Wedoany.com Report on Jan 31st, A research team from the University of Pretoria in South Africa recently conducted a multi-objective optimization study exploring the synergistic application of commercial and industrial photovoltaic systems with compressed air energy storage technology. The study, tailored to conditions in South Africa, aims to reduce the total system investment and operational costs, improve power supply reliability, and increase the share of renewable energy in the grid.
Corresponding author Tshilumba Kalala explained to pv magazine: "The core innovation of this work lies in adopting a holistic optimization approach. Unlike the traditional method of designing compressed air energy storage capacity based on worst-case scenarios, which often leads to costly over-sizing, our developed multi-objective framework can simultaneously optimize both the photovoltaic system and the compressed air energy storage components."
The research team simulated a grid-connected hybrid microgrid system for a commercial building in South Africa. This system includes a photovoltaic array, an adiabatic compressed air energy storage unit, and a backup diesel generator. The adiabatic compressed air energy storage consists of three parts: a compressor, a turbine, and an air storage tank.
The optimization model employs a multi-objective mixed-integer nonlinear programming method, incorporating both continuous and binary variables. The researchers tested four scenarios: two under normal lighting conditions and two under low-irradiance extreme conditions, with each scenario examining daily load shedding of 2 hours and 6 hours respectively.
Kalala pointed out: "Compared to the traditional sequential design approach, the co-optimized photovoltaic-compressed air energy storage system can reduce the total capital cost by 15-20% while maintaining or even improving grid stability and renewable energy utilization. The results clearly show that there is no universal compressed air energy storage configuration; the optimal power-to-energy ratio depends on the local electricity demand curve and solar irradiance characteristics."
The analysis revealed a trade-off relationship between system performance and investment cost. A high-performance configuration (37.5 kW PV, 200 m³ storage capacity, 10 bar pressure, 20 kW turbine) could achieve a renewable energy penetration rate of 41.5% and a power supply reliability of 94.1%, but requires a capital investment of $13.57 million. A cost-optimized configuration (28.15 kW PV, 3 bar compressed air energy storage) reduces the upfront investment by 32% to $9.2 million, with corresponding reliability at 92% and renewable energy share at 18.6%.
Regarding future research directions, Kalala stated that the team is developing an artificial intelligence-driven energy management system to achieve real-time dynamic control and dispatch of compressed air energy storage. This intelligent control system uses machine learning to predict energy flows and adapt to grid conditions, aiming to enhance the operational efficiency, service life, and economic benefits of the energy storage assets.
