Abstract

     The modern active distribution systems necessitate integrating storage systems, thereby facilitating the large-scale proliferation of photovoltaic (PV) energy resources. This further calls for the optimal planning of energy storage systems, satisfying all the operational and economic constraints. This article describes an exhaustive storage integration method, deeming the life cycle of the battery energy storage, the uncertainty of load and PV output, and the islanded mode of operation of the system. A two-stage mixed-integer linear programming problem is formulated that determines the capacity and the number of discharge cycles of the batteries in the first stage. The lifetime of the battery based on the partial depth of discharge is analyzed in the second stage. Furthermore, the uncertainty and variability of PV and demand are taken into account through probabilistic analysis and time-period clustering. The method is validated on a standard 33-bus radial distribution network for the allocation of distributed lithium-ion batteries. Also, the method’s scalability is validated on a practical Indian distribution network and a 141-bus distribution network of metropolitan area of Caracas with distributed PV installations on various nodes.

Introduction

     S MART grid is a gratuity to the consumers and the stakeholders involved in the electric grid’s generation, transmission, and distribution sections. It includes the complete modernization of the power system with smart meters, automation devices, intelligent monitoring, demand-side management, and self-healing capability schemes. Along with these activities, generation units are set up in the distribution section, i.e., nearer to the consumer premises, to reduce the transmission losses. This shift of the electric grid from a passive network to a bidirectional active network is due to the small generators being set up in the distribution grid known as distributed/decentralized generation. These distributed generators (DGs) mainly generate energy using renewable energy resources, such as photovoltaic (PV), wind, biomass, tidal, etc., as they are clean sources of energy and promote sustainable development. However, the intermittent nature and excess penetration of these resources pose problems, such as reverse power flow, voltage fluctuations, increase in losses, harmonic distortion, and instantaneous matching of demand and supply [1], [2]. Although integrating storage systems in the distribution network can help alleviate the aforementioned problems if allocated optimally. Also, India’s target of 40-GW PV rooftop installations by the year 2022 and the declining cost of batteries will provide a fillip to the installation of energy storage systems (ESSs) [3]. Therefore, an ESS can be seen as a game changer in the ever-changing power system paradigm and can serve multiple ranges of benefits. To achieve these benefits, methods for optimal sizing, allocation, and operation of ESSs are required

Conclusion

     The integration of renewable energy resources necessitates the allocation of distributed energy storage to mitigate voltage deviation and defer distribution upgrades. This article illustrated a two-stage procedure for integrating lithium-ion batteries in a PVpenetrated active distribution network. The planning methodology considered an ac-OPF model with the uncertainty of PV and demand to determine the batteries’ accurate size. Moreover, the reverse power flow due to excess PV penetration was utilized and the voltage was within limits after the integration of batteries in the system. The battery’s life cycle was examined in the second stage with the partial cycle of discharge and complete discharge cycles. For lithium-ion batteries, partial cycle discharging was fine as there was no memory effect, and the battery does not need full discharges periodically. From the results, it was illustrated that with the partial cycle of discharge, the battery will last longer. Moreover, the method used here was the mathematical programming, which provides a robust solution, but may require significant computation time for large and complex problems. Also, the maximum discharging capacity of the battery gradually declined with its cycles, which can be kept as a future research direction. Furthermore, to enhance the stability and cost-effectiveness, a smart charging and discharging operation can be added to the problem. In addition, different types of DGs, such as wind, micro-turbines, and fuel cells, can be considered an extension to the OPF formulation.