This article proposes a plan to replace real-time power with constant power from the grid to reduce costs and reduce the impact of the micro-grid on the main grid at the same time. Most of the peak electricity consumption periods of universities or some enterprise institutions are during the daytime. If solar energy can be used reasonably at this time, it can provide a good guarantee of peak power. In this study, a grid-linked solar-plus-storage micro-grid was used to supply power to a university located in Okinawa, Japan. The non-dominated sorting genetic algorithm II (NSGA-II) was used to optimize the model size, and the loss of power supply probability (LPSP), life cycle cost (LCC), and waste of energy (WE) were taken as the optimization indicators. For this study, three scenarios were considered where the first scheme (Case 1) was a comparison scheme, which used a PV battery and real-time power from the infinity bus. Both the second and third cases used constant power. While Case 2 used constant power throughout the year, Case 3 used daily constant power. The optimal solutions for the power supply units were grouped into three cases where Case 1 was found to be the most expensive one. It was found that the costs of Cases 2 and 3 were 62.8% and 63.3% less than Case 1. As a result, the waste of energy was found to be more significant than Case 1: 70 times and 60 times, respectively. On the contrary, Case 1 had 15.2% and 16.7% less carbon emissions than Case 2 and Case 3, respectively. This article put forward the idea of constant power supply growth at the financial markets, which breaks the traditional way in which the power supply side follows the user’s consumption. While reducing costs, it reduces the impact on large-scale power grids and can also ensure the reliability of campus microgrids.

Combating climate change issues resulting from excessive use of fossil fuels comes with huge initial costs, thereby posing difficult challenges for the least developed countries in Sub-Saharan Africa (SSA) to invest in renewable energy alternatives, especially with rapid industrialization. However, designing renewable energy systems usually hinges on different economic and environmental criteria. This paper used the Multi-Objective Particle Swarm Optimization (MOPSO) technique to optimally size ten grid-connected hybrid blocks selected amongst Photo-Voltaic (PV) panels, onshore wind turbines, biomass combustion plant using sugarcane bagasse, Battery Energy Storage System (BESS), and Diesel Generation (DG) system as backup power, to reduce the supply deficit in Sierra Leone. Resource assessment using well-known methods was done for PV, wind, and biomass for proposed plant sites in Kabala District in Northern and Kenema District in Southern Sierra Leone. Long term analysis was done for the ten hybrid blocks projected over 20 years whilst ensuring the following objectives: minimizing the Deficiency of Power Supply Probability (DPSP), Diesel Energy Fraction (DEF), Life Cycle Costs (LCC), and carbon dioxide (CO ${}_2$ ) emissions. Capacity factors of $27.41\%$ and $31.6\%$ obtained for PV and wind, respectively, indicate that Kabala district is the most feasible location for PV and wind farm installations. The optimum results obtained are compared across selected blocks for DPSP values of 0–50% to determine the most economical and environmentally friendly alternative that policy makers in Sierra Leone and the region could apply to similar cases.

The Virtual Synchronous Generator (VSG) can give virtual inertia and imitate primary control of the synchronous generator. This paper focuses on grid-following (GFL) type VSG for easy parallel operation compared to voltage source inverter. The grid frequency support of GFL-VSG can be achieved by controlling active power output using frequency deviation. However, this control causes the delay because of feedback for the virtual inertia. This paper proposes the GFL-VSG with the parallel installation of the governor and swing equation to reduce the delay. The numerical simulation was carried out to verify its effect and grid frequency support of the proposed GFL-VSG. The result shows that grid frequency support by GFL-VSG is faster compared with the conventional method, and the gate frequency is more stable with multiple several VSGs.

The rapid growth of renewable energies specifically wind and solar with their intermittent phenomena has recently become an exciting and important topic among researchers worldwide. The integration of such devices in distribution networks comprising of battery energy storage systems (BESSs) is considered as efficient and complementation approach which their well-managed configuration and scheduling via optimal arrangement yield not only productive and reliable power delivery to end-users, but cost-saving opportunity for utility managers. When dealing with photovoltaic (PV), reactive power capability of PV inverter is another aspect of interest that can contribute in enhancing effective penetration of wind, solar, and storage system into distribution networks. In this paper, a robust and novel optimization technique epsilon multiobjective genetic algorithm $\epsilon$ -MOGA is proposed and applied to a real 162-bus distribution network. A three-objective function optimization encompassing environmental, technical, and economic aspect is developed to size the centralized wind-farm and BESS parameters, distributed rooftop PV, and PV inverter reactive power control in an optimal manner. The proposed arrangement is simulated in MATLAB and compared with several cases to show its superior implementation concerning carbon emission reduction, better technical aspect, annual saving, and grid power alleviation.

Renewable energy resources (RERs) such as wind and solar are said to be considerable promising of the power system worldwide, and Afghanistan is evaluated for abundant and feasible electricity generation capacity from these resources. It fortifies merging of RER to the electric power system of Afghanistan where power quality issue sums up with scheduled and unscheduled load shedding due to the shortage of electricity. This research study presents an optimal solution comprising of rooftop solar photovoltaic (PV) as distributed generation to a real and substantial 162‐bus electric distribution network (EDN) in Kabul, the capital of Afghanistan. Genetic algorithm (GA) based on Newton–Raphson power flow with the objective of power loss minimisation is put forward for sizing and placement of the solar PV at practically available locations or candidate buses of the network. This approach tends to reduce the dependency on the import power and at the same time improves the performance of the current system through minimisation of the total power loss and voltage deviation. The proposed method is simulated by MATLAB ^® software to compare and demonstrate the performance of the system under different scenarios of the PV allocations.