Daily optimization of maintenance routing and scheduling in a large-scale photovoltaic power plant with time-varying output power

Abstract

Photovoltaic (PV) power generation is an environmentally friendly and clean energy source with extensive and widespread installations worldwide. Consequently, the maintenance planning for PV power plants has become exceedingly significant. Owing to the time-varying output power of PV power plants, the optimization of daily maintenance planning for PV power plants should encompass not only the optimization of maintenance routes and task assignments, but also the consideration of reducing downtime costs resulting from failures and maintenance activities. To find the optimal route that minimizes the total cost, including travel, technician, downtime, and penalty costs, a PV power plant maintenance routing problem and its solution approach were investigated in this study. First, considering the influence of daily variations in solar radiation and temperature on output power, this study proposed an optimization problem for maintenance routing in a PV power plant. Next, linearization techniques with controllable error precision were developed to construct a mixed-integer linear programming model. Then, to efficiently solve large-scale problems, a heuristic algorithm was customized based on an adaptive large neighborhood search approach. Finally, the proposed approach was implemented at the Talatan PV power plant in Qinghai Province, China. Compared to the traditional shortest route model, which neglects the time-varying output power, the proposed method significantly reduces the total cost.