Spatial matching of large-scale grid-connected photovoltaic power generation with utility demand in Peninsular Malaysia

摘要

Deployment of large-scale grid-connected photovoltaics (PV) power plants requires very reliable technical evaluation to reduce electricity demand and achieve efficient utilization of electricity generated from PV. At lower PV penetration levels, it is likely that the energy mix could under-supply utility demand, thus requiring extra units of generators, while at higher penetration levels it may oversupply demands, thus wasting generator capacity. Thus, determining the optimum installed capacity, technical limits, and economic benefits of large-scale PV systems are the main issues for both power utilities and decision makers. This study describes the development and validation of an alternative method (called the generation-demand matching model, GDMM) for evaluating the large-scale implementation of grid connected PV power plants in Peninsular Malaysia relative to its interface with the traditional power grid system. The method explicitly provides a detailed assessment of the temporal and spatial factors that facilitate the match between PV generated electricity and electricity demand. These evaluation factors are analyzed using simulations of PV performance located at optimal sites. Optimal sites along with physical constraints were mapped using geographic information systems (GIS) for visualization and representation by location. PV electricity generation at different levels of penetration was predicted hourly for a year using time series analysis. This allowed comparison of electricity generation with electricity demand to evaluate the impacts of increasing levels of PV penetration. A novel feature of the proposed method is its combination of topographical and topological map data with metric data. The ability of the new method to accurately predict the performance of PV compared to PVWatts demonstrates the robustness of the method in evaluating the technical limits of PV systems in conventional power systems. (C) 2017 Elsevier Ltd. All rights reserved.