Multi-Objective Optimization Scheduling Strategies for Distribution Networks With High-Density Photovoltaics and Energy Storage

doi:10.12096/j.2096-4528.pgt.260120

Abstract

Abstract:

Objectives To better integrate high-density photovoltaic (PV) energy, energy storage devices are integrated into distribution networks to achieve peak shaving and valley filling of electrical loads and alleviate the effect of distributed PV on grid voltage. Methods Through appropriate selection of energy storage scheduling and distributed PV output strategies, a multi-objective optimization method is used to optimize the power flow of distribution networks, aiming to minimize network losses and voltage deviations. A comprehensive evaluation system is established using the analytic hierarchy process-entropy weight method, and an optimization model is developed using particle swarm optimization algorithm to obtain the optimal scheduling strategy for PV and energy storage coordination. Results Calculations and analysis are conducted on a 30-node simulation model and practical cases. It is demonstrated that the energy storage system effectively mitigates PV fluctuations, ensures voltage stability at nodes, and improves energy utilization efficiency. Compared with traditional algorithms, the proposed method shows significant advantages in power quality and network loss control. Conclusions The method can achieve efficient and stable operation of distribution networks and reduce system network losses.

Key words: distribution network optimization, high-density photovoltaics, energy storage, multi-objective optimization, PV-storage coordination, particle swarm optimization, optimal scheduling, comprehensive evaluation

CLC Number:

TK 01

Kewen LIU, Langbo HOU, Hao SUN, Heng CHEN. Multi-Objective Optimization Scheduling Strategies for Distribution Networks With High-Density Photovoltaics and Energy Storage[J]. Power Generation Technology, 2026, 47(1): 214-224.

Figures/Tables 7

Fig. 1 Architecture of distribution network control

Fig. 2 Simulation of 30-node example

Fig. 3 Time-of-use electricity pricing of system

Fig. 4 Temporal distribution of PV output and energy storage charging/discharging

Fig. 5 Voltage distribution at nodes before and after coordinated optimization of PV-storage system

Fig. 6 Frequency distribution of voltage after optimization using different algorithms

Fig. 7 Active network loss after optimization using different algorithms

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