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PDF(1108 KB)
风电接入下计及碳交易的机组组合问题研究
Research on Unit Commitment Problem Taking Into Account Carbon Trading Under Wind Power Integration
针对目前广泛关注的环境污染问题,利用粒子群优化算法求解风电接入下计及碳交易的机组组合问题。在模型方面,对火电机组产生的CO2,引入碳交易机制,采用限量排放、差额交易的形式;对于其他污染物,如SO2、氮氧化合物(NOx)、总悬浮颗粒物(total suspended particulates,TSP)等,将排放成本与火电机组出力建立函数关系,作为惩罚成本进行计算。在算法方面,提出一种改进的二进制粒子群优化(binary particle swarm optimization,BPSO)算法,将计及污染成本的机组组合问题变成一个双层优化问题。外层采用改进BPSO算法确定机组启停状态,内层通过改进λ迭代算法计算机组组合及风电出力。在算例方面,通过与其他算法进行对比说明算法的有效性,分析变化的碳排放配额、碳交易成本对机组组合结果的影响,得到兼顾环保性和经济性的结果。
In order to solve the problem of environmental pollution, particle swarm optimization algorithm is proposed to solve the unit commitment problem taking into account carbon trading under wind power integration. In the aspect of model, For the CO2 produced by thermal power units, the carbon trading mechanism is introduced, and the form of limited emission and balance trading is adopted. For other pollutants such as SO2, nitrogen oxides (NOx), total suspended particulates (TSP), the emission cost is calculated as a penalty cost by establishing a functional relationship with the output of thermal power units. In terms of algorithm, this paper presents an improved binary particle swarm optimization (BPSO) algorithm, which turns the unit commitment problem with pollution cost into a two-layer optimization problem. The improved BPSO is used to determine the start-stop state of the unit in the outer layer, and the improved λ iterative algorithm is used to calculate the unit commitment and wind power output in the inner layer. In terms of calculation examples, the effectiveness of the algorithm is demonstrated by comparing it with other algorithms, the impact of changing carbon emission quotas and carbon trading costs on the unit commitment results is analyzed, and the results that balance environmental protection and economy are obtained.
wind power integration / carbon trading / unit commitment / particle swarm optimization algorithm
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