Multi-objective Optimal Configuration of Electricity-Hydrogen Hybrid Energy Storage System in Zero-Carbon Park

doi:10.12204/j.issn.1000-7229.2022.08.001

Abstract

Abstract:

With the rapid development of hydrogen production and storage technology, the development of hydrogen energy storage systems (HESSs) will bring fundamental changes to energy and power system structure. The coordinated optimization of HESS and battery energy storage system (BESS) can solve the imbalance between supply and demand of various energy sources and improve energy efficiency. In order to ensure the effectiveness of BESS and HESS planning, minimum life cycle cost (LCC), system network loss, switching power deviation, load fluctuation, and voltage fluctuation are chosen as the fitness function in this paper. Meanwhile, a non-dominated sorting genetic algorithm-II (NSGA2) with elite strategy is used to solve energy storage system (ESS) Pareto non-dominated solution set of site-constant volume planning scheme. The grey target decision based on entropy weight method (EWM) is used to select the best compromise solution in Pareto non-dominated solution set. Additionally, typical operation scenarios of source load are obtained by fuzzy kernel C-means (FKCM) clustering algorithm, and the simulation analysis is carried out on the basis of the extended IEEE 33-node system. Simulation results show that NSGA2 algorithm not only achieves the minimum LCC of the electricity-hydrogen hybrid energy storage system, but also improves voltage quality, power stability, network loss and load fluctuation compared to that of other algorithms.

Key words: zero-carbon park, battery energy storage systems (BESSs), hydrogen energy storage systems (HESSs), fuzzy kernel C-means (FKCM), non-dominated sorting genetic algorithm-II (NSGA2)

CLC Number:

TM715

ZHANG Chi, ZHOU Jun, ZHAO Bin, LI Jiale, YANG Bo. Multi-objective Optimal Configuration of Electricity-Hydrogen Hybrid Energy Storage System in Zero-Carbon Park[J]. ELECTRIC POWER CONSTRUCTION, 2022, 43(8): 1-12.

Figures/Tables 15

Fig.1 Typical configuration of BESSs and HESSs directly connected to power grid

Fig.2 The flowchart diagram of the proposed NSGA2 used for electricity-hydrogen hybrid energy storage system

Fig.3 Typical diurnal curves of load, wind and PV power

Table 1 Clustering results of composition scene

Fig.4 Topology of the extended IEEE 33-node system

Table 2 Main parameters of power grid

Table 3 Main parameters of BESS and HESS

Table 4 Time-of-use electricity price

Table 5 Pareto optimization results of the model obtained by two algorithms

Table 6 BESS and HESS configuration schemes and optimization results obtained by two algorithms

Fig.5 Charging and discharging power of BESS and HESS in one day

Fig.6 Optimization results of network obtained by two algorithms in the extended IEEE 33-node system

Fig.7 Pareto front of parallel coordinate system under extended IEEE 33-node system

Table 7 Comparison of optimization results with different energy storage configurations

Fig.8 Grid optimization results of different energy storage systems based on NSGA2 algorithm

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