This study focuses on the real-time operation of a microgrid (MG). A novel approximate dynamic programming based spatiotemporal decomposition approach is developed to incorporate efficient management of distributed energy storage systems into MG real-time operation while considering uncertainties in renewable generation. The original dynamic energy management problem is decomposed into single-period and single-unit sub-problems, and the value functions are used to describe the interaction among the sub-problems. A two-stage procedure is further designed for the real-time decisions of those sub-problems. In the first stage, empirical data is utilised offline to approximate the value functions. Then in the second stage, each sub-problem can make immediate and independent decision in both temporal and spatial dimensions to mitigate adverse effects of intermittent renewable generation in a MG. No central operator intervention is required, and the near optimal decisions can be obtained at a very fast speed. Case studies based on a six-bus MG and an actual island MG are conducted to demonstrate the effectiveness of the proposed algorithm.
Electric loads are essential for power system dynamic simulation. However, load modeling is one of the most challenging topics due to the diversity and time-varying behavior of the load. When considering the intervention of rapidly developing distributed generation (DG), load modeling becomes more difficult. In this paper, a new solution for determining the unknown generalized load model is proposed. The radial basis function (RBF) neural network-based sub-models of generalized load are stored in the form of a sub-model bank. A recursive Bayesian approach is used to identify the sub-models and then merge them into one generalized load model according to their probabilities. The proposed method can be implemented online and adapt to describing the diversity and time-varying behavior of the generalized load. Numerical studies are carried out using both simulation data and actual measurements. The comparisons with other load modeling methods verify the advantages of the proposed method.
This study presents a fully decentralised robust optimisation (RO) approach for multi-area economic dispatch (MA-ED) in the presence of wind power uncertainty. Unlike traditional algorithms, the authors formulate this MA-ED problem as dynamic programming problem, and decompose the centralised robust MA-ED problem into a series of sub-problems based on approximate dynamic programming algorithm. The value functions are proposed for each area to iteratively estimate the impacts of its dispatches on the dispatches of other areas which make decisions subsequently. The proposed algorithm does not require a central operator but only needs to exchange a small amount of information among neighbouring areas to achieve fully decentralised decision-making. It is practical in cases where the centralised operator cannot be implemented considering the dispatch independence and the detailed data of one area is unavailable considering the privacy. Additionally, the accuracy, adaptability and computational efficiency of the proposed algorithm are illustrated using numerical simulations on two test systems and an actual power system.
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