The area of a Microgrid (μG) is a very fast-growing and promising system for overcoming power barriers. This paper examines the impacts of a microgrid system considering Electric Vehicle Grid Integration (EVGI) based on stochastic metaheuristic methods. One of the biggest challenges to slowing down global climate change is the transition to sustainable mobility. Renewable Energy Sources (RESs) integrated with Evs are considered a solution for the power and environmental issues needed to achieve Sustainable Development Goal Seven (SDG7) and Climate Action Goal 13 (CAG13). The aforementioned goals can be achieved by coupling Evs with the utility grid and other RESs using Vehicle-to-Grid (V2G) technology to form a hybrid system. Overloading is a challenge due to the unknown number of loads (unknown number of Evs). Thus, this study helps to establish the system impact of the uncertainties (arrival and departure Evs) by proposing Stochastic Monte Carlo Method (SMCM) to be addressed. The main objective of this research is to size the system configurations using a metaheuristic algorithm and analyze the impact of an uncertain number of Evs on the distribution of residential power in Tripoli-Libya to gain a cost-effective, reliable, and renewable system. The Improved Antlion Optimization (IALO) algorithm is an optimization technique used for determining the optimal number of configurations of the hybrid system considering multiple sources, while the Rule-Based Energy Management Strategy (RB-EMS) controlling algorithm is used to control the flow of power in the electric power system. The sensitivity analysis of the effect parameters has been taken into account to assess the expected impact in the future. The results obtained from the sizing, controlling, and sensitivity analyses are discussed.

Using a PV emulator (PVE) simplifies testing of the Photovoltaic (PV) generation system. However, conventional controller used for the PVE suffers from oscillating output voltage, requiring a high number of iterations, or it is too complex to be implemented. This paper proposes a controller based on a resistance feedback control strategy, which produces stable and fast converging operating point for the PVE. The resistance feedback control strategy requires a new type of PV model, which is the Current-Resistance (I-R) PV model. This model is computed using a Binary Search Method at a fast convergence rate. It is combined with a closed-loop buck converter using a PI controller to form the resistance feedback control strategy. The PVE's controller is implemented into dSPACE ds1104 hardware platform for experimental validation. The acquired experimental results show that the proposed PVE is able to follow Current-Voltage characteristic of PV module accurately. In addition, the PVE's efficiency is more than 90% under maximum power point operation. The transient response of the proposed PVE is similar to the PV panel during irradiance changes.

Electric vehicle charging stations (EVCSs) and renewable energy sources (RESs) have been widely integrated into distribution systems. Electric vehicles (EVs) offer advantages for distribution systems, such as increasing reliability and efficiency, reducing pollutant emissions, and decreasing dependence on non-endogenous resources. In addition, vehicle-to-grid (V2G) technology has made EVs a potential form of portable energy storage, alleviating the random fluctuation of renewable energy power. This paper simulates the optimal design of a photovoltaic/wind/battery hybrid energy system as a power system combined with an electric vehicle charging station (EVCS) using V2G technology in a grid-connected system. The rule-based energy management strategy (RB-EMS) is used to control and observe the proposed system power flow. A multi-objective improved arithmetic optimization algorithm (MOIAOA) concept is proposed to analyze the optimal sizing of the proposed system components by calculating the optimal values of the three conflicting objectives: grid contribution factor (GCF), levelled cost of electricity (LCOE), and energy sold to the grid (${\mathrm{E}}_{\mathrm{S}\mathrm{O}\mathrm{L}\mathrm{D}}$). This research uses a collection of meteorological data such as solar radiation, temperature, and wind speed captured every ten minutes for one year for a government building in Al-Najaf Governorate, Iraq. The results indicated that the optimal configuration of the proposed system using the MOIAOA method consists of eight photovoltaic modules, two wind turbines, and thirty-three storage batteries, while the fitness value is equal to 0.1522, the LCOE is equal to 2.66 × ${10}^{-2}$ USD/kWh, the GCF is equal to 7.34 × ${10}^{-5}$ kWh, and the ${\mathrm{E}}_{\mathrm{S}\mathrm{O}\mathrm{L}\mathrm{D}}$ is equal to 0.8409 kWh. The integration of RESs with an EV-based grid-connected system is considered the best choice for real applications, owing to their remarkable performance and techno-economic development.

This article reports on an investigation into the effects of thermal aging on the structure and dielectric properties of polypropylene (PP) nanocomposites. Magnesium aluminate (MgAl ₂ O ₄ ), calcium carbonate (CaCO ₃ ), and surface-modified calcium carbonate (CaCO ₃ T) nanofillers have been added to PP and the resulting nanocomposites have been subjected to aging at 110 °C and 140 °C. The results show that unfilled PP experiences a 13% reduction in its dc breakdown strength after aging at 110 °C and that its dc breakdown strength reduces by 27% after aging at 140 °C. In contrast, the reduction in the dc breakdown strength of all investigated PP nanocomposites is much less pronounced than that of the unfilled PP after aging. Specifically, the dc breakdown strength of PP/MgAl ₂ O ₄ nanocomposites, PP/CaCO ₃ nanocomposites, and PP/CaCO ₃ T nanocomposites reduces by 8%, 14%, and 14%, respectively, after aging at 140 °C. Possible mechanisms affecting the dc breakdown strength of the unfilled PP and PP nanocomposites before and after aging are consequently discussed.

Minimizing energy costs while maintaining consumer satisfaction is a very challenging task in a smart home. The contradictory nature of these two objective functions (cost of energy and satisfaction level) requires a multi-objective problem formulation that can offer several trade-off solutions to the consumer. Previous works have individually considered the cost and satisfaction, but there is a lack of research that considers both these objectives simultaneously. Our work proposes an optimum home appliance scheduling method to obtain an optimum satisfaction level with a minimum cost of energy. To achieve this goal, first, an energy management system (EMS) is developed using a rule-based algorithm to reduce the cost of energy by efficient utilization of renewable energy resources and an energy storage system. The second part involves the development of an optimization algorithm for optimal appliance scheduling based on consumer satisfaction level, involving their time and device-based preferences. For that purpose, a multi-objective grey wolf accretive satisfaction algorithm (MGWASA) is developed, with the aim to provide trade-off solutions for optimal load patterns based on cost per unit satisfaction index (Cs_index) and percentage satisfaction (%S). The MGWASA is evaluated for a grid-connected smart home model with EMS. To ensure the accuracy of the numerical simulations, actual climatological data and consumer preferences are considered. The Cs_index is derived for six different cases by simulating (a) optimal load, (b) ideal load, and (c) base (random) load, with and without EMS. The results of MGWASA are benchmarked against other state-of-the-art optimization algorithms, namely, binary non-dominated sorting genetic algorithm-2 (NSGAII), multi-objective binary particle swarm optimization algorithm (MOBPSO), Multi-objective artificial bee colony (MOABC), and multi-objective evolutionary algorithm (MOEA). With the proposed appliance scheduling technique, a % reduction in annual energy cost is achieved. MGWASA yields Cs_index at 0.049$ with %S of 97%, in comparison to NSGAII, MOBPSO, MOABC, and MOEA, which yield %S of 95%, 90%, 92%, and 94% at 0.052$, 0.048$, 0.0485$, and 0.050$, respectively. Moreover, various related aspects, including energy balance, PV utilization, energy cost, net present cost, and cash payback period, are also analyzed. Lastly, sensitivity analysis is carried out to demonstrate the impact of any future uncertainties on the system inputs.

This paper presents a stochastic simulation using Monte Carlo technique to size a battery to meet dual objectives of demand shift at peak tariff times and outage protection (an uninterruptible power supply function) for commercial buildings. Both functions require battery storage and the sizing of battery using numerical optimization is popularly used. However, outage and demand peaks are not of deterministic nature or location. A given battery size can only offer a particular probability of surviving an outage or completing a demand shift, this offers an opportunity to battery sizing using stochastic methods. In this work, the Monte Carlo method takes into account the historical outage statistics and building load profiles. Further, it describes the life-cycle cost of the system and the ratio of saving and investment for ten cases of demand shifting. It also reports on the customer outage costs which were calculated based on a weekday load profile for different outage durations. By considering the statistics of both problems together, customers can assess the risk of not being able to survive in an outage and the success rate of meeting the demand shift capability based on the results of the proposed method.