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The trend of today's power system is to incorporate more green power in the conventional grid to mitigate the power demand and to minimize environmental pollution. This paper proposes a multi‐energy system with wind thermal–pump storage plant aiming to minimize the total cost of operation. In this paper, a forecasted data of wind speed is considered. Because of the variable nature of wind power, real‐time generation scheduling may lead to deviation of forecasted thermal power generation. This paper works on a power management strategy (PMS) where the gap between the day‐ahead scheduled thermal power and the real‐time scheduled power is minimized optimizing the operation cost. Cultural algorithm (CA) is used here very efficiently in a modified UI 62 bus. It is observed from the simulation results that the proposed strategy can effectively reduce the power deviation fluctuations with reducing the operating as well as runtime cost. A comparison of total cost between CA and particle swarm optimization (PSO) is drawn in this paper to observe the efficacy of the proposed method.
Currently the renewable energies including wind power and photovoltaic power have been increasingly deployed in power system to achieve contamination free and environmental-friendly power production. However, due to the natural characteristics of wind and solar, both wind power and photovoltaic power contain uncertainty and randomness which may significantly impact the stability, security, and economic efficiency of the conventional power system mainly consisted by hydropower and thermal power. To deal with the issue, this paper presents a two-stage robust model which is able to achieve the optimal day-ahead dispatch strategy in the worst-case scenario of wind and photovoltaic outputs. Because of the strong interactions between the two stages, the original optimization has been decomposed into the day-ahead dispatch master problem and the additional adjustment subproblem considering the uncertainty and randomness of the wind and the photovoltaic outputs. Also, the piecewise linearization technique is employed to convert the original problem into a MILP problem. Afterward, the dualization of the additional adjustment subproblem can be obtained by using linear programming strong duality theory. Additionally, the Big-M method enables the linearization of the dual model. The interacted-iterations between the master problem and the subproblem are successfully implemented which can ultimately figure out the optimal day-ahead dispatch strategy of the power system with conventional and renewable energies.
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