Calculation of Receipt of Renewable Energy Resources and Operation Modes of Power Plants

The main objective of the work is to develop principles for the formation of the optimal composition of the energy complex from all the given power plants based on renewable energy sources for an autonomous consumer, taking into account the variable energy loads of the consumer, changing climatic conditions and the possibility of using local fuel and energy resources. As a result of solving this optimization problem, in addition to the optimal configuration of the power complex, it is also necessary to solve the problem of optimizing the joint operation of different types of power plants from the selected optimal configuration, that is, it is necessary to determine the optimal modes of operation of power plants and the optimal share of their participation in providing consumers at every moment in time. A numerical method for analyzing and optimizing the parameters and operating mode of the energy complex with the most accurate consideration of the schedule of changes in consumer load and software that automates the solution of this optimization problem are also presented.

Seamless Control and Unified Dynamic Energy Management in a Renewable/Clean Energy Integrated Self-Reliant DC Microgrid

The growing demand for electrical energy calls for the assimilation of renewable energy sources to the main utility grid. Multiple renewable energy sources (RESs) like solar PV array, wind turbine, micro-hydro plant, etc. can be combined and controlled to form a microgrid. In spite of the availability of different microgrid topologies, DC microgrid largely facilitates the injection of DC power from various renewable energy sources into the stabilised DC power pool. The requirement for a minimal number of conversion stages, simple structure, economic operation, and numerous localised applications are driving factors for the DC microgrid technology. The mettle of the DC microgrid technology lies in choosing the appropriate microgrid participants for energy interchange and the suitable supervisory control to tap power from the microgrid partakers even after respecting their operating constraints. The use of high gain DC-DC converters is inevitable in DC microgrid due to the low terminal voltage levels of different RESs.

Sizing and Placement of Battery-Sourced Solar Photovoltaic (B-SSPV) Plants in Distribution Networks

This chapter proposes a mixed-integer optimization using genetic algorithm (MIOGA) for determining the optimum sizes and placements of battery-sourced solar photovoltaic (B-SSPV) plants to reduce the total energy losses in distribution networks. Total energy loss index (TELI) is formulated as the main objective function and meanwhile bus voltage deviations and PV penetrations of B-SSPV plants are calculated. To deal the stochastic behavior of solar irradiance, 15 years of weather data is modeled by using beta probability density function (Beta-PDF). The proposed algorithm is applied on IEEE 33 bus and IEEE 69 bus test distribution networks and optimum results are acquired for different time varying voltage dependent load models. From the results, it is known that, compared to PV only, the integration of B-SSPV plants in the distribution networks resulted in higher penetration levels in distribution networks. The proposed algorithm was very effective in terms of determining the sizes of the PV plant and the battery storage, and for the charging and discharging of the battery storage.

Comparative Analysis of MCDM Methods for the Evaluation of Optimum Green Energy Sources

The aim of this article is to select the optimum green energy sources for sustainable planning from a given set of energy alternatives. This study examines the combined behavior of multi-criteria decision-making approaches-TOPSIS, MOOSRA and COPRAS are used to evaluate the green energy sources–solar, hydro, biogas and biomass and to identify the optimum source by appraising its functioning features based on entropy probability technique. An illustrative case study is presented in order to demonstrate the application feasibility of the combined approaches for the ranking of optimum green energy sources. The analyzed results show that biogas is the optimum green energy source having the highest score value obtained by combined approaches. The sensitivity analysis shows the robustness of the combined approaches with the highest effectiveness. The study not only considers the various cost criteria but other actors like power generation, implementation period and useful life are also considered to select the optimum green energy sources for future project investment.

Artificial Bee Colony-Based Optimization of Hybrid Wind and Solar Renewable Energy System

In this chapter, the artificial bee colony (ABC) algorithm was applied to optimize hybrids of wind and solar renewable energy system. The main objective of this research is to minimize the total annual cost of the system by determining appropriate numbers of wind turbine, solar panel, and batteries, so that the desired load can be economically and reliably satisfied based on the given constraints. ABC is a recently proposed meta heuristic algorithm which is inspired by the intelligent behavior of honey bees such as searching for food source and collection and processing of nectar. Instead of gradient and Hessian matrix information, ABC uses stochastic rules to escape local optima and find the global optimal solutions. The proposed methodology was applied to this hybrid system by the help of MATLAB code and the results were discussed. Additionally, it is shown that ABC can be efficiently solve the optimum sizing real-world problems with high convergence rate and reliability. The result was compared with the results of PSO.

Newly-Developed Swarm Intelligence Algorithms Applied to Renewable Energy-Based Load Dispatch Real-World Problems

Swarm intelligence is a promising field of biologically-inspired artificial intelligence, which is based on the behavioral models of social insects. This article covers Swarm Intelligence Algorithm, i.e., grasshopper optimization algorithm (GOA) which is based on the social communication nature of the grasshopper, applied to renewable energy based economic and emission dispatch problems. Based on Weibull probability density function (W-pdf), the stochastic wind speed including optimization problem is numerically solved for a 2 renewable wind energy incorporating 6 and 14 thermal units for 3 different loads. Moreover, to improve the solution superiority and convergence speed, quasi oppositional based learning (QOBL) is included with the main GOA algorithm. The performance of GOA and QOGOA is evaluated and the simulation results as well as statistical results obtained by these methods along with different other algorithms available in the literature are presented to demonstrate the validity and effectiveness of the proposed GOA and QOGOA schemes for practical applications.

Application of Renewable Energy System With Fuzzy Logic

There has been rapid surge in energy consumption owing to the industrialization and the growing population. There has been a shift from agrarian economy to the industrial economy. This transformation has led to increased energy consumption in tandem with the emissions associated with it. Thus, the energy consumption has led to environmental concerns. Therefore, the planning and modeling of energy resources has become critical to economic growth and should be efficiently done for securing the health of the environment as well. Looking at the importance of modeling and planning, the present chapter is an attempt to explore the fuzzy based models used for the renewable systems and in particular the wind energy systems. It has been found that the fuzzy based models have been used extensively for installation of wind farms, for optimization of the parameters related to wind systems and for the site selection of the different wind energy farms.

Robust Economic Dispatch of Microgrid With Highly Penetrated Renewables and Energy Storage System

Economic dispatch (ED) of a grid-connected and renewable integrated microgrid is considered in this article. Here, the renewable energy sources (RES) taken into consideration are wind farms. A parameter worst-case-transaction-cost which arises due to the stochastic availability and uncontrollable nature of wind farms is also emphasised and efforts have been taken to minimize it too. Hence, the article focuses on separately optimizing the generation costs and the worst-case transaction costs. It also optimises the net microgrid cost as a whole, which is the summation of generation costs and the worst-case transaction costs. Two different cases with highly varying transaction prices are studied. Various types of loads, ramp rates of generators, charging and discharging limits of the batteries are taken into consideration while minimizing the microgrid cost. Four meta-heuristic soft computing algorithms are applied for optimization and a comparative analysis among them is studied. Numerical results are tabulated to justify the effectiveness of the novel approach.

Renewable Energy Sources Development in Rural Areas of African Countries

The prospects of increasing access to electricity for the population of rural areas of Africa are considered. The main international funds and organizations aimed at sustainable energy development in Africa are described. An analysis of the state and possible options for using renewable energy sources for this purpose in decentralized energy supply through the creation of mini-grids or stand-alone systems is given. The risks by developing renewable energy sources in rural areas and modern mechanisms for financing in solar energy are presented.

Renewable Energy and Economic Growth

The global commitment to drastically curb greenhouse gas emissions towards a sustainable development is strongly connected to the development and usage of renewable energy (RE), such as solar and wind. Between 2006 and 2016, world's total RE consumption, excluding hydro-electricity, increased by almost 350%, and RE investment grew from US $47 billion in 2004 to 279.8 billion in 2017. The importance of RE has attracted a lot of attention from the economic literature as well, since a growing body of empirical research is investigating the relationships between RE and economic growth. The general outcome is the existence of a positive bi-directional (direct and reverse) link between RE consumption and real GDP, though it also emerges evidence showing no statistically significant relationship. This Chapter provides i) an overview of the recent world's trends of RE production and investment, ii) an extensive and detailed review of the recent advances in the RE-growth empirical literature, highlighting the main methodologies adopted and the main findings emerged.