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This paper proposes a novel four-stage time aggregation method that can extract the underlying structure of years of historical renewable energy data. As opposed to the conventional time aggregation approaches, the proposed method starts with finding the best pair matches between patterns from one year to the next using Minimum Bipartite Graph Matching. The proposed method can capture the typical structure of patterns by taking into account the inter-annual relationships between observations collected from different years. The proposed method is evaluated by both data-based and model-based criteria. Regarding the former, we examine the quality of representative periods from different aspects such as the mean, standard deviation, maximum hourly ramp up/down, annual duration curve, and annual ramp duration curve. For the model-based evaluation, we consider a unit commitment problem in a modified IEEE 24-bus system consisting of an energy storage unit. We evaluate the methods in replicating the annual operational cost, wind energy curtailment, and energy throughput of energy storage. We evaluate the methods on three out-of-sample data sets to show the effectiveness of the proposed method in generalizing the clusters rather than over-fitting them to the in-sample observations.

Day-ahead wind power forecast (DWPF) is an indispensable component for accommodating wind energy in power systems. Traditional DWPF models focus on providing information, merely, around the average wind power for day-ahead periods. This article goes beyond the traditional approach and formulates a DWPF problem that renders information on intra-period wind variability with high resolution (second-wise), by predicting the intra-period temporal distribution of wind power for day-ahead horizons. This additional information could be of particular interest to decision-makers when high-resolution wind variability is crucial for optimal decisions, e.g., wind power reserve scheduling. First, a parametric and several entropy-based losses are tailored to this problem to acquire candidate solutions using classical approaches. Then, a differentiable loss, based on the Wasserstein distance (WD), is dedicatedly developed to overcome the inherent limitations of the tailored losses. The superiority of the proposed WD-based loss is, first, verified by comparing its predictions with those of classical losses, using on-site wind measurements. Furthermore, the superiority of the predictions obtained by the proposed WD loss, in the context of day-ahead wind power scheduling, is further verified using operational electricity market data, with profit erosion as an error measure.

This paper proposes a new time aggregation method that can properly reflect the spatial aspect of the data sources alongside their temporal behaviour when multiple variable energy resources are involved. Each representative period, constructed by the proposed method, can include more than one representative day, which, in itself, represents a group of data sources that are highly associated with each other. It also preserves the spatial relationship among data sources as a whole by constructing a hybrid centroid. Two case studies, each with different data sources and networks, with and without energy storage, are considered for this study. In the second case, we add an storage to reflect the significance of preserving shape of patterns in the representative periods. The duration curves of wind farms' and solar farms' generations, electricity demand, and net demand are used as data-based evaluation indices to compare the performance of the proposed method against other approaches. In addition, an IEEE 24-bus and a 6-bus test system are considered for the case studies. Two model-based evaluation indices are used to measure the performance of the clustering methods, including the power flow of all the lines in the system and the annual operation cost of the system. In addition, the performance of the proposed method in the presence of energy storage is evaluated and compared with existing methods.

In this article, a new short-term solar power forecasting method is proposed which has a closed-loop structure composed of point-estimating and range-classifying parts. If the forecasts generated by these parts for solar power are inconsistent, the feedback loop sends appropriate signals to them to correct their predictions. The feedback loop iterates until consistent forecasts are generated for the solar power by the point-estimating and range-classifying parts. This enables the proposed closed-loop forecasting method to enhance its solar power prediction accuracy and reliability. Furthermore, a novel sample selection approach, different from feature selection methods, is devised to mine the historical data for finding the most informative training samples for training the proposed forecasting engine. The effectiveness of the proposed solar power forecasting method is illustrated by testing it on some real-world solar farms and comparing its results with the results of several state-of-the-art solar power prediction methods.

Microgrids have emerged as a promising solution to integrate distributed energy resources (DERs) and supply reliable and efficient electricity. The operation of a microgrid involves the coordination of different DERs and loads. To date, various control methods have been developed to maximize the overall benefit while satisfying various constraints. Now it is urgently needed to understand and comprehend these approaches to further stimulate the deployment of microgrids. This paper presents an overview for researchers on economic model predictive control (EMPC) methods of microgrids to achieve a variety of objectives such as cost minimization and benefit maximization. The fundamental principle of the EMPC theory is explained in detail. The most popular and important strategies applied to stand-alone microgrids, grid-connected microgrids, residential smart homes, as well as networked microgrids are discussed. Future trends are also highlighted.