The integration of renewable energy sources (RESs) is a strategic goal in Saudi Arabia. The energy source diversification plan comprises the penetration of various technologies, including solar photovoltaic (PV) and wind energy. In this research, an optimal microgrid system design is proposed and analyzed at the Islamic University of Madinah. The research intends to facilitate the decision-making process in the incorporation of RESs in Saudi universities. A pilot project has been established at the Faculty of Engineering and the measured load profile has been incorporated. Three alternatives are investigated, and their technical and economic performance is determined (i.e., PV system, wind system, and hybrid system). To enhance the accuracy of the simulated models, on-the-ground weather data have been utilized to formulate a typical meteorological year profile. The results demonstrate that a PV system of 1.5 MW installed capacity can cover up to 3.03% of the university’s annual electrical consumption, with a levelized cost of energy (LCOE) of 0.051 USD/kWh. The PV alternative can generate annual energy of 2.68 GWh with a capacity factor of 20.2% and a simple payback period of 18.6 years. The wind energy system has a capacity factor of 1.1 MW and yields a higher ratio of energy production to installed capacity, owing to a higher capacity factor at 29.5%, and annual energy of 2.71 GWh. However, due to the higher initial cost and insufficiency of wind resources at the proposed location, this wind energy alternative results in higher LCOE at 0.064 USD/kWh and a simple payback period of 23.6 years. The hybrid alternative facilitates the integration of diverse RESs. It has a capacity factor of 1.37 MW, leading to an annual generation of 3.27 GWh and a renewable fraction of 3.7%. The LCOE of the hybrid option is determined to be 0.061 USD/kWh and the simple payback period at 20.7 years. All alternatives help in the reduction of carbon dioxide (CO₂), sulfur dioxide (SO₂), and nitric oxide (NO_x) between 0.11 million kg and 54.6 million kg annually. Each of the systems can provide opportunities at the technical, economic, and environmental levels. The implications of this research facilitate Saudi universities in supporting the integration of RESs, considering the strategic goals of Saudi Arabia.

The global electricity demand from electric vehicles (EVs) increased by 3631% over the last decade, from 2600 gigawatt hours (GWh) in 2013 to 97,000 GWh in 2023. The global electricity demand from EVs will rise to 710,000 GWh by 2030. These EVs will depend on smart grids (SGs) for their charging requirements. Like EVs, SGs are a booming market. In 2021, SG technologies were valued at USD 43.1 billion and are projected to reach USD 103.4 billion by 2026. As EVs become more prevalent, they introduce additional complexity to the SG landscape, with EVs not only consuming energy, but also potentially supplying it back to the grid through vehicle-to-grid (V2G) technologies. The entry of numerous independent sellers and buyers, including EV owners, into the market will lead to intense competition, resulting in rapid fluctuations in electricity prices and constant energy transactions to maximize profit for both buyers and sellers. Blockchain technology will play a crucial role in securing data publishing and transactions in this evolving scenario, ensuring transparent and efficient interactions between EVs and the grid. This survey paper explores key research challenges from an engineering design perspective of SG operation, such as the potential for voltage instability due to the integration of numerous EVs and distributed microgrids with fluctuating generation capacities and load demands. This paper also delves into the need for a synergistic balance to optimize the energy supply and demand equation. Additionally, it discusses policies and incentives that may be enforced by national electricity carriers to maintain grid reliability and manage the influx of EVs. Furthermore, this paper addresses emerging issues of SG technology providing primary charging infrastructure for EVs, such as incentivizing green energy, the technical difficulties in integrating diverse hetero-microgrids based on HVAC and HVDC technologies, challenges related to the speed of energy transaction processing during fluctuating prices, and vulnerabilities concerning cyber-attacks on blockchain-based SG architectures. Finally, future trends are discussed, including the impact of increased EV penetration on SGs, advancements in V2G technologies, load-shaping techniques, dynamic pricing mechanisms, and AI-based stability enhancement measures in the context of widespread SG adoption.

This paper presents an energy storage system (ESS) sizing model and reliability assessment framework to quantify reliability improvements due to ESS of electric energy systems with high penetration of renewables. The model formulation takes into account: (a) variable generation (VG) forced outage rates (FORs), (b) reserve and demand requirements, and (c) conventional generation (CG) operational constraints and practices. Correlation coefficients among wind speed, solar radiation, and demand are computed using the least square estimation method (LSE). Depending on the correlation, probabilistic models for the composite demand are computed and integrated into the ESS sizing model. Subsequently, the ESS optimal sizes are computed along with the ESS optimal charging and discharging schedules. Then, the reliability is quantified using the probabilistic production costing (PPC) method. A case study is presented in which one VG was approximated as independent variable because of its low correlation with demand and other VG. The case study compares the error when treating the VG as independent.

This paper presents a comprehensive model for optimal energy storage system (ESS) design for an isolated microgrid. The model presented is a mixed integer linear program (MILP) that considers seasonal varying generation (VG) demand, more specifically seasonal solar cell generator (SCG) demand, SCG maintenance (failure and restoration) rates, and practical operation of conventional generation (CG) while satisfying the required demand and reserve. The model is based on unit commitment (UC) to simulate real operations and physical constraints of CG units, the power balance, and reserve requirements. The objective function aims at minimizing the associated cost of CG, namely, production (fuel), costs of startup and shutdown procedures, and the investment cost of power and energy. The proposed model is assessed on a case study system consisting of multiple SCGs in addition to CG to meet a specific demand. The proposed model showed that the ESS sizing when considering Li-Ion technology and a SCG penetration of 25% was on average approximately 3 MWh and 1.70 MW. Meeting the demand and reserve requirements were the two major constraints when determining the optimal ESS sizing. Moreover, introducing the ESS substantially reduced the operating cost of the system.

This paper presents a mixed integer linear program (MILP) to optimally size power and energy of energy storage systems (ESSs). The sizing model takes into account conventional generation (CG) operation constraints in addition to seasonal and locational wind speed and solar radiation variations, and variable generation (wind turbine systems (WTSs) and solar cell generators (SCGs)) forced outages. Subsequently, the outcomes of the ESS sizing model are inputted to the probabilistic production method (PCC) to assess the reliability of the integrated system. All aforementioned analyses have been applied to a system with different penetration levels. The method is demonstrated with case studies on a system consisting of 10 CG units and VG penetration levels of 20% and 30%. For each penetration level, ESS sizing is computed and then reliability assessment is performed.