Energy storage system (ESS) possesses tremendous potential to counter both the rapid growth of intermittent renewable energy resources (RESs) and provide frequency support to the microgrid (MG). Since the deployment of ESS has overcome the imbalance between generation and consumption, however, their massive cost, as well as degradation tendency, are the restricting considerations that demand alternative solutions to provide stable microgrid operation. To assist ESS, the electric vehicles (EVs) are incorporated into the system. EVs have been gradually commercially viable and considerable focus has been paid to vehicle-to-grid technologies. Appropriate collaboration between ESS and EVs has good capability to manage the frequency irregularities to ensure the efficient operation of the MG. This article presents a novel combination of two control techniques i.e., model predictive control (MPC) and adaptive droop control (ADC), to tackle the frequency regulation issue in the isolated MG, by effectively controlling the ESS and EVs during the large-scale integration of RESs or huge change in load demand. Firstly, the MPC regulates the ESS according to the system frequency deviation, and secondly, the ADC manages the power of EVs according to system specifications by retaining the least possible power for potential usage of EVs. Moreover, an advanced genetic algorithm is applied to tune the MPC and ADC parameters in order to achieve optimized performance. An isolated MG is modeled and verified in MATLAB/Simulink using the above-mentioned control techniques. Further, different case studies are taken into account to validate the combination of ADC and MPC for frequency regulation of an isolated MG. Additionally, the proposed MPC controller is compared with fuzzy logic proportional-integral (FPI) controller and proportional-integral (PI) controller, the MPC provides better performance results as compared with FPI and PI controllers.

After nearly a century with internal combustion engines dominating the transportation sector, it now appears that electric vehicles (EVs) are on the brink of enjoying rapid development due to numerous useful features they possess, such as less operational cost and reduced carbon emissions. EVs can act as load as well as source, by utilizing the technique known as Vehicle-to-Grid (or Grid-to-Vehicle technique if EVs are used as a load). This technique adds key features to an industrial microgrid in the form of primary frequency control and congestion management. In this paper, two controllers (grid regulation and charger controller) are proposed by considering different charging profiles, state of charge of electric vehicle batteries, and a varying number of electric vehicles in an electric vehicle fleet. These controllers provide bidirectional power flow, which can provide primary frequency control during different contingencies that an industrial microgrid may face during a 24-hour period. Simulation results prove that the proposed controllers provide reliable support in terms of frequency regulation to an industrial microgrid during contingencies. Furthermore, simulation results also depict that by adding more electric vehicles in the fleet during the vehicle-to-grid mode, the frequency of an industrial microgrid can be improved to even better levels. Different case studies in this article constitute an industrial microgrid with varied distributed energy resources (i.e. solar and wind farm), electric vehicles fleet, industrial and residential load along with diesel generator. These test cases are simulated and results are analyzed by using MATLAB/SIMULINK.

As non-renewable energy sources become depleted, the world is transitioning toward renewable energy sources (RERs), with several significant advantages, including cost savings, large-scale availability, and minimal losses. Pakistan is in the sixth spot among the countries affected by global warming and abrupt climatic conditions. Severe floods, catastrophic rains, and wind gusts have created new challenges in the country regarding resilient and reliable power supply. Due to harsh weather conditions, the power shut down, blackouts, and prolonged fault in the transmission systems are common and cause millions of economic losses annually. The main challenge of providing a reliable power supply while satisfying the climatic conditions to use renewable energy resources has a global significance. This article aims to develop a hybrid power network (PV/Battery/Grid) for King Abdullah Campus, The University of Azad Jammu and Kashmir, Muzaffarabad, Pakistan. The results for the proposed system are compared with the existing system while they are supplying the same power to the load. The cost of energy (COE) from the existing on-grid Hybrid system is Rs.27.32/kWh thus. It can be seen that energy cost is minimized up to Rs.-.251/kWh by the effective use of solar energy. Optimization results conclude that the on-grid hybrid (PV/Battery/Convertor/Grid) is more efficient than all other configurations. Therefore, it is concluded that the proposed hybrid power system for Grid-connected sites is most efficient, stable, cost-effective and environmentally friendly. The ultimate goal is to design an economically feasible hybrid power system and analyze the techno-socio impacts of the PV hybrid system for the campus. Data is initially collected for the desired site and then simulated via the HOMER simulation tool, which determines the net load and feasibility of the proposed system. This techno-economic analysis of the PV hybrid system will enhance the utilization of solar power in other educational or commercial facilities in the region may follow the path in the future.

This paper presents a novel optimal coordinated strategy for frequency regulation (FR) through electric vehicles (EVs) under variable power system operation states (PSOSs). The methodology ensures a secure and economical operation of the power system through the coordination of the frequency regulation, the power of the electric vehicles and generators with multiple optimization objectives. In the normal state of operation of the power system, the battery degradation cost is taken into account and accordingly the minimum FR cost is utilized as an objective. On the other hand, for abnormal operation, the optimization objective considers the minimum frequency restoration duration. Different scenarios have been investigated to validate the proposed method. The simulation results confirm the usefulness and superior performance of the proposed optimized coordinated control strategy.

This paper presents an industrial approach to assess the performance of large-scale solar plants (LSSPs) has been developed using a novel performance ratio (PR) formula model based on energy production. The PR is an important quality indicator used for the LSSP projects with a nominal installed capacity of 20 MWdc upward. When solar power plants are put into operation on the national grid, the PR can be used as a performance indicator to ensure the energy guarantee and the plant’s efficiency required to guarantee a level of performance. Then, PR gives the real face of the plant’s efficiency. This is not an easy task because it has no constant value over the years due to the effective incident global irradiation (GlobInc) value always not being available. For this purpose, the proposed PR formula model is used to compute the correct performance guarantee of the grid-connected 100MWdc-LSSP in Pakistan’s Quaid-e-Azam Solar Park (QASP). The main purpose of this study is to incorporate a PR formula based on energy production evaluation to achieve the energy guarantee (excess energy, bonus energy, or energy shortfall) in LSSPs. The 1-year on-site plant data is utilized to investigate the plant’s performance energy inputs such as production, irradiance, PR, and the excess energy. Based on the actual annual energy output and irradiance of the plant, the following results are obtained: Actual PR is achieved at a higher level than the target benchmark, so no shortfall or liquefied damage (LD) is imposed due to excess energy. Thus, 100MWdc-LSSP’s power generation is found to be 5% higher than expected by using the theoretical calculations of PR. In FY (2018–2019), the system fed 0.34% higher PR, with an actual PR of 76.58% higher than the target PR of 76.24%, and extra energy of 710.72MWh produced, as well as bonus energy of 142,143.62 USD. The majority of LSSP projects are determined to be successfully operational and generate a significant amount of power, but they fail to meet the contractual requirements for energy guarantee because they use the incorrect PR formula and also fail to provide the desired energy to stakeholders owing to a poor theoretical assessment of the PR criteria based on energy production. As a result, solar power plants confront a great energy shortfall for many years to come.

The integration of electric vehicles (EVs) in the power grid has attracted considerable attention due to its potential benefits, such as demand response and power quality improvement. However, the intermittent and unpredictable nature of EVs’ charging and discharging behavior can cause significant challenges to the grid’s stability and power quality. This research study explores the use of a droop-ANN model to improve power quality in vehicle-to-grid (V2G) systems. The proposed approach integrates an artificial neural network (ANN) into the droop control technique to accurately predict the voltage and frequency of the charger. Through simulations, the model’s effectiveness in reducing power fluctuations and enhancing power quality was validated. The results indicate that the droop-ANN model significantly improves power quality across various battery state of charge (SoC) and charging/discharging scenarios. The findings highlight the potential of the droop-ANN model to enhance stability and reliability in V2G systems. Further research is needed to validate the model in real-world applications and explore its full potential. Overall, the droop-ANN model offers a promising solution for improving power quality in V2G systems.