Optimal Allocation of Photovoltaic Systems and Energy Storage Systems based on Vulnerability Analysis

Abstract

There is a growing need to connect renewable energy systems (REs), such as photovoltaic systems (PVs), to the power grid for solving environmental problems such as global warming. However, an electricity grid with RE is vulnerable to problems of power shortage and surplus owing to the uncertainty of RE outputs and grid failures. Energy storage systems (ESSs) can be used to solve supply reliability problems, but their installation should be minimized considering their high costs. This study proposes a method to optimize the allocations of PVs and ESSs based on vulnerability analysis, and utilizes our proposed concept of "slow" and "fast" ESSs, which can reflect the influences of both uncertainties: PV outputs and grid failures. Accordingly, this paper demonstrates an optimal allocation of PVs and ESSs that minimizes the amount of ESSs while satisfying the PV installation target and the constraints on supply reliability indices for power shortage and power surplus in the event of a grid failure.

A Stackelberg Game-Based Model of Distribution Network-Distributed Energy Storage Systems Considering Demand Response

“…Literature [7] takes a leadacid battery storage system as an example to model the battery aging model and optimize its control strategy. Literature [8] provides an energy storage allocation method based on vulnerability analysis, which takes into account the factors of sudden grid faults as well as the probabilistic output of DPVs in order to facilitate the quick determination of the optimal allocation of DPVs and energy storage in unexpected conditions. Literature [9] introduces the power demand and non-black start (NB-S) generating unit capacity accessibility concept and proposes an optimal selection and siting scheme for battery storage and DPV.…”

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“…These studies are focused on minimizing the annual investment cost, operation and maintenance, installation cost, power loss cost, interruption cost and maximizing the utility, reliability, safety, stability, consumption of renewable energy. Most of these studies use optimal power flow (OPF), genetic algorithm (GA), particle swarm optimisation (PSO) and block coordinate decent (BCD) algorithms programmes [61][62][63][64][65][66][67][68][69][70][71]. The constraints considered in these studies are based on conventional power system constraints, cost constraints, ESS constraints and renewable energy constraints.…”

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“…Moreover, some researchers had studied a method to optimize the allocations of the PVs and energy storage systems (ESSs), based on vulnerability analysis. They utilized the proposed concept of 'slow' and 'fast' ESSs [12]. In terms of the social acceptance of renewable energy technologies (RETs), the study covered the topic of awareness and willingness to invest in the renewable energy technology [13], but it did not consider the allocation of SHS sustainable development.…”