With the globally increasing electricity demand, its related uncertainties are on the rise as well. Therefore, a deeper insight of load forecasting techniques for projecting future electricity demands becomes imperative for business entities and policy makers. The electricity demand is governed by a set of different variables or “electricity demand determinants”. These demand determinants depend on forecasting horizons (long term, medium term, and short term), the load aggregation level, climate, and socio-economic activities. In this paper, a review of different electricity demand forecasting methodologies is provided in the context of a group of low and middle income countries. The article presents a comprehensive literature review by tabulating the different demand determinants used in different countries and forecasting the trends and techniques used in these countries. A comparative review of these forecasting methodologies over different time horizons reveals that the time series modeling approach has been extensively used while forecasting for long and medium terms. For short term forecasts, artificial intelligence-based techniques remain prevalent in the literature. Furthermore, a comparative analysis of the demand determinants in these countries indicates a frequent use of determinants like the population, GDP, weather, and load data over different time horizons. Following the analysis, potential research gaps are identified, and recommendations are provided, accordingly.

NEOM City in Saudi Arabia is planned to be the first environmentally friendly city in the world that is powered by renewable energy sources minimizing CO2 emissions to reduce the effect of global warming according to Saudi Arabia’s Vision 2030. In recent years, Saudi Arabia has had a problem with water scarcity. The main factors affecting water security are unequal water distribution, wrong use of water resources and using bad or less efficient irrigation techniques. This paper is aimed to provide a detailed feasibility and techno-economic evaluation of using several scenarios of a stand-alone hybrid renewable energy system to satisfy the electrical energy needs for an environmentally friendly seawater desalination plant which feeds 150 m−3 day−1 of freshwater to 1000 people in NEOM City, Saudi Arabia. The first scenario is based on hybrid solar photovoltaic PV, fuel cells (FC) with a hydrogen storage system and batteries system (BS), while the second and third scenarios are based on hybrid PV/BS and PV/FC with a hydrogen storage system, respectively. HOMER® software was used to obtain the optimal configuration based on techno-economic analysis of each component of the hybrid renewable energy systems and an economic and environmental point of view based on the values of net present cost (NPC) and cost of energy (COE). Based on the obtained results, the best configuration is PV/FC/BS. The optimal size and related costs for the optimal size are 235 kW PV array, 30 kW FC, 144 batteries, 30 kW converter, 130 kW electrolyzer, and 25 kg hydrogen tank is considered the best option for powering a 150 m3 reverse osmosis (RO) desalination plant. The values of net present cost (NPC) and the cost of energy (COE) are $438,657 and $0.117/kWh, respectively. From the authors’ point view, the proposed system is one among the foremost environmentally friendly systems to provide electric energy to the seawater desalination plant, especially when connecting to the utility grid, because it is ready to reduce a large amount of greenhouse gas emissions due to using oil/nature gas in utility generation stations to reduce the effect of global warming.

There is a burden of adequate energy supply for meeting demand and reducing emission to avoid the average global temperature of above 2 °C of the pre-industrial era. Therefore, this study presents the exergoeconomic and environmental analysis of a proposed integrated multi-generation plant (IMP), with supplemental biomass-based syngas firing. An in-service gas turbine plant, fired by natural gas, was retrofitted with a gas turbine (GT), steam turbine (ST), organic Rankine cycle (ORC) for cooling and power production, a modified Kalina cycle (KC) for power production and cooling, and a vapour absorption system (VAB) for cooling. The overall network, energy efficiency, and exergy efficiency of the IMP were estimated at 183 MW, 61.50% and 44.22%, respectively. The specific emissions were estimated at 122.2, 0.222, and 3.0 × 10−7 kg/MWh for CO2, NOx, and CO, respectively. Similarly, the harmful fuel emission factor, and newly introduced sustainability indicators—exergo-thermal index (ETI) and exergetic utility exponent (EUE)—were obtained as 0.00067, 0.675, and 0.734, respectively. The LCC of $1.58 million was obtained, with a payback of 4 years, while the unit cost of energy was estimated at 0.0166 $/kWh. The exergoeconomic factor and the relative cost difference of the IMP were obtained as 50.37% and 162.38%, respectively. The optimum operating parameters obtained by a genetic algorithm gave the plant’s total cost rate of 125.83 $/hr and exergy efficiency of 39.50%. The proposed system had the potential to drive the current energy transition crisis caused by the COVID-19 pandemic shock in the energy sector.

This paper discusses a novel technique and implementation to perform nonlinear control for two different forced model state oscillators and actuators. The paper starts by discussing the Duffing oscillator which features a second order non-linear differential equation describing complex motion whereas the second model is the Van der Pol oscillator with non-linear damping. A first order actuator is added to both models to expand on the chaotic behavior of the oscillators. In order to control the system without comprising linearization, Lyapunov non-linear control was used. A control Lyapunov function was tailored to the system. This led to improved maneuverability of the controller and the performance of the overall system. The controller was found to be highly efficient in system tracking and had swift response time. Simulations were performed on both the uncontrolled and controlled cases. Both simulation results ultimately confirmed the effectiveness of the proposed controller.

The aim of this research is to perform an in-depth performance comparison of ground-mounted and rooftop photovoltaic (PV) systems. The PV modules are tilted to receive maximum solar irradiance. The efficiency of the PV system decreases due to the mutual shading impact of parallel tilted PV modules. The mutual shading decreases with the increasing interrow distance of parallel PV modules, but a distance that is too large causes an increase in land cost in the case of ground-mounted configuration and a decrease in roof surface shading in the case of rooftop configuration, because larger sections of roof are exposed to sun radiation. Therefore, an optimized interrow distance for the two PV configurations is determined with the aim being to minimize the levelized cost of energy (LCoE) and maximize the energy yield. The model of the building is simulated in EnergyPlus software to determine the cooling load requirement and roof surface temperatures under different shading scenarios. The layout of the rooftop PV system is designed in Helioscope software. A detailed comparison of the two systems is carried out based on energy output, performance ratio, capacity utilization factor (CUF), energy yield, and LCoE. Compared to ground-mounted configuration, the rooftop PV configuration results in a 2.9% increase in CUF, and up to a 23.7% decrease in LCoE. The results of this research show that installing a PV system on a roof has many distinct advantages over ground-mounted PV systems such as the shading of the roof, which leads to the curtailment of the cooling energy requirements of the buildings in hot regions and land cost savings, especially for urban environments.

This paper presents eight hybrid renewable energy (RE) systems that are derived from solar, wind and biomass, with energy storage, to meet the energy demands of an average household in the six geopolitical zones of Nigeria. The resource assessments show that the solar insolation, wind speed (at 30 m hub height) and biomass in the country range, respectively, from 4.38–6.00 kWh/m2/day, 3.74 to 11.04 m/s and 5.709–15.80 kg/household/day. The HOMER software was used to obtain optimal configurations of the eight hybrid energy systems along the six geopolitical zones’ RE resources. The eight optimal systems were further subjected to a multi-criteria decision making (MCDM) analysis, which considers technical, economic, environmental and socio-cultural criteria. The TOPSIS-AHP composite procedure was adopted for the MCDM analysis in order to have more realistic criteria weighting factors. In all the eight techno-economic optimal system configurations considered, the biomass generator-solar PV-battery energy system (GPBES) was the best system for all the geopolitical zones. The best system has the potential of capturing carbon from the atmosphere, an attribute that is desirous for climate change mitigation. The cost of energy (COE) was seen to be within the range of 0.151–0.156 US$/kWh, which is competitive with the existing electricity cost from the national grid, average 0.131 US$/kWh. It is shown that the Federal Government of Nigeria favorable energy policy towards the adoption of biomass-to-electricity systems would make the proposed system very affordable to the rural households.

Monitoring and control systems in the energy sector are specialized information structures that are not governed by the same information technology standards as the rest of the world’s information systems. Such industrial control systems are also used to handle important infrastructures, including smart grids, oil and gas facilities, nuclear power plants, water management systems, and so on. Industry equipment is handled by systems connected to the internet, either via wireless or cable connectivity, in the present digital age. Further, the system must work without fail, with the system’s availability rate being of paramount importance. Furthermore, to certify that the system is not subject to a cyber-attack, the entire system must be safeguarded against cyber security vulnerabilities, threats, and hazards. In addition, the article looks at and evaluates cyber security evaluations for industrial control systems, as well as their possible impact on the accessibility of industrial control system operations in the energy sector. This research work discovers that the hesitant fuzzy-based method of the Analytic Hierarchy Process (AHP) and the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) is an operational procedure for estimating industrial control system cyber security assessments by understanding the numerous characteristics and their impacts on cyber security industrial control systems. The author evaluated the outputs of six distinct projects to determine the quality of the outcomes and their sensitivity. According to the results of the robustness analysis, alternative 1 shows the utmost effective cybersecurity project for the industrial control system. This research work will be a conclusive reference for highly secure and managed monitoring and control systems.

There are three main parts of an electric power system—power generation, transmission, and distribution. For electric companies, it is a tough challenge to reduce losses of the power system and deliver lossless and reliable power from the generating station to the consumer end. Nowadays, modern power systems are more complex due to gradually increasing loads. In the electrical power system, especially in transmission and distribution networks, there are power losses due to many reasons such as overloading of the line, long distribution lines, low power factors, corona losses, and unsuitable conductor size. The main performance factor of the power system is reliability. Reliability means continuity of the power supply without any interruptions from the generating station to the demand side. Thus, due to these power losses, there are voltage stability problems and economic losses in the electrical system. The voltage stability of the power system can be increased by improving the voltage profile. In this paper, different techniques are analyzed that include the integration of wind power, the integration of photovoltaic power, and reactive power injection by integrating FACTS devices. These techniques are applied to the IEEE 57 bus system with standard data using simulation models developed in MATLAB. Thus, the results of the analysis of these techniques have been compared with each other.