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.

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 proposes a coordination method for the voltage control devices based on optimal settings of the D-STATCOMs, on the load tap changer (OLTC) transformer and the distributed generations (DGs)-based renewable energy resources (RERs). The central controller is introduced to handle the active smart distribution network (SDN) problems to maintain the voltage profile within its permissible limits, minimize power losses in different operating conditions, and minimize the energy wastage from the distributed renewable energy resources. These problems are formulated as a multi-objective optimization problem. With increasing load demand and RERs in the distribution system, voltage coordination threatens real-time efficiency. In this research work, central controller-based Gorilla Troops optimization (GTO) algorithm is proposed to detect the optimum solutions for the voltage coordination problem. The load demand uncertainty and the stochastic nature of power generated from RERs (PV panels and wind turbines) are considered in the voltage coordination problem due to their significant effects on the operation and planning of the SDN. The proposed SDN has been represented based on the Internet of Things (IoT) communication protocol. It enhances the data and information transfer between the system-connected agents. A practical test system, NDEDC-24 bus radial distribution network from the North Delta Electrical Distribution Company in Egypt and IEEE-33 node system are used to test and evaluate the proposed method. The results are compared with other well-known evolutionary methods. The proposed method obtains more accurate results than the other methods.

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.

In recent times, the significance of renewable energy generation has increased and photovoltaic‐thermoelectric (PV‐TE) technologies have emerged as a promising solution. However, the incorporation of these technologies still faces difficulties in energy storage and optimization. This review paper addresses these challenges by providing a comprehensive overview of the latest advancements in PV‐TE technologies. The paper emphasizes the integration of phase change materials (PCMs) for thermal energy storage, also buttressing the use of encapsulated PCM for thermal storage and efficiency, and the use of hybrid PCM to enhance overall performance. Furthermore, reviews on the use of machine learning techniques for efficient optimization and the integration of thermoelectric modules into tandem perovskite silicon solar cells have been comprehensively analyzed. The advancements in photovoltaic‐thermoelectric systems, as reviewed in this article, signify significant progress in attaining sustainable and effective energy production and storage. This review comprehensively addresses the 4Es, underlining their importance. It not only consolidates recent developments but also charts a path for future research in the field of PV‐TE technologies, offering precise insights to guide upcoming studies and innovations.