Two-stage stochastic-robust model for the self-scheduling problem of an aggregator participating in energy and reserve markets

Wang, Jian; Xie, Ning; Huang, Chunyi; Wang, Yong

doi:10.1186/s41601-023-00320-y

Cited by 7 publications

(4 citation statements)

References 28 publications

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“…This paper presents a two-stage robust optimization model with a two-stage, threelayer, max-min-max problem. This problem could not be solved directly using a solver but could be solved iteratively by decomposing the original problem into two problems, the main problem and the subproblem by the CCG algorithm [30,31]. The method used to address the uncertainty of the real-time price of electricity was the scenario method, which was based on historical real-time tariff data.…”

Section: Derivation Of the Solution Processmentioning

confidence: 99%

A Two-Stage Robust Pricing Strategy for Electric Vehicle Aggregators Considering Dual Uncertainty in Electricity Demand and Real-Time Electricity Prices

Wang,

Sun

2024

Sustainability

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To enable the regulation and utilization of electric vehicle (EV) load resources by the power grid in the electricity market environment, a third-party electric vehicle aggregator (EVA) must be introduced. The strategy of EVA participation in the electricity market must be studied. During operation, the EVA faces a double uncertainty in the market, namely, electricity demand and electricity price, and must optimize its market behavior to protect its own interests. To achieve this goal, we propose a robust pricing strategy for the EVA that takes into account the coordination of two-stage market behavior to enhance operational efficiency and risk resistance. A two-stage robust pricing strategy that takes into account uncertainty was established by first considering day-ahead pricing, day-ahead electricity purchases, real-time electricity management, and EV customer demand response for the EVA, and further considering the uncertainty in electricity demand and electricity prices. The two-stage robust pricing model was transformed into a two-stage mixed integer programming by linearization method and solved iteratively by the columns and constraints generation (CCG) algorithm. Simulation verification was carried out, and the results show that the proposed strategy fully considers the influence of price uncertainty factors, effectively avoids market risks, and improves the adaptability and economy of the EVA’s business strategy.

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Section: Derivation Of the Solution Processmentioning

confidence: 99%

A Two-Stage Robust Pricing Strategy for Electric Vehicle Aggregators Considering Dual Uncertainty in Electricity Demand and Real-Time Electricity Prices

Wang,

Sun

2024

Sustainability

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“…User's expected profit consisting of DR incentive profit and comfortable utility will be different for different choices. To analyze the dynamic trend of the population of users choosing to be in DR, the replication dynamic equation is designed according to the form in (9). Simulation results show that the DR incentive mechanism plays an important role in promoting the user population to participate in DR.…”

Section: ) Evolutionary Gamementioning

confidence: 99%

“…In recent years, distributed energy technologies, such as photovoltaic (PV) and wind power generation, have had an influence in relieving supply-demand conflict from the energy source side [6], [7]. In addition to increasing the installed capacity of distributed generation on the source side, addressing the issue from the demand side is also a viable and effective solution [8], [9]. Generally, demand users can be divided into residential, commercial, and industrial users according to their energy consumption characteristics [10].…”

mentioning

confidence: 99%

Game-Theoretic Applications for Decision-Making Behavior on the Energy Demand Side: a Systematic Review

Ji,

Liu,

Tang

2024

Prot. Control Mod. Power Syst.

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As an essential characteristic of the smart grid, energy demand users are being transformed from passive roles to active decision-makers. To analyze their decision-making behaviors, game theory has been widely applied on the demand side. This paper focuses on the classification and in-depth analysis of recent studies that propose game-theoretic approaches for decision optimization of multiple demand users. This analysis classifies scenarios into various game participant categories, including distributed energy prosumers, small-and middle-sized users, and large energy consumers. The in-depth analysis of each scenario, covering non-cooperative game, cooperative game, Stackelberg game, Bayesian game, and evolutionary game, is conducted by analyzing market operation mechanisms, model assumptions/formulations, and solution methods. Based on a comprehensive review of such studies, it is concluded that game-theoretic applications on the demand side can benefit both the grid and the users, e.g., reductions in the peak-to-average ratios and energy costs of the users. The prospects for the applications of game theory on the demand side are discussed, including application scenarios and methodologies. The overview presented in this paper is expected to support researchers in comprehending typical game-theoretic concepts, keeping with the latest research developments, and identifying new and innovative applications for the energy demand side.

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“…Meanwhile, the output uncertainty of renewable energy poses great challenges to the allocation of DG [21]. Stochastic optimization (SO) [22,23] and robust optimization (RO) [24,25] are the two most widely used uncertainty-handling methods. The optimal solution is usually obtained in the worst-case scenario for RO, which can lead to an overly conservative allocation scheme.…”

Section: Introductionmentioning

confidence: 99%

Optimal Allocation Stochastic Model of Distributed Generation Considering Demand Response

He,

Liu

2024

Energies

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Demand response (DR) can improve the accommodation of renewable energy and further affect the distributed generation (DG) allocation strategy. In this context, this paper proposes a stochastic optimal allocation model of DG, considering DR. Firstly, to address the uncertainty of wind and solar power outputs, a large number of scenarios of wind and solar power are generated based on the scenario method, which are then clustered into 10 typical scenarios by the k-means method. Secondly, with the goal of maximizing the total cost, the DR cost and corresponding constraints are introduced. Then, the stochastic planning model for DG is established, where the planning level aims to minimize the investment cost while the operation level minimizes the total operation expectation cost. For the non-linear term in the DR cost and power flow constraint, the Taylor expansion method and second-order conic relaxation method are both adopted to transform the original mixed-integer non-linear model to the mixed-integer second-order conic planning model. Finally, the whole planning model for DG is solved by CPLEX 12.6.0. The results show that DR can reduce the total cost and improve the accommodation of renewable energy in the DG planning process, which should be paid more attention to in the DG planning model.

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Two-stage stochastic-robust model for the self-scheduling problem of an aggregator participating in energy and reserve markets

Cited by 7 publications

References 28 publications

A Two-Stage Robust Pricing Strategy for Electric Vehicle Aggregators Considering Dual Uncertainty in Electricity Demand and Real-Time Electricity Prices

A Two-Stage Robust Pricing Strategy for Electric Vehicle Aggregators Considering Dual Uncertainty in Electricity Demand and Real-Time Electricity Prices

Game-Theoretic Applications for Decision-Making Behavior on the Energy Demand Side: a Systematic Review

Optimal Allocation Stochastic Model of Distributed Generation Considering Demand Response

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