A rule-based method to downscale provincial level power sector projection results to plant level

Usually, previous studies on the future development pathway of coal power are based on the economic models to provide the administrative pathways, or the coal-fired power plants dataset to provide bottom-up pathways with the multi-scenario hypothesis. However, these two methods above are difficult to be combined: there is a gap between the comprehensive consideration of economic, policy, and environmental factors, with the high spatial resolution of technology and space. This study narrows the gap between regional projection and unit data, and also considers the uncertainty of the operating units with the Monte Carlo Method. Firstly, we evaluate the score of each unit according to its technical parameters and other attribute information, which is based on a sufficient dataset of coal-fired power units with their geographical spatial coordinates. And next, the probability distribution function is built according to the scores of the candidate units. Then, we do sampling from the candidate units until the total capacity reaches the regional projection of the coal power development goal. Based on this method, we could identify the spatial distribution probability of coal-fired power units in the future, and therefore it can help us explore the environmental impacts in high-resolution space. • The method calculates the probability of operating status of candidate units using technical and attribute information-base scores with Monte Carlo method. • This paper describes the uncertainties in determining the spatial distribution of future power plants, and verifies the robustness of the results. • This method narrows the scale gap between regional projection and unit-level data.