Optimal Scheduling of the Wind-Photovoltaic-Energy Storage Multi-Energy Complementary System Considering Battery Service Life

Abstract

Under the background of “peak carbon dioxide emissions by 2030 and carbon neutrality by 2060 strategies” and grid-connected large-scale renewables, the grid usually adopts a method of optimal scheduling to improve its ability to cope with the stochastic and volatile nature of renewable energy and to increase economic efficiency. This article proposes a short-term optimal scheduling model for wind–solar storage combined-power generation systems in high-penetration renewable energy areas. After the comprehensive consideration of battery life, energy storage units, and load characteristics, a hybrid energy storage operation strategy was developed. The model uses the remaining energy in the system after deducting wind PV and energy storage output as the “generalized load”. An improved particle swarm optimization (PSO) is used to solve the scheduling schemes of different running strategies under different objectives. The optimization strategy optimizes the battery life-loss coefficient from 0.073% to 0.055% under the target of minimizing the mean squared deviation of “generalized load”, which was optimized from 0.088% to 0.053% under the minimized fluctuation of combined system output and optimized from 0.092% to 0.081% under the minimized generation costs of the combined system. The results show that the model can ensure a stable operation of the combined system, and the operation strategy proposed in this article effectively reduces battery life loss while reducing the total power generation cost of the system. Finally, the superiority of the improved PSO algorithm was verified.

Tariff-Based Optimal Scheduling Strategy of Photovoltaic-Storage for Industrial and Commercial Customers

“…The existing research on user-side PV-ESS primarily focuses on cost reduction, load shifting or peak demand reduction, and time of use (TOU) pricing arbitrage [15]. In [16], the study employs an improved particle swarm optimization (PSO) algorithm to address the scheduling scheme with varying objectives, aiming to minimize battery degradation while achieving optimal generation cost reduction. However, the investigation was limited to a single sunny day, precluding the assessment of system performance and optimal scheduling strategies across diverse weather conditions.…”

“…It directly affects the economic benefits and efficiency of the system. The previous research [16][17][18] primarily focused on analyzing a single weather condition and did not consider the influence of varying weather conditions on the capacity configuration of the PV-ESS. This assumption of a single weather condition may lead to inaccurate system design and erratic performance in actual variable meteorological environment.…”