This article studied the load frequency control (LFC) of a multi-source microgrid with the presence of renewable energy sources. To maintain a sustainable power supply, the frequency of the system must be kept constant. A Proportional–Integral–Derivative (PID) controller is presented as a secondary controller to control the frequency of the microgrid in island mode, and the integral of squared time multiplied by error squared (ISTES) is used as a performance index. The use of the Craziness-Based Particle Swarm Optimization (CRPSO), which is an improved version of Particle Swarm Optimization (PSO), improves the convergence speed in optimizing the nonlinear problem of load and frequency controller design. The test microgrid is composed of the load and distributed generation units such as diesel generators, photovoltaics and wind turbines. The proposed controller provided the desired response to adjusting the microgrid frequency, achieving the final response after a short time and making it more stable and less oscillatory compared with the conventional system.

Today, the use of renewable energy is increasing day by day, and this development requires the optimization of these technologies in various dimensions. Solar systems have a higher acceptance due to their high availability and accessibility; the most common solar technology is photovoltaic cell. In this research, modeling was done to achieve the most economically optimal arrangement of photovoltaic panels, inverters, and module placement to generate more electrical energy by considering economic parameters, for which the CPSO algorithm was used. Four different combinations of module and inverter were studied in this research, among which the second combination, which included PV module type one and inverter type two, was the best case. One of the significant results of the present study is 191,430 kWh of electrical energy during the studied year by the solar cell connected to the grid, which requires $42,792,727 to produce.

Dealing with multi-objective problems has several interesting benefits, one of which is that it supplies the decision-maker with complete information regarding the Pareto front, as well as a clear overview of the various trade-offs that are involved in the problem. The selection of such a representative set is, in and of itself, a multi-objective problem that must take into consideration the number of choices to show the uniformity of the representation and/or the coverage of the representation in order to ensure the quality of the solution. In this study, day-ahead scheduling has been transformed into a multi-objective optimization problem due to the inclusion of objectives, such as the operating cost of multi-energy multi-microgrids (MMGs) and the profit of the Distribution Company (DISCO). The purpose of the proposed system is to determine the best day-ahead operation of a combined heat and power (CHP) unit, gas boiler, energy storage, and demand response program, as well as the transaction of electricity and natural gas (NG). Electricity and gas are traded by MGs with DISCO at prices that are dynamic and fixed, respectively. Through scenario generation and probability density functions, the uncertainties of wind speed, solar irradiation, electrical, and heat demands have been considered. By using mixed-integer linear programming (MILP) for scenario reduction, the high number of generated scenarios has been significantly reduced. The ɛ-constraint approach was used and solved as mixed-integer nonlinear programming (MINLP) to obtain a solution that meets the needs of both of these nonlinear objective functions.

This study aims to develop an optimization strategy for determining the optimal type and capacity of batteries in a building‐applied photovoltaic system, taking into account battery degradation, consumption profiles, and regional solar irradiation. Key performance indicators such as peak shaving, savings, net present value, self‐consumption, return on investment, and payback period are examined. The best trade‐off among these indicators is determined using the fuzzy decision‐making method. A study was conducted using real data from Kpenergy Company, focusing on a building with a 50 kW photovoltaic system located in Stockholm. Three cases were examined in MATLAB software, each categorized based on the type of contract between the utility (Vattenfall Company) and the subscriber. The results of these case studies highlight the effectiveness of the proposed optimization approach. Using the proposed approach, optimal batteries are determined, minimizing subscriber costs while maximizing profit.

Nowadays, generating energy is discussed as a paramount option in waste management, because the share of energy from waste is increasing annually. The ever‐increasing need for energy and limitations of using fossil resources, and also the increase in environmental pollution from consuming these resources have made the use of renewable energies of especial importance. In this research, we first studied renewable energies, followed by technologies related to the biogas system. Biogas systems have been referred to by landfill and discussed comprehensively in this scheme for utilization. For this purpose, the input source of this system is considered as the volume of garbage in the city of Germi as the study area. In the next step, the amount of garbage produced in this city was extracted and the Landfill software was used for methane production potential assessment from this system and the Homer software was used for economic analysis and reliability evaluation. The most important results are payback period, which is about 10 years, and the amount of electricity produced per year, which is 11658.265 MWh.