Due to uncertainties associated with the power output of offshore wind farms, the active power balance and frequency security control of power systems with lots of offshore wind farms are highly challenging. To addres...
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Due to uncertainties associated with the power output of offshore wind farms, the active power balance and frequency security control of power systems with lots of offshore wind farms are highly challenging. To address this problem, in this study, a new stochastic economic dispatch model of a power system with offshore wind farms considering the system frequency security constraints is established to obtain economic and secure dispatch decisions. Furthermore, the nonlinear convexity of frequency security constraints provides considerable theoretical support for the global optimality of decision-making, and a golden section search-based approximate linear constraint generation algorithm is designed to approximate nonlinear frequency security constraints for improving computational efficiency. Next, a non-iterative distributed approximate dynamic programming algorithm based on the equivalent projection method is designed for the distributed solution of the established model. In the algorithm, first, the model is decoupled from time periods. Next, the high-dimensional feasible region of the offshore wind farm optimization model is projected into a low-dimensional feasible region and substituted into the transmission grid optimization model, and solves the models of the transmission grid and the offshore wind farms sequentially to achieve the non-iterative distributed solution. Finally, case studies on a modified IEEE 39-bus system with two offshore wind farms and an actual provincial system with seven offshore wind farms demonstrate the effectiveness and superiority of the proposed model and algorithm, reducing solution time by over 86.4% compared to the alternating direction method of multipliers-based distributed approximate dynamic programming algorithm.
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...
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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 dynamicprogramming 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.
The authors consider efficient mechanisms to optimize the power consumption within a home, industrial facility, college campus, or other facility or set of facilities. The system is controlled centrally by an energy m...
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The authors consider efficient mechanisms to optimize the power consumption within a home, industrial facility, college campus, or other facility or set of facilities. The system is controlled centrally by an energy management controller (EMC), which determines the timing of the operation of some of the devices within the facilities. The authors introduce an approximatedynamicprogramming (ADP) algorithm for this problem and show that the ADP outperforms a recent dynamicprogramming (DP) algorithm. However, even the ADP fails to solve sufficiently quickly when applied to larger instances. Therefore, the authors also propose several heuristic scheduling policies that provide accurate solutions in a fraction of the time required by the ADP. The authors discuss the computational performance of the ADP algorithm and scheduling policies, and insights gained from the models.
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