Evolution and current status of demand response (DR) in electricity markets: Insights from PJM and NYISO

Walawalkar, Rahul; Fernands, Stephen; Thakur, Netra; Chevva, Konda Reddy

doi:10.1016/j.energy.2009.09.017

Cited by 202 publications

(94 citation statements)

References 8 publications

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“…Understanding how consumers react to various pricing strategies is crucial to control electricity demand effectively. Several studies investigate the relationship between time-of-use tariffs and energy consumption (Bernard et al, 2011;Garcia-Cerrutti, 2000;Kamerschen and Porter, 2004;Olmos et al, 2011;Walawalkar et al, 2010). Pilot studies have reported significant demand reductions in the industrial and commercial sectors for some time-based pricing experiments (Barbose et al, 2004).…”

Section: Time-based Pricingmentioning

confidence: 99%

“…Several countries such as the member states of the European Union have already agreed to trigger the introduction of Demand Response programs. In fact, policy issues are broadly discussed by governments throughout the world (Cappers et al, 2010;Walawalkar et al, 2010;Ming et al, 2013). Greening (2010), for example, suggests that state regulators will need to regulate through the development of incentive mechanisms.…”

Section: Policy Implicationsmentioning

confidence: 99%

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Measuring the financial impact of demand response for electricity retailers

Feuerriegel

Neumann

2014

Energy Policy

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Due to the integration of intermittent resources of power generation such as wind and solar, the amount of supplied electricity will exhibit unprecedented fluctuations. Electricity retailers can partially meet the challenge of matching demand and volatile supply by shifting power demand according to the fluctuating supply side. The necessary technology infrastructure such as Advanced Metering Infrastructures for this so-called Demand Response (DR) has advanced. However, little is known about the economic dimension and further effort is strongly needed to realistically quantify the financial impact. To succeed in this goal, we derive an optimization problem that minimizes procurement costs of an electricity retailer in order to control Demand Response usage. The evaluation with historic data shows that cost volatility can be reduced by 7.74 %; peak costs drop by 14.35 %; and expenditures of retailers can be significantly decreased by 3.52 %.

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Section: Time-based Pricingmentioning

confidence: 99%

Section: Policy Implicationsmentioning

confidence: 99%

Measuring the financial impact of demand response for electricity retailers

Feuerriegel

Neumann

2014

Energy Policy

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“…Maximum limits have to be imposed for load reduction (4); price caps are also considered (5). The balance between load and generation, which is the main constraint of any power system, has to be guaranteed (6). The consideration of load response is formulated based on price elasticity of demand (7), therefore the elasticity should be included in the formulation, since it shows the relation between power and price variation and makes them mutually dependent.…”

Section: Maximizementioning

confidence: 99%

“…One of the areas expected to grow in the scope of EMs is the Demand Response (DR), as it appears as a very promising opportunity for consumers and brings several advantages for the whole system [5,6]. This is due to the fact that power systems' infrastructure is highly capital-intensive and DR is one of the cheaper resources available to operate the system [7].…”

Section: Introductionmentioning

confidence: 99%

Demand response in electrical energy supply: An optimal real time pricing approach

Faria

Vale

2011

Energy

287

124

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a b s t r a c tIn competitive electricity markets with deep concerns for the efficiency level, demand response programs gain considerable significance. As demand response levels have decreased after the introduction of competition in the power industry, new approaches are required to take full advantage of demand response opportunities. This paper presents DemSi, a demand response simulator that allows studying demand response actions and schemes in distribution networks. It undertakes the technical validation of the solution using realistic network simulation based on PSCAD. The use of DemSi by a retailer in a situation of energy shortage, is presented. Load reduction is obtained using a consumer based price elasticity approach supported by real time pricing. Non-linear programming is used to maximize the retailer's profit, determining the optimal solution for each envisaged load reduction. The solution determines the price variations considering two different approaches, price variations determined for each individual consumer or for each consumer type, allowing to prove that the approach used does not significantly influence the retailer's profit.The paper presents a case study in a 33 bus distribution network with 5 distinct consumer types. The obtained results and conclusions show the adequacy of the used methodology and its importance for supporting retailers' decision making.

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“…One area expected to grow in the scope of electricity markets is Demand Response (DR), as it appears as a very promising opportunity for consumers and brings several advantages for the whole system [1,3]. This is due to the fact that power systems infrastructure is highly capital intensive and demand response is one of the cheaper resources available to prevent investment needs, by peak-shaving and strategic load curtailment in congestion situations [4].…”

Section: Introductionmentioning

confidence: 99%

Decision Support Concerning Demand Response Programs Design and Use - A Conceptual Framework and Simulation Tool

Faria¹,

Vale²

2014

Appl. Math. Inf. Sci.

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Abstract:Demand response can play a very relevant role in the context of power systems with an intensive use of distributed energy resources, from which renewable intermittent sources are a significant part. More active consumers participation can help improving the system reliability and decrease or defer the required investments. Demand response adequate use and management is even more important in competitive electricity markets. However, experience shows difficulties to make demand response be adequately used in this context, showing the need of research work in this area. The most important difficulties seem to be caused by inadequate business models and by inadequate demand response programs management. This paper contributes to developing methodologies and a computational infrastructure able to provide the involved players with adequate decision support on demand response programs and contracts design and use. The presented work uses DemSi, a demand response simulator that has been developed by the authors to simulate demand response actions and programs, which includes realistic power system simulation. It includes an optimization module for the application of demand response programs and contracts using deterministic and metaheuristic approaches. The proposed methodology is an important improvement in the simulator while providing adequate tools for demand response programs adoption by the involved players. A machine learning method based on clustering and classification techniques, resulting in a rule base concerning DR programs and contracts use, is also used. A case study concerning the use of demand response in an incident situation is presented.

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Evolution and current status of demand response (DR) in electricity markets: Insights from PJM and NYISO

Cited by 202 publications

References 8 publications

Measuring the financial impact of demand response for electricity retailers

Measuring the financial impact of demand response for electricity retailers

Demand response in electrical energy supply: An optimal real time pricing approach

Decision Support Concerning Demand Response Programs Design and Use - A Conceptual Framework and Simulation Tool

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