CAJ | 학술논문

提出一种可促进电力系统碳减排的新型机组组合模型。相比于传统模型，该模型在以下两方面进行了改进：一是综合考虑供应侧资源与需求响应、电动汽车、分布式可再生能源发电等低碳的需求侧资源的最优组合；二是机组调度的规则在经济目标之外充分考虑碳排放目标，提出可计及目标相对优先级的模糊双目标优化方法。另外，在求解优化模型时，对粒子群优化算法进行改进，引入了遗传算法中的“交叉”、“变异”两个算子，提高了粒子群算法的全局寻优能力。通过对10机系统进行算例分析，验证了模型与算法的有效性。
제출일충가촉진전력계통탄감배적신형궤조조합모형。상비우전통모형，해모형재이하량방면진행료개진：일시종합고필공응측자원여수구향응、전동기차、분포식가재생능원발전등저탄적수구측자원적최우조합；이시궤조조도적규칙재경제목표지외충분고필탄배방목표，제출가계급목표상대우선급적모호쌍목표우화방법。령외，재구해우화모형시，대입자군우화산법진행개진，인입료유전산법중적“교차”、“변이”량개산자，제고료입자군산법적전국심우능력。통과대10궤계통진행산례분석，험증료모형여산법적유효성。
A novel unit commitment model to promote carbon reduction of a power system is proposed.Compared with traditional models,this one is improved in the following two aspects.On the one hand,low-carbon demand side resources, such as demand response,vehicle to grid and distributed renewable energy generation,are considered together with power supply resources to achieve an optimal schedule.On the other hand,a new fuzzy bi-objective optimization approach that can reflect the relevant priority between objectives is presented to strike an effective balance between economic objective and carbon emission objective.To solve the unit commitment optimization problem,the particle swarm optimization (PSO) is improved by employing crossover operator and mutation operator from the genetic algorithm,which enhances the global optimization ability of PSO.Numerical studies of a 10-unit system have verified the effectiveness of the model and the algorithm.