“Smart” power control for receivers up to 1000 V at underground mining enterprises

Abstract

The paper considers “smart” control of power consumption by electrical receivers with a voltage of up to 1000 V in anunderground iron ore enterprise to optimize daily power costs. The relevance of the work is determined by the high level of energycosts for mining processes in underground iron ore extraction. Since the operating schedule of electrical receivers up to 1000 V is notstrictly fixed, this allows for flexible control of power consumption, taking into account hourly changes in power tariffs throughoutthe day. A heuristic approach to “smart” power consumption control based on a genetic algorithm is proposed, which is a goodalternative to classical approaches, such as mixed integer linear programming, which is limited to online use due to its offline natureand inability to handle complex constraints. The object of the study is the power consumption of electrical receivers of several blocksof an iron ore mine, including both permanently (ventilation, water drainage) and variably (drilling rigs, winches) operating duringthe load time. The objective function was formulated in such a way as to minimize the total power costs of an underground iron oreenterprise over a 24-hour period by optimizing the distribution of the connection time to the power grid for each receiver. During thestudy, three parameters of the genetic algorithm were analyzed: population size, type of crossover function, and number of elitephenotypes. The lowest energy costs (41,454.99 UAH) were achieved with a population size of 100 phenotypes, 10 elite phenotypesin each population, and the Laplace crossover function. These costs were 4.86% lower than the worst result (43,470.55 UAH), whichoccurred with a population of 200 phenotypes, 0 elite phenotypes, and a single-point crossover type. It turned out that the number ofelite phenotypes significantly improves the quality of the genetic algorithm and speeds up optimization. The transition from 0 to 20elite phenotypes reduced the number of generations required to minimize power costs from 300 to 91 (by 69.7%). It was also foundthat the minimum power costs of an underground mining enterprise do not always coincide with the lowest values of the root meansquare power, since the most important factor here is the distribution of power consumption relative to the time when peak tariffs arein effect. The most effective genetic algorithm settings ensured balanced power consumption throughout the day, avoidingconsumption during periods when tariffs are highest. The results of the study confirmed the feasibility of using a genetic algorithm tomanage power consumption and integrate similar systems when implementing smart grid technologies in industrial power systems.