A bilevel model for electricity retailers' participation in a demand response market environment

Zugno, Marco; Morales, Juan M.; Pinson, Pierre; Madsen, Henrik

doi:10.1016/j.eneco.2012.12.010

Cited by 281 publications

(214 citation statements)

References 23 publications

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“…Note also that there exist only a few gametheoretic references where the price paid by consumers is a function of the total consumption profile in the aggregator pool; this makes shiftable load scheduling coupling between the different consumers through an endogenous tariff mechanism [27,18]. Close to our model is also the work of Zugno et al [41] who model the interactions between consumers and the aggregator as a Stackelberg game. However, their model does not allow the possibility for consumers to switch energy supplier and they do not consider private information.…”

Section: Introductionmentioning

confidence: 85%

“…These predictions are reported to the aggregator in day ahead and the consumer will be penalized in real time depending on the deviation between his forecasted and realized demand profiles. Similar assumption was made in [31,37,41] where each consumer is asked to provide in advance a prediction for his consumption during a specific period and is then charged a Prediction-Of-Use tariff depending on the gap between base load prediction and real consumption. The electrical load forecasting has become more and more important in recent years due to the electricity market deregulation and integration of renewable resources.…”

Section: Expected Deviations From the Forecast Consumption Of Base Loadsmentioning

confidence: 99%

“…This device is responsible for the scheduling of the consumers' shiftable loads: its output is the result of an integer optimization problem that depends on the price signal sent by the aggregator. The aggregator task is made of two parts that occur simultaneously: taking forward positions in the day-ahead electricity market to compensate the uncertainty associated with the base load estimation (similar problem was considered in [31,37,41]); and, pricing his supply service so as to reach a net targeted profit for a fixed and finite number of periods, which is the maximum value guaranteeing the stability of the coalition under some cost-sharing mechanism. In the second part of the aggregator's task, the created value for the aggregator comes from the stochastic nature of the base load demand.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Designing coalition-based fair and stable pricing mechanisms under private information on consumers’ reservation prices

Cadre¹,

Pagnoncelli

Homem-de-Mello

et al. 2019

European Journal of Operational Research

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We model the relation between an aggregator and consumers joining a coalition to reduce the risk resulting from the unpredictability of their base load demand, as a Stackelberg game formulated as a mathematical bilevel program with private information on the consumers' reservation prices. At the upper-level of the Stackelberg game, the aggregator optimizes his daily price profile so as to reach a net targeted profit which is the maximum value guaranteeing that no consumer will leave the coalition -to contract with a conventional retailer considered here as a fixed alternative -while meeting fairness criterion imposed by the cost-sharing mechanism. At the lower-level, the consumers are asked to provide in day ahead an estimate of their base load hourly demand profile and to schedule their shiftable loads depending on the price signal sent by the aggregator. We provide algorithms that determine the unique price profile and consumer shiftable load schedules as functions of the reservation price estimates. The Stackelberg game between the aggregator and the consumers being repeated for a period of time, the aggregator has the possibility to update his estimates of the reservation prices relying on a feedback function which depends on the percentage of activated loads. A randomized algorithm for consumers' reservation price learning based on regret minimization is provided. For four cost-sharing mechanisms such as uniform allocation, stand-alone cost, Shapley value, separable and non-separable costs, we determine the closed form of the aggregator's optimal net targeted profit guaranteeing the stability of the coalition. We also determine conditions guaranteeing the core non-emptiness and prove that for a profit-maximizing aggregator, the stand-alone cost is always preferable to the Shapley value, which coincides with the uniform allocation. Furthermore, the optimal size of the coalition -in terms of the aggregator's profit -can be determined analytically when the Shapley value is implemented as cost-sharing mechanism. The results are illustrated on a case study where we show that there exists an optimal net targeted profit below which the consumers energy bill is lower when joining the aggregator than with the conventional retailer. Coalition dynamics is also analyzed numerically depending on the consumer inertia in their energy supplier choice process, for each cost-sharing mechanism.

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Section: Introductionmentioning

confidence: 85%

Section: Expected Deviations From the Forecast Consumption Of Base Loadsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Designing coalition-based fair and stable pricing mechanisms under private information on consumers’ reservation prices

Cadre¹,

Pagnoncelli

Homem-de-Mello

et al. 2019

European Journal of Operational Research

View full text Add to dashboard Cite

show abstract

“…Therefore, customers have enough time to plan their responses, such as shifting use (often by shifting load to off-peak hours or by using onsite generation) or hedging dayahead prices with other products if they cannot curtail their demand [10]. Hence, the day-ahead RTP is more suitable for day-ahead optimization of distribution networks.…”

Section: Definitionmentioning

confidence: 99%

“…In [6], the implementation of price based on demand response by an industrial consumer can increase the proportion of using wind power electricity. A game theoretical model accounting for the Stackelberg relationship between retailers and consumers in a dynamic price environment is proposed in [10]. These efforts are aimed at saving payment of wholesale markets or resolving problems in present electric power systems by RTP.…”

Section: Introductionmentioning

confidence: 99%

Economic optimization of smart distribution networks considering real-time pricing

Zhang

Zhao

et al. 2014

J. Mod. Power Syst. Clean Energy

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With the development of smart meters, a realtime pricing (RTP) demand response is becoming possible for households in distribution networks. The power flow can be bidirectional in distribution networks which become smarter with distributed generators (DGs). It is expensive to import electricity from the generation far from load centers because of the cost of power loss and network use, so that it is more economical to use electricity generated by local distributed generators. Therefore, in order to curtail operating costs of distribution networks, this paper proposes a model of economic optimization conducted by distribution network operators. The electricity purchasing costs for distribution network operators are minimized by optimizing electric power from transmission systems and local distributed generators. Further, based on price elasticity, the formulations of load demand considering RTP are proposed with economic optimization of distribution networks. The economic optimization problems are resolved by an interior point method. The case study shows that peak load demand can be reduced about 3.5% because the household RTP and electricity purchasing costs of distribution network operators can save 28.86 £ every hour.

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Integrated TOU price‐based demand response and dynamic grid‐to‐vehicle charge scheduling of electric vehicle aggregator to support grid stability

Sharma

Jain

2019

Int Trans Electr Energ Syst

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Summary Unregulated and simultaneous charge scheduling of large‐scale electric vehicles (EVs) may lead to grid instability and peak demand rise. EV aggregator (EVA) can adopt coordinated and distributed charging rates for EVs over the connection hours providing stability and regulation to system operator (SO). However, against flat rate, price‐based demand response (PBDR) integrated scheduling motivates EVs to charge in off peak hours and utilize their flexibility for load levelling and peak reduction. PBDR reduces EV charging cost as well. Real‐time price (RTP) demand response (DR), mostly adopted in literature, has low acceptance rate by EVs for being too dynamic to response and being infeasible for few charge cycles in a day. Time of use (TOU), being less dynamic, is natural price signal for EVs under PBDR but may reduce EVA's profit. Considering this, an integrated TOU‐PBDR model for EVA's grid‐to‐vehicle (G2V) charge scheduling is proposed for energy and regulation market. EV owner perspective for charging cost minimization is considered along with EVA profit maximization. TOU is designed from RTP using agglomerative hierarchical clustering (AHC) method. EVs mobility uncertainty characterization makes scheduling dynamic. Sensitivity analysis based on charging rate upper limit and number of EVs validates the method. Results show interesting trends regarding EV charging cost, EVA profit, and regulation provision to SO.

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A bilevel model for electricity retailers' participation in a demand response market environment

Cited by 281 publications

References 23 publications

Designing coalition-based fair and stable pricing mechanisms under private information on consumers’ reservation prices

Designing coalition-based fair and stable pricing mechanisms under private information on consumers’ reservation prices

Economic optimization of smart distribution networks considering real-time pricing

Integrated TOU price‐based demand response and dynamic grid‐to‐vehicle charge scheduling of electric vehicle aggregator to support grid stability

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