Reducing the Cost of Electricity by Optimizing Real-Time Consumer Planning Using a New Genetic Algorithm-Based Strategy

• Published in: Mathematics (Basel) 2020-07, Vol.8 (7), p.1144
• Main Authors: Ionescu, Laurentiu-Mihai, Bizon, Nicu, Mazare, Alin-Gheorghita, Belu, Nadia
• Format: Article
• Language: English
• Subjects: Case studies; Communication; Computer simulation; consumer planning; Consumers; consumption monitoring; Distributors; Electricity; Electricity consumption; Electricity distribution; Energy consumption; Energy distribution; Food science; Genetic algorithms; Literature reviews; Nuclear energy; Nuclear power plants; Optimization; Parameters; Planning; Production capacity; real-time strategy; renewable energy; Renewable energy sources; Sensors; Simulation; Smart grid technology; Tariffs
• ISSN: 2227-7390; 2227-7390
• DOI: 10.3390/math8071144

To ensure the use of energy produced from renewable energy sources, this paper presents a method for consumer planning in the consumer–producer–distributor structure. The proposed planning method is based on the genetic algorithm approach, which solves a cost minimization problem by considering several input parameters. These input parameters are: the consumption for each unit, the time interval in which the unit operates, the maximum value of the electricity produced from renewable sources, and the distribution of energy production per unit of time. A consumer can use the equipment without any planning, in which case he will consume energy supplied by a distributor or energy produced from renewable sources, if it is available at the time he operates the equipment. A consumer who plans his operating interval can use more energy from renewable sources, because the planning is done in the time interval in which the energy produced from renewable sources is available. The effect is that the total cost of energy to the consumer without any planning will be higher than the cost of energy to the consumer with planning, because the energy produced from renewable sources is cheaper than that provided from conventional sources. To be validated, the proposed approach was run on a simulator, and then tested in two real-world case studies targeting domestic and industrial consumers. In both situations, the solution proposed led to a reduction in the total cost of electricity of up to 25%.