利用建筑虚拟储能能提升电热联合系统的风电消纳能力,但若只考虑建筑结构储能而不考虑室内空气储能会导致虚拟储能利用不足。针对此问题,在奖惩阶梯碳交易背景下提出一种利用建筑虚拟储能提升风电消纳能力的电热联合系统优化调度方法。首先,以室内空气和建筑结构热平衡模型为基础建立精细化建筑虚拟储能模型;其次,在系统中引入奖惩阶梯碳交易机制及需求响应机制;再次,考虑电、热功率平衡以及室内温度等约束,以电热联合系统总成本最低为目标函数,并提出改进型平衡器优化算法对目标函数进行优化求解;最后,对不同场景下电热联合系统对风电的消纳、CO2排放及经济性进行分析,结果表明：所提方法不仅提高了电热联合系统的风电消纳率,降低了CO2的排放量,且提升了经济性。
Abstract
The wind power accommodation capability of combined electric and thermal system can be enhanced by using building virtual energy storage. As much concerned for the utilization rate, it will lead to the underutilizaion of virtual energy storage if only taking building structure energy storage into consideration except the indoor air energy storage. In this paper, we propose an optimal scheduling method for combined electric and thermal system under the circumstance of reward and punishment ladder-type carbon trading, which can enhance wind power accommodation capability by using building virtual energy storage. Firstly, a refined building virtual energy storage model is established based on indoor air and building structure heat balance model; secondly, a reward and punishment ladder carbon trading mechanism and demand response mechanism are introduced to the system; thirdly, by considering factors of constraints like electric, thermal power balance and room temperature, and setting the objective function with the lowest total cost of the combined electric and thermal system, we propose an improved equilibrium optimizer algorithm to solve the objective function optimally; finally, the analysis of wind power accommodation, CO2 emission and economics of combined electric and thermal system under different scenarios shows that the propose method not only improves the wind power accommodation rate, reduces CO2 emission of combined electric and thermal system, but also improves the economics.
关键词
风电消纳 /
建筑虚拟储能 /
平衡优化器算法 /
奖惩阶梯碳交易 /
综合需求响应
Key words
wind power accommodation /
building virtual energy storage /
equilibrium optimizer algorithm /
reward and punishment ladder-type carbon trading /
integrated demand response
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基金
新疆维吾尔自治区重点实验室开放课题（2023D04081）; 国家自然科学基金（52067020）
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