基于机会约束目标规划的多电-气互联综合能源系统分布式优化模型

多个综合能源系统间通过电能交互能有效提高系统风电消纳水平，寻求考虑风电不确定性下的多区域能量管理最优策略成为必要的研究工作。针对风电的随机出力特性和区域间电能传输的安全可靠性问题，将随机优化与分层协调技术相结合，建立了基于机会约束目标规划的多电-气互联综合能源系统分布式优化模型。首先，对单个电-气互联综合能源系统精细化建模，从系统经济性出发，引入区域间联络线灵活性机会约束。进一步，采用二阶锥松弛和确定性等价方法将该模型转化为混合整数二阶锥规划模型，利用广义Benders分解（general Benders decomposition，GBD）法，将模型分解为主、子问题反复迭代求解。最后，算例表明求解方法具备较好的收敛特性和计算效率，有效保障不同区域内用户信息隐私并有较强自适应性，而且机会约束目标规划模型比传统机会约束具有更好的经济性和灵活性。

Distributed Optimal Dispatch Based on Chance Constrained Goal Programming for Multi-Area Integrated Electricity-Natural Gas Energy Systems

The interaction of multiple integrated energy systems through electrical energy can effectively improve the level of wind power consumption in the system. It will become a necessary research work to seek the optimal strategy of multi-regional energy management under the uncertainty of wind power. In view of the random output characteristics of wind power and the safety and reliability of electrical energy transmission between regions， random optimization and hierarchical coordination techniques are combined to establish a distributed optimization model of multiple electricity-gas interconnected integrated energy systems on the basis of chance-constrained goal programming. The model firstly refines the modeling of a single electricity-gas interconnected integrated energy system. From the perspective of system economics， it introduces the opportunity constraint on the flexibility of interregional tie lines. The second-order cone relaxation and deterministic equivalent methods are further used to transform the model into a mixed integer second-order cone programming model. Using the general Benders decomposition （GBD） method， the model is decomposed into main and sub-problems and iteratively solved. Finally， the calculation example shows that the solution method has better convergence characteristics and computational efficiency， effectively guarantees the privacy of user information in different regions and has strong adaptability； and the establishment of the chance constrained goal programming model has a better economy and flexibility than the traditional chance constrained method.