Assessment of electricity consumption level influence at system loses

Denysiuk, Serhii; Opryshko, Vitalii; Danilin, Oleksandr

doi:10.1109/ess50319.2020.9160106

Cited by 1 publication

(1 citation statement)

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“…Despite sufficient volumes of power generation at power plants to meet the country's needs, Ukraine's power problem has worsened in recent decades, which is a result of a number of factors [3,4], including the relationship between generating and distributing power structures on the one hand, and the Ukrainian power market and power consumers on the other. And while the former need to ensure the stability of power consumption levels by customers, including in terms of hours of the day, the latter need transparent pricing, preferably predictable, to enable them to optimize power consumption and costs.…”

Section: Introductionmentioning

confidence: 99%

Simulation of power consumption control of receivers at underground iron ore mining enterprises

Sinchuk,

Mykhailenko,

Kobeliatskyi

et al. 2023

AAIT

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The article presents the results of developing a concept for controlling the process of power consumption by electrical receivers with a voltage of up to 1000 V at underground mining enterprises in the function of hourly tariffs, which are characterized by variable pricing for the day ahead in current market conditions. The following parameters are considered: limitations on operation duration of a separate electrical unit during the day, the maximum load on underground substation transformers, and the amount of power ordered by the enterprise, the excess of which leads to the application of penalties. To solve the control problem, it is proposed to apply a heuristic optimization method based on a genetic algorithm. System efficiency is studied by determining settings of the evolutionary search algorithm that would ensure the lowest cost of power purchase. In particular, the crossover function (one-point, two-point, or Laplace) and the number of elite individuals in the population are modified. The experiments are carried out on the basis of the Global Optimization Toolbox in the MATLAB software package. Simulation of system efficiency through different settings of the genetic algorithm demonstrates that the minimum power cost can be ensured by using the Laplace crossover method with 10 individuals in a population of 100, of which 10 are elite and pass to the next generation unchanged. This option allows obtaining an average of 2.62 % lower daily power cost than the other parameters studied. The proposed method of power consumption control allows us to identify the achievable potential for reducing the energy component in the final product cost of iron ore mining at underground mining enterprises. It can be recommended for practical implementation at both operating and projected enterprises

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Section: Introductionmentioning

confidence: 99%