Reliability evaluation and optimization of multi-energy systems considering energy storage devices effects under weather uncertainties

Abstract

Since traditional fossil energy sources such as coal and oil are non-renewable, increasing energy efficiency and expanding broad use of renewable energy sources have become an inevitable choice to solve energy shortages. The multi-energy system (MES) can realize the cascade utilization of energy through various forms of energy coupling such as electricity, gas, cold/heat, etc., and effectively improve the comprehensive utilization efficiency of energy. Moreover, through its coupling characteristics and complementary substitution, multi-energy system can make up for the obvious intermittence and random fluctuation of renewable energy, such as wind power and solar energy, and promote the development and usage of renewable energy. In addition, the energy storage system plays a critical role in the multi-energy system. Due to its bidirectional charge-discharge feature, it can compensate for insufficient energy supply caused by system failure and store excess energy when the system is operating normally, which improves the flexibility of the multi-energy system. Therefore, this project presents an approach for evaluating and optimizing reliability under weather uncertainty considering various energy storage configuration strategies. Firstly, the simulation model of the whole MES is created in Matlab/Simulink. Secondly, sequential Monte Carlo simulation is used to evaluate MES reliability. Then, a multi-objective reliability optimization is proposed aiming at minimizing system interruption duration and reliability cost. SAIDI (system average interruption index) reliability index is adopted as the reliability indicator to reflect MES reliability performance. The cost indicator includes both system interruption costs and investment costs. Different meta-heuristic optimization algorithms have been considered such as NSGA-II (non-dominated sorting genetic algorithm II), MOPSO (multiple objective particle swarm optimization), and SPEA2 (strength Pareto evolution algorithm 2) to compare the reliability optimization results and demonstrate the feasibility of the proposed methodology. Case studies that are applied on two typical MES layouts of increasing complexity are also given afterward. Both case studies’ results show that NSGA-II leads to the best optimal values and converges the fastest among the three algorithms. After optimization, the SAIDI declined by 90.78% in case study 1 and 86.92% in case study 2, respectively. Last but not least, this project also investigates the resilient analysis of MES. The resilient modeling and evaluation of MES are given to investigate the effect of the resilience enhancement strategies on different wind speeds. The simulation results have concluded that the priority of resilience strengthening actions may vary depending on the wind speed profile. Improving line robustness gives significant improvements in resilience index at high wind speed, whereas redundancy case is most significant at extreme wind speeds.

Date of Award	31 Dec 2022
Original language	English
Awarding Institution	The University of Manchester
Supervisor	Jovica Milanovic (Supervisor) & Alessandra Parisio (Supervisor)