Abstract
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. Then, an hourly bidding profile is created for the DER aggregator to bid successfully in the day-ahead power market. The proposed data-driven 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.
Highlights
This paper proposes the framework of a data-driven bidding strategy for a distributed energy resources (DERs) aggregator in the day-ahead power market considering uncertainties
The proposed data-driven model was validated by comparing it with three benchmark forecasting models: long short-term memory (LSTM), recurrent neural network (RNN), and SVR
The comparison results of the prediction, R2, and the errors revealed that the proposed model outperforms the other models in terms of forecasting accuracy and contributes toward reducing the penalty costs
Summary
Kim et al.: Data-Driven Bidding Strategy for DER Aggregator Based on GRU-ELPSO nGRU yp, yq yp, yq. RRN π1 π2 π3 f min, f max δ α, β yrieal , ypi red , ymi ean Iteration number of GRU Actual and predicted values of training data Actual and predicted values of validation data Input random, target, and average variable. PPVt , PWt T PPMt , PDt R PBt ase PEt SS PRt ,,PV PRt ,WT PRt ,Base Xt Uch, Udch λst ell. Target value, and second target value Supplementary variable Velocity and position of particle Inertia weight Self-cognition and influence coefficient Random variables Local bests solution of PSO Global best solution of PSO Revenue of DER aggregator Revenue from sellingpower tocustomers Revenue from selling DR Cost of purchasing power from market Incentives provided to customers
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