Nature-Inspired Optimization Algorithms for Renewable Energy Generation, Distribution and Management—A Comprehensive Review

Renewable energy requirement has surged in the recent times in order to cope up with the climate change laws and to reduce the dependence on fossil fuels for a clean and green tomorrow. Hydro, wind, solar, geothermal and bio-energy are the predominant sources of renewables, and it is the requirement of the time to optimize these sources to ensure maximized power generation with minimized costs and losses while dealing with various constraints on resources, storage, distribution, etc. Various optimization techniques/algorithms are deployed to efficiently optimize the renewable energies in the literature while dealing with numerous constraints in the literature. Among these techniques, nature-inspired meta-heuristic optimization algorithms have attracted attention with their reliability and excellent solution quality to deal with the various hurdles towards generation, distribution, integration and management of renewables. This chapter provides a comprehensive outlook of the various nature-inspired algorithms and their deployment to the optimization of various renewable energy systems. Initially, the growth of renewables in the recent times is analysed with the data from International Energy agency, and the recent trends pertaining to individual renewable source are mapped and studied. The distribution of renewable energy sources for various demographic areas is provided. Following it is a survey of the role of nature-inspired algorithms (NIAs) and their contribution to the betterment of various renewables over last two decades. This survey compares and examines various NIAs with their objective and constraint functions alongside their area of application. The various aspects like maximum power point tracking and optimization for solar and wind systems, battery and energy management optimization systems, improving the efficiency of PV panels and wind farms, resource allocation and management, demand management issues, integration with grid, investment costs, profits and lifespan, maintenance costs, thermal efficiency, etc., through NIAs are presented for both small units and larger systems. The management of renewables like economic dispatch, with the influence of capital investment costs, maintenance costs, depreciation costs are also studied. The constraining factors like cost of operations, resource limitations, battery storage limitations, depreciation of the equipment, load management constraints, pumped storage constraints, environmental and land area limitations, budget constraints, power flow constraints, power rating constraints, rate of failure of equipment, power system faults, etc., are studied, and their impact on the system is analysed. The formulation of the objective function and the conflicting constraints in each scenario are analysed, and the performances of the NIAs are studied. A comparative study is also done for various NIAs for the same type of optimization problem where the robustness and the optimality with feasible solutions are compared. Additionally, the different renewables systems with their mechanics either for a standalone or grid-connected (integrated) systems and the various problems encountered are studied and analysed. Apart from NIAs, various optimization tools and paradigms for enhancing renewable energy systems are also examined and compared for standalone and grid-connected systems.