Optimized Energy Storage System Configuration for Voltage Regulation of Distribution Network With PV Access

Abstract

With the large-scale integration of renewable energy such as wind power and PV, it is necessary to maintain the voltage stability of power systems while increasing the use of intermittent renewable energy sources. The rapid development of energy storage technologies permits the deployment of energy storage systems (ESS) for voltage regulation support. This paper develops an ESS optimization method to estimate the optimal capacity and locations of distributed ESS supporting the voltage regulation of a distribution network. The electrical elements of the network integrated with PV and ESS are first modelled to simulate the voltage profile of the network. Then an improved multi-objective particle swarm optimization (PSO) algorithm is employed to minimise a weighted sum of the overall nodal voltage deviation from the nominal level across the network and across the time horizon and the energy capacity of ESS reflecting the associated investment. The improved PSO algorithm adaptively adjusts the inertia weight associated with each particle based on its distance from the best known particle of the population and introduces the cross-mutation operation for a small distance to avoid falling into local optimal solutions. Then the dynamic dense distance arrangement is taken to update the non-inferior solution set and indicate potential global optimal solutions so as to keep the scale and uniformity of the optimal Pareto solution set. To mitigate the impact of decision makers’ preference, the information entropy based technique for order of preference by similarity to ideal solution is used to select the optimal combination of the ESS access scheme and capacity from the Pareto solution set. The proposed ESS optimization method is tested based on the IEEE 24-bus system with additional imports from high-voltage power supply. The voltage profile of the network simulated without the ESS or with the random or optimized ESS placement is compared to illustrate the effectiveness of the optimized ESS in performing voltage regulation under normal operation and supporting emergency power supply during high-voltage transmission failures.

Voltage Optimization in Active Distribution Networks—Utilizing Analytical and Computational Approaches in High Renewable Energy Penetration Environments

“…This approach is demonstrated to be more effective than the standard PV smoothing mode. In [62], the author introduces an improved MOPSO algorithm for determining the optimal capacity and locations of ESSs. The algorithm aims to minimize voltage deviations and ESS capacity costs.…”

Functional-Combination-Based Comprehensive Benefit Evaluation of Energy Storage Projects under Source-Grid-Load Scenarios via Super-Efficiency DEA

“…(2) Capacity cost management: an ESS is used to store electric energy during the valley period of power consumption, and discharge it during the peak period of power consumption, which can replace part of the power supply of the power grid, thereby reducing the cost of capacity management [29]. (3) Improve user power quality: an ESS can reduce problems such as voltage rise and frequency fluctuation to reduce the loss caused by power quality events [30]. (4) Demand response: the ESS responds to market price signals, incentive mechanisms, or instructions issued by operators to change its short-or long-term operation strategies [31].…”

An Improved Analysis of Distributed Resource Access Location and Capacity on Voltage Stability of Distribution Networks in Smart Grid

“…The benefits of using DRALC include improved voltage stability, reduced losses, and better utilization of available network resources. As distribution networks increasingly incorporate distributed energy resources (DERs), DRALC might be a vital component in guaranteeing the smart grid's dependable and secure operation [7]. One important consideration in the planning and management of distribution networks in an intelligent grid is the Distributed Resource Access Location and Capacity (DRALC).…”