パターン化された年間運用計画にもとづくマイクログリッドシステムの設備容量の最適化

抄録

When introducing a microgrid system (MGS) for consumers, installing equipment according to loads of consumers is essential. Small MGS requires algorithms that ensure computational scalability because they have consumer-specific circumstances and are difficult to consider versatility. Therefore, we optimize the capacity of every MGS facility from the viewpoint of life cycle cost (LCC), which is the sum of the initial cost and running cost. As an optimization method suitable for this, we propose a method to optimize the capacity of MGS facilities by particle swarm optimization (PSO) based on an operation plan simulating one year of MGS using pattern data and mixed-integer linear programming (MILP). The problem with this method is that it takes time to calculate annual operations. Expressing one year of customer data with four types of pattern data while keeping the characteristics of data for each season reduces the amount of computation required for operational planning. It provides the optimum facility capacity within a reasonable time. By running the proposed method under several conditions and conducting a case study of the installed capacity, we have found an LCC that has less computational complexity and less installed capacity than when searching for everything. This demonstrates the effectiveness of this approach, although there is room for improved PSO retrieval effectiveness.