Search

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 FROM AUTHOR]

It is an effective way to regard the electric vehicles as the demand response for reducing the negative impact of large‐scale introduction on the power system. Aiming at the microgrid with demand response, the adaptive uncertainty sets‐based two‐stage robust optimisation method is established in this study. The coordination of micro‐gas turbine, energy storage, and demand response etc. are considered in the economic dispatch model. To effectively consider the uncertain variable contained in the microgrid, the concept of adaptive uncertainty sets is proposed in this study. The uncertainty sets are achieved by the long short‐term memory network and modified fuzzy information granulation. To handle the adaptive uncertainty sets‐based robust optimisation model, the column and constraint generation algorithm and strong duality theory are introduced to decompose the model into a master problem and a subproblem with mixed‐integer linear structure. To verify the performance of the proposed adaptive uncertainty sets‐based two‐stage robust optimisation method, measured data from a plateau city of China are introduced in the simulation test. The simulation results demonstrate the effectiveness of the model and solution strategy. [ABSTRACT FROM AUTHOR]

It is an effective way to regard the electric vehicles as the demand response for reducing the negative impact of large-scale introduction on the power system. Aiming at the microgrid with demand response, the adaptive uncertainty sets-based two-stage robust optimisation method is established in this study. The coordination of micro-gas turbine, energy storage, and demand response etc. are considered in the economic dispatch model. To effectively consider the uncertain variable contained in the microgrid, the concept of adaptive uncertainty sets is proposed in this study. The uncertainty sets are achieved by the long short-term memory network and modified fuzzy information granulation. To handle the adaptive uncertainty sets-based robust optimisation model, the column and constraint generation algorithm and strong duality theory are introduced to decompose the model into a master problem and a subproblem with mixed-integer linear structure. To verify the performance of the proposed adaptive uncertainty sets-based two-stage robust optimisation method, measured data from a plateau city of China are introduced in the simulation test. The simulation results demonstrate the effectiveness of the model and solution strategy. [ABSTRACT FROM AUTHOR]