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This paper presents a robust planning of distributed battery energy storage systems (DBESSs) from the viewpoint of distribution system operator (DSO) to increase the network flexibility. Initially, the deterministic model of the proposed problem is expressed by minimizing the difference between the DBESS planning, degradation and operation (charging) costs and the revenue of DBESS from selling its stored energy subject to the constraints of AC power flow equations in the presence of RESs and DBESSs, and technical limits of the network indexes, variable renewable energy sources (vRESs) and DBESSs. This problem is modeled as a non-linear programming (NLP), then, an equivalent linear programming (LP) model is proposed using the first-order expansion of Taylor's series for linearization of power flow equations and a polygon for linearization of circular inequalities. Also, to model the uncertain parameters in the proposed problem including forecasted active and reactive loads, energy and charging/discharging prices and the output power of vRES, the bounded uncertainty-based robust optimization (BURO) framework is proposed in the next step. Finally, the proposed scheme is applied to 19-bus MV CIGRE benchmark grid by GAMS software to investigate the capability and efficiency of the model. • Investment planning of distributed battery energy storage systems is modeled. • The planning problem is modeled linearly from DSO viewpoint. • Simple degradation cost of batteries is considered in the optimization. • Robust optimization is implemented to handle the uncertainty of PVs. [ABSTRACT FROM AUTHOR]

Summary: Renewable energy such as wind power plays an important role in power generation. Large amount of renewable energy can allow these producers to change the market price and be as a price maker in the market. Thus, we propose an optimal bidding strategy in the day‐ahead market for the hybrid power plant that participates as a price maker in the day‐ahead market and as a price taker in the balancing market. Hybrid power plant is composed of wind power producer, battery storage and demand response. The uncertainty of day‐ahead market price is modeled through Robust Optimization. In case studies, bidding strategy of hybrid power plant as a price maker and price taker configuration is presented. Then, joint operation of hybrid power plant is compared to disjoint operation and the effect of participating in market as coordinated unit on total profit is investigated. Moreover, the changes in profit according to risk level are determined and finally, offering curves are obtained by dividing the confidence bounds of day‐ahead market price to some subintervals. The simulation is done based on Nord Pool Market and the results validate hybrid power plant can behave as a price maker in the day‐ahead market and the profit of all units increases in joint operation. [ABSTRACT FROM AUTHOR]