Pool-based Demand Response Exchange: Concept and modeling

Nguyen, The-Vinh; Negnevitsky, M; Groot, M. de

doi:10.1109/pes.2011.6039051

Cited by 61 publications

(79 citation statements)

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“…[17] proposes some models for the upcoming retail markets in the power system. However, the concept where DR is treated as a trading commodity in the retail market was introduced in few literature [18][19][20]. Nguyen et al [18] devise a DR Exchange, in which buyers and sellers trade DR in a pool-based market.…”

Section: Retail Marketmentioning

confidence: 99%

“…However, the concept where DR is treated as a trading commodity in the retail market was introduced in few literature [18][19][20]. Nguyen et al [18] devise a DR Exchange, in which buyers and sellers trade DR in a pool-based market. This market is modified in [19] where Walrasian market clearing technique is used instead of the former pool-based method.…”

Section: Retail Marketmentioning

confidence: 99%

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The rise of AGILE demand response: Enabler and foundation for change

Babar

Nyugen

Ćuk

et al. 2016

Renewable and Sustainable Energy Reviews

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Section: Retail Marketmentioning

confidence: 99%

Section: Retail Marketmentioning

confidence: 99%

The rise of AGILE demand response: Enabler and foundation for change

Babar

Nyugen

Ćuk

et al. 2016

Renewable and Sustainable Energy Reviews

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“…In TBR programs (also known as price-based DR programs [6]), time-varying prices are given to consumers based on the electricity price in different time periods, encouraging them to change their consumption level in response to the changing price signals. On the other hand, in IBP schemes,…”

Section: Introductionmentioning

confidence: 99%

Study of the Effect of Time-Based Rate Demand Response Programs on Stochastic Day-Ahead Energy and Reserve Scheduling in Islanded Residential Microgrids

et al. 2017

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Abstract:In recent deregulated power systems, demand response (DR) has become one of the most cost-effective and efficient solutions for smoothing the load profile when the system is under stress. By participating in DR programs, customers are able to change their energy consumption habits in response to energy price changes and get incentives in return. In this paper, we study the effect of various time-based rate (TBR) programs on the stochastic day-ahead energy and reserve scheduling in residential islanded microgrids (MGs). An effective approach is presented to schedule both energy and reserve in presence of renewable energy resources (RESs) and electric vehicles (EVs). An economic model of responsive load is also proposed on the basis of elasticity factor to model the behavior of customers participating in various DR programs. A two-stage stochastic programming model is developed accordingly to minimize the expected cost of MG under different TBR programs. To verify the effectiveness and applicability of the proposed approach, a number of simulations are performed under different scenarios using real data; and the impact of TBR-DR actions on energy and reserve scheduling are studied and compared subsequently.

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“…However, making the demand for resources flexible comes with its own set of challenges, mostly geared towards finding the right price. A significant body of literature has been dedicated to studying the different aspects of electricity pricing in the presence of flexible loads (see, e.g., [1]- [6]). An implied assumption in these works is that electricity is the sole congestible resource that electric loads need.…”

Section: Introductionmentioning

confidence: 99%

Optimal Electricity Pricing for Societal Infrastructure Systems

Alizadeh

Wai

Goldsmith³

et al. 2017

Proceedings of the 50th Hawaii International Conference on System Sciences (2017)

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Abstract-We develop a general framework for pricing electricity in order to optimally manage the electricity load of societal infrastructures that interact with power systems through their price-responsive electricity load. In such infrastructure systems, electricity is not the sole resource needed to serve users' needs. Examples include cloud computing infrastructure or electric transportation networks. In these cases, other shared networked resources such as charging stations or communication links and data centers are also required to serve users. Hence, pricing of electricity becomes intertwined to managing other congestible resources not priced by the power system operator, leading to a complex economic dispatch problem. For brevity of notation, our analysis is performed under a static setting. We discuss how the power system operator should model the effects of the mobility of loads and congestion in the infrastructure in the economic dispatch. We numerically study the performance of our algorithms using the example of a simple electric transportation network.

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Pool-based Demand Response Exchange: Concept and modeling

Cited by 61 publications

References 0 publications

The rise of AGILE demand response: Enabler and foundation for change

The rise of AGILE demand response: Enabler and foundation for change

Study of the Effect of Time-Based Rate Demand Response Programs on Stochastic Day-Ahead Energy and Reserve Scheduling in Islanded Residential Microgrids

Optimal Electricity Pricing for Societal Infrastructure Systems

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