Multi-objective evolutionary optimization with genetic algorithm for the design of off-grid PV-wind-battery-diesel syste
- PDF / 5,371,834 Bytes
- 20 Pages / 595.276 x 790.866 pts Page_size
- 7 Downloads / 214 Views
(0123456789().,-volV) (0123456789().,-volV)
METHODOLOGIES AND APPLICATION
Multi-objective evolutionary optimization with genetic algorithm for the design of off-grid PV-wind-battery-diesel system Rajendran Joseph Rathish1 • Krishnan Mahadevan1 • Senthil Kumaran Selvaraj2 • Jayapalan Booma3
Springer-Verlag GmbH Germany, part of Springer Nature 2020
Abstract With the increasing hazard to the environment and progressions in renewable energy technologies, hybrid renewable energy systems can meet the energy demand. Total dependability can be attained by adding battery banks to hybrid systems. This paper aims to optimally design a multi-source grid isolated hybrid generation system for future smart cities in the state of Tamil Nadu, India. This off-grid power generation consists of wind turbine generators, photovoltaic panels, inverters, diesel generators, and batteries. The design intents at minimizing Net Present Cost, Unmet Load, and CO2 emissions which are conventionally contradictory to each other. This paper analyzes the optimal off-grid combination of components and control strategies, utilizing improved Hybrid Optimization using the Genetic Algorithm. The results obtained portrays that a mix of hybrid renewable energy generators at off-grid locations without diesel generators can be a cost-effective choice of new smart cities, and it is sustainable, environmentally viable, and techno-economically feasible. Subvention is imperative to take on the large-scale system due to the high capital cost. Keywords Multi-objective optimization Genetic algorithm Evolutionary algorithm Renewable energy Hybrid systems List of symbols Ngen main Number of main algorithm generations Ngenmain max Maximum number of generations of the main algorithm Ngensec Number of generations of the secondary algorithm Ngensec max Maximum number of secondary algorithm generations Nnon dom Tally of non-dominated solutions
Communicated by V. Loia. & Rajendran Joseph Rathish [email protected]; [email protected] 1
Department of Electrical and Electronics Engineering, PSNA College of Engineering and Technology, Dindigul, Tamil Nadu, India
2
Department of Manufacturing Engineering, School of Mechanical Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India
3
Department of Electronics and Communication Engineering, PSNA College of Engineering and Technology, Dindigul, Tamil Nadu, India
1 Introduction Globalization has led to rapid industrialization over the past three decades. The demand for electrical energy has seen an unprecedented increase, and this is due to advancements in new technologies and hike in energy usage of household goods and services. The infrastructure of Power is a specific constituent for the acceleration of constant economy growth. The input of power is a hinge around that all economic actions turn round. Availability of affordable, dependable, and quality power is a basic necessity for any country. With a swelling population, the development of new smart cities has become vital for
Data Loading...
