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The penetration of distributed power sources has been increasing with the continuous promotion of clean renewable energy sources. This paper seeks to improve the utilization rate of clean energy and reduce the cost of microgrid operation by first establishing a double-layer wind power prediction error model based on a comprehensive consideration of the time-of-use price and the operating characteristics of different types of clean energy sources, such as wind power, photovoltaic power, thermal power, and transmission tie lines. A combined cooling, heating, and power microgrid collaborative optimization model that considers wind power forecast uncertainty is established with the goal of minimizing economic cost, environmental cost, and degree of power-generation unit output asynchrony of the microgrid. The established multiobjective optimization model is solved using an improved intelligent optimization algorithm that combines the non-dominated sorting genetic algorithm (NSGA) with co-evolution theory and the beetle antennae search algorithm. This algorithm employs a variety of groups in the NSGA to help with correcting the approximations of group members through competition and cooperation. Therefore, the proposed algorithm can combine the excellent convergence of the NSGA and the powerful searching ability of co-evolutionary algorithms. Finally, a practical microgrid system in Northwest China is simulated as a case study, and the performance of the proposed algorithm is compared with that of the conventional NSGA. The simulation results demonstrate the superiority of the global search performance and the rapid convergence performance of the proposed hybrid algorithm.

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Combined cooling, heating, and power (CCHP) microgrids are a special form of a microgrid that is attracting increasing attention. This study contributes to the goal of minimising the operation cost of CCHP microgrids by proposing a hierarchical two‐stage robust optimisation dispatch model for multiple CCHP microgrid systems. The uncertainties associated with wind power output, electric power, heating, and cooling loads, and transmission line failures are considered in the proposed model. Moreover, the electricity purchasing and selling prices of each microgrid are independently determined. The proposed model applies the outputs of fuel cells, energy storage devices, and gas turbines, the distribution factor of waste heat, and the power transmission between the microgrids and an external grid as control variables. The optimised dispatch problem is solved using McCormick envelopes relaxation and a novel column and constraint generation algorithm that provides enhanced optimisation performance by implementing co‐evolutionary theory. In this way, the microgrid system is divided into several sections, and each section is represented as an individual min–max–min problem. The rationality and validity of the proposed model and the superiority of the solution performance of the improved algorithm are verified through simulation case studies involving a system composed of four CCHP microgrids.

Abstract:The rapid incorporation of wind power resources in electrical power networks has significantly increased the volatility of transmission systems due to the inherent uncertainty associated with wind power. This paper addresses this issue by proposing a transmission network expansion planning (TEP) model that integrates wind power resources, and that seeks to minimize the sum of investment costs and operation costs while accounting for the costs associated with the pollution emissions of generator infrastructure. Auxiliary relaxation variables are introduced to transform the established model into a mixed integer linear programming problem. Furthermore, the novel concept of extreme wind power scenarios is defined, theoretically justified, and then employed to establish a two-stage robust TEP method. The decision-making variables of prospective transmission lines are determined in the first stage, so as to ensure that the operating variables in the second stage can adapt to wind power fluctuations. A Benders' decomposition algorithm is developed to solve the proposed two-stage model. Finally, extensive numerical studies are conducted with Garver's 6-bus system, a modified IEEE RTS79 system and IEEE 118-bus system, and the computational results demonstrate the effectiveness and practicability of the proposed method.