You searched for subject:(demand response).
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Georgia Tech
1.
Tsybina, Evgeniya.
RESIDENTIAL DEMAND RESPONSE USING A HOUSE AS A BATTERY.
Degree: MS, Electrical and Computer Engineering, 2020, Georgia Tech
URL: http://hdl.handle.net/1853/64118 
► The growth in the number of residential intelligent electrical appliances and home energy management systems creates the potential to provide residential demand response services to…
(more)
▼ The growth in the number of residential intelligent electrical appliances and home energy management systems creates the potential to provide residential
demand response services to the electricity grid. Simultaneously, direct control of individual devices by utilities can cause coordination and privacy concerns. A method to overcome this challenge is to combine all devices in a house into a single unit for the purposes of
demand response. It allows to provide
demand response service without giving up information on or control over specific appliances.
The contribution of this thesis is the development of a proof of concept for a home energy management system that can combine several devices into a single unit of
demand response. This study suggests an adjusted 2d bin packing problem with partial trimming and a recursive join algorithm to optimize bidding of an individual house. It tests the algorithm with the use of controllable devices in an experimental house. It further uses simulations to establish whether the use of a house as a battery causes a reduction in available
demand response capacity and whether
demand response can provide financial incentives to individual users. It also solves an additional problem that emerged along the way: the problem of predicting the charge of HVAC systems.
The thesis serves as an intermediate step between existing theoretical research and possible future steps, such as commercial prototyping of systems that provide residential
demand response services at the point of common coupling.
Advisors/Committee Members: Grijalva, Santiago (advisor), Meliopoulos, Athanasios (advisor), Molzahn, Daniel (advisor).
Subjects/Keywords: Demand response; Minkowski sum
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APA (6th Edition):
Tsybina, E. (2020). RESIDENTIAL DEMAND RESPONSE USING A HOUSE AS A BATTERY. (Masters Thesis). Georgia Tech. Retrieved from http://hdl.handle.net/1853/64118
Chicago Manual of Style (16th Edition):
Tsybina, Evgeniya. “RESIDENTIAL DEMAND RESPONSE USING A HOUSE AS A BATTERY.” 2020. Masters Thesis, Georgia Tech. Accessed February 27, 2021.
http://hdl.handle.net/1853/64118.
MLA Handbook (7th Edition):
Tsybina, Evgeniya. “RESIDENTIAL DEMAND RESPONSE USING A HOUSE AS A BATTERY.” 2020. Web. 27 Feb 2021.
Vancouver:
Tsybina E. RESIDENTIAL DEMAND RESPONSE USING A HOUSE AS A BATTERY. [Internet] [Masters thesis]. Georgia Tech; 2020. [cited 2021 Feb 27].
Available from: http://hdl.handle.net/1853/64118.
Council of Science Editors:
Tsybina E. RESIDENTIAL DEMAND RESPONSE USING A HOUSE AS A BATTERY. [Masters Thesis]. Georgia Tech; 2020. Available from: http://hdl.handle.net/1853/64118
IUPUI
2.
Amini, Amin.
A novel approach to forecast and manage electrical maximum demand.
Degree: 2017, IUPUI
URL: http://hdl.handle.net/1805/12825
► Indiana University-Purdue University Indianapolis (IUPUI)
Electric demand charge is a large portion (usually 40%) of electric bill in residential, commercial, and manufacturing sectors. This charge…
(more)
▼ Indiana University-Purdue University Indianapolis (IUPUI)
Electric demand charge is a large portion (usually 40%) of electric bill in residential, commercial, and manufacturing sectors. This charge is based on the greatest of all demands that have occurred during a month recorded by utility provider for an end-user. During the past several years, electric demand forecasting have been broadly studied by utilities on account of the fact that it has a crucial impact on planning resources to provide consumers reliable power at all time; on the other hand, not many studies have been conducted on consumer side. In this thesis, a novel Maximum Daily Demand (MDD) forecasting method, called Adaptive-Rate-of-Change (ARC), is proposed by analysing real-time demand trend data and incorporating moving average calculations as well as rate of change formularization to develop a forecasting tool which can be applied on either utility or consumer sides. ARC algorithm is implemented on two different real case studies to develop very short-term load forecasting (VSTLF), short-term load forecasting (STLF), and medium-term load forecasting (MTLF). The Chi-square test is used to validate the forecasting results. The results of the test reveal that the ARC algorithm is 84% successful in forecasting maximum daily demands in a period of 72 days with the P-value equals to 0.0301. Demand charge is also estimated to be saved by $8,056 (345.6 kW) for the first year for case study I (a die casting company) by using ARC algorithm. Following that, a new Maximum Demand Management (MDM) method is proposed to provide electric consumers a complete package. The proposed MDM method broadens the electric consumer understanding of how MDD is sensitive to the temperature, production, occupancy, and different sub-systems. The MDM method are applied on two different real case studies to calculate sensitivities by using linear regression models. In all linear regression models, R-squareds calculated as 0.9037, 0.8987, and 0.8197 which indicate very good fits between fitted values and observed values. The results of proposed demand forecasting and management methods can be very helpful and beneficial in decision making for demand management and demand response program.
Advisors/Committee Members: Razban, Ali, Chen, Jie, Goodman, David.
Subjects/Keywords: Load Forecasting; Maximum Demand; Demand Management; Demand Response; Load Shedding
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APA (6th Edition):
Amini, A. (2017). A novel approach to forecast and manage electrical maximum demand. (Thesis). IUPUI. Retrieved from http://hdl.handle.net/1805/12825
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
Amini, Amin. “A novel approach to forecast and manage electrical maximum demand.” 2017. Thesis, IUPUI. Accessed February 27, 2021.
http://hdl.handle.net/1805/12825.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Amini, Amin. “A novel approach to forecast and manage electrical maximum demand.” 2017. Web. 27 Feb 2021.
Vancouver:
Amini A. A novel approach to forecast and manage electrical maximum demand. [Internet] [Thesis]. IUPUI; 2017. [cited 2021 Feb 27].
Available from: http://hdl.handle.net/1805/12825.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Amini A. A novel approach to forecast and manage electrical maximum demand. [Thesis]. IUPUI; 2017. Available from: http://hdl.handle.net/1805/12825
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Texas A&M University
3.
Ming, Hao.
A Household-level Incentive-based Demand Response: Theory, Platform and Experiment.
Degree: PhD, Electrical Engineering, 2018, Texas A&M University
URL: http://hdl.handle.net/1969.1/173756
► This work concentrates on issues with demand response on residential end-consumers. It is motivated by the fact that demand response (DR) provides flexibility to the…
(more)
▼ This work concentrates on issues with
demand response on residential end-consumers. It is motivated by the fact that
demand response (DR) provides flexibility to the power system, especially with the integration of uncertain renewable energy resources such as wind and solar. As an innovative type of DR, coupon incentive-based
demand response (CIDR) incentivizes load shedding and reshaping of end-consumers by issuing some coupon prices in peak-price hours. EnergyCoupon is a first-of-its-kind implementation of CIDR; a small-scale experimental result indicates behavior changes and load reshaping achieved by active participants. At the market level,
demand response provider (DRP) serves as an aggregator of DR from a group of end-consumers without the obligation of serving the load. While it is accepted by multiple ISOs as a participant into the wholesale markets, it also draws great concern and heated discussions on the treatment of DRPs in the wholesale market, such as the revenue inadequacy problem. To this end, this dissertation focuses on the following issues: 1) modeling CIDR with bounded rational consumers; 2) introducing EnergyCoupon as an implementation of incentive-based DR, and analyzing customer behavior change during the experiment; 3) providing an innovative scenario-based economic dispatch considering uncertain DRPs; 4) discussing and providing solutions to the revenue inadequacy problem caused by DRPs in the wholesale market.
Advisors/Committee Members: Xie, Le (advisor), Kezunovic, Mladen (committee member), Hou, I-Hong (committee member), Castillo, Marco (committee member).
Subjects/Keywords: Demand response; EnergyCoupon; Demand response provider; Scenario approach; Revenue inadequacy
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APA ·
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CSE |
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Manager
APA (6th Edition):
Ming, H. (2018). A Household-level Incentive-based Demand Response: Theory, Platform and Experiment. (Doctoral Dissertation). Texas A&M University. Retrieved from http://hdl.handle.net/1969.1/173756
Chicago Manual of Style (16th Edition):
Ming, Hao. “A Household-level Incentive-based Demand Response: Theory, Platform and Experiment.” 2018. Doctoral Dissertation, Texas A&M University. Accessed February 27, 2021.
http://hdl.handle.net/1969.1/173756.
MLA Handbook (7th Edition):
Ming, Hao. “A Household-level Incentive-based Demand Response: Theory, Platform and Experiment.” 2018. Web. 27 Feb 2021.
Vancouver:
Ming H. A Household-level Incentive-based Demand Response: Theory, Platform and Experiment. [Internet] [Doctoral dissertation]. Texas A&M University; 2018. [cited 2021 Feb 27].
Available from: http://hdl.handle.net/1969.1/173756.
Council of Science Editors:
Ming H. A Household-level Incentive-based Demand Response: Theory, Platform and Experiment. [Doctoral Dissertation]. Texas A&M University; 2018. Available from: http://hdl.handle.net/1969.1/173756
Texas A&M University
4.
Ming, Hao.
A Household-level Incentive-based Demand Response: Theory, Platform and Experiment.
Degree: PhD, Electrical Engineering, 2018, Texas A&M University
URL: http://hdl.handle.net/1969.1/173851
► This work concentrates on issues with demand response on residential end-consumers. It is motivated by the fact that demand response (DR) provides flexibility to the…
(more)
▼ This work concentrates on issues with
demand response on residential end-consumers. It is motivated by the fact that
demand response (DR) provides flexibility to the power system, especially with the integration of uncertain renewable energy resources such as wind and solar. As an innovative type of DR, coupon incentive-based
demand response (CIDR) incentivizes load shedding and reshaping of end-consumers by issuing some coupon prices in peak-price hours. EnergyCoupon is a first-of-its-kind implementation of CIDR; a small-scale experimental result indicates behavior changes and load reshaping achieved by active participants. At the market level,
demand response provider (DRP) serves as an aggregator of DR from a group of end-consumers without the obligation of serving the load. While it is accepted by multiple ISOs as a participant into the wholesale markets, it also draws great concern and heated discussions on the treatment of DRPs in the wholesale market, such as the revenue inadequacy problem. To this end, this dissertation focuses on the following issues: 1) modeling CIDR with bounded rational consumers; 2) introducing EnergyCoupon as an implementation of incentive-based DR, and analyzing customer behavior change during the experiment; 3) providing an innovative scenario-based economic dispatch considering uncertain DRPs; 4) discussing and providing solutions to the revenue inadequacy problem caused by DRPs in the wholesale market.
Advisors/Committee Members: Xie, Le (advisor), Kezunovic, Mladen (committee member), Hou, I-Hong (committee member), Castillo, Marco (committee member).
Subjects/Keywords: Demand response; EnergyCoupon; Demand response provider; Scenario approach; Revenue inadequacy
Record Details
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Share »
Record Details
Similar Records
Cite
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❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Ming, H. (2018). A Household-level Incentive-based Demand Response: Theory, Platform and Experiment. (Doctoral Dissertation). Texas A&M University. Retrieved from http://hdl.handle.net/1969.1/173851
Chicago Manual of Style (16th Edition):
Ming, Hao. “A Household-level Incentive-based Demand Response: Theory, Platform and Experiment.” 2018. Doctoral Dissertation, Texas A&M University. Accessed February 27, 2021.
http://hdl.handle.net/1969.1/173851.
MLA Handbook (7th Edition):
Ming, Hao. “A Household-level Incentive-based Demand Response: Theory, Platform and Experiment.” 2018. Web. 27 Feb 2021.
Vancouver:
Ming H. A Household-level Incentive-based Demand Response: Theory, Platform and Experiment. [Internet] [Doctoral dissertation]. Texas A&M University; 2018. [cited 2021 Feb 27].
Available from: http://hdl.handle.net/1969.1/173851.
Council of Science Editors:
Ming H. A Household-level Incentive-based Demand Response: Theory, Platform and Experiment. [Doctoral Dissertation]. Texas A&M University; 2018. Available from: http://hdl.handle.net/1969.1/173851
Virginia Tech
5.
Rahman, Md Moshiur.
Design and Implementation of a Web-based Home Energy Management System for Demand Response Applications.
Degree: MS, Computer Engineering, 2013, Virginia Tech
URL: http://hdl.handle.net/10919/23689
► The objective of this work is to design and implement an architectural framework for a web-based demand management system that allows an electric utility to…
(more)
▼ The objective of this work is to design and implement an architectural framework for a web-based
demand management system that allows an electric utility to reduce system peak load by automatically managing end-use appliances based on homeownersṕreferences. The proposed framework comprises the following components: human user interface, home energy management (HEM) algorithms, web services for
demand response communications, selected ZigBee and smart energy profile features for appliance interface, and security aspects for a web-based HEM system. The proposed web-based HEM system allows homeowners to be more aware about their electricity consumption by allowing visualization of their real-time and historical electricity consumption data. The HEM system enables customers to monitor and control their household appliances from anywhere with an Internet connection. It offers a user-friendly and attractive display panel for a homeowner to easily set his/her preferences and comfort settings. An algorithm to autonomously control appliance operation is incorporated in the proposed web-based HEM system, which makes it possible for residential customers to participate in
demand response programs. In this work, the algorithm is demonstrated to manage power-intensive appliances in a single home, keeping the total household load within a certain limit while satisfying preset comfort settings and user preferences. Furthermore, an extended version of the algorithm is demonstrated to manage power-intensive appliances for multiple homes within a neighborhood. As one of the
demand response (DR)-enabling technologies, the web services-based DR communication has been developed to enable households without smart meters or advanced metering infrastructure (AMI) to participate in a DR event via the HEM system. This implies that an electric utility can send a DR signal via a web services-enabled HEM system, and appropriate appliances can be controlled within each home based on homeowner preferences. The interoperability with other systems, such as utility systems, third-party Home Area Network (HAN) systems, etc., is also taken into account in the design of the proposed web services-based HEM system. That is, it is designed to allow interaction with authorized third-party systems by means of web services, which are collectively an interface for machine-to-machine interaction. This work also designs and implements device organization and interface for end-use appliances utilizing ZigBee Device Profile and Smart Energy Profile. Development of the Home Area Network (HAN) of appliances and the HAN Coordinator has been performed using a ZigBee network. Analyses of security risks for a web-based HEM system and their mitigation strategies have been discussed as well.
Advisors/Committee Members: Rahman, Saifur (committeechair), Silva, Luiz A. (committee member), Pipattanasomporn, Manisa (committee member).
Subjects/Keywords: Home Energy Management System; Demand Response/Load Control; Controllable Loads; Demand Response Algorithm; Demand Compensation
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APA ·
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Manager
APA (6th Edition):
Rahman, M. M. (2013). Design and Implementation of a Web-based Home Energy Management System for Demand Response Applications. (Masters Thesis). Virginia Tech. Retrieved from http://hdl.handle.net/10919/23689
Chicago Manual of Style (16th Edition):
Rahman, Md Moshiur. “Design and Implementation of a Web-based Home Energy Management System for Demand Response Applications.” 2013. Masters Thesis, Virginia Tech. Accessed February 27, 2021.
http://hdl.handle.net/10919/23689.
MLA Handbook (7th Edition):
Rahman, Md Moshiur. “Design and Implementation of a Web-based Home Energy Management System for Demand Response Applications.” 2013. Web. 27 Feb 2021.
Vancouver:
Rahman MM. Design and Implementation of a Web-based Home Energy Management System for Demand Response Applications. [Internet] [Masters thesis]. Virginia Tech; 2013. [cited 2021 Feb 27].
Available from: http://hdl.handle.net/10919/23689.
Council of Science Editors:
Rahman MM. Design and Implementation of a Web-based Home Energy Management System for Demand Response Applications. [Masters Thesis]. Virginia Tech; 2013. Available from: http://hdl.handle.net/10919/23689
University of Alberta
6.
Dong,Ming.
Decomposition Techniques for Power System Analysis.
Degree: PhD, Department of Electrical and Computer
Engineering, 2013, University of Alberta
URL: https://era.library.ualberta.ca/files/j96021808
► In recent years, the increased public awareness of energy conservation has attracted serious attention to detailed energy consumption monitoring and management for the end users…
(more)
▼ In recent years, the increased public awareness of
energy conservation has attracted serious attention to detailed
energy consumption monitoring and management for the end users of
power system. Load decomposition is a technique that can extract
detailed sub-load information from compound load information. This
technique decomposes a compound load such as an entire residential
house into specific sub-load levels such as different home
appliances by using only the aggregated metering data of the
compound load. Through load decomposition, users can better
understand the usage patterns of individual loads or load groups
and therefore decide on how to save energy. On the utility side,
load decomposition can be very helpful for load forecast, demand
response program development, and Time-Of-Use price design. In the
past, traditional methods are either too costly or inaccurate.
Therefore, some researchers proposed a non-intrusive load
monitoring (NILM) approach that can identify and track major
sub-loads based on only the total signal collected from the
meter-side with acceptable error. Recently, the vast deployment of
smart meters has raised considerable interests in this approach.
However, many critical problems still need to be solved before it
truly becomes technically available for ordinary end-users. To
solve the above problems, at the beginning, this thesis presents a
novel NILM method based on event detection and load signature
studies. The key idea is to model the entire operating cycle of a
load and make identification based on event-window candidates. The
proposed technique makes NILM more applicable for complex loads,
more robust for load inventory change and can also simplify the
training process; on the other hand, the thesis addresses a new and
critical problem that previous researchers ignored – the
non-intrusive extraction of load signatures. The proposed approach
is an unsupervised non-intrusive approach which can automatically
extract load signatures by using the meter-side data and requires
almost zero effort from users. This thesis also discusses how to
estimate the energy for several key components in a residential
house such as the NILM identified appliances, load groups and
background power. Based on estimation, residential energy
characteristics are discussed with respect to the Time-of-use
price.
Subjects/Keywords: Demand response; Load decomposition; Smart grid
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APA (6th Edition):
Dong,Ming. (2013). Decomposition Techniques for Power System Analysis. (Doctoral Dissertation). University of Alberta. Retrieved from https://era.library.ualberta.ca/files/j96021808
Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete
Chicago Manual of Style (16th Edition):
Dong,Ming. “Decomposition Techniques for Power System Analysis.” 2013. Doctoral Dissertation, University of Alberta. Accessed February 27, 2021.
https://era.library.ualberta.ca/files/j96021808.
Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete
MLA Handbook (7th Edition):
Dong,Ming. “Decomposition Techniques for Power System Analysis.” 2013. Web. 27 Feb 2021.
Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete
Vancouver:
Dong,Ming. Decomposition Techniques for Power System Analysis. [Internet] [Doctoral dissertation]. University of Alberta; 2013. [cited 2021 Feb 27].
Available from: https://era.library.ualberta.ca/files/j96021808.
Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete
Council of Science Editors:
Dong,Ming. Decomposition Techniques for Power System Analysis. [Doctoral Dissertation]. University of Alberta; 2013. Available from: https://era.library.ualberta.ca/files/j96021808
Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete
University of Michigan
7.
Beil, Ian Burkley.
Fast-timescale Control Strategies for Demand Response in Power Systems.
Degree: PhD, Electrical Engineering: Systems, 2015, University of Michigan
URL: http://hdl.handle.net/2027.42/116627
► Concerns over climate change have spurred an increase in the amount of wind and solar power generation on the grid. While these resources reduce carbon…
(more)
▼ Concerns over climate change have spurred an increase in the amount of wind and solar power generation on the grid. While these resources reduce carbon emissions, the physical phenomena that they rely on - wind and sunlight - are highly stochastic, making their generated power less controllable.
Demand-side strategies, which modulate load in a controllable manner, have been proposed as a way to add flexibility to the grid.
Resources with innate flexibility in their load profile are particularly suited to
demand response (DR) applications. This work examines two such loads: heating, ventilation, and air conditioning (HVAC) systems, and plug-in electric vehicle (PEV) fleets.
HVAC systems can vary the timing of power consumption due to the thermal inertia inherent in their associated building(s). The first part of this thesis explores the efficacy of using commercial HVAC for DR applications. Results are presented from an experimental testbed that quantify performance, in terms of accuracy in perturbing the load in a desired manner, as well as the efficiency of this process.
PEVs offer very fast
response times and may eventually represent a significant load on the power system. The second part of this thesis develops several control strategies to manage PEV power consumption in an environment where communication resources are limited, both to prevent detrimental system effects such as transformer overload, and to provide ancillary services such as frequency regulation to the grid.
Advisors/Committee Members: Hiskens, Ian (committee member), Lynch, Jerome P (committee member), Hofmann, Heath (committee member), Mathieu, Johanna (committee member), Backhaus, Scott (committee member).
Subjects/Keywords: Power Systems; Demand Response; Electrical Engineering; Engineering
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APA ·
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APA (6th Edition):
Beil, I. B. (2015). Fast-timescale Control Strategies for Demand Response in Power Systems. (Doctoral Dissertation). University of Michigan. Retrieved from http://hdl.handle.net/2027.42/116627
Chicago Manual of Style (16th Edition):
Beil, Ian Burkley. “Fast-timescale Control Strategies for Demand Response in Power Systems.” 2015. Doctoral Dissertation, University of Michigan. Accessed February 27, 2021.
http://hdl.handle.net/2027.42/116627.
MLA Handbook (7th Edition):
Beil, Ian Burkley. “Fast-timescale Control Strategies for Demand Response in Power Systems.” 2015. Web. 27 Feb 2021.
Vancouver:
Beil IB. Fast-timescale Control Strategies for Demand Response in Power Systems. [Internet] [Doctoral dissertation]. University of Michigan; 2015. [cited 2021 Feb 27].
Available from: http://hdl.handle.net/2027.42/116627.
Council of Science Editors:
Beil IB. Fast-timescale Control Strategies for Demand Response in Power Systems. [Doctoral Dissertation]. University of Michigan; 2015. Available from: http://hdl.handle.net/2027.42/116627
Cornell University
8.
Ge, Jing.
Understanding Urban Residential Electricity Usage Behavior Under Time-Of-Use Pricing: A Case Study In Jiangsu Province, China.
Degree: M.S., Human-Environment Relations, Human-Environment Relations, 2015, Cornell University
URL: http://hdl.handle.net/1813/40942
► an important role for residential customers to engage in peak shifting or energy conservation behaviors. As a result of this study, based on strong empirical…
(more)
▼ an important role for residential customers to engage in peak shifting or energy conservation behaviors. As a result of this study, based on strong empirical and measured data, the design and implementation strategies of pricing program, as well as supporting approaches including communication, are proposed to further shift peak load, enhance gird reliability, and facilitate carbon emissions mitigation. 3
Advisors/Committee Members: Hua,Ying (chair), Alvarez Daziano,Ricardo (committee member).
Subjects/Keywords: Demand Response; Energy Saving; Consumer Behavior
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APA ·
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to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Ge, J. (2015). Understanding Urban Residential Electricity Usage Behavior Under Time-Of-Use Pricing: A Case Study In Jiangsu Province, China. (Masters Thesis). Cornell University. Retrieved from http://hdl.handle.net/1813/40942
Chicago Manual of Style (16th Edition):
Ge, Jing. “Understanding Urban Residential Electricity Usage Behavior Under Time-Of-Use Pricing: A Case Study In Jiangsu Province, China.” 2015. Masters Thesis, Cornell University. Accessed February 27, 2021.
http://hdl.handle.net/1813/40942.
MLA Handbook (7th Edition):
Ge, Jing. “Understanding Urban Residential Electricity Usage Behavior Under Time-Of-Use Pricing: A Case Study In Jiangsu Province, China.” 2015. Web. 27 Feb 2021.
Vancouver:
Ge J. Understanding Urban Residential Electricity Usage Behavior Under Time-Of-Use Pricing: A Case Study In Jiangsu Province, China. [Internet] [Masters thesis]. Cornell University; 2015. [cited 2021 Feb 27].
Available from: http://hdl.handle.net/1813/40942.
Council of Science Editors:
Ge J. Understanding Urban Residential Electricity Usage Behavior Under Time-Of-Use Pricing: A Case Study In Jiangsu Province, China. [Masters Thesis]. Cornell University; 2015. Available from: http://hdl.handle.net/1813/40942
Texas A&M University
9.
Sharma, Gaurav.
Optimal Demand Response Schemes for Renewable Integration using Thermal Inertial Loads.
Degree: PhD, Electrical Engineering, 2016, Texas A&M University
URL: http://hdl.handle.net/1969.1/157838
► We consider a smart microgrid environment where renewable power sources like wind generators are available to service the thermal inertial load along with conventional non-renewable…
(more)
▼ We consider a smart microgrid environment where renewable power sources like wind generators are available to service the thermal inertial load along with conventional non-renewable energy sources. The flexibility in power consumption of thermal inertial loads, like air-conditioners can be used towards absorbing the fluctuations in intermittently available renewable power sources. Several optimization schemes can be used towards this goal. We discuss and analyze some of these optimization models. An optimization model which promotes renewable consumption by penalizing non-renewable consumption, but does not account for variations in the load requirements, lead to an optimal solution in which all the loads’ temperatures behave in a lockstep fashion. That is, the power is allocated in such a fashion that all the temperatures are brought to a common value and they are kept the same after that time, resulting in synchronization among all the loads. We show that under a model which additionally penalizes the comfort range violation, the optimal solution is in fact of a de-synchronizing nature, where the temperatures are intentionally kept apart to avoid power surges resulting from simultaneous comfort violation across many loads.
In the sequel, we additionally take into account several other factors, such as the privacy requirements from the users of loads, architectural simplicity, and tractability of the solution. We propose a
demand response architecture where no information from the end-user is required to be transferred in order to optimally co-ordinate their power consumption. We propose a simple threshold value based policy which is architecturally simple, computationally inexpensive, and achieves optimal staggering among loads to smooth the variations in non-renewable power requirements. We show that it is possible to compute the optimal solution in a number of scenarios, and give a heuristic approach to approximate the optimal solution for the scenarios where information such as cooling/heating rates, etc. is not available.
Advisors/Committee Members: Kumar, Panganamala R. (advisor), Hou, I-Hong (advisor), Xie, Le (committee member), Howard, Peter (committee member).
Subjects/Keywords: demand response; power system; optimization; renewable integration
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APA (6th Edition):
Sharma, G. (2016). Optimal Demand Response Schemes for Renewable Integration using Thermal Inertial Loads. (Doctoral Dissertation). Texas A&M University. Retrieved from http://hdl.handle.net/1969.1/157838
Chicago Manual of Style (16th Edition):
Sharma, Gaurav. “Optimal Demand Response Schemes for Renewable Integration using Thermal Inertial Loads.” 2016. Doctoral Dissertation, Texas A&M University. Accessed February 27, 2021.
http://hdl.handle.net/1969.1/157838.
MLA Handbook (7th Edition):
Sharma, Gaurav. “Optimal Demand Response Schemes for Renewable Integration using Thermal Inertial Loads.” 2016. Web. 27 Feb 2021.
Vancouver:
Sharma G. Optimal Demand Response Schemes for Renewable Integration using Thermal Inertial Loads. [Internet] [Doctoral dissertation]. Texas A&M University; 2016. [cited 2021 Feb 27].
Available from: http://hdl.handle.net/1969.1/157838.
Council of Science Editors:
Sharma G. Optimal Demand Response Schemes for Renewable Integration using Thermal Inertial Loads. [Doctoral Dissertation]. Texas A&M University; 2016. Available from: http://hdl.handle.net/1969.1/157838
University of Waterloo
10.
Alarfaj, Omar.
Energy Management and Demand Response of Industrial Systems.
Degree: 2018, University of Waterloo
URL: http://hdl.handle.net/10012/14177
► Energy management is an important concept that has come to the forefront in recent years under the smart grid paradigm. Energy conservation and management can…
(more)
▼ Energy management is an important concept that has come to the forefront in recent years under the smart grid paradigm. Energy conservation and management can help defer some capacity addition requirements in the long-term, which is very significant in the context of continuously growing demand for energy. It can also alleviate the adverse environmental impacts of commissioning new generation plants. Therefore, there is a continuous need for the development of appropriate tools to ensure efficient energy usage by existing and new loads and the efficient integration of distributed energy resources (DER).
There is a need for energy conservation in the industrial sector as it accounts for the largest share of energy consumption among all customer sectors. Also considering their high energy density, industrial facilities have significant potential for participating in demand side management (DSM) programs and help in reducing the system peak demand by reducing or shifting their load in response to energy price signals. However industrial demand response (DR) is typically constrained by the operational requirements such as process interdependencies and material flow management.
An EMS framework is proposed in this thesis for optimal load management of industrial loads which includes improved load estimation technique and uncertainty mitigation using MPC. The framework has been applied to a water pumping system (WPS) where an equipment level load modeling is implemented using a NN-based model. Another EMS framework is proposed for an oil refinery process. The refinery EMS is developed based on power demand modeling of the oil refinery process, considering an on-site cogeneration facility. A joint electrical-thermal model is proposed for the cogeneration units to account for the electricity and steam production costs.
In addition to load management, DR for industrial loads is investigated as another energy management application. However since DR requires interaction between the energy supplier and the customer, this thesis considers DR from both the local distribution company's (LDC) and industrial customer's perspectives. From the LDC's perspective, the objective is to reduce the network operational costs by minimizing peak demand and flattening the load profile for better utilization of system resources. From the industrial customer's perspective, the objective is to minimize the energy cost using both load management decisions and DR signals sent by the LDC. While the developed EMS models are used to represent the industrial customer's operations, a distribution optimal power flow (DOPF) model is developed to represent distribution system operations.
The DR strategy proposed in this thesis is based on effective communication between the customer's EMS and the LDC's operations using a day-ahead contractual mechanism between the two parties, and a real-time operational scheme to mitigate the uncertainties through improved forecasts for energy prices and power demand. Two types of DR signals are proposed; a desired…
Subjects/Keywords: Demand response; Energy management; Industrial load
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APA (6th Edition):
Alarfaj, O. (2018). Energy Management and Demand Response of Industrial Systems. (Thesis). University of Waterloo. Retrieved from http://hdl.handle.net/10012/14177
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
Alarfaj, Omar. “Energy Management and Demand Response of Industrial Systems.” 2018. Thesis, University of Waterloo. Accessed February 27, 2021.
http://hdl.handle.net/10012/14177.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Alarfaj, Omar. “Energy Management and Demand Response of Industrial Systems.” 2018. Web. 27 Feb 2021.
Vancouver:
Alarfaj O. Energy Management and Demand Response of Industrial Systems. [Internet] [Thesis]. University of Waterloo; 2018. [cited 2021 Feb 27].
Available from: http://hdl.handle.net/10012/14177.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Alarfaj O. Energy Management and Demand Response of Industrial Systems. [Thesis]. University of Waterloo; 2018. Available from: http://hdl.handle.net/10012/14177
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Texas Tech University
11.
Mohammed, Rezgar.
Essays on differentiated yogurt market in the United States.
Degree: PhD, Agricultural and Applied Economics, 2018, Texas Tech University
URL: http://hdl.handle.net/2346/73812
► This dissertation examines three issues in yogurt market, while taking into consideration the most competing brands of yogurt in the market, accounting for the brand…
(more)
▼ This dissertation examines three issues in yogurt market, while taking into consideration the most competing brands of yogurt in the market, accounting for the brand of Chobani. It is important to note that the brand of Chobani is included in this research for the first time in studying yogurt marketing. Chapter 2 examines household
demand relations of yogurt differentiated by brand level and style level in the U.S. with quadratic almost ideal
demand system approach. The main conclusion is that
demand for a new brand of Chobani and private label are substantially elastic compared to other national brands of Dannon and Yoplait. At the style level, groups have the same magnitude with respect to the expenditure elasticity, while higher uncompensated own-price elasticity for the Greek yogurt. The variation in estimated own-prices elasticities of yogurt that can be explained by yogurt prices and households’ sociodemographic. Chapter 3 estimated the likelihood that households buy a given brand and also remain loyal to a brand of yogurt in the presence of a store’s price promotion. There is a strong evidence to support the hypotheses that the choice of yogurt brands and their brand loyalty are sensitive to the frequency of price promotion as an increase in the frequency of price promotion by a given store will impact positively on the brand choice and impact negatively on brand loyalty. Chapter 4 explores the strategic price
response to better understand the responses of brands to each other’s prices, in addition to estimation of price promotion duration for main brands in the U.S. yogurt market. Results show that prices are not random and brands lead Yoplait in their price setting. Results of Markov-switching regression answer the question whether private labels have price reduction like other national brands by showing that private labels go on price promotion as frequently as major national brands in the yogurt market.
Advisors/Committee Members: Chidmi, Benaissa (committee member), Carpio, Carlos (committee member), Hudson, Darren (committee member), Murova, Olga (Committee Chair).
Subjects/Keywords: Yogurt; Demand Elasticity; Price Promotion; Price Response
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APA (6th Edition):
Mohammed, R. (2018). Essays on differentiated yogurt market in the United States. (Doctoral Dissertation). Texas Tech University. Retrieved from http://hdl.handle.net/2346/73812
Chicago Manual of Style (16th Edition):
Mohammed, Rezgar. “Essays on differentiated yogurt market in the United States.” 2018. Doctoral Dissertation, Texas Tech University. Accessed February 27, 2021.
http://hdl.handle.net/2346/73812.
MLA Handbook (7th Edition):
Mohammed, Rezgar. “Essays on differentiated yogurt market in the United States.” 2018. Web. 27 Feb 2021.
Vancouver:
Mohammed R. Essays on differentiated yogurt market in the United States. [Internet] [Doctoral dissertation]. Texas Tech University; 2018. [cited 2021 Feb 27].
Available from: http://hdl.handle.net/2346/73812.
Council of Science Editors:
Mohammed R. Essays on differentiated yogurt market in the United States. [Doctoral Dissertation]. Texas Tech University; 2018. Available from: http://hdl.handle.net/2346/73812
University of Wisconsin – Milwaukee
12.
Kinsella, Peter.
Investigation of Distributed Model Predictive Control for Economic Load Shifting in Building HVAC Systems.
Degree: MS, Engineering, 2015, University of Wisconsin – Milwaukee
URL: https://dc.uwm.edu/etd/811
► One of the major challenges that building owners and operators face is maintaining a low cost of operation. In certain markets within the U.S.,…
(more)
▼ One of the major challenges that building owners and operators face is maintaining a low cost of operation. In certain markets within the U.S., electrical cost varies throughout the day; it is higher during times of peak
demand. This leaves the customer the incentive to cut back electrical use during peak
demand periods. Since 40% of the peak electrical
demand is due to the operation of the building HVAC system alone, the opportunity exists for shifting the building cooling load to off-peak hours. This can be done by pre-cooling the space, thereby using the building mass as a sort of thermal battery, which can then discharge later, alleviating the cooling load off the HVAC system during peak times. It is in this thesis that a peak load reduction strategy is presented using model predictive control (MPC). Furthermore, the system modeled in this paper is a two-zone system, each having a dedicated controller. First the problem is explored with a single, centralized MPC which calculates the optimal trajectory for the entire building. Secondly, the load reduction strategy control is distributed to each individual controller. The advantage to distributed control is the reduction of computing resources which brings with it a cost reduction on its own. Lastly, both MPC approaches are compared to the traditional PI-only control scheme. Results showed that the distributed scheme proved favorable next to the centralized MPC benchmark, and both MPC approaches produced favorable results over the traditional PI-only control.
Advisors/Committee Members: Yingchun Yuan.
Subjects/Keywords: Control; Demand Response; HVAC; MPC; Mechanical Engineering
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Manager
APA (6th Edition):
Kinsella, P. (2015). Investigation of Distributed Model Predictive Control for Economic Load Shifting in Building HVAC Systems. (Thesis). University of Wisconsin – Milwaukee. Retrieved from https://dc.uwm.edu/etd/811
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
Kinsella, Peter. “Investigation of Distributed Model Predictive Control for Economic Load Shifting in Building HVAC Systems.” 2015. Thesis, University of Wisconsin – Milwaukee. Accessed February 27, 2021.
https://dc.uwm.edu/etd/811.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Kinsella, Peter. “Investigation of Distributed Model Predictive Control for Economic Load Shifting in Building HVAC Systems.” 2015. Web. 27 Feb 2021.
Vancouver:
Kinsella P. Investigation of Distributed Model Predictive Control for Economic Load Shifting in Building HVAC Systems. [Internet] [Thesis]. University of Wisconsin – Milwaukee; 2015. [cited 2021 Feb 27].
Available from: https://dc.uwm.edu/etd/811.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Kinsella P. Investigation of Distributed Model Predictive Control for Economic Load Shifting in Building HVAC Systems. [Thesis]. University of Wisconsin – Milwaukee; 2015. Available from: https://dc.uwm.edu/etd/811
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
University of Illinois – Chicago
13.
Wang, Yong.
Joint Production and Energy Modeling for Sustainable Manufacturing Systems.
Degree: 2015, University of Illinois – Chicago
URL: http://hdl.handle.net/10027/19834
► This dissertation proposes a framework for addressing challenges of joint production and energy modeling for manufacturing systems. The knowledge generated is used to improve the…
(more)
▼ This dissertation proposes a framework for addressing challenges of joint production and energy modeling for manufacturing systems. The knowledge generated is used to improve the technological readiness of manufacturing enterprises for the transition towards sustainable manufacturing in the context of smart electric grids. Detailed research tasks of the framework on the modeling of production, energy efficiency, electricity
demand, cost, and
demand response decision making have been implemented. Specifically, the dynamics and performance measures of general manufacturing systems with multiple machines and buffers have been modeled. Expressions of electricity energy efficiency and cost have been established based on the electricity pricing profile. Production scheduling problem formulations and the solution technique are discussed. New insights are acquired based on the applications of the established model in system parameter monotonicity analysis, rate plan switching decision making, and
demand response scheduling. The findings based on case studies show that with appropriate adjustment of production routines, significant improvement in energy efficiency and substantial savings in energy cost can be achieved without sacrificing production. Appropriate implementation of this research outcome may lead to energy-efficient, electricity-
demand-responsive, and cost-effective operations and thus improve the sustainability of modern manufacturing systems. The new knowledge generated can be implemented to discrete manufacturing in various industries such as automotive, electronics, appliances, aerospace, etc.
Advisors/Committee Members: Li, Lin (advisor), He, David (committee member), Banerjee, Prashant (committee member), Darabi, Houshang (committee member), Lin, Jie (committee member).
Subjects/Keywords: Manufacturing; Energy; Sustainability; Production; Demand Response
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Manager
APA (6th Edition):
Wang, Y. (2015). Joint Production and Energy Modeling for Sustainable Manufacturing Systems. (Thesis). University of Illinois – Chicago. Retrieved from http://hdl.handle.net/10027/19834
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
Wang, Yong. “Joint Production and Energy Modeling for Sustainable Manufacturing Systems.” 2015. Thesis, University of Illinois – Chicago. Accessed February 27, 2021.
http://hdl.handle.net/10027/19834.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Wang, Yong. “Joint Production and Energy Modeling for Sustainable Manufacturing Systems.” 2015. Web. 27 Feb 2021.
Vancouver:
Wang Y. Joint Production and Energy Modeling for Sustainable Manufacturing Systems. [Internet] [Thesis]. University of Illinois – Chicago; 2015. [cited 2021 Feb 27].
Available from: http://hdl.handle.net/10027/19834.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Wang Y. Joint Production and Energy Modeling for Sustainable Manufacturing Systems. [Thesis]. University of Illinois – Chicago; 2015. Available from: http://hdl.handle.net/10027/19834
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Texas A&M University
14.
An, Jaeyong.
On Price Responsive Consumer Behavior in Electricity Markets: to Machina Economicus from Homo Agens.
Degree: PhD, Computer Engineering, 2019, Texas A&M University
URL: http://hdl.handle.net/1969.1/188707
► The electricity power market is well known for its highly volatile nature due to its innate variability characteristic of demand and the absence of practical…
(more)
▼ The electricity power market is well known for its highly volatile nature due to its innate variability characteristic of
demand and the absence of practical bulk storage at reasonable cost. Any discordance between rapid fluctuation in wholesale prices and near flat retail prices not only incurs economic inefficiency in terms of social welfare, but also creates price-inelastic wholesale
demand which severely exacerbates the volatility of wholesale electricity prices. While the market has a fundamental dynamic nature, the behavioral aspect of power consumption in
response to price changes is not well understood. This necessitate to develop a empirical modeling methodology of
demand which can potentially provide practical insights into
demand response. In the former part of this work, we focus on dynamic aspect of
demand response in Chapter 2. We first show that (i)
demand is well responsive to outlier high price surges, and (ii)
demand response can incur a certain amount of delay. Examining further data, it appears that
demand is responsive to anticipated prices. This is in conformity with our previous observations on the inertia of
demand, and testing the hypothesis that
demand actually responds to anticipated prices rather than actual real time prices is an important next step.
While it is impractical to obtain a particular individual’s own price prediction, We propose to test the hypothesis with day-ahead electricity prices (DAP). In addition, as an initial step toward the derivation of a quantitative model of electricity load and price, we propose a model of “appliance” usage as a representative basic component of electricity load. In the latter part of this work, we investigate more fundamental aspect of data-centric modeling in Chapter 3. First, we show the limitation of pure data-centric modeling strategy by proving that having a perfect knowledge on the joint distribution on price and load does not identify the load behavior in
response to price. As it turns out that the causal structure of the variables of interest is the central matter that determines load behavior identifiability, we derive a minimal identifiable causal structure of
demand response from the preexisting economic theories. Based on the discovered causal structure, we propose a minimal Bayesian model representation called “stochastic neuron” which connects machine learning technique to
demand response modeling. We show that a stochastic neuron is an explainable tool as expressive as an ordinary neural network, and well extends the arguments from “appliance” usage model.
Advisors/Committee Members: Kumar, Panganamala R. (advisor), Xie, Le (committee member), Hou, I-Hong (committee member), Chakravorty, Suman (committee member).
Subjects/Keywords: Demand Response; Electricity Markets; Bayesian Neural Networks
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APA ·
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Manager
APA (6th Edition):
An, J. (2019). On Price Responsive Consumer Behavior in Electricity Markets: to Machina Economicus from Homo Agens. (Doctoral Dissertation). Texas A&M University. Retrieved from http://hdl.handle.net/1969.1/188707
Chicago Manual of Style (16th Edition):
An, Jaeyong. “On Price Responsive Consumer Behavior in Electricity Markets: to Machina Economicus from Homo Agens.” 2019. Doctoral Dissertation, Texas A&M University. Accessed February 27, 2021.
http://hdl.handle.net/1969.1/188707.
MLA Handbook (7th Edition):
An, Jaeyong. “On Price Responsive Consumer Behavior in Electricity Markets: to Machina Economicus from Homo Agens.” 2019. Web. 27 Feb 2021.
Vancouver:
An J. On Price Responsive Consumer Behavior in Electricity Markets: to Machina Economicus from Homo Agens. [Internet] [Doctoral dissertation]. Texas A&M University; 2019. [cited 2021 Feb 27].
Available from: http://hdl.handle.net/1969.1/188707.
Council of Science Editors:
An J. On Price Responsive Consumer Behavior in Electricity Markets: to Machina Economicus from Homo Agens. [Doctoral Dissertation]. Texas A&M University; 2019. Available from: http://hdl.handle.net/1969.1/188707
Uppsala University
15.
Shepero, Mahmoud.
Modelling the Penetration Effect of Photovoltaics and Electric Vehicles on Electricity Demand and Its Implications on Tariff Structures.
Degree: Industrial Engineering & Management, 2016, Uppsala University
URL: http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-295426
► The shift towards more renewable energy sources is imminent, this shift is accelerated by the technological advancement and the rise of environmental awareness. However,…
(more)
▼ The shift towards more renewable energy sources is imminent, this shift is accelerated by the technological advancement and the rise of environmental awareness. However, this shift causes major operational problems to the current grid that is optimised for unidirectional power flow. Besides the operational problems, there are problems related to the optimal tariff scheme. In this thesis a study on the effect of the adoption of photovoltaic solar panels and the electric vehicles on the households' electricity demand profile is presented. The change on the demand profile is going to affect the current tariffs, this effect is also explored in this thesis. In this thesis real life data on household electricity use and photovoltaic power production was used. For electric vehicle charging simulated data was used. Besides that, a demand response scheme for electric vehicle is proposed in order to estimate the savings potential of this demand response on the electricity bill. The results show that the change in the demand profile is not merely a change in the total energy consumption, but it is a change in the power peaks as well. The peaks change significantly in condominiums and rental apartments, in this households' type it increases by around 80%, while in detached and row houses little change is noticed on the peaks, yet they still increase by around 10%. The demand response shows around 1- 12% savings in the distribution bill depending on the household, however it showed more incentives for condominiums and rental apartments. The current distribution tariffs perform asymmetrically with the various households. However, one tariff ensures 11.7 MSEK financial revenue for the distribution system operator, this is higher than the other tariffs' revenue by more than 28.5%. The new prospective situation requires totally different tariffs that ensure a balance between firstly a reasonable revenue for the distribution system operator and secondly incentives for consumers to self produce electricity as well as to reduce their peaks.
Subjects/Keywords: Photovoltaics; Electric vehicles; Tariffs; Demand response
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APA ·
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Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Shepero, M. (2016). Modelling the Penetration Effect of Photovoltaics and Electric Vehicles on Electricity Demand and Its Implications on Tariff Structures. (Thesis). Uppsala University. Retrieved from http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-295426
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
Shepero, Mahmoud. “Modelling the Penetration Effect of Photovoltaics and Electric Vehicles on Electricity Demand and Its Implications on Tariff Structures.” 2016. Thesis, Uppsala University. Accessed February 27, 2021.
http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-295426.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Shepero, Mahmoud. “Modelling the Penetration Effect of Photovoltaics and Electric Vehicles on Electricity Demand and Its Implications on Tariff Structures.” 2016. Web. 27 Feb 2021.
Vancouver:
Shepero M. Modelling the Penetration Effect of Photovoltaics and Electric Vehicles on Electricity Demand and Its Implications on Tariff Structures. [Internet] [Thesis]. Uppsala University; 2016. [cited 2021 Feb 27].
Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-295426.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Shepero M. Modelling the Penetration Effect of Photovoltaics and Electric Vehicles on Electricity Demand and Its Implications on Tariff Structures. [Thesis]. Uppsala University; 2016. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-295426
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Uppsala University
16.
Andersson, Karin.
Development of models for quantifying the environmental impact of demand response in electrical power distribution.
Degree: Industrial Engineering & Management, 2015, Uppsala University
URL: http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-263282
► In this report some possible consequences of introducing demand response in the electric power grid are studied. Demand response is a part of the…
(more)
▼ In this report some possible consequences of introducing demand response in the electric power grid are studied. Demand response is a part of the Smart Grid, which is a technology being developed to use our electric power grids more efficiently. Demand response programs aim to move people’s power usage over different times of the day, for example to distribute the power usage more evenly throughout the day or to permit a larger share of renewable, intermittent power sources in the system without making the delivery of electric power less stable. A distribution system operator (DSO) can encourage customers to shift their power usage between different hours by various tariffs, for example by using time-differentiated or power dependent tariffs. In this thesis, the change in power losses and possible environmental impact of introducing due to a power shift is studied. Power input curves from a DSO, Sala-Heby Energi AB, are studied and modified to simulate a power shift with an evened out electric power usage. The studies made show that in the best-case scenario, that is a electric power usage evened out to 100% each day, the power losses in the whole grid can be reduced with 2.6%. The environmental study shows that the result varies greatly with what method is chosen to do the calculations. The results are presented in kg CO2-equivalents (CO2e), and depending on method used they can either decrease or increase. The environmental study show that the environmental impact from the power usage is more dependent on the shift in power usage between hours than the decrease in electric power losses.
Subjects/Keywords: smart grid; demand response; power loss
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Export
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APA (6th Edition):
Andersson, K. (2015). Development of models for quantifying the environmental impact of demand response in electrical power distribution. (Thesis). Uppsala University. Retrieved from http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-263282
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
Andersson, Karin. “Development of models for quantifying the environmental impact of demand response in electrical power distribution.” 2015. Thesis, Uppsala University. Accessed February 27, 2021.
http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-263282.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Andersson, Karin. “Development of models for quantifying the environmental impact of demand response in electrical power distribution.” 2015. Web. 27 Feb 2021.
Vancouver:
Andersson K. Development of models for quantifying the environmental impact of demand response in electrical power distribution. [Internet] [Thesis]. Uppsala University; 2015. [cited 2021 Feb 27].
Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-263282.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Andersson K. Development of models for quantifying the environmental impact of demand response in electrical power distribution. [Thesis]. Uppsala University; 2015. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-263282
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Delft University of Technology
17.
Budez Vergara, Jonathan (author).
Quantifying the Contribution of distributed flexible loads to congestion management.
Degree: 2019, Delft University of Technology
URL: http://resolver.tudelft.nl/uuid:9cd7c0c5-6f3f-4875-802c-663262f30a88
► The electrical network must have adequate capacity to avoid congestion for all but the highest load levels. The most common solution to manage this congestion…
(more)
▼ The electrical network must have adequate capacity to avoid congestion for all but the highest load levels. The most common solution to manage this congestion is to gradually invest in new infrastructure. An alternative solution for congestion management is the use of flexible loads, or demand response. In order to determine the relative value of physical assets and demand response it is necessary to quantify the contribution of these flexible loads. This is not straightforward, particularly when the number of them is small and their response can be unreliable. The objective of this thesis is to quantify the contribution of flexible loads, especially for small group of households, by using the theory of capacity values. The approach is investigated using simple probabilistic examples
Sustainable Energy Technology
Advisors/Committee Members: Tindemans, Simon (mentor), Delft University of Technology (degree granting institution).
Subjects/Keywords: Demand response; Flexible load; Congestion management
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APA (6th Edition):
Budez Vergara, J. (. (2019). Quantifying the Contribution of distributed flexible loads to congestion management. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:9cd7c0c5-6f3f-4875-802c-663262f30a88
Chicago Manual of Style (16th Edition):
Budez Vergara, Jonathan (author). “Quantifying the Contribution of distributed flexible loads to congestion management.” 2019. Masters Thesis, Delft University of Technology. Accessed February 27, 2021.
http://resolver.tudelft.nl/uuid:9cd7c0c5-6f3f-4875-802c-663262f30a88.
MLA Handbook (7th Edition):
Budez Vergara, Jonathan (author). “Quantifying the Contribution of distributed flexible loads to congestion management.” 2019. Web. 27 Feb 2021.
Vancouver:
Budez Vergara J(. Quantifying the Contribution of distributed flexible loads to congestion management. [Internet] [Masters thesis]. Delft University of Technology; 2019. [cited 2021 Feb 27].
Available from: http://resolver.tudelft.nl/uuid:9cd7c0c5-6f3f-4875-802c-663262f30a88.
Council of Science Editors:
Budez Vergara J(. Quantifying the Contribution of distributed flexible loads to congestion management. [Masters Thesis]. Delft University of Technology; 2019. Available from: http://resolver.tudelft.nl/uuid:9cd7c0c5-6f3f-4875-802c-663262f30a88
Delft University of Technology
18.
Tzanakou, Eleni (author).
Demand Response under cyber-attacks.
Degree: 2017, Delft University of Technology
URL: http://resolver.tudelft.nl/uuid:01f1d0ca-9eef-443c-b252-2d992c5503e1
► Nowadays, by using the new ICT technologies the functional efficiency of the Smart Grid is increased but at the same time leads to a more…
(more)
▼ Nowadays, by using the new ICT technologies the functional efficiency of the Smart Grid is increased but at the same time leads to a more vulnerable system and makes the Smart Grid a potential target for cyber-attacks. In fact, cyber-attacks are becoming one of the most serious threats in critical infrastructures. More than 80% of energy companies dealt with a growth in the incidents of successful cyber-attacks in 2015 (Herring, 2016). These facts show the importance of investigating the impacts caused by cyber-attacks on the system, and the need for a systematic way to assess it. In order to gain insights into the degree of influence of cyber-attacks regarding the energy prices and the power
demand, the objective of this thesis is to shed light on the impact of two types of cyber-attacks, targeting the signals along the communication link, on the normal behavior of the system and their influence on the behavior of consumers and utilities, when a price based
Demand Response (DR) program is used.
Advisors/Committee Members: Herder, Paulien (mentor), Herdeiro Teixeira, André (mentor), Annema, Jan Anne (graduation committee), Delft University of Technology (degree granting institution).
Subjects/Keywords: Demand Response; Cyber-attacks; Smart Grid
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APA (6th Edition):
Tzanakou, E. (. (2017). Demand Response under cyber-attacks. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:01f1d0ca-9eef-443c-b252-2d992c5503e1
Chicago Manual of Style (16th Edition):
Tzanakou, Eleni (author). “Demand Response under cyber-attacks.” 2017. Masters Thesis, Delft University of Technology. Accessed February 27, 2021.
http://resolver.tudelft.nl/uuid:01f1d0ca-9eef-443c-b252-2d992c5503e1.
MLA Handbook (7th Edition):
Tzanakou, Eleni (author). “Demand Response under cyber-attacks.” 2017. Web. 27 Feb 2021.
Vancouver:
Tzanakou E(. Demand Response under cyber-attacks. [Internet] [Masters thesis]. Delft University of Technology; 2017. [cited 2021 Feb 27].
Available from: http://resolver.tudelft.nl/uuid:01f1d0ca-9eef-443c-b252-2d992c5503e1.
Council of Science Editors:
Tzanakou E(. Demand Response under cyber-attacks. [Masters Thesis]. Delft University of Technology; 2017. Available from: http://resolver.tudelft.nl/uuid:01f1d0ca-9eef-443c-b252-2d992c5503e1
Delft University of Technology
19.
Broere, Lennart (author).
Optimisation of smart-and Vehicle-to-Grid charging strategies in distribution networks: based on charging behaviour analysis.
Degree: 2018, Delft University of Technology
URL: http://resolver.tudelft.nl/uuid:74dfef2c-bbd8-4e0d-a18c-45c514e805ce
► Currently in the energy system of the Netherlands, lower level consumer demand flexibility is rather obsolete due to sufficient capacity limits of the distribution network…
(more)
▼ Currently in the energy system of the Netherlands, lower level consumer
demand flexibility is rather obsolete due to sufficient capacity limits of the distribution network and supply of capacity mechanisms by large industrial actors. However, in the upcoming 10- 20 years, the power system is in transition to become decentralised with a higher share of renewable energy sources and significant increase in consumption. An operational control structure in the power system, where private consumers provide flexible capacity, is an effective and economical efficient approach to make sure the regulated process of electricity generation to supply at consumers is secure and reliable. Currently as a result of the EU Energy Efficiency Directive of 2012, an institutional base is presented for development initiatives of
demand response in Europe [17]. Technical and regulatory standards now enable
demand response flexibility to be offered on the wholesale and retail energy market and allow for consumer participation [34].
Demand response schemes are usually distinguished by the various motivation methods offered to the participating consumers. Programs include in general two control methods, centralised direct load control or time-based and incentive-based DR. Because these schemes rely on
demand response decision-making by means of a centralised (multi) aggregator perspective, direct load control can be precisely adjusted to technical (local) grid constraints [69]. Practically, the objective of DR in this research is used to reduce congestion in distribution grids by moving part of BEV energy
demand from (evening) peaks to the afternoon or night with direct control. By achieving these measures potential benefits arise, including the most profound in the distribution grid [29]: • Optimising local grid assets by increasing the utilisation factor, and thereby maximise asset efficiency and subsequently decrease costs, which is beneficial for the DSO • Scheduling of peak charging
demand to aid congestion in distribution grids. The modelling of the
demand response charging strategies in Amsterdam fills the knowledge gap towards handling congestion for the DSO. It also provides a new study that addresses the potential to postpone future distribution grid investments by using charging strategies specifically for Amsterdam. The main research question that this study addresses is therefore: What is the value of
demand response management in a Vehicle-to-Grid network and does it provide increased benefits to smart charging for consumers and the distribution system operator in Amsterdam? In order to grasp the
subject of congestion prevention within the time limits of graduation, the scope of this study is limited to assess the first mentioned item by modelling charging
demand, and subsequently simulate optimal
demand response charging strategies for a case study in Amsterdam’s local power grid. The motivation for this study is threefold. Firstly, providing insight and recommendations in Amsterdam’s BEV…
Advisors/Committee Members: Hakvoort, Rudi (mentor), Kroesen, Maarten (mentor), Delft University of Technology (degree granting institution).
Subjects/Keywords: Smart charging; Vehicle-to-Grid; Demand Response
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APA (6th Edition):
Broere, L. (. (2018). Optimisation of smart-and Vehicle-to-Grid charging strategies in distribution networks: based on charging behaviour analysis. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:74dfef2c-bbd8-4e0d-a18c-45c514e805ce
Chicago Manual of Style (16th Edition):
Broere, Lennart (author). “Optimisation of smart-and Vehicle-to-Grid charging strategies in distribution networks: based on charging behaviour analysis.” 2018. Masters Thesis, Delft University of Technology. Accessed February 27, 2021.
http://resolver.tudelft.nl/uuid:74dfef2c-bbd8-4e0d-a18c-45c514e805ce.
MLA Handbook (7th Edition):
Broere, Lennart (author). “Optimisation of smart-and Vehicle-to-Grid charging strategies in distribution networks: based on charging behaviour analysis.” 2018. Web. 27 Feb 2021.
Vancouver:
Broere L(. Optimisation of smart-and Vehicle-to-Grid charging strategies in distribution networks: based on charging behaviour analysis. [Internet] [Masters thesis]. Delft University of Technology; 2018. [cited 2021 Feb 27].
Available from: http://resolver.tudelft.nl/uuid:74dfef2c-bbd8-4e0d-a18c-45c514e805ce.
Council of Science Editors:
Broere L(. Optimisation of smart-and Vehicle-to-Grid charging strategies in distribution networks: based on charging behaviour analysis. [Masters Thesis]. Delft University of Technology; 2018. Available from: http://resolver.tudelft.nl/uuid:74dfef2c-bbd8-4e0d-a18c-45c514e805ce
Victoria University of Wellington
20.
Alzaanin, Hatem I.
The Potential of Utilising Residential Demand Response to Balance the Fluctuation of Wind Power in New Zealand.
Degree: 2014, Victoria University of Wellington
URL: http://hdl.handle.net/10063/4381
► The substantial penetration of wind power introduces increased flexibility requirements on the power system and puts increased pressure on the instantaneous reserve levels required. Instantaneous…
(more)
▼ The substantial penetration of wind power introduces increased flexibility requirements on the power system and puts increased pressure on the instantaneous reserve levels required. Instantaneous reserves are a security product that ensures that electricity
demand can continue to be met in the event of unplanned generation or transmission interruptions. This reserve must be available to respond very quickly to generation-
demand variability. While this is an integral component of the power system, providing instantaneous reserve increases the production cost of power. More calls from energy researchers and stakeholders ask for loads to play an increasingly important role in balancing the short timescale fluctuations in generated wind power. The purpose of this study is to assess the current level of
demand responsiveness among domestic refrigerators, freezers, and water heaters and their potential to contribute towards instantaneous reserve and balance the fluctuation of wind. Refrigerators, freezers, and water heaters can generally store energy due to their thermal mass. Interrupting these domestic loads for short time by employing direct load control strategies makes it possible to control these appliances by turning them on or off before their reach their maximum or minimum temperatures or by slightly modifying their temperature set point. Using this strategy helps to ensure that the overall satisfaction of consumers should not be affected. This study first modelled the load profiles of the participated residential appliances and statistically assessed the potential of controlling these residential loads using direct load control strategies to contribute towards instantaneous reserves to mitigate and balance the fluctuation of wind power in the years: 2014, 2020 and 2030. In the second section, it demonstrated the capabilities of the assessed residential responsive loads within Wellington Region network to compensate for and balance the fluctuation of wind power generated from the West Wind Farm in seven selected days in 2013-2014 as a showcase. Such technology can enable a power system operator to remove the burden of both providing instantaneous reserve from conventional sources, and instead maintain such capacity from available residential
demand response. The study ends with recommendations to engage residential loads in fast timescale
demand response and suggests directions for future research.
Advisors/Committee Members: Rayudu, Ramesh.
Subjects/Keywords: Demand response; Wind power; Power system
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APA (6th Edition):
Alzaanin, H. I. (2014). The Potential of Utilising Residential Demand Response to Balance the Fluctuation of Wind Power in New Zealand. (Masters Thesis). Victoria University of Wellington. Retrieved from http://hdl.handle.net/10063/4381
Chicago Manual of Style (16th Edition):
Alzaanin, Hatem I. “The Potential of Utilising Residential Demand Response to Balance the Fluctuation of Wind Power in New Zealand.” 2014. Masters Thesis, Victoria University of Wellington. Accessed February 27, 2021.
http://hdl.handle.net/10063/4381.
MLA Handbook (7th Edition):
Alzaanin, Hatem I. “The Potential of Utilising Residential Demand Response to Balance the Fluctuation of Wind Power in New Zealand.” 2014. Web. 27 Feb 2021.
Vancouver:
Alzaanin HI. The Potential of Utilising Residential Demand Response to Balance the Fluctuation of Wind Power in New Zealand. [Internet] [Masters thesis]. Victoria University of Wellington; 2014. [cited 2021 Feb 27].
Available from: http://hdl.handle.net/10063/4381.
Council of Science Editors:
Alzaanin HI. The Potential of Utilising Residential Demand Response to Balance the Fluctuation of Wind Power in New Zealand. [Masters Thesis]. Victoria University of Wellington; 2014. Available from: http://hdl.handle.net/10063/4381
California State University – Sacramento
21.
Paranjpe, Mithila.
Security and privacy in demand response systems in smart grid.
Degree: MS, Computer Science, 2011, California State University – Sacramento
URL: http://hdl.handle.net/10211.9/874
► Demand response programs are used in smart grid to improve stability of the electric grid and to reduce consumption of electricity and costs during peak…
(more)
▼ Demand response programs are used in smart grid to improve stability of the electric grid and to reduce consumption of electricity and costs during peak times. One of the key aspects in
demand response programs is for utilities to provide secure information to customers. Various threats and attacks could hinder the propagation of crucial information related to price, electricity usage, device and customer information. Privacy of utility, customer and other
demand response entities could be at risk due to the vulnerabilities. This project analyzes security issues, best practices, and research issues in
demand response program. It illustrates various security issues and possible best practices for the
demand response program. It covers proper data handling practices for maintaining confidentiality, integrity, availability, and accountability. Some of the countermeasures are inadequate for Smart Grid architecture and hence require more research. Some of the research issues are discussed. In conclusion, this project helped evaluate security privacy issues and possible best practices for energy efficient solutions associated with
demand response system.
Advisors/Committee Members: Ghansah, Isaac.
Subjects/Keywords: OpenADR; Mithila; Demand response; Smart grid
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Manager
APA (6th Edition):
Paranjpe, M. (2011). Security and privacy in demand response systems in smart grid. (Masters Thesis). California State University – Sacramento. Retrieved from http://hdl.handle.net/10211.9/874
Chicago Manual of Style (16th Edition):
Paranjpe, Mithila. “Security and privacy in demand response systems in smart grid.” 2011. Masters Thesis, California State University – Sacramento. Accessed February 27, 2021.
http://hdl.handle.net/10211.9/874.
MLA Handbook (7th Edition):
Paranjpe, Mithila. “Security and privacy in demand response systems in smart grid.” 2011. Web. 27 Feb 2021.
Vancouver:
Paranjpe M. Security and privacy in demand response systems in smart grid. [Internet] [Masters thesis]. California State University – Sacramento; 2011. [cited 2021 Feb 27].
Available from: http://hdl.handle.net/10211.9/874.
Council of Science Editors:
Paranjpe M. Security and privacy in demand response systems in smart grid. [Masters Thesis]. California State University – Sacramento; 2011. Available from: http://hdl.handle.net/10211.9/874
California State University – Sacramento
22.
Mukkamala, Rohan.
Analysis of SIP's applicability in Smart Grid environment.
Degree: MS, Computer Science, 2011, California State University – Sacramento
URL: http://hdl.handle.net/10211.9/1211
► Smart Grid is the modernization of the existing power grid, which utilizes the digital technology for the two-way exchange of information and electricity between the…
(more)
▼ Smart Grid is the modernization of the existing power grid, which utilizes the digital technology for the two-way exchange of information and electricity between the utilities and consumers. It requires a ???communication overlay??? network, which facilitates intelligent communication between various nodes of the heterogeneous Smart Grid network. SIP is widely used in similar ???communication overlay??? networks for the IP telephony as an established de-facto protocol for ???session management???. This project discusses the use of SIP in the Smart Grid environment.
This project gives a detailed analysis on the appropriateness of using SIP (Session Initiation Protocol) in the Smart Grid environment. The project mainly investigates the role of SIP in HAN (Home Area Network) and
Demand Response System. In HAN, one of the network segments of the Smart Grid network, the interaction of the residential or commercial devices (such as TV, washing machine, surveillance camera, etc.) with the smart meter/energy management system and vice-versa is analyzed. An observation of the proposed SIP-Zigbee interworking architecture involving the SIP presence framework is done. The Event-service model of SIP is analyzed which makes it an effective protocol and architecture to realize open automated
demand response system in the smart grid.
In this report, SIP???s support for mobile metering, the realization of an efficient SIP-Zigbee Internetworking architecture and an Open-ADR system based on SIP???s Presence Framework and Event Service model respectively strengthens the candidacy of SIP for deployment in various components of the Smart Grid infrastructure.
Advisors/Committee Members: Ghansah, Isaac.
Subjects/Keywords: Presence framework; Demand response; Home area networks
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APA ·
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Manager
APA (6th Edition):
Mukkamala, R. (2011). Analysis of SIP's applicability in Smart Grid environment. (Masters Thesis). California State University – Sacramento. Retrieved from http://hdl.handle.net/10211.9/1211
Chicago Manual of Style (16th Edition):
Mukkamala, Rohan. “Analysis of SIP's applicability in Smart Grid environment.” 2011. Masters Thesis, California State University – Sacramento. Accessed February 27, 2021.
http://hdl.handle.net/10211.9/1211.
MLA Handbook (7th Edition):
Mukkamala, Rohan. “Analysis of SIP's applicability in Smart Grid environment.” 2011. Web. 27 Feb 2021.
Vancouver:
Mukkamala R. Analysis of SIP's applicability in Smart Grid environment. [Internet] [Masters thesis]. California State University – Sacramento; 2011. [cited 2021 Feb 27].
Available from: http://hdl.handle.net/10211.9/1211.
Council of Science Editors:
Mukkamala R. Analysis of SIP's applicability in Smart Grid environment. [Masters Thesis]. California State University – Sacramento; 2011. Available from: http://hdl.handle.net/10211.9/1211
University of Georgia
23.
Zahedi, Cameron Saeed.
Economic welfare gains from demand response and real-time pricing for industrial processes.
Degree: 2016, University of Georgia
URL: http://hdl.handle.net/10724/35519"
► Sustainable electricity supply has come to the forefront of current affairs in the U.S. The power grid of the future requires significant system-level upgrades to…
(more)
▼ Sustainable electricity supply has come to the forefront of current affairs in the U.S. The power grid of the future requires significant system-level upgrades to an outdated infrastructure. A combination of new technology and new energy
management strategies will be necessary. An important topic of discussion is Demand Response (DR), and usually coupled with it is Real-Time Electricity Pricing (RTP). The former refers to electricity users' reducing their demand, often receiving some
kind of benefit in exchange, which theoretically helps grid reliability during system stress and levels out market prices. The latter refers to the grid's pricing electricity based on current generation, which theoretically allows for more accurate price
effects. Through analyzing a model of industrial load-shifting as a demand response method employed by a customer with day-ahead real-time pricing, this paper demonstrates the economic welfare benefits that can accrue from real-time pricing and demand
response in modern power systems.
Subjects/Keywords: Energy Economics; Demand Response; Real-Time Pricing
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APA (6th Edition):
Zahedi, C. S. (2016). Economic welfare gains from demand response and real-time pricing for industrial processes. (Thesis). University of Georgia. Retrieved from http://hdl.handle.net/10724/35519"
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
Zahedi, Cameron Saeed. “Economic welfare gains from demand response and real-time pricing for industrial processes.” 2016. Thesis, University of Georgia. Accessed February 27, 2021.
http://hdl.handle.net/10724/35519".
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Zahedi, Cameron Saeed. “Economic welfare gains from demand response and real-time pricing for industrial processes.” 2016. Web. 27 Feb 2021.
Vancouver:
Zahedi CS. Economic welfare gains from demand response and real-time pricing for industrial processes. [Internet] [Thesis]. University of Georgia; 2016. [cited 2021 Feb 27].
Available from: http://hdl.handle.net/10724/35519".
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Zahedi CS. Economic welfare gains from demand response and real-time pricing for industrial processes. [Thesis]. University of Georgia; 2016. Available from: http://hdl.handle.net/10724/35519"
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Delft University of Technology
24.
Alfreðsson, Andri (author).
Facilitating large-scale EV penetration in Iceland: Coordination of charging load with demand response to increase distribution grid utilisation.
Degree: 2020, Delft University of Technology
URL: http://resolver.tudelft.nl/uuid:5e5661f6-dfdb-4cee-8d4e-b02cc8e1fee5
► With increased awareness of anthropogenic emissions, industries and sectors worldwide are changing rapidly. One of those sectors is the transport sector which has seen immense…
(more)
▼ With increased awareness of anthropogenic emissions, industries and sectors worldwide are changing rapidly. One of those sectors is the transport sector which has seen immense change with the increase of electric vehicles in recent years. Although these electric vehicles reduce emissions and are a welcoming sign of change, they greatly increase electrical demand, especially on the residential distribution grids. Case studies and research on demand response with EVs has been increasing over the last years in an attempt to reduce this load impact. This thesis aims to explore how the charging load of a large EV fleet impacts the distribution grid of the capital region in Iceland and how it can be minimised with demand response strategies. A load model was created for the distribution grid and the results indicate that large-scale EV penetration can have a huge load impact. Furthermore, the results showed that demand response strategies can greatly reduce that impact and offer significant peak reductions. However, based on a bottom-up approach, the lower levels of the distribution grid seem to be worst affected, with and without demand response strategies. Future research should be focused on mapping these local grid effects and conducting more in-depth analyses on that level.
Complex Systems Engineering and Management (CoSEM)
Advisors/Committee Members: Hakvoort, R.A. (mentor), Warnier, M.E. (graduation committee), Delft University of Technology (degree granting institution).
Subjects/Keywords: Demand Response; EV; Load modelling; Distribution Grid
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APA (6th Edition):
Alfreðsson, A. (. (2020). Facilitating large-scale EV penetration in Iceland: Coordination of charging load with demand response to increase distribution grid utilisation. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:5e5661f6-dfdb-4cee-8d4e-b02cc8e1fee5
Chicago Manual of Style (16th Edition):
Alfreðsson, Andri (author). “Facilitating large-scale EV penetration in Iceland: Coordination of charging load with demand response to increase distribution grid utilisation.” 2020. Masters Thesis, Delft University of Technology. Accessed February 27, 2021.
http://resolver.tudelft.nl/uuid:5e5661f6-dfdb-4cee-8d4e-b02cc8e1fee5.
MLA Handbook (7th Edition):
Alfreðsson, Andri (author). “Facilitating large-scale EV penetration in Iceland: Coordination of charging load with demand response to increase distribution grid utilisation.” 2020. Web. 27 Feb 2021.
Vancouver:
Alfreðsson A(. Facilitating large-scale EV penetration in Iceland: Coordination of charging load with demand response to increase distribution grid utilisation. [Internet] [Masters thesis]. Delft University of Technology; 2020. [cited 2021 Feb 27].
Available from: http://resolver.tudelft.nl/uuid:5e5661f6-dfdb-4cee-8d4e-b02cc8e1fee5.
Council of Science Editors:
Alfreðsson A(. Facilitating large-scale EV penetration in Iceland: Coordination of charging load with demand response to increase distribution grid utilisation. [Masters Thesis]. Delft University of Technology; 2020. Available from: http://resolver.tudelft.nl/uuid:5e5661f6-dfdb-4cee-8d4e-b02cc8e1fee5
University of Texas – Austin
25.
Hernandez, Guillermo, active 21st century.
Utility management of plug-in electric vehicle residential charging.
Degree: MSin Engineering, Electrical and Computer Engineering, 2014, University of Texas – Austin
URL: http://hdl.handle.net/2152/26005
► The purpose of this study is to identify realistic opportunities and barriers regarding PEV charge management by analyzing real-world PEV data from customers in the…
(more)
▼ The purpose of this study is to identify realistic opportunities and barriers regarding PEV charge management by analyzing real-world PEV data from customers in the Austin Energy service area and evaluating direct, quantifiable economic value benefits as it relates new revenue, cost avoidance, CO2 reductions, and MW potential for peak shaving. The main objective is to provide business analysis to support the strategic road-map for Austin Energy PEV home charging programs. Three main charge program implementations are considered: Uncontrolled Charging, Time of Use Rates, and One Way Utility Control.
The data used for the analysis includes 45 households with PEVs from Mueller area; 24 were under a Time of Use trial with pricing incentives to charge at night, and 21 receive normal Austin Energy rates. Data analysis shows that 66% of Time of Use trial group successfully shifted PEV load to Off Peak hours (10:00PM to 6:00AM).
The potential of One Way control, based on load availability for interruption, shows that it will not be possible to implement until there are 37,000 PEVs in the Austin Energy area. Uncontrolled Charging represents a risk by increasing load during the residential peak. Time of Use Rates program will incentivize load shifting, reduce wholesale energy costs for Austin Energy while allowing customers to reduce their overall electricity bill.
Advisors/Committee Members: Baldick, Ross (advisor), Webber, Michael E., 1971- (advisor).
Subjects/Keywords: Electric vehicle; Time of use; Demand response
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APA (6th Edition):
Hernandez, Guillermo, a. 2. c. (2014). Utility management of plug-in electric vehicle residential charging. (Masters Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/26005
Chicago Manual of Style (16th Edition):
Hernandez, Guillermo, active 21st century. “Utility management of plug-in electric vehicle residential charging.” 2014. Masters Thesis, University of Texas – Austin. Accessed February 27, 2021.
http://hdl.handle.net/2152/26005.
MLA Handbook (7th Edition):
Hernandez, Guillermo, active 21st century. “Utility management of plug-in electric vehicle residential charging.” 2014. Web. 27 Feb 2021.
Vancouver:
Hernandez, Guillermo a2c. Utility management of plug-in electric vehicle residential charging. [Internet] [Masters thesis]. University of Texas – Austin; 2014. [cited 2021 Feb 27].
Available from: http://hdl.handle.net/2152/26005.
Council of Science Editors:
Hernandez, Guillermo a2c. Utility management of plug-in electric vehicle residential charging. [Masters Thesis]. University of Texas – Austin; 2014. Available from: http://hdl.handle.net/2152/26005
University of Tennessee – Knoxville
26.
McKee, Evan.
Deep Reinforcement Learning for Real-Time Residential HVAC Control.
Degree: MS, Electrical Engineering, 2019, University of Tennessee – Knoxville
URL: https://trace.tennessee.edu/utk_gradthes/5579
► The model-free Deep Reinforcement Learning (DRL) environment developed for this work attempts to minimize energy cost during residential heating, ventilation, and air conditioning (HVAC) operation.…
(more)
▼ The model-free Deep Reinforcement Learning (DRL) environment developed for this work attempts to minimize energy cost during residential heating, ventilation, and air conditioning (HVAC) operation. The HVAC load associated with heating and cooling is an ideal candidate for price optimization through automation for two reasons: Its power footprint in a typical home is sizeable, and the required level of participation from an inhabitant is passive. HVAC is difficult to accurately model and unique for every home, so online machine learning is used to allow for real-time readjustment in performance. Energy cost for the cooling unit shown in this work is minimized by scheduling on/off commands around dynamic prices. By taking advantage of precooling events that take place when the price is low, the agent is able to reduce operational cost without violating user comfort. This work applies to multi-zone cooling operation, where each zone’s indoor temperature affects the others, and will be extended to include heating as well as management of other home loads. After training in simulation, the learner was tested in a real home where it achieved a 21.4% cost reduction when compared to rule-based, fixed-setpoint operation.
Advisors/Committee Members: Fangxing Li, Amir Sadovnik, Hector Pulgar.
Subjects/Keywords: machine learning; deep learning; demand response; optimization
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APA (6th Edition):
McKee, E. (2019). Deep Reinforcement Learning for Real-Time Residential HVAC Control. (Thesis). University of Tennessee – Knoxville. Retrieved from https://trace.tennessee.edu/utk_gradthes/5579
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
McKee, Evan. “Deep Reinforcement Learning for Real-Time Residential HVAC Control.” 2019. Thesis, University of Tennessee – Knoxville. Accessed February 27, 2021.
https://trace.tennessee.edu/utk_gradthes/5579.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
McKee, Evan. “Deep Reinforcement Learning for Real-Time Residential HVAC Control.” 2019. Web. 27 Feb 2021.
Vancouver:
McKee E. Deep Reinforcement Learning for Real-Time Residential HVAC Control. [Internet] [Thesis]. University of Tennessee – Knoxville; 2019. [cited 2021 Feb 27].
Available from: https://trace.tennessee.edu/utk_gradthes/5579.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
McKee E. Deep Reinforcement Learning for Real-Time Residential HVAC Control. [Thesis]. University of Tennessee – Knoxville; 2019. Available from: https://trace.tennessee.edu/utk_gradthes/5579
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
University of Tennessee – Knoxville
27.
Asadinejad, Ailin.
Electricity Market Designs for Demand Response from Residential Customers.
Degree: 2017, University of Tennessee – Knoxville
URL: https://trace.tennessee.edu/utk_graddiss/4378
► The main purpose of this dissertation is to design an appropriate tariff program for residential customers that encourages customers to participate in the system while…
(more)
▼ The main purpose of this dissertation is to design an appropriate tariff program for residential customers that encourages customers to participate in the system while satisfying market operators and utilities goals. This research investigates three aspects critical for successful programs: tariff designs for DR, impact of renewable on such tariffs, and load elasticity estimates. First, both categories of DR are modeled based on the demand-price elasticity concept and used to design an optimum scheme for achieving the maximum benefit of DR. The objective is to not only reduce costs and improve reliability but also to increase customer acceptance of a DR program by limiting price volatility. A time of use (TOU) program is considered for a PB scheme designed using a monthly peak and off peak tariff. For the IBDR, a novel optimization is proposed that in addition to calculation of an adequate and a reasonable amount of load change for the incentive also finds the best times to request DR.
Second, the effect of both DR programs under a high penetration of renewable resources is investigated. LMP variation after renewable expansion is more highly correlated with renewable’s intermittent output than the load profile. As a result, a TOU program is difficult to successfully implement; however, analysis shows IBDR can diminish most of the volatile price changes in WECC. To model risk associated with renewable uncertainty, a robust optimization is designed considering market price and elasticity uncertainty.
Third, a comprehensive study to estimate residential load elasticity in an IBDR program. A key component in all demand response programs design is elasticity, which implies customer reaction to LSEs offers. Due to limited information, PB elasticity is used in IBDR as well. Customer elasticity is calculated using data from two nationwide surveys and integrated with a detailed residential load model. In addition, IB elasticity is reported at the individual appliance level, which is more effective than one for the aggregate load of the feeder. Considering the importance of HVAC in the aggregate load signal, its elasticity is studied in greater detail and estimated for different customer groupings.
Subjects/Keywords: Demand Response; Incentive Based Demand Response; Residential Elasticity; Renewable Energy; Power and Energy
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APA (6th Edition):
Asadinejad, A. (2017). Electricity Market Designs for Demand Response from Residential Customers. (Doctoral Dissertation). University of Tennessee – Knoxville. Retrieved from https://trace.tennessee.edu/utk_graddiss/4378
Chicago Manual of Style (16th Edition):
Asadinejad, Ailin. “Electricity Market Designs for Demand Response from Residential Customers.” 2017. Doctoral Dissertation, University of Tennessee – Knoxville. Accessed February 27, 2021.
https://trace.tennessee.edu/utk_graddiss/4378.
MLA Handbook (7th Edition):
Asadinejad, Ailin. “Electricity Market Designs for Demand Response from Residential Customers.” 2017. Web. 27 Feb 2021.
Vancouver:
Asadinejad A. Electricity Market Designs for Demand Response from Residential Customers. [Internet] [Doctoral dissertation]. University of Tennessee – Knoxville; 2017. [cited 2021 Feb 27].
Available from: https://trace.tennessee.edu/utk_graddiss/4378.
Council of Science Editors:
Asadinejad A. Electricity Market Designs for Demand Response from Residential Customers. [Doctoral Dissertation]. University of Tennessee – Knoxville; 2017. Available from: https://trace.tennessee.edu/utk_graddiss/4378
28.
Martins, Hugo Emanuel Chainho da Costa.
Consumo inteligente de energia elétrica.
Degree: 2015, Repositório Científico do Instituto Politécnico de Lisboa
URL: https://www.rcaap.pt/detail.jsp?id=oai:repositorio.ipl.pt:10400.21/5607
► Trabalho Final de Mestrado para obtenção do grau de Mestre em Engenharia de Redes de Comunicação e Multimédia
Este trabalho realiza uma solução de Demand…
(more)
▼ Trabalho Final de Mestrado para obtenção do grau de Mestre em Engenharia de Redes de Comunicação e Multimédia
Este trabalho realiza uma solução de Demand Response(DR) que tem como objetivo, implementar um sistema informático distribuído para controlo do consumo de cargas elétricas.
A implementação deste sistema baseia-se na norma OpenADR2.0, onde são definidas as entidades Virtual Top Node(VTN) e Virtual End Node (VEN). O VTN é responsável por gerir e controlar todas as atividades de DR (ex. utilities,
Independent System Operators) que são depois enviadas para os VENs. Os VENs têm como função controlar cargas, mediante um conjunto de mensagens definidas na norma OpenADR2.0. É implementado um VTN, com a criação de uma aplicação Web. O servidor Webutilizado no trabalho foi o Apache Tomcat versão 8.0.23. O VEN, é implementado com uma aplicação Java. Realiza-se também um ensaio onde o VEN é instalado num Raspberry PI paraa ativação de uma lâmpada que simboliza uma carga no consumidor.
É também proposto a criação da noção de estado de um VEN, e um programa de DR que cumpra os objetivos deste trabalho. O programa de DR proposto tem dois modos de funcionamento. No modo para diminuição de picos de carga despacham-se as cargas de forma a diminuir os valores máximos do diagrama de carga previsto. No modo para aproveitamento máximo do potencial renovável o despacho das cargas desloca o início do seu funcionamento, quando o valor da previsão de produção renovável em relação á previsão de carga é mais elevado.
Como principais conclusões, tem-se que a aplicação da norma OpenADR2.0, revelou-se ser vantajosa permitindo criar um modelode dados e padrões de troca de informação de forma eficaz e simples. Tanto o VTN como o VEN implementados realizam todas as funcionalidades idealizadas para este trabalho. O programa de DR proposto cumpre também os objetivos propostos.
This work presents a Demand Response (DR) solution, which aims to implement a distributed computing system to controlthe consumption of electrical loads.
The system implemented is based on OpenADR2.0 standard, which defines the entities Virtual Top Node (VTN) and Virtual End Node (VEN). The VTN is responsible for managing and controlling all DR activities (eg. Utilities,
Independent System Operators) which are then sent to VENs. The VENs have the function to control loads, through a set of defined messages in OpenADR2.0 standard. This work implements a VTN, with the creation of a Web application.
The Web server used was the Apache Tomcat version 8.0.23. The VEN, is implemented with a Java application. This work conducts also an experiment where the VEN is installed on a Raspberry PI for activation of a lamp, which symbolizes a load on the consumer.
A VEN state notion, and a DR program that meets theobjectives of this work are also proposed. The proposed DR program has two modes of operation. In the Peak Clipping mode, the…
Advisors/Committee Members: Barata, Manuel Martins.
Subjects/Keywords: Demand side management; Demand response; OpenADR2.0; Raspberry Pi
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APA (6th Edition):
Martins, H. E. C. d. C. (2015). Consumo inteligente de energia elétrica. (Thesis). Repositório Científico do Instituto Politécnico de Lisboa. Retrieved from https://www.rcaap.pt/detail.jsp?id=oai:repositorio.ipl.pt:10400.21/5607
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
Martins, Hugo Emanuel Chainho da Costa. “Consumo inteligente de energia elétrica.” 2015. Thesis, Repositório Científico do Instituto Politécnico de Lisboa. Accessed February 27, 2021.
https://www.rcaap.pt/detail.jsp?id=oai:repositorio.ipl.pt:10400.21/5607.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Martins, Hugo Emanuel Chainho da Costa. “Consumo inteligente de energia elétrica.” 2015. Web. 27 Feb 2021.
Vancouver:
Martins HECdC. Consumo inteligente de energia elétrica. [Internet] [Thesis]. Repositório Científico do Instituto Politécnico de Lisboa; 2015. [cited 2021 Feb 27].
Available from: https://www.rcaap.pt/detail.jsp?id=oai:repositorio.ipl.pt:10400.21/5607.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Martins HECdC. Consumo inteligente de energia elétrica. [Thesis]. Repositório Científico do Instituto Politécnico de Lisboa; 2015. Available from: https://www.rcaap.pt/detail.jsp?id=oai:repositorio.ipl.pt:10400.21/5607
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
University of New South Wales
29.
Malik, Anam.
Demand Response Opportunities with Residential Appliances in Smart Grids.
Degree: Electrical Engineering & Telecommunications, 2018, University of New South Wales
URL: http://handle.unsw.edu.au/1959.4/61531
► Supply-demand mismatch and demand peaks jeopardize the reliability of power systems and incur severe implications such as huge network investment, increased generation capacity and electricity…
(more)
▼ Supply-
demand mismatch and
demand peaks jeopardize the reliability of power systems and incur severe implications such as huge network investment, increased generation capacity and electricity cost to customers. The increased penetration of renewable energy in future will reduce the carbon footprint, however, their reduced inertia and variable output will make the power system vulnerable to disturbance, further exacerbating the problem by reducing controllability of generators. In the past,
demand side management has shown great potential in reducing power mismatch as well as
demand peaks. The potential of
demand response in peak clipping, valley filling and reducing price volatility is already explored in the literature. Residential appliances such as refrigerators, air-conditioners, hot water systems (EWHs) have large thermal inertia, and therefore represent energy storage opportunities showing great
demand response potential. However, increased penetration of renewables as well as high energy consuming residential appliances such as aircons and EWHs bring new challenges to
demand response making it meaningful to discover novel
demand control strategies. The thesis presents a detailed study of
demand control approaches and peak
demand drivers as well as their limitations. These are addressed by undertaking a study of centralized and decentralized control system topologies, modelling power network setup with conventional diesel and wind generation, modelling refrigerators as control loads for power balancing, studying large scale residential appliance level data from Australia’s largest network provider, Ausgrid and using the data to investigate major
demand drivers in summer and winter
demand peaks as well as studying
demand response opportunities with them. Contributions of the thesis include introducing a novel hybrid control topology for supply-
demand balancing by using domestic refrigerators as control loads in a hybrid power network setup, in addition to analysing Smart Grid Smart City data to study the actual contribution of aircons and other residential appliances. Clustered operational models combined with aircon load control strategies are used to study possible peak load reductions from aircons at the New South Wales state level. Also, winter and summer peak data are studied for many zone substations (ZS) and
demand response opportunities with potential controllable loads at the ZS are investigated.
Advisors/Committee Members: Ravishankar, Jayashri, Electrical Engineering & Telecommunications, Faculty of Engineering, UNSW, MacGill, Iain, Electrical Engineering & Telecommunications, Faculty of Engineering, UNSW.
Subjects/Keywords: Demand Response; Smart Grids; Residential; Frequency Regulation; Peak Demand
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APA ·
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MLA ·
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CSE |
Export
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Manager
APA (6th Edition):
Malik, A. (2018). Demand Response Opportunities with Residential Appliances in Smart Grids. (Doctoral Dissertation). University of New South Wales. Retrieved from http://handle.unsw.edu.au/1959.4/61531
Chicago Manual of Style (16th Edition):
Malik, Anam. “Demand Response Opportunities with Residential Appliances in Smart Grids.” 2018. Doctoral Dissertation, University of New South Wales. Accessed February 27, 2021.
http://handle.unsw.edu.au/1959.4/61531.
MLA Handbook (7th Edition):
Malik, Anam. “Demand Response Opportunities with Residential Appliances in Smart Grids.” 2018. Web. 27 Feb 2021.
Vancouver:
Malik A. Demand Response Opportunities with Residential Appliances in Smart Grids. [Internet] [Doctoral dissertation]. University of New South Wales; 2018. [cited 2021 Feb 27].
Available from: http://handle.unsw.edu.au/1959.4/61531.
Council of Science Editors:
Malik A. Demand Response Opportunities with Residential Appliances in Smart Grids. [Doctoral Dissertation]. University of New South Wales; 2018. Available from: http://handle.unsw.edu.au/1959.4/61531
NSYSU
30.
Astrid, Erita.
Congestion Management Considering Demand Response with Multiple Fuels.
Degree: Master, IMEPE, 2016, NSYSU
URL: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0520116-121250
► A new framework of real time congestion management in deregulated and competitive power system based on a combination of Demand Response program and generation re-dispatch…
(more)
▼ A new framework of real time congestion management in deregulated and
competitive power system based on a combination of
Demand Response program and generation re-dispatch is proposed in this research.
Demand Response program is implemented through customerâs participation who volunteer to reduce their consumption during congestion. The program is modeled based on
demand elasticity and considering incentives. Different level of
demand elasticity values are introduced to the customers in the simulation to examine their contribution in congestion relief. The simulation test is conducted on IEEE 30 bus system using Optimal Power Flow. The results of this optimization are the cost to manage congestion and optimal re-dispatch of generators with participation of customers in
Demand Response Program.
Advisors/Committee Members: Ta-Peng Tsao (chair), Jen-Hao Teng (chair), Wei-Min Lin (committee member).
Subjects/Keywords: Generation Re-dispatch; Demand Elasticity; Incentive; Optimal Power Flow; Demand Response; Congestion Management
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APA ·
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APA (6th Edition):
Astrid, E. (2016). Congestion Management Considering Demand Response with Multiple Fuels. (Thesis). NSYSU. Retrieved from http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0520116-121250
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
Astrid, Erita. “Congestion Management Considering Demand Response with Multiple Fuels.” 2016. Thesis, NSYSU. Accessed February 27, 2021.
http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0520116-121250.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Astrid, Erita. “Congestion Management Considering Demand Response with Multiple Fuels.” 2016. Web. 27 Feb 2021.
Vancouver:
Astrid E. Congestion Management Considering Demand Response with Multiple Fuels. [Internet] [Thesis]. NSYSU; 2016. [cited 2021 Feb 27].
Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0520116-121250.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Astrid E. Congestion Management Considering Demand Response with Multiple Fuels. [Thesis]. NSYSU; 2016. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0520116-121250
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
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Open Access Theses and Dissertations