The participation of a renewable energy-based aggregator in real-time market: A Bi-level approach

Sheikhahmadi, Pouria; Bahramara, Salah

doi:10.1016/j.jclepro.2020.123149

Cited by 37 publications

(19 citation statements)

References 18 publications

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“…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. However, these studies did not consider the physical constraints of the distribution system.…”

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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show abstract

“…The network needs to be upgraded with new investments that can reduce uncertainties and foster small producers' participation, increasing competition level ([23]). Literature on regulation is also focused on how to design tariffs ( [24]) and how to regulate old and new agents: this is particularly true for network operators ( [25]; [26]) and aggregators ( [27]; [28]; [29]). Medium and low voltage networks see the participation of many renewable energy producers, or prosumers, characterized by small size: in this context, the figure of the aggregator has taken hold as a subject capable of bringing into the market very diversified operators.…”

Section: Literature Backgroundmentioning

confidence: 99%

The participation of small-scale variable distributed renewable energy sources to the balancing services market

et al. 2021

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“…The authors in [4] proposed a stochastic linear programming-based optimal bidding strategy for DER aggregators in a dayahead market that involves DER uncertainties. In [5], an optimal bidding strategy for renewable-based aggregators in the real-time market was proposed. It is based on bi-level optimization and aims at solving the decision-making problem of DER aggregators and the real-time market clearing problem as a price-maker players.…”

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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The participation of a renewable energy-based aggregator in real-time market: A Bi-level approach

Cited by 37 publications

References 18 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

The participation of small-scale variable distributed renewable energy sources to the balancing services market

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

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