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Economical Evaluation and Optimal Energy Management of a Stand-Alone Hybrid Energy System Handling in Genetic Algorithm Strategies

Hybrid renewable energy systems are a promising technology for clean and sustainable development. In this paper, an intelligent algorithm, based on a genetic algorithm (GA), was developed and used to optimize the energy management and design of wind/PV/tidal/ storage battery model for a stand-alone hybrid system located in Brittany, France. This proposed optimization focuses on the economic analysis to reduce the total cost of hybrid system model. It suggests supplying the load demand under different climate condition during a 25-years interval, for different possible cases and solutions respecting many constraints. The proposed GA-based optimization approach achieved results clear highlight its practicality and applicability to any hybrid power system model, including optimal energy management, cost constraint, and high reliability.
- Shanghai Maritime University China (People's Republic of)
- Northern Technical University Iraq
- Southern Technical University Iraq
- Shanghai Maritime University China (People's Republic of)
- Southern Technical University Iraq
energy management, TK7800-8360, [SPI.NRJ]Engineering Sciences [physics]/Electric power, stand-alone system, genetic algorithm, Electronics, economical cost, optimization, hybrid energy system, [SPI.NRJ] Engineering Sciences [physics]/Electric power
energy management, TK7800-8360, [SPI.NRJ]Engineering Sciences [physics]/Electric power, stand-alone system, genetic algorithm, Electronics, economical cost, optimization, hybrid energy system, [SPI.NRJ] Engineering Sciences [physics]/Electric power
citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).57 popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.Top 1% influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).Top 10% impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.Top 10%
