Optimal Demand-Side Management Using Flat Pricing Scheme in Smart Grid

Albogamy, Fahad R.; Ashfaq, Yasir; Hafeez, Ghulam; Murawwat, Sadia; Khan, Sheraz; Ali, Faheem; Khan, Farrukh Aslam; Rehman, Khalid Mehmood Ur

doi:10.3390/pr10061214

Cited by 19 publications

(5 citation statements)

References 77 publications

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“…They formalize the problem of power control as an optimization problem of planning operation periods of electrical equipment over a certain time period. The majority of the research is based on algorithms that determine the optimal solution by imitating the principles by which wildlife systems function, like genetic algorithms [10,11], the particle swarm method [12,13], the ant colony algorithm [14], the ant lion algorithm [15], the cuckoo search algorithm [16], the gray wolf hunting optimization algorithm [17], the symbiotic organisms search algorithm [18], the hybrid genetic-air optimization algorithm [19], and the whale search algorithm [20].…”

Section: Electrical Complexes and Systemsmentioning

confidence: 99%

Heuristic control of power consumption by up to 1000 V electrical loads at mining enterprises

Sinchuk,

Rogoza,

Mykhailenko

et al. 2024

Nauk. visn. nat. hirn. univ.

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Purpose. To develop a method for synthesizing the structure and algorithm of the system for automated control of power consumption by up to 1000 V electrical receivers at mining enterprises with iron ore underground mining methods. This enables direct control of the load connection to the industrial power grid to ensure minimum power costs depending on its cost per day ahead. Methodology. The problem of controlling power consumption of electrical receivers at iron ore underground mines is formalized as a binary form of mixed integer programming. To solve it, a binary implementation of the heuristic genetic algorithm is used. The mathematical modeling method analyzes the impact of genetic algorithm settings, such as the number of phenotypes in the population, the number of elite phenotypes that pass unchanged to the next generation, and the method of phenotype crossover on its quality. Findings. As a result of the research, it is found that the most effective way to control the process of power consumption based on an evolutionary genetic algorithm is to use the Laplace crossover function and keep the percentage of elite phenotypes in the population at 10 %. Moreover, at the smallest population size, the best accuracy is observed when using the Laplace function, while at one- and two-point crossover functions, it worsens, but not significantly (no more than 0.2 %). However, as the number of elite phenotypes increases, the duration of the evolutionary search in the control process is reduced by almost a factor of two in the case of one- and two-point crossovers. Originality. For the first time, the structure of a heuristic system for automated control of power consumption by underground electrical receivers with a supply voltage of up to 1000 V at iron ore underground mines has been developed on the basis of an evolutionary genetic algorithm. Depending on the designed volumes of ore production and the daily power cost per day, this allows determining the optimal power load schedule of underground distribution substations in advance, which, subject to the accepted limits on hourly and daily power, minimizes the cost of purchasing power, and thus reduces the cost of the final product. Practical value. The architecture of a heuristic system for controlling power consumption by electrical receivers with a voltage of up to 1000 V based on an evolutionary genetic algorithm is developed and recommended when optimizing the power load schedule of transformer substations of mining and metallurgical enterprises, in particular, of iron ore underground mines operating in this voltage class.

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Section: Electrical Complexes and Systemsmentioning

confidence: 99%

Heuristic control of power consumption by up to 1000 V electrical loads at mining enterprises

Sinchuk,

Rogoza,

Mykhailenko

et al. 2024

Nauk. visn. nat. hirn. univ.

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“…For smart grids simulators, it is essential to look at the power grid and the communication infrastructure in parallel. There are several tools available that deal with, for example, optimising power generation and consumption within smart grids [34] or smart buildings [35].…”

Section: Available Smart Grids Simulators Comparisonmentioning

confidence: 99%

Investigating the Possibilities for Simulation of the Interconnected Electric Power and Communication Infrastructures

et al. 2022

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In recent years, we have seen an increasing dependency between different urban critical infrastructures, which raises the demands on their analysis and cybersecurity. Current open-source solutions do not provide simulations of interconnected data networks and power grids, which are essential for analysing of both infrastructures weak parts and reducing the risk of emerging threats. The main purpose of this paper is to describe the design of a simulation platform that provides this type of simulation to customers using the virtual user interface. The paper describes the development of a complex model of interconnected power and data infrastructures. Created virtualization platform that consists of several open-source tools is described in detail. Outputs of this paper can be used for analysis of critical infrastructures at the level of urban networks.

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“…Ref. [23] develops an Ant Colony Optimization (ACO) algorithm to minimize PAR, energy costs, and carbon emissions by balancing demand and power generation. In [24], the authors propose a multiobjective version of the bald eagle search optimization algorithm to find the optimal scheduling modes, aiming to reduce energy costs, microgrid emission costs, and PAR.…”

Section: Introductionmentioning

confidence: 99%

Integrated Demand Response Optimization for Consumer With Herd Mentality: A Genetic Simulated Annealing Approach

Huo,

Yu,

Pang

et al. 2024

IEEE Access

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In this paper, the integrated demand response (IDR) optimization problem is studied under the consideration of herd mentality. The contradiction between power supply and demand is becoming increasingly prominent. IDR can maintain the balance between power supply and demand, ensuring the safe operation of the system. The establishment of an IDR model that takes into account the herd mentality of consumers and the renewable energy supply, fully enhances consumers' enthusiasm for participating in demand response and input renewable energy into the comprehensive energy system to improve energy conversion rate and supply, which is closer to the actual IDR process. Using the Genetic Simulated Annealing Algorithm (GSA) to solve multi-objective optimization problems with comprehensive energy efficiency, operating costs and peak-to-average ratio as optimization objectives has significant advantages in terms of iteration speed and solution accuracy. The analysis of the simulation shows that the IDR optimization model can not only improve the stability and comprehensive energy efficiency of the system but also reduce the operating cost of the system, which proves the effectiveness of the proposed method.INDEX TERMS Integrated demand response, load management, herd mentality, peak-to-average ratio, genetic simulated annealing algorithm.

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Optimal Demand-Side Management Using Flat Pricing Scheme in Smart Grid

Cited by 19 publications

References 77 publications

Heuristic control of power consumption by up to 1000 V electrical loads at mining enterprises

Heuristic control of power consumption by up to 1000 V electrical loads at mining enterprises

Investigating the Possibilities for Simulation of the Interconnected Electric Power and Communication Infrastructures

Integrated Demand Response Optimization for Consumer With Herd Mentality: A Genetic Simulated Annealing Approach

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