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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]

• Two-level voltage control strategy. • Combining optimal tap position of OLTC, and battery energy storage systems. • Usage the reactive power compensation capability of inverters to regulate the voltage. • A network partition-based zonal voltage control. • Linear formulations for both power flow and the AC inverter. Over-voltage in low voltage feeders due to the increasing penetration level of photovoltaic (PV) is a crucial issue to be addressed. There is a need to use more advanced voltage control methods to satisfy the response time of the control system. In this paper, a two-level voltage control strategy is presented. In the first level, based on day-ahead PV production scenario, Both the on-load tap changer (OLTC) and the battery energy storage systems (ESSs) are applied to deal with over-voltage in the peak of PV generation and as well the voltage drop in the peak of demand. In this level, the batteries and the tap position of the feeder transformer are optimally set in order to improve the voltage profile for the entire planning horizon (next day) taking into account the uncertainties in the PV production. In the second level, based on the partitioning of the distribution network, reactive power compensation capability of PV inverters is employed to fine-tune the voltage profile for the next operating hour. To model the uncertainty pertaining to the output power of PV units, the parameters of beta distribution function are estimated for each hour time interval, and then Monte Carlo simulation method is used to generate daily scenarios. In order to reduce the complexities and computational burden, the linearized model of power flow equations and PV inverters have been implemented. A real and practical, 10 kV, 37-bus system is used to test the performance of the proposed method. [ABSTRACT FROM AUTHOR]