Voltage Regulation Based on Deep Reinforcement Learning Algorithm in Distribution Network with Energy Storage System

2021 ◽  
Author(s):  
Wentao Zhou ◽  
Ning Zhang ◽  
Zhenbo Cao ◽  
Yi Chen ◽  
Mingjuan Wang ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Energy Storage ◽  
Learning Algorithm ◽  
Storage System ◽  
Distribution Network ◽  
Energy Storage System ◽  
Voltage Regulation ◽  
Reinforcement Learning Algorithm

scholarly journals VOLTAGE REGULATION CONTROL USING BATTERY ENERGY STORAGE SYSTEM IN DISTRIBUTION NETWORK WITH HIGH PV PENETRATION STRENGTH

2021 ◽  
Vol 83 (6) ◽  
pp. 203-209
Author(s):  
Nur Muhammad Alif Ramli ◽  
Siti Maherah Hussin ◽  
Dalila Mat Said ◽  
Norzanah Rosmin ◽  
Amirjan Nawabjan
Keyword(s):  
Energy Storage ◽  
Storage System ◽  
Distribution Network ◽  
Energy Storage System ◽  
Voltage Regulation ◽  
Battery Energy Storage System ◽  
Battery Energy Storage ◽  
Voltage Rise ◽  
Battery Energy ◽  
Pv Penetration

In recent years, the increasing integration of PV generations into distribution network systems is becoming a huge concern as it introduces various complications such as voltage rise problems, especially during high PV penetration levels. Conventional mitigation methods using voltage regulating devices are not designed to mitigate this particular problem while emerging methods requires sacrifices in term of cost and profit to be made by PV system owners. Thus, mitigation using a battery energy storage system (BESS) is proposed in this paper, where it is specifically designed to solve the voltage rise problem in the distribution system during high PV penetration. This is achieved by controlling the charging and discharging of the BESS accordingly. To validate the effectiveness of the proposed BESS, a simulation using MATLAB/Simulink software of 25 distributed PV generations with respective loads connected to a distribution network power system is done. The penetration level is set from 0% to 100% and the voltage level is measured at the point of common coupling for each increment. The findings show that the BESS can regulate the voltage rise that occurred during high PV penetration of 80% and 100% from 1.11 p.u. and 1.13 p.u. to an acceptable voltage of 1.01 pu.

Optimal allocation of distributed energy storage in active distribution network via hybrid teaching learning and multi-objective particle swarm optimization algorithm

2021 ◽  
pp. 002072092098369
Author(s):  
Jijun Liu ◽  
Yuxin Bai ◽  
Yingfeng He
Keyword(s):  
Particle Swarm Optimization ◽  
Energy Storage ◽  
Optimal Allocation ◽  
Storage System ◽  
Distribution Network ◽  
Particle Swarm ◽  
Economic Cost ◽  
Energy Storage System ◽  
Swarm Optimization ◽  
Active Distribution Network

This work aims at solving complex problems of the optimal scheduling model of active distribution network, teaching strategies are proposed to improve the global search ability of particle swarm optimization. Moreover, based on the improved Euclidean distance cyclic crowding sorting strategy, the convergence ability of Li Zhiquan algorithm is improved. With the cost and voltage indexes of the energy storage system of the distribution network as the goal, different optimized configuration schemes are constructed, and the improved HTL-MOPSO algorithm is adopted to find the solution. The results show that compared with the traditional TV-MOPSO algorithm, the proposed algorithm has better convergence performance and optimization ability, and has a lower economic cost. In short, the algorithm proposed can provide a basis for improving the optimization of active distribution network scheduling strategies.

Sign in / Sign up

Export Citation Format

Share Document