Abstract
Optimal operation of energy storage systems plays an important role in enhancing their lifetime and efficiency. This paper combines the concepts of the cyber–physical system (CPS) and multi-objective optimization into the control structure of the hybrid energy storage system (HESS). Owing to the time-varying characteristics of HESS, combining real-time data with physical models via CPS can significantly promote the performance of HESS. The multi-objective optimization model designed in this paper can improve the utilization of supercapacitors, reduce energy consumption, and prevent the state of charge (SOC) of HESS from exceeding the limitation. The new control scheme takes the characteristics of the components of HESS into account and is beneficial in reducing battery short-term power cycling and high discharge currents. The rain-flow counting algorithm is applied for battery life prediction to quantify the benefits of the HESS under the control scheme proposed. A much better power-sharing relationship between the supercapacitor and the lithium–ion battery (LiB) can be observed from the SIMULINK results and the case study with our new control scheme. Moreover, compared to the traditional low-pass filter control method, the battery lifetime is quantifiably increased from 3.51 years to 10.20 years while the energy efficiency is improved by 1.56%.
Highlights
With the significant increase in the penetration of renewable energy power generation such as photovoltaics power and wind power in the power grid, the contradictions between the randomness of renewable energy power generation and the safe operation of the power grid have become increasingly prominent [1,2,3]
This paper proposes a hybrid energy storage system and a corresponding control scheme for photovoltaic generation
This paper focuses on the multi-objective optimization control of hybrid energy storage system (HESS) based on cyber–physical system (CPS)
Summary
With the significant increase in the penetration of renewable energy power generation such as photovoltaics power and wind power in the power grid, the contradictions between the randomness of renewable energy power generation and the safe operation of the power grid have become increasingly prominent [1,2,3]. The CPS system monitors the SOC of HESS in real time for further optimization, which is intended to minimize the system loss and stabilize the battery power by increasing the supercapacitor utilization. Choi et al presented a power management system that provides the optimal solution to control the current flow in each energy storage element by solving multi-objective function with boundary parameters found through the multiplicative-increase-additive-decrease principle [20]. For the real-time computing and stability requirements, the weighted sum scalarization method used in this article converts a multi-optimization problem into a single objective function which sacrifices the number of optimal solutions in exchange for computing performance to better adapt to the CPS system. The system can obtain the optimal power output of the hybrid energy storage system at every moment to ensure the real-time performance of the algorithm in practical applications.
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