A multi-energy microgrid configuration method in remote rural areas considering the condition value at risk

The uncertainty caused by the growing use of renewable energy sources, such as wind and solar energy, makes it difficult to forecast the operation costs of micro-energy systems, particularly those in remote rural areas. Motivated by this point, this paper analyzes the possible operational risks and then introduces Condition Value at Risk (CVaR) to quantify the cost of the operational risk. On this basis, stochastic programming based on a multi-energy microgrid planning model that minimizes the investment cost, the operating cost, and the cost of operational risk, while considering the physical limitations of the multi-energy microgrid, is presented. Especially, scenarios of wind and solar energy output are generated using the Latin hypercube sampling method and reduced using the crowding measure-based scenario reduction method. After piecewise linearization and second-order cone relaxation, the model proposed in this paper is processed as a mixed integer linear model and solved by CPLEX. According to the achieved typical scenarios processed by the reduction method, the simulation shows that the presented configuration model can balance the investment cost and the cost of the operational risk, which effectively enhances the system’s ability to cope with uncertainties and fluctuations. Moreover, by adjusting the risk preference coefficient, the conservativeness of the planning scheme can be correspondingly adjusted.