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.

Photovoltaic (PV) energy generation in microgrids (MGs) is increasing. Battery energy storage systems (BESSs) reduce the fluctuations in PV outputs caused by the intermittent availability of solar energy. Although BESSs are advantageous for stable MG operation, they are still relatively expensive. By remaining within the operational limits during normal and contingency operation, optimal sizing of BESS is required to maintain security considering cost of MG system. This paper proposes a BESS sizing optimization approach for MGs by solving the security constrained optimal power flow (SCOPF), considering the stochastic errors in forecasting the PV outputs. The degree of compensation for the solar energy forecasting error is firstly configured. To address these errors, the combined PV and BESS operation system is modeled by applying a control strategy to smooth PV fluctuations and minimize battery life degradation. BESS sizing optimization, under a certain degree of compensation, minimizes the PV penalty cost and BESS operation cost. The optimal BESS capacity and schedule are then obtained for the MG. To enhance the convergence and computational efficiency, decomposed-probabilistic security constrained optimal power flow (D-PSCOPF) is proposed. It efficiently solves the problem by dividing it into a master problem and a slave problem. The base case solution is computed in the slave problem, which induces the partial optimal size of BESS. By adding the feasibility cut through the violation of the slave problem, the master problem derives the optimal BESS capacity. Different case studies were analyzed, confirming the superiority and computational efficiency of the proposed approach. shutdown 𝐶 𝑡𝑠0 𝐶 𝑡𝑠𝑐 𝐶 𝑡𝑠𝑑 Weighted price vectors summarizing contributi ons to the value of terminal storage 𝛾 𝑡 Probability of making it to period t without branch off the central path in a contingency in periods 𝑃 𝑃𝑉,𝑎𝑐𝑡𝑢𝑎𝑙 Actual power output of PV 𝑃 𝑃𝑉,𝑓𝑜𝑟𝑒𝑐𝑎𝑠𝑡 Forecasted power output of PV 𝑃 𝑟𝑎𝑡𝑒 Required rated power of the BESS 𝑃 𝐵𝐸𝑆𝑆 Actual charging and discharging power of the B ESS 𝐸 Energy fluctuation of BESS at the sampling tim e relative to the initial state 𝐸 𝑟𝑎𝑡𝑒 Rated capacity of the BESS 𝑄 𝑐𝑦𝑐𝑙𝑒 Capacity loss at the time instants 𝑃 𝐺