Combined Cool and Heat and Power Multi-objective Scheduling Considering Carbon Emissions Trading Using Algorithm of Fuzzy Self-correction Particle Swarm Optimization
ZHOU Renjun;LI Shaojin;CHEN Ruixian;LI Hongying;YANG Yuwei;CHEN Yun;Smart Grid Operation and Control Key Laboratory of Hunan Province (Changsha University of Science and Technology);Hunan Electric Power Corporation Training Center;Shangrao Electric Power Supply Company of Jiangxi Province;
With the advancement of carbon emissions quotas and trading mechanism, CO2 emissions have been no longer just in the form fixed as the environment punishment cost. Its emission rights can also be used for transactions, and the transaction prices have been dominated by market. To consider carbon emissions influence on combined cool and heat and power(CCHP), a carbon emissions trading cost function was introduced, and a CCHP low carbon dispatching multi-objective optimization model was established, which considered the carbon trading cost, fuel cost and environmental cost. A fuzzy self-correction particle swarm optimization(FS-PSO) algorithm was proposed to solve this optimization problem. A membership function that responds to the own fitness of particles was established by using the fuzzy reasoning mechanism. The value of inertia weight was modified by the current membership function value of the particle fitness during optimization, which can improve particle precocity defect and enhance its global searching ability. On the premise of guaranteeing system load demand, the analysis results show that the model can control the CO2 emissions effectively and get additional earnings and reduce the integrated operation cost of CCHP.