Abstract
Energy storage systems (ESSs) are promising solutions for the mitigation of power fluctuations and the management of load demands in distribution networks (DNs). However, the uncertainty of load demands and wind generations (WGs) may have a significant impact on the capacity allocation of ESSs. To solve the problem, a novel optimal ESS capacity allocation scheme for ESSs is proposed to reduce the influence of uncertainty of both WG and load demands. First, an optimal capacity allocation model is established to minimize the ESS investment costs and the network power loss under constraints of DN and ESS operating points and power balance. Then, the proposed method reduces the uncertainty of load through a comprehensive demand response system based on time-of-use (TOU) and incentives. To predict the output of WGs, we combined particle swarm optimization (PSO) and backpropagation neural network to create a prediction model of the wind power. An improved simulated annealing PSO algorithm (ISAPSO) is used to solve the optimization problem. Numerical studies are carried out in a modified IEEE 33-node distribution system. Simulation results demonstrate that the proposed model can provide the optimal capacity allocation and investment cost of ESSs with minimal power losses.
Highlights
Energy storage systems (ESSs) are promising solutions for the mitigation of power fluctuations and the management of load demands in distribution networks (DNs)
This study presents a new approach to the optimal capacity allocation of ESSs in DN, which introduces a comprehensive demand response (DR) to reduce the uncertainty of high-penetration wind generations (WGs) and load demand using computational swarm intelligence
Several studies have explored solutions to accommodate the uncertainties from WGs and load demands. e uncertainty of wind power output has been studied in the DN [6, 7]
Summary
Where PWG,t is the power of WGs at time t; Pgrid,t is the power purchased from the upper system at time t; Pcbess,t and Pdbess,t represent the charging and discharging power at time t, respectively; and Pload,t and Ploss,t represent the load power demand and network power loss at time t, respectively
Sign-in/Register to view highlights & summary 
Published Version (
Free)
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
