考虑需求响应的电/热/气云储能优化配置策略

面向越来越开放的能源交易市场,为充分调动用户侧资源,提出了一种考虑需求响应(demand response, DR)的电/热/气云储能(cloud energy storage,CES)优化配置策略。建立含电/热/气云储能能源集线器(energy hub, EH)结构,从参与云储能商业模式的用户侧与云储能提供商出发,构建两主体双层优化模型。底层基于长短期记忆和贝叶斯神经网络的概率预测方法,刻画新能源出力的不确定性,建立考虑需求响应的用户侧云储能充放能模型,以用户总成本最小为目标优化决策用户侧充放能行为,并将决策信息传递到云储能提供商。顶层以云储能提供商的总成本最小为目标,集中优化决策实体储能功率和容量的配置问题。通过大M法对目标以及约束中的非线性部分进行松弛线性化,将其转化为混合整数线性规划模型。最后,建立4个典型应用场景,通过Matlab中的YALMIP工具箱调用CPLEX优化求解器对不同场景下的模型进行求解,联合对比在4种不同场景下的整体成本与收益,验证该策略在资源共享、节约系统整体成本等方面的优越性。

Optimal Configuration of Electricity-Heat-Gas Cloud Energy Storage Considering Demand Response

In order to fully mobilize user-side resources in an increasingly open energy trading market, this paper proposes an optimal allocation strategy for electricity-heat-gas cloud energy storage (CES) considering demand response (DR). The proposed optimized configuration establishes an energy hub (EH) structure with electricity-heat-gas cloud energy storage, and a two-subject two-layer optimized model from the view of users and providers participating in the CES business model is established. The lower layer describes the uncertainty of new energy output according to the probability prediction method based on long-term and short-term memory and Bayesian neural network; a user-side CES charging and discharging model considering demand response, which is optimized aiming to minimize the user’s total cost, is established; and the decision information will be informed to the CES provider. The upper layer, aimed to minimize the investment and construction cost of CES providers, concentrates on optimizing the allocation of energy storage power and capacity of decision-making entities. The big M method is adopted to relax and linearize the nonlinear part of the objective and constraints, and then it is transformed into a mixed-integer linear optimization problem. Finally, four typical application scenarios are established. As to the verification of the superiority of the strategy, the CPLEX optimization solver is called through the YALMIP toolbox in Matlab to solve the models in different scenarios, and the overall costs and benefits are jointly compared.