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This research introduces an innovation strategy aimed at bolstering the reliability of a renewable energy resource, which is hybrid energy systems, through the application of a metaheuristic algorithm. The growing need for sustainable energy solutions underscores the importance of integrating various energy sources effectively. Concentrating on the intermittent characteristics of renewable sources, this study seeks to create a highly reliable hybrid energy system by combining photovoltaic (PV) and wind power.

To obtain efficient renewable energy resources, system designers aim to enhance the system’s reliability. Generally, for this purpose, the reliability redundancy allocation problem (RRAP) method is utilized. The authors have also introduced a new methodology, named Reliability Redundancy Allocation Problem with Component Mixing (RRAP-CM), for optimizing systems’ reliability. This method incorporates heterogeneous components to create a nonlinear mixed-integer mathematical model, classified as NP-hard problems. We employ specially crafted metaheuristic algorithms as optimization strategies to address these challenges and boost the overall system performance.

The study introduces six newly designed metaheuristic algorithms. Solve the optimization problem. When comparing results between the traditional RRAP method and the innovative RRAP-CM method, enhanced reliability is achieved through the blending of diverse components. The use of metaheuristic algorithms proves advantageous in identifying optimal configurations, ensuring resource efficiency and maximizing energy output in a hybrid energy system.

The study’s findings have significant social implications because they contribute to the renewable energy field. The proposed methodologies offer a flexible and reliable mechanism for enhancing the efficiency of hybrid energy systems. By addressing the intermittent nature of renewable sources, this research promotes the design of highly reliable sustainable energy solutions, potentially influencing global efforts towards a more environmentally friendly and reliable energy landscape.

The research provides practical insights by delivering a comprehensive analysis of a hybrid energy system incorporating both PV and wind components. Also, the use of metaheuristic algorithms aids in identifying optimal configurations, promoting resource efficiency and maximizing reliability. These practical insights contribute to advancing sustainable energy solutions and designing efficient, reliable hybrid energy systems.

This work is original as it combines the RRAP-CM methodology with six new robust metaheuristics, involving the integration of diverse components to enhance system reliability. The formulation of a nonlinear mixed-integer mathematical model adds complexity, categorizing it as an NP-hard problem. We have developed six new metaheuristic algorithms. Designed specifically for optimization in hybrid energy systems, this further highlights the uniqueness of this approach to research.

Priorities of decarbonizing the mining sector together with an availability of cost-effective technological solutions lead renewable energy (RE) to become an attractive energy source for the mining industry. Several pilot projects are run as hybrid systems, providing additional capacity to traditional energy systems. The purpose of this paper is to develop a mathematical model as a decision-making tool. The decision refers to a replacement of the fossil fuel system contains by the hybrid system in the sense of no return.

Four systems are considered. System one contains only a diesel plant. System two consists of a hybrid energy system with a photovoltaic (PV) part and a genset as back-up. System three includes a conventional natural gas combined cycle (CGCC) plant. Finally, system four covers a hybrid energy system with a PV part and CGCC turbine. The mathematical model is based upon the well-known concept of levelized cost of electricity.

The scenarios account for the degradation rate of PV modules, the PV yields of mines in different locations and the greenhouse gas emissions impact. The results show the break-even times of each scenario and the years of no return for the four systems in each scenario.

The solution of the model is performed for two case-studies. Case study 1 compares the diesel and hybrid PV-diesel systems. Case study 2 compares the CGCC and hybrid PV-natural gas systems.

This model can be generalized to all mining settings, with specific practical implications for off-grid mines.

The results of this paper bring a valuable contribution to carbon dioxide emissions reduction.

The paper aims to enhance the attention of decision-makers on fossil fuel and RE technologies increase the attractiveness of RE in powering mining operations.

This study aims to analyze the effects of variations in annual real interest rates in the assessment of the techno-economic feasibility of a hybrid renewable energy system (HRES) for an off-grid community.

Hybrid Optimization of Multiple Energy Resources (HOMER) software is used to propose an HRES for Abadam community in northern Nigeria. The HRES was designed to meet the basic needs of the community over a 25-year project lifespan. Based on the available energy resources in the community, photovoltaic (PV), wind turbine, diesel generator and battery were suggested for integration to serve the load requirements.

When the annual real interest rates were taken as 10 and 8 per cent, the total amount of total energy fraction from PV, wind turbine and the diesel generator is 28, 57 and 15 per cent, respectively. At these interest rates, wind turbines contributed more energy across all months than other energy resources. The energy resource distribution for 0, 2,4 and 6 per cent annual real interest rates have a similar pattern, but PV contributed a majority of the energy.

This study has used annual real interest and inflation rates dynamic behavior to determine optimal HRES for remote communities. Hence, its analysis will equip decision-makers with the necessary information for accurate planning.

The results of this study can be used to plan and design HRES infrastructure for off-grid communities around the world.

Renewable generation is a main component of most hybrid generation systems. However, randomness on its generation is a characteristic to be considered due to its direct impact on reliability and performance of these systems. For this reason, renewable generation usually is accompanied with other generation elements to improve their general performance. The purpose of this paper is to analyze the power generation system, composed of solar, wind and diesel generation and power outsourcing option from the grid as means of reserve source. A multi-objective optimization for the design of hybrid generation system is proposed, particularly using the cost of energy, two different reliability indexes and the percentage of renewable energy as objectives. Further, the uncertainty of renewable sources and demand is modeled with a new technique that permits to evaluate the reliability quickly.

The multi-state model of the generators and the load is modeled with the Universal Generating Function (UGF) to estimate the reliability indexes for the whole system. Then an evolutionary algorithm NSGA-II (Non-dominated Sorting Genetic Algorithm) is used to solve the multi-objective optimization model.

The use of UGF methodology reduces the computation time, providing effective results. The validation of reliability assessment of hybrid generation systems using the UGF is carried out taking as a benchmark the results obtained with the Monte Carlo simulation. The proposed multi-objective algorithm gives as a result different generators combinations that outline hybrid systems, where some of them could be preferred over others depending on its results for each independent objective. Also it allows us to observe the changes produced on the resulting solutions due to the impact of the power fluctuation of the renewable generators.

The main contributions of this paper are: an extended multi state model that includes different types of renewable energies, with emphasis on modeling of solar energy; demonstrate the performance improvement of UGF against SMC regarding the computational time required for this case; test the impact of different multi-states numbers for the representation of the elements; depict through multi-objective optimization, the impact of combining different energies on the cost and reliability of the resultant systems.

This study aims to find the best location for constructing green energy facilities in India and reducing CO₂ emissions. Incorporating green energy is a priority for many countries under the Paris Agreement. This task is challenging due to factors that affect implementation, and making the wrong decision wastes resources. India’s goals are net-zero emissions by 2070 and 50% renewable electricity by 2030. Other developing nations should emulate India’s renewable energy strategy. India ranks fourth in renewable energy and wind power, and fifth in solar power capacity. This research aims to identify the best locations in India for implementing green energy projects.

To identify the optimal green energy implementation sites in India, this research uses the hybrid multicriteria decision analysis (MCDA) in an uncertain environment. This research uses the Delphi method to identify the most suitable green energy implementation sites in India. It adapts the elements for this investigation. In addition, the utilization of the Fermatean fuzzy weighted aggregated sum product assessment technique is implemented to effectively prioritize the factors that impact the selection of these sites. This study used the MEREC method (method based on the removal effects of criteria) to identify the most suitable areas in India for implementing green energy. The highest accuracy is attained through the amalgamation of these hybrid methods.

Following the computation data by hybrid MCDA in uncertainty environment, NP Kunta in Andhra Pradesh emerges as the recommended green energy site among the 11 considered. Also among the factors political strategies and objectives hold the highest priority among them.

This study is pioneering in its efforts to provide a comprehensive perspective on the development and management of green energy operations in India. The study proves advantageous for diverse sites in the successful adoption and management of green energy. The study is additionally valuable in informing policy development aimed at promoting the use of green energy by employees through the utilization of MCDA methods in uncertain environments.

The purpose of this paper is to examine the impact of renewable energy on the power supply chain and to study whether the renewable generator or the power grid that purchases power from the power spot market is better when the actual generation of renewable energy is insufficient. The authors want to compare and analyze the different power supply chain operation modes and discuss the optimal mode selection for renewable energy generator and power grid in different situations.

This paper studies the grid-led price competition game in the power supply chain, in which the power grid as a leader decides the price of transmission and distribution, and generators determine the power grid price. The renewable energy power generator and the traditional energy power generator conduct a price competition game; on the other hand, the power grid and power generators conduct Stackelberg games. The authors analyze the power supply of single power generator and two power generators, respectively, and research on the situation that the renewable energy cannot be fully recharged when the actual power generation is insufficient.

The study finds that both renewable and traditional power grid prices decline as price sensitivity coefficient of demand and installed capacity of renewable energy generators increase. Power grid premium decreases as the price sensitivity coefficient of demand increases, but rises as the installed capacity of renewable energy generator increases. When there is a shortage of power, if the installed capacity of renewable energy is relatively small and price sensitivity coefficient of demand is relatively large, the grid purchases the power from power spot market and shares cost with renewable energy generators, leading to higher expected profits of the renewable energy generators. On the contrary, the renewable energy generators prefer to make up power shortage themselves. For the power grid, purchasing the power by the renewable energy generators when there is a power shortage can bring more utility to the power grid when the installed capacity of renewable energy is lower and the demand price sensitivity coefficient is higher. When the installed capacity of renewable energy is high and the price sensitivity coefficient of demand is moderate, or the installed capacity of renewable energy is moderate and the demand price sensitivity coefficient is high, a generator that simultaneously possesses two kinds of energy source will bring more utility to the power grid. If the installed capacity of renewable energy and the demand price sensitivity coefficient both are small or the installed capacity of renewable energy and the price sensitivity coefficient of demand both are large, the power grid prefers to purchase the power by itself when there is a power shortage.

The goal of our paper analysis is to explore the implications of the theoretical model and address the series of research questions regarding the impact of the renewable energy on the power supply chain. The results of this study have key implications for reality. This paper sheds light on the power supply chain operation mode selection, which can potentially be used for the renewable energy generators to choose their operating mode and can also help traditional energy generators and power grid enterprises maximize their utility. This paper also has some references for the government to formulate the corresponding renewable energy development policy.

This paper studies the power operation mode under the uncertainty of supply and demand, and compares the advantages and disadvantages of renewable energy generator that makes up the shortage or the power grid purchases the power from power spot market then shares cost with the renewable energy generator. This paper analyzes the power grid-led coordination problem in a power supply chain, compares and analyzes the price competition game model of single power generator and dual power generators, and compares the different risk preferences of power grid.

The important factors, which should be considered in the design of a hybrid system of photovoltaic and wind energy are discussed in this study. The current load demand for electricity, as well as the load profile of solar radiation and wind power of the specified region chosen in Iran, is the basis of design and optimization in this study. Hybrid optimization model for electric renewable (HOMER) software was used to simulate and optimize hybrid energy system technically and economically.

HOMER software was used to simulate and optimize hybrid energy system technically and economically.

The maximum radiation intensity for the study area is 7.95 kwh/m²/day for July and the maximum wind speed for the study area is 11.02 m/s for January.

This research is the result of the original studies.

The application of hybrid renewable energy system (HRES) can mitigate inadequate access to clean, stable and sustainable energy among households in sub-Saharan Africa (SSA). Available studies on HRES seem to concentrate only on its techno-economic and environmental viability. In so doing, these studies do not seem to underline the likely challenges that follow the acquisition of HRES by especially low-income households. The ensuing reality is, of course, a limitation in the use of HRES in homes with low incomes. It is therefore imperative to analyze how a household with low income can afford this kind of energy system. The purpose of this study, therefore, lies in presenting a techno-economic, environmental and affordability analysis of how HRES is acquired.

To arrive at a grounded analysis, a typical household in SSA is used as an example. The analysis focused on the pattern of energy use, and this is obtained by visiting an active site to evaluate the comprehensive load profile. In the course of analysis, an optimal techno-economic design and sizing of a hybrid PV, wind and battery were undertaken. Additionally, an acquisition analysis was done based on loan amortization.

The interesting result is that a combination of the photovoltaic-gasoline-battery system is the most cost-effective energy system with a net present cost of $2,682. The system combination can lead to an emission reduction of approximately 98.3 per cent, compared to the use of gasoline generating sets, common mostly in SSA. If an amortized loan is used to purchase the energy system, and the payment plan is varied such that the frequency of payments is made quarterly, annually, semi-annually, bi-monthly, semi-monthly and bi-weekly, it will be observed that low-income household can conveniently acquire a HRES.

The result presented a framework by which a low-income household can purchase and install HRES. To facilitate this, it is recommended that low-income households should be given interest-friendly loans, so as to enhance the acquisition of HRES.

This paper aims to propose a comprehensive methodology for setting up rural electrifications for indigenous villages with minimum budgets and the lowest possible cost of electricity (COE). The electricity accessibility of rural area in Malaysia is not fully covered and the cost of extending the grid to these areas can be high as RM 2.7m per km. Lack of vigorous policies and economic attraction of the rural areas are also the main barriers to rural electrification. Electricity is an essential element of economic activities and the lack of electricity exacerbates poverty and contributes to its perpetuation. Therefore, a hybrid standalone power system can be an alternative solution for the rural electrification. A hybrid standalone power system is studied to investigate the potential of the implementation and the budget required.

A site survey has been carried out in a village in Peninsular Malaysia, namely, Kampung Ulu Lawin Selatan. A standalone hybrid system is modeled in HOMER Pro software and the data collected from the selected site are used to obtain the system configuration with the lowest COE. The load following and cycle charging energy dispatch methods are compared to identify the optimal system configuration that yields the lowest COE. The diesel generator-only system is chosen as a benchmark for comparisons.

The results show that the hybrid system constituted from the diesel generator, photovoltaic (PV), micro-hydro and energy storage using the load following energy dispatch method yields the lowest COE of RM 0.519 per kWh. The COE of the hybrid system is 378 per cent lower than that of the diesel generator-only system. The lead-acid energy storage system (ESS) is able to reduce 40 per cent of COE as compared to the system without ESS.

The results indicate that the COE of the diesel-micro hydro-PV-ESS system with load following dispatch strategy is RM 0.519 per kWh, and this value is 35 per cent higher than the average electricity price in Malaysia. However, it is important to note that the costs of extending the grid to the rural area are not taken into account. If this cost is considered into the electricity price, then the standalone hybrid power system proposed by this study is still a competitive alternative for rural electrification.

Earlier most of the research groups have designed and developed hybrid renewable energy system models with technological, scientific and industrial advancement for the energy systems, but slight attention has been paid towards the grid-connected sustainable urban residential energy systems (SUR_eS) for metropolitan cities. The current research wishes to design, model and analyze grid-connected energy system for residential applications for sustainable urban residential energy system. The works aims to explore the potential of the augmented energy system for grid-connected energy system.

The proposed grid-connected SUR_eS are validated for a sample location at New Delhi (India) with a hybrid optimization model for electric renewable (HOMER) software to define and understand the various load profile. It presents the sensitivity analysis approach to validate the design of the proposed energy system.

The obtained results reports the key barriers, proposed model and scenarios for sustainable urban energy system development.

Similar approaches can be replicated to design and develop an independent, self-sustainable cleaner and environmental-friendly energy system in the future scenario for the extension of complex grid infrastructures.

It will assist the stakeholder in solving the complex urban sustainability issues raised due to the shortage of energy.

It will offer a clean and environment friendly sustainable energy resources with reduced carbon emissions. It will benefit sustainable energy resources with a mix of challenges and opportunities, to suggest an approach for implementation of efficient energy policies to optimize the existing and forthcoming energy systems.

The current research offers a design and model to analyze grid-connected energy system sustainable urban residential applications. It explores the potential of the augmented energy system. The proposed model are validated for a sample location with HOMER simulation software to define and understand various scenarios of the multiple load profile. The work presents the sensitivity analysis approach to validate the proposed energy system.