考虑CCUS的电-气-热综合能源系统鲁棒优化调度

Robust Optimal Dispatch of Electricity-Gas-Heat Integrated Energy System Considering Carbon Capture, Utilization and Storage

为提升电–气–热综合能源系统运行的经济性并减少系统的碳排放量,以系统总运行成本最小为目标函数,建立了考虑碳捕集、利用与封存(carbon capture, utilization and storage, CCUS)的综合能源系统日前优化调度模型。根据拟合精度评价指标综合对比正态分布、柯西分布、拉普拉斯分布以及高斯混合模型,得到基于数据驱动的风电预测功率误差不确定集。采用二阶锥松弛和分段线性化处理目标函数及约束条件中的非线性项,将其转换为混合整数二阶锥规划模型。最后利用鲁棒线性优化技术处理风电功率的不确定性,并调用Gurobi求解器对该优化问题进行求解。以IEEE39节点电网、6节点气网和6节点热网构成的电–气–热综合能源系统为例进行仿真研究,研究结果表明,基于数据驱动的不确定集能更准确地描述风电功率的波动范围,从而降低系统运行的保守性;此外,引入CCUS装置后系统总成本降低5.53%,碳排放量减少36.79%。研究结果验证了所提方法的合理性和有效性。

In order to improve the operation economy and reduce the carbon emission of electricity-gas-heat integrated energy system, the day-ahead optimal scheduling model of integrated energy system considering carbon capture, utilization and storage(CCUS) is established to minimize the total operation cost of the system. According to the fitting accuracy evaluation index, the normal distribution, Cauchy distribution, Laplace distribution and Gaussian mixture model are compared, and the uncertainty set of wind power prediction error based on data-driven is obtained. The second order cone relaxation and piecewise linearization are adopted to deal with the nonlinear term in the objective function, which is transformed into a mixed integer second-order cone programming model. Finally,the robust linear optimization technology is used to deal with the uncertainty of wind power, and the Gurobi solver is employed to solve the optimization problem. The electricity-gas-heat integrated energy system composed of IEEE39-node grid, 6-node gas grid and 6-node heating grid is taken as an example for simulation investigation. The results show that the uncertainty set based on data-driven can describe the fluctuation range of wind power more accurately, thus reducing the conservatism of system operation. In addition, after the introduction of CCUS device, the total cost of the system is reduced by 5.53%, and the carbon emission is reduced by 36.79%. The simulation results verify the rationality and effectiveness of the proposed method.