In order to solve the influence of load uncertainty on hydrothermal power system operation and achieve the optimal objectives of system power generation consumption, pollutant emissions, and first-stage hydropower station storage capacity, this paper introduced CVaR method and built a multiobjective optimization model and its solving method. In the optimization model, load demand’s actual values and deviation values are regarded as random variables, scheduling objective is redefined to meet confidence level requirement and system operation constraints and loss function constraints are taken into consideration. To solve the proposed model, this paper linearized nonlinear constraints, applied fuzzy satisfaction, fuzzy entropy, and weighted multiobjective function theories to build a fuzzy entropy multiobjective CVaR model. The model is a mixed integer linear programming problem. Then, six thermal power plants and three cascade hydropower stations are taken as the hydrothermal system for numerical simulation. The results verified that multiobjective CVaR method is applicable to solve hydrothermal scheduling problems. It can better reflect risk level of the scheduling result. The fuzzy entropy satisfaction degree solving algorithm can simplify solving difficulty and get the optimum operation scheduling scheme.
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The power industry's participation in carbon trading and green certificate trading is an effective market-based approach to solve the negative externalities of power production. In this paper, the Virtual power plant (VPP) is taken as the aggregator to coordinate and optimize the carbon trading and green certificate trading between the power purchasing end and the power selling end, so as to achieve the goal of maximizing the comprehensive benefits of the VPP. Firstly, the operation mode of VPP aggregating various types of distributed energy and different users participating in green certificate market and carbon trading market is analyzed; Secondly, a two-level collaborative optimization model of VPP participating in power purchase and sale transaction and green certificate transaction is constructed. On the one hand, the cost of power purchase and green certificate acquisition is minimized by combining various types of power generation resources at the power purchase end, and on the other hand, the power purchased is distributed among various types of users at the power sale end, so as to maximize the power sale income and green certificate sales income. On this basis, the VPP as a whole participates in the electric energy market, carbon trading market and green certificate trading market to maximize the comprehensive income. Finally, a VPP is taken as an example to verify the economy and effectiveness of the proposed model in this paper.INDEX TERMS Green certificate trading; carbon trading; VPP; optimal scheduling, decision optimization
Carbon emissions of power industry in China have accounted for more than half of the total emissions. How to decrease them is important for realizing carbon emission reduction. This paper proposes a carbon market feedback mechanism to power market, comprehensively considering the influence of generation structure, carbon intension, and technological progress on carbon emission reduction in power industry, and builds a potential model based on dynamic system. Operation system results show that the increasing trend of carbon emission can be controlled effectively but always with a lag. At the same time, sensitivity analysis results show that carbon emission reduction can be better realized by adjusting power structure and improving technological level; the former can reduce 32% and the latter can reduce 60% at most.
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