Capacity Value of Solar Power: Report of the IEEE PES Task Force on Capacity Value of Solar Power

This paper reviews methods used for adequacy risk assessment considering solar power, and for assessment of the capacity value of solar power. The properties of solar power are described as seen from the perspective of the balancing authority, comparing differences in energy availability and capacity factors with those of wind. Methodology for risk calculations considering variable generation (VG) are then surveyed, including the probability background, statistical estimation approaches, and capacity value metrics. Issues in incorporating VG in capacity markets are described, followed by a review of applied studies considering solar power. Finally, recommendations for further research will be presented

A Novel Closed-Loop Clustering Method for Hierarchical Load Forecasting

Hierarchical load forecasting (HLF) is an approach to generate forecasts for hierarchical loadtime series. The performance of HLF can be improved by optimizing the forecasting model and the hierarchical structure. Previous studies mostly focus on the forecasting model while the hierarchical structure is usually formed by clustering of natural attributes like geography, customer type, or the similarities between load profiles. A major limitation of these natural hierarchical structures is the mismatched objectives between clustering and forecasting. Clustering aims to minimize the dissimilarity among customers of a group while forecasting aims to minimize their forecasting errors.The two independent optimizations could limit the overall forecasting performance. Hence, this paper attempts to integrate the hierarchical structure and the forecasting model by a novel closed-loopclustering (CLC) algorithm. It links the objectives of forecasting and clustering by a feedback mechanism to return the goodness-of-fit as the criterion for the clustering. In this way, the hierarchical structure is enhanced by re-assigning the cluster membership and the parameters of the forecasting models are updated iteratively. The method is comparatively assessed with existing HLF methods. Using the same forecasting model, the proposed hierarchical structure outperforms the bottom-up structure by 52.20%, ensemble-based structure by 26.89%, load-profile structures by 19.90%, respectively. <br/