Researchers from South Africa's University of Pretoria have conducted a multi-objective optimization study to combine commercial and industrial (C&I) PV systems with compressed air energy storage (CAES). The study is aimed to minimize total system investment (Capex) and operational costs (Opex), while improving reliability and maximizing renewable energy penetration under South African conditions.
“The core innovation of this work lies in its holistic, optimization-driven approach. Unlike traditional methods that size CAES based on worst-case scenarios, which often lead to costly over-design, we developed a multi-objective framework that simultaneously optimizes the PV system and CAES components in real-time,” corresponding author Tshilumba Kalala told pv magazine.
“Our model dynamically balances energy capacity, power output, thermodynamic efficiency, and economic constraints, ensuring an optimally sized system that is both technically robust and economically viable for a wide range of microgrid applications,” he added. “It effectively bridges a critical gap between theoretical CAES design and practical, cost-effective deployment alongside solar PV.”
The team has simulated a grid-connected hybrid microgrid for an unspecified commercial building in South Africa. It has three main components: a PV array, an adiabatic CAES (A-CAES), and a backup diesel generator. The A-CAES comprises three parts: compressors, turbines, and an air storage tank.
The optimization problem was formulated as a multi-objective mixed-integer nonlinear programming (MINLP) model with continuous and binary variables. It was solved for four scenarios: two normal conditions, where the irradiance is high, and the amount of power generated by the solar energy sources in the microgrid is high; and two extreme conditions, where the microgrid has lower levels of solar irradiance. Each condition was tested with 2 and 6 hours of load shedding per day.
“One of the most compelling findings was the significant cost reduction achievable through simultaneous optimization. Our model demonstrated that a co-optimized PV-CAES system could reduce total capital costs by up to 15-20% compared to conventional sequential sizing, while maintaining or even improving grid stability and renewable energy utilization,” Kalala said. “Furthermore, the results clearly showed that there is no ‘one-size-fits-all' CAES configuration; the optimal power-to-energy ratio is highly sensitive to local demand profiles and solar irradiation patterns, underscoring the necessity of tailored design tools like the one we propose.”
The analysis also showed a clear trade-off exists between system performance and capital expenditure. A high-performance configuration of 37.5 kW PV, 200 m3 storage, 10 bar pressure, and 20 kW turbomachinery was found to achieve a 41.5% renewable energy penetration and 94.1% reliability, while requiring a high capital investment of $13.57 million. On the other hand, a cost-constrained configuration of 28.15 kW PV and 3 bar CAES reduced the upfront cost by 32% to $9.2 million. However, it attained a lower reliability of 92% and an 18.6% renewable share.
“We are currently working on moving from optimal design to optimal operation,” Kalala said about the future directions of the research. “We are developing an AI-driven energy management system to dynamically control and dispatch a CAES system in real-time within a microgrid. The goal is to maximize the efficiency, lifespan, and economic return of the storage asset by using machine learning to predict energy flows and adapt to grid conditions in real time. This ‘smart CAES' controller is the next critical step to unlock the full value and grid-support capabilities of CAES technology in renewable-heavy grids.”
The system was presented in “Simultaneous sizing of a photovoltaic system and compressed air energy storage in a microgrid,” published in Energy Conversion and Management: X.
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