Robust bidding strategy for demand response aggregators in electricity market based on game theory

Abapour, Saeed; Mohammadi‐Ivatloo, Behnam; Hagh, Mehrdad Tarafdar

doi:10.1016/j.jclepro.2019.118393

Cited by 84 publications

(35 citation statements)

References 29 publications

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“…In [22], the gap decision theory was applied for the optimal bidding of a DERA to address uncertainties. In [23], Abapour et al suggested a robust optimal bidding strategy using game theory, considering multiple DERAs. In [24], P. Sheikhahmadi and S. Bahramara proposed a DERA as price maker in real-time market with a Bi-level approach.…”

Section: A Literature Focusing On the Dera's Optimal Bidding Considering Uncertaintiesmentioning

confidence: 99%

Prequalification Scheme of a Distribution System Operator for Supporting Wholesale Market Participation of a Distributed Energy Resource Aggregator

et al. 2021

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Increasing penetration of distributed renewable energy sources (DRESs) has resulted in the emergence of distributed energy resource aggregators (DERAs). A DERA participates in the transmissionlevel market operated by a transmission system operator (TSO), and the DERA's resources are connected to a jurisdiction of the distribution system operator (DSO). Inspired by the structure of the Korean power industry, this study assumes a minimal DSO that cannot directly dispatch the resources in its system. In this study, we develop a detailed procedure for prequalification wherein the DSO checks the DERA's bids that are submitted to the TSO markets. The proposed prequalification enables the DSO to secure the reliability of its system by providing limited network information to the DERA. The DERA modifies its bid until potential overvoltage and overflow problems are resolved, even in the worst case, including uncertainties. The proposed prequalification process is verified using the IEEE 33-bus distribution network. Compared to previous studies, the results demonstrate that the proposed prequalification can deal with distribution system constraints, even though uncertainties are included. The proposed prequalification process can be applied to power industries where the DSO does not have the full dispatch authority on DRESs.

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Section: A Literature Focusing On the Dera's Optimal Bidding Considering Uncertaintiesmentioning

confidence: 99%

Prequalification Scheme of a Distribution System Operator for Supporting Wholesale Market Participation of a Distributed Energy Resource Aggregator

et al. 2021

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“…In [23], the uncertainty in the electricity market was examined using IGDT. In [24], a game theory-based model was proposed to assist the distribution grid operator in choosing trusted sources for grid demand response. In [25], a method for obtaining the tender strategy in the electricity market for large consumers using the IGDT was proposed.…”

Section: Literature Reviewmentioning

confidence: 99%

Risk-Averse Scheduling of Combined Heat and Power-Based Microgrids in Presence of Uncertain Distributed Energy Resources

et al. 2021

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In this paper, a robust scheduling model is proposed for combined heat and power (CHP)-based microgrids using information gap decision theory (IGDT). The microgrid under study consists of conventional power generation as well as boiler units, fuel cells, CHPs, wind turbines, solar PVs, heat storage units, and battery energy storage systems (BESS) as the set of distributed energy resources (DERs). Additionally, a demand response program (DRP) model is considered which has a successful performance in the microgrid hourly scheduling. One of the goals of CHP-based microgrid scheduling is to provide both thermal and electrical energy demands of the consumers. Additionally, the other objective is to benefit from the revenues obtained by selling the surplus electricity to the main grid during the high energy price intervals or purchasing it from the grid when the price of electricity is low at the electric market. Hence, in this paper, a robust scheduling approach is developed with the aim of maximizing the total profit of different energy suppliers in the entire scheduling horizon. The employed IGDT technique aims to handle the impact of uncertainties in the power output of wind and solar PV units on the overall profit.

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“…Unlike SO, RO has been discussed widely which considers the worst-case of uncertainty. A non-cooperative static game-based robust bidding strategy of a DER aggregator was introduced in [7]. where the uncertainty of the day-ahead market price was modeled by the robust method.…”

Section: B Literatrue Reviewmentioning

confidence: 99%

Data-Driven Bidding Strategy for DER Aggregator Based on Gated Recurrent Unit–Enhanced Learning Particle Swarm Optimization

2021

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Distributed energy resources (DERs) such as wind turbines (WTs), photovoltaics (PVs), energy storage systems (ESSs), local loads, and demand response (DR) are highly valued for environmental protection. However, their volatility poses several risks to the DER aggregator while formulating a profitable strategy for bidding in the day-ahead power market. This study proposes a data-driven bidding strategy framework for a DER aggregator confronted with various uncertainties. First, a data-driven forecasting model involving gated recurrent unit-enhanced learning particle swarm optimization (GRU-ELPSO) with improved mutual information (IMI) is employed to model renewables and local loads. It is critical for a DER aggregator to accurately estimate these components before bidding in the day-ahead power market. This aids in reducing the penalty costs of forecasting errors. Second, an optimal bidding strategy that is based on the information gap decision theory (IGDT) is formulated to address market price uncertainty. The DER aggregator is assumed to be risk-averse (RA) or risk-seeker (RS), and the corresponding bidding strategies are formulated according to the risk preferences thereof. Thenr, an hourly bidding profile is created for the DER aggregator to bid successfully in the day-ahead power market. The proposed datadriven bidding framework is evaluated using an illustrative system wherein a dataset is obtained from the PJM market. The results reveal the effectiveness of handling uncertainty by providing accurate forecasting results. In addition, the DER aggregator can bid effectively in the day-ahead power market according to its preference for robustness or high profit, with a suitable bidding profile.

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Robust bidding strategy for demand response aggregators in electricity market based on game theory

Cited by 84 publications

References 29 publications

Prequalification Scheme of a Distribution System Operator for Supporting Wholesale Market Participation of a Distributed Energy Resource Aggregator

Prequalification Scheme of a Distribution System Operator for Supporting Wholesale Market Participation of a Distributed Energy Resource Aggregator

Risk-Averse Scheduling of Combined Heat and Power-Based Microgrids in Presence of Uncertain Distributed Energy Resources

Data-Driven Bidding Strategy for DER Aggregator Based on Gated Recurrent Unit–Enhanced Learning Particle Swarm Optimization

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