The paper investigates the design and operation of microgrid arrangements, with a focus on renewable power systems, system architectures, and storage solutions. The research evaluates stochastic and multi-objective optimization methods to show how demand response systems improve operational flexibility. The study evaluates 183 journal articles to select those that address microgrid design in conjunction with optimization models and demand response approaches. The articles are classified into three essential categories, which include microgrid design optimization methods and demand response integration. The review establishes that microgrid performance depends on three fundamental design parameters, which include energy generation systems, storage capabilities, and load demand control mechanisms. The review demonstrates that advanced optimization approaches, such as stochastic and multi-objective optimization methods, offer effective solutions for managing renewable energy variability. The paper demonstrates that demand response strategies are crucial for reducing costs and enhancing system flexibility. However, current published research falls short of establishing an integrated system that combines real-time demand response with stochastic optimization. This integration, while not yet fully realized, is suggested as a critical advancement for ensuring both system performance optimization and long-term sustainability. Therefore, this paper calls for further research to develop resilient hybrid renewable microgrids that integrate flexibility with sustainability through advanced optimization models and demand response strategies.

The research develops a multi-stage stochastic Mixed-Integer Linear Programming (MILP) model for managing dispatch schedules in microgrids with significant renewable energy integration. The primary objective is to optimize the integration of renewable energy sources with energy storage systems and grid power, concurrently aiming to reduce operational costs and address uncertainties associated with renewable energy resources. The model effectively captures the variability inherent in renewable sources through the use of scenarios and implements a multi-stage MILP formulation that incorporates storage and load constraints. The methodology employs stochastic optimization techniques to regulate fluctuations in renewable generation by analyzing diverse energy availability scenarios. The optimization process is designed to minimize grid power consumption while maximizing the utilization of renewable energy via storage and load constraints that guarantee a balanced energy supply. The model achieves optimal operational costs by producing results that amount to 46,600 USD while successfully controlling renewable energy variability. The research demonstrates two main achievements by integrating high renewable penetration levels and providing valuable insights into how energy storage systems and grid independence lower costs.

The paper investigates the design and operation of microgrid arrangements, with a focus on renewable power systems, system architectures, and storage solutions. The research evaluates stochastic and multi-objective optimization methods to show how demand response systems improve operational flexibility. The study evaluates 200 journal articles to select those that address microgrid design in conjunction with optimization models and demand response approaches. The articles are classified into three essential categories, which include microgrid design optimization methods and demand response integration. The review establishes that microgrid performance depends on three fundamental design parameters, which include energy generation systems, storage capabilities, and load demand control mechanisms. The review demonstrates that advanced optimization approaches, such as stochastic and multi-objective optimization methods, offer effective solutions for managing renewable energy variability. The paper demonstrates that demand response strategies are crucial for reducing costs and enhancing system flexibility. However, current published research falls short of establishing an integrated system that combines real-time demand response with stochastic optimization. This integration, while not yet fully realized, is suggested as a critical advancement for ensuring both system performance optimization and long-term sustainability. Therefore, this paper calls for further research to develop resilient hybrid renewable microgrids that integrate flexibility with sustainability through advanced optimization models and demand response strategies.

The shift towards renewable energy demands decision-making tools that unite economic performance with environmental stewardship and social equity. The conventional evaluation methods fail to consider these interconnected factors, which results in substandard investment results. The paper establishes a sustainability accounting system that uses the Elimination and Choice Expressing Reality (ELECTRE) method to optimize investment distribution between solar power, wind power, and bioenergy systems. The evaluation framework uses six performance indicators, which include cost efficiency and return on investment, together with CO₂ emissions intensity, job creation, energy output, and financial sustainability indicators, like Net Present Value (NPV) and payback period. The barrier optimization algorithm solved the model in 10 iterations, which took 0.10 s to achieve an optimal objective value of 1.6929. The wind energy source demonstrated superior performance in every evaluation criterion because it achieved the highest concordance scores, lowest discordance levels, best payback period, and strongest NPV. The maximum allocation went to wind at 53.3%, while bioenergy received 31.0%, and solar received 16.7%. The optimized portfolio reached a total sustainability index (SI) of 1.70, which validates the method’s strength. The research shows that using ELECTRE with sustainability accounting creates an exact and open system for renewable energy investment planning. The framework reveals wind as the core alternative yet demonstrates how bioenergy and solar work together to support sustainable development across environmental and economic and social dimensions.

Energy plays a major task in the development of any nation’s economy. This may not be unconnected to its inter-relationship with countries’ national gross domestic product (GDP). In most developing countries, a reasonable percentage of national income initiates from the rural areas engaged in agricultural and related activities. Unfortunately, these communities depend predominantly on conventional sources of fuels which have detrimental environmental effects, as their source of supply of electricity. Consequently, there is little or no improvement in the standard of living in rural communities from their present low level without access to sustainable electricity. The aim of this study is to explore the prospect of meeting electricity demand from a solar wind-diesel hybrid system to a rural community isolated from the main utility grid in a coastal area of Nigeria. Based on the available renewable energy sources present at the site, a feasibility study has been carried out on the optimal hybrid renewable energy system (HRES) model to meet the electricity demand of a community of about 700 people. Hybrid Optimization Model for Electric Renewables (HOMER) is used to define the optimum configuration based on the available metrological resource. The outcome of the analysis is a list of feasible HRES configurations ranked according to their present cost (NPC). For this case study, the optimal configuration at the present interest rate of 12% in Nigeria is a combination of 20kW PV, 10 2.5kW, wind turbine, 50kW diesel generator, 17kW converter and 100 S4KS25P batteries. The NPC for this system configuration is N27,181,044 and LCOE is N71.676/kWh. The emission of CO2 the principal pollutant can be reduced by 97.1% using HRES instead of standalone diesel generator. Furthermore, relationship of economic indices and the interest rate is also evaluated. In all, HRES though has a higher initial cost; it has a lower NPC, Levelized Cost of Energy (LCOE) and reduced greenhouse gas emission compared to diesel only generator.