CAJ | 학술논문

针对国内外广泛关注的电动汽车移动储能技术，综合考虑电网约束、电池约束、车主使用需求，建立了电动汽车移动储能系统模型，并采用粒子群优化（PSO）算法对模型进行求解。由于标准粒子群优化（SPSO）算法在处理高维问题时更易出现早熟收敛，根据仿生算法思想对其进行了改进，提出了基于防碰撞粒子群优化（CAPSO）算法的电动汽车移动储能控制策略。几个典型基准测试函数的测试结果表明，改进算法较PSO算法性能更优。最后，通过平抑负荷、平抑可再生能源发电功率波动、平抑计及可再生能源出力的负荷3个算例，对实际系统进行了定量模拟，验证了模型和控制策略的有效性。
침대국내외엄범관주적전동기차이동저능기술，종합고필전망약속、전지약속、차주사용수구，건립료전동기차이동저능계통모형，병채용입자군우화（PSO）산법대모형진행구해。유우표준입자군우화（SPSO）산법재처리고유문제시경역출현조숙수렴，근거방생산법사상대기진행료개진，제출료기우방팽당입자군우화（CAPSO）산법적전동기차이동저능공제책략。궤개전형기준측시함수적측시결과표명，개진산법교PSO산법성능경우。최후，통과평억부하、평억가재생능원발전공솔파동、평억계급가재생능원출력적부하3개산례，대실제계통진행료정량모의，험증료모형화공제책략적유효성。
The electric vehicle mobile energy storage technology has drawn widespread attention nationally as well as internationally. The electric vehicle mobile energy storage system is modeled as an optimization problem considering power system constraints, battery constraints and vehicle owners' constraints. The particle swarm optimization (PSO) algorithm is adopted to solve the optimization problem of the proposed model. Since the standard particle swarm optimization (SPSO) algorithm is more likely to get premature convergence when it comes to high-dimension problems, based on the ideas of the bionic algorithm, an improvement is made to the SPSO. A control strategy of electric vehicle mobile energy storage system is proposed based on the collision avoidance particle swarm optimization (CAPSO). The testing results of several benchmark functions show that the improved algorithm has better performance than SPSO. The quantitative simulation results of various cases including the load-smoothing, the renewable energy output fluctuation smoothing and the load-smoothing considering renewable energy generation validate the efficacy of the proposed model and the control strategy.