Open Access

ARTICLE

Source-Load Coordinated Optimal Scheduling Considering the High Energy Load of Electrofused Magnesium and Wind Power Uncertainty

Juan Li¹, Tingting Xu^1,*, Yi Gu², Chuang Liu¹, Guiping Zhou², Guoliang Bian¹

1 School of Electrical Engineering, Northeast Electric Power University, Jilin, 132013, China
2 Ministry of Science and Technology Communication, State Grid Liaoning Electric Power Co., Ltd., Shenyang, 110000, China

* Corresponding Author: Tingting Xu. Email:

Energy Engineering 2024, 121(10), 2777-2795. https://doi.org/10.32604/ee.2024.052331

Received 30 March 2024; Accepted 06 June 2024; Issue published 11 September 2024

Abstract

In fossil energy pollution is serious and the “double carbon” goal is being promoted, as a symbol of fresh energy in the electrical system, solar and wind power have an increasing installed capacity, only conventional units obviously can not solve the new energy as the main body of the scheduling problem. To enhance the system scheduling ability, based on the participation of thermal power units, incorporate the high energy-carrying load of electro-melting magnesium into the regulation object, and consider the effects on the wind unpredictability of the power. Firstly, the operating characteristics of high energy load and wind power are analyzed, and the principle of the participation of electrofused magnesium high energy-carrying loads in the elimination of obstructed wind power is studied. Second, a two-layer optimization model is suggested, with the objective function being the largest amount of wind power consumed and the lowest possible cost of system operation. In the upper model, the high energy-carrying load regulates the blocked wind power, and in the lower model, the second-order cone approximation algorithm is used to solve the optimization model with wind power uncertainty, so that a two-layer optimization model that takes into account the regulation of the high energy-carrying load of the electrofused magnesium and the uncertainty of the wind power is established. Finally, the model is solved using Gurobi, and the results of the simulation demonstrate that the suggested model may successfully lower wind abandonment, lower system operation costs, increase the accuracy of day-ahead scheduling, and lower the final product error of the thermal electricity unit.

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