基于奖惩阶梯型碳价机制的能源枢纽低碳优化策略

Low-carbon optimization strategy for energy hub based on reward-punishment ladder carbon price mechanism

为进一步降低碳排放水平以及源-荷不确定性对系统运行的影响,文中提出一种基于奖惩阶梯型碳价机制和分布式模型预测控制(distributed model predictive control,DMPC)的能源枢纽(energy hub,EH)日前-日内-实时多时间尺度低碳优化调度策略。引入奖惩阶梯型碳价计算方法,构建EH日前低碳优化调度模型,并制定基于DMPC的日内滚动和实时调整的反馈闭环优化策略,降低源-荷预测误差,提高传统模型预测控制(model predictive control,MPC)的求解效率。在日内阶段,构建以阶梯型碳成本、运行成本和储能调整惩罚成本之和最小为目标的日内滚动优化模型;在实时阶段,分解整体优化问题,建立基于DMPC的多智能体实时调整模型。算例结果表明,文中所提策略能够有效提升系统经济效益,降低源-荷不确定性,实现EH的低碳经济、稳定可靠运行。

In order to reduce carbon emissions and the impact of source-load uncertainty on system operation,a multi-timescale low-carbon optimization scheduling strategy in day-ahead,intra-day and real-time operations for energy hub(EH) based on a reward-punishment ladder carbon price mechanism and distributed model predictive control(DMPC) is proposed.A reward-punishment ladder carbon price calculation method is introduced and a day-ahead low-carbon optimization scheduling model for EH is constructed.A feedback closed-loop optimization strategy based on DMPC for intra-day rolling and real-time adjustments is formulated.The optimization strategy reduces source-load prediction errors and improves the efficiency of traditional model predictive control(MPC) solving.In the intra-day stage,a rolling optimization model with the objective of minimizing the sum of the ladder carbon price cost,operational cost,and penalty cost for energy storage adjustment is constructed.In the real-time stage,the overall optimization problem is decomposed,and a multi-agent real-time adjustment model based on DMPC is established.The simulation results indicate that the proposed strategy is effective in enhancing the economic efficiency of the system,reducing the uncertainty of source and load,and achieving the low-carbon,economic,stable,and reliable operation for EH.