An increase in human activities and population growth have significantly increased the world’s energy demands. The major source of energy for the world today is from fossil fuels, which are polluting and degrading the environment due to the emission of greenhouse gases. Hydrogen is an identified efficient energy carrier and can be obtained through renewable and non-renewable sources. An overview of renewable sources of hydrogen production which focuses on water splitting (electrolysis, thermolysis, and photolysis) and biomass (biological and thermochemical) mechanisms is presented in this study. The limitations associated with these mechanisms are discussed. The study also looks at some critical factors that hinders the scaling up of the hydrogen economy globally. Key among these factors are issues relating to the absence of a value chain for clean hydrogen, storage and transportation of hydrogen, high cost of production, lack of international standards, and risks in investment. The study ends with some future research recommendations for researchers to help enhance the technical efficiencies of some production mechanisms, and policy direction to governments to reduce investment risks in the sector to scale the hydrogen economy up.
This paper describes the usefulness of renewable energy throughout the world to generate power. Renewable energy adds a remarkable scope in power system. Renewable energy sources act as the prime mover of a microgrid. The Microgrid is a small network of power system with distributed generation (DG) units connected in parallel. The integration challenges of renewable energy sources and the control of microgrid are described in this paper. The varied nature of DG system produces voltage and frequency deviation. The unknown nature of the load produces un-modeled dynamics. This un-modeled dynamic introduces measurable effects on the performance of the microgrid. This paper investigates the performance of the microgrid against different scenarios. The voltage of the microgrid is controlled by using different controllers and their results are also investigated. The performance of controllers is investigated using MATLAB/Simulink SimPowerSystems.
Multi-energy systems (MESs) contribute to increasing energy utilization efficiency and renewable energy accommodation by coupling multiple energy sectors. The preferable characteristic of MESs raises the need for optimizing the configuration of MESs across multiple energy sectors at the planning stage. Based on the energy hub (EH) model, this research presents a two-stage mixed-integer linear programming approach for district level MES planning considering distributed renewable energy integration. The approach models an MES as a directed acyclic graph with multiple layers. The proposed EH configuration planning procedure includes two stages: 1) optimizing what equipment (e.g., energy converters, distributed renewable energy sources and storages) should be invested in for each layer and 2) optimizing the connection relationships between the invested equipment in each two adjacent layers. The proposed approach is able to optimize both the equipment selection and the MES configuration. It can be applied to both expansion planning and initial planning of MESs from scratch. An illustrative example of planning a typical MES is given. A sensitivity analysis is performed to show the impacts of load profiles, energy prices and equipment parameters on the optimal MES configuration. A case study based on the MES in Beijing's new subsidiary administrative center is conducted using the proposed approach.
Abstract This paper estimates the impact of renewable energy on economic growth in West African countries using panel dynamic ordinary least squares (DOLS) by employing a sample of 15 West African countries covering the 1995-2014 period. The results indicated that renewable energy consumption slows down economic growth in these countries. This is attributed to the nature and source of renewable energy used in West Africa, which is majorly wood biomass. The wood biomasses used in West Africa are usually unclean and highly polluting when burnt. On the other hand, the use of clean energy sources like solar, wind and hydropower which does not have a side effect on human health and the environment is less in West Africa. As such, renewable energy use can slow down economic growth by lowering productivity when unclean and inefficient sources are used. The study recommends that (1) cleaner technologies should be employed to optimize the benefits of wood biomass as a renewable source of energy while minimizing its adverse effects; (2) the share of other renewable energy components such as solar, wind and geothermal should be increased in the renewable energy mix of the sub-region of West Africa and (3) greater commitment to achieving sustainable renewable energy by West African authorities is needed.
Forecasting for wind and solar renewable energy is becoming more important as the amount of energy generated from these sources increases. Forecast skill is improving, but so too is the way forecasts are being used. In this paper, we present a brief overview of the state‐of‐the‐art of forecasting wind and solar energy. We describe approaches in statistical and physical modeling for time scales from minutes to days ahead, for both deterministic and probabilistic forecasting. Our focus changes then to consider the future of forecasting for renewable energy. We discuss recent advances which show potential for great improvement in forecast skill. Beyond the forecast itself, we consider new products which will be required to aid decision making subject to risk constraints. Future forecast products will need to include probabilistic information, but deliver it in a way tailored to the end user and their specific decision making problems. Businesses operating in this sector may see a change in business models as more people compete in this space, with different combinations of skills, data and modeling being required for different products. The transaction of data itself may change with the adoption of blockchain technology, which could allow providers and end users to interact in a trusted, yet decentralized way. Finally, we discuss new industry requirements and challenges for scenarios with high amounts of renewable energy. New forecasting products have the potential to model the impact of renewables on the power system, and aid dispatch tools in guaranteeing system security.
Abstract Wind power, as one of the most promising renewable energy sources, plays a pivotal role in the transition to a low-carbon energy system and the achievement of the "dual carbon" goals. However, wind power time series exhibit significant non-stationarity, strong randomness, and multi-scale temporal characteristics, which pose considerable challenges to the predictive accuracy and robustness of conventional forecasting models. To address these issues, this paper introduces the Uncertainty-Aware Forecasting Transformer (UAFformer) framework for wind power forecasting. UAFformer integrates several key mechanisms, including modal decomposition, multi-scale attention, dynamic weight reconstruction, and distribution modeling, to systematically enhance both the accuracy and stability of wind power predictions. Specifically, the model employs a hybrid decomposition strategy that combines Variational Mode Decomposition (VMD) and Singular Spectrum Analysis (SSA) to preprocess the original wind power sequence. This approach improves signal stationarity and extracts sub-modes with physical interpretations. Subsequently, a multi-scale temporal attention mechanism is introduced to capture dependencies across varying time scales. A dynamic weight learning module is designed to facilitate channel-adaptive reconstruction. In the feature fusion layer, an adaptive feature fusion gating structure is proposed to effectively integrate outputs from both the Transformer and BiGRU. At the output layer, an uncertainty perception module is developed to jointly model the mean and variance of the predicted power, thereby endowing the model with enhanced robustness and risk perception capabilities. Extensive experiments, ablation studies, and interpretability analyses were conducted using multiple real-world wind power datasets. The results demonstrate that UAFformer achieves competitive performance across multiple evaluation metrics, with notable improvements in accuracy and robustness. Specifically, UAFformer attains 8.7–12.3% lower mean absolute error during high-volatility intervals compared to state-of-the-art baselines, indicating enhanced stability under non-stationary conditions and strong potential for practical deployment.
Vijithra Nedunchezhian, Muthukumar Kandasamy, Renugadevi Thangavel
et al.
The optimal allocation of Photovoltaic (PV) and wind-based renewable energy sources and Battery Energy Storage System (BESS) capacity is an important issue for efficient operation of a microgrid network (MGN). The impact of the unpredictability of PV and wind generation needs to be smoothed out by coherent allocation of BESS unit to meet out the load demand. To address these issues, this article proposes an efficient Energy Management System (EMS) and Demand Side Management (DSM) approaches for the optimal allocation of PV- and wind-based renewable energy sources and BESS capacity in the MGN. The DSM model helps to modify the peak load demand based on PV and wind generation, available BESS storage, and the utility grid. Based on the Real-Time Market Energy Price (RTMEP) of utility power, the charging/discharging pattern of the BESS and power exchange with the utility grid are scheduled adaptively. On this basis, a Jellyfish Search Algorithm (JSA)-based bi-level optimization model is developed that considers the optimal capacity allocation and power scheduling of PV and wind sources and BESS capacity to satisfy the load demand. The top-level planning model solves the optimal allocation of PV and wind sources intending to reduce the total power loss of the MGN. The proposed JSA-based optimization achieved 24.04% of power loss reduction (from 202.69 kW to 153.95 kW) at peak load conditions through optimal PV- and wind-based DG placement and sizing. The bottom level model explicitly focuses to achieve the optimal operational configuration of MGN through optimal power scheduling of PV, wind, BESS, and the utility grid with DSM-based load proportions with an aim to minimize the operating cost. Simulation results on the IEEE 33-node MGN demonstrate that the 20% DSM strategy attains the maximum operational cost savings of €ct 3196.18 (reduction of 2.80%) over 24 h operation, with a 46.75% peak-hour grid dependency reduction. The statistical analysis over 50 independent runs confirms the sturdiness of the JSA over Particle Swarm Optimization (PSO) and Osprey Optimization Algorithm (OOA) with a standard deviation of only 0.00017 in the fitness function, demonstrating its superior convergence characteristics to solve the proposed optimization problem. Finally, based on the simulation outcome of the considered bi-level optimization problem, it can be concluded that implementation of the proposed JSA-based optimization approach efficiently optimizes the PV- and wind-based resource allocation along with BESS capacity and helps to operate the MGN efficiently with reduced power loss and operating costs.
Simon Halvdansson, Lucas Ferreira Bernardino, Brage Rugstad Knudsen
Decarbonization of isolated or off-grid energy systems through phase-in of large shares of intermittent solar or wind generation requires co-installation of energy storage or continued use of existing fossil dispatchable power sources to balance supply and demand. The effective CO2 emission reduction depends on the relative capacity of the energy storage and renewable sources, the stochasticity of the renewable generation, and the optimal control or dispatch of the isolated energy system. While the operations of the energy storage and dispatchable sources may impact the optimal sizing of the system, it is challenging to account for the effect of finite horizon, optimal control at the stage of system sizing. Here, we present a flexible and computationally efficient sizing framework for energy storage and renewable capacity in isolated energy systems, accounting for uncertainty in the renewable generation and the optimal feedback control. To this end, we implement an imitation learning approach to stochastic neural model predictive control (MPC) which allows us to relate the battery storage and wind peak capacities to the emissions reduction and investment costs while accounting for finite horizon, optimal control. Through this approach, decision makers can evaluate the effective emission reduction and costs of different storage and wind capacities at any price point while accounting for uncertainty in the renewable generation with limited foresight. We evaluate the proposed sizing framework on a case study of an offshore energy system with a gas turbine, a wind farm and a battery energy storage system (BESS). In this case, we find a nonlinear, nontrivial relationship between the investment costs and reduction in gas usage relative to the wind and BESS capacities, emphasizing the complexity and importance of accounting for optimal control in the design of isolated energy systems.
Southeast Asian countries stand at a crossroads concerning their shared energy future and heavily rely on fossil fuels for transport and electricity. Within Asia, especially India and China lead the world renewable energy generation undergoing a period of energy transition and economic transformation. Southeast Asian countries have huge potentials for sustainable energy sources. However they are yet to perform globally in renewable energy deployment due to various challenges. The primary objective of the study is to examine the renewable energy growth and analyse the government policies to scale up the deployment of renewables for power generation substantially. The study also offers policy recommendations to accelerate renewable energy exploitation sustainably across the region. To achieve the ambitious target of 23% renewables in the primary energy mix by 2025, ASEAN Governments should take proactive measures like removal of subsidies of fossil fuels, regional market integration and rapid implementation of the existing project. Eventually, each of this strategy will necessitate sustained leadership, political determination, and concrete actions from stakeholders, in particular, increased cooperation across the region.
Water and energy are two key factors in human life that always control the growth and development of human societies. Climate changes, increasing the population in urban areas and industrialization, have increased the demands for freshwater around the world. Estimates show that a small percentage of all freshwater produced in the world is from renewable sources. By developing the technology, lowering equipment prices and increasing attention to the environmental problems of fossil fuels, utilizing renewable energy is growing. By providing a wide variety of conventional desalination methods driven by various types of renewable energy technologies in the world, water and energy legislators should choose different methods to meet the needs based on the local potentials by paying attention to the desalination processes and power systems. In some cases, concentrated solar power for thermal desalination or electricity generated by the photovoltaic plants for membrane desalination systems can be used in arid areas. Definitely, the most problem of using renewable sources is their unsteady natures, which using storage systems or combining with other renewable sources can solve this problem. This chapter provides extensive information about renewables, desalination and performance analysis of power systems. Reverse osmosis technique is a practical process in desalination which 69% of desalination plants use this system. Solar energy is an important source of energy for hybrid systems. The geothermal has a steady performance at a specified depth. Ultimately, obtained results from energy and exergy analysis would have provided a better insight.
Abstract Renewable energy sources (RESs) and energy storage systems (ESSs) are the key technologies for smart grid applications and provide great opportunities to de-carbonize urban areas, regulate frequency, voltage deviations, and respond to severe time when the load exceeds the generation. Nevertheless, uncertainty and inherent intermittence of renewable power generation units impose severe stresses on power systems. Energy storage systems such as battery energy storage system enables the power grid to improve acceptability of intermittent renewable energy generation. To do so, a successful coordination between renewable power generation units, ESSs and the grid is required. Nonetheless, with the existing grid architecture, achieving the aforementioned targets is intangible. In this regard, coupling renewable energy systems with different generation characteristics and equipping the power systems with the battery storage systems require a smooth transition from the conventional power system to the smart grid. Indeed, this coordination requires not only robust but also innovative controls and models to promote the implementation of the next-generation grid architecture. In this context, the present research proposes a smart grid architecture depicting a smart grid consisting of the main grid and multiple embedded micro-grids. Moreover, a focus has been given to micro-grid systems by proposing a “Micro-grid Key Elements Model” (MKEM). The proposed model and architecture are tested and validated by virtualization. The implementation of the virtualized system integrates solar power generation units, battery energy storage systems with the proposed grid architecture. The virtualization of the proposed grid architecture addresses issues related to Photovoltaic (PV) penetration, back-feeding, and irregularity of supply. The simulation results show the effect of Renewable Energy (RE) integration into the grid and highlight the role of batteries that maintain the stability of the system.
Meeting the needs for both renewable energy production and increased food supply to sustain growing communities remains a global challenge. Agrivoltaic greenhouses can meet these dual needs in one plot of land, mitigating land competition. Luminescent solar concentrators (LSCs) benefit these systems by providing additional design flexibility for crop-specific spectrum modification while allowing sufficient light transmission for crop growth. Silicon quantum dots (Si QDs) have received growing interest as a material candidate for LSC greenhouses as well. We present an investigation into the impact of Si QD film concentration on the energy demands of an LSC greenhouse in Phoenix, Arizona through a comprehensive modelling framework. We then expand upon one Si QD concentration and simulate LSC greenhouses in 48 locations across the United States. We demonstrate LSC greenhouses can supply their annual energy demands in warm climates, where greenhouse heating demands remain low. LSC greenhouses can also be as profitable as the conventional glass greenhouse if the crop yield remains comparable or if the greenhouse can benefit from net metering.
Abstract In recent years, the transition from internal combustion engines to electric vehicles (EVs) has gained momentum due to their potential to reduce greenhouse gas emissions significantly. This shift presents various opportunities but also introduces challenges in energy management within electric and transportation systems. Proper Electric Vehicle management can alleviate grid stress and minimize user wait times. Moreover, leveraging renewable energy sources for Electric Vehicle charging can reduce the grid’s load. Vehicle-to-grid (V2G) technology emerges as a promising solution to handle sudden surges in grid demand. Strategic planning is essential for the future of electric mobility systems. This paper delves into the effects of Electric Vehicle energy consumption on the grid and reviews the latest advancements in Electric Vehicle charging control and optimization aimed at cost optimization. It also explores strategies for large-scale Electric Vehicle charging to improve energy management. This paper identifies key research gaps in the Electric Vehicle field through a comprehensive literature review, offering insights and recent solutions to crucial issues.
Electrical energy is the most dependable form of energy. The advancement of technology demands substantial energy use. Conventional energy sources are producing pollution, and fossil fuels are diminishing daily, so paving the way for renewable energy sources. Wind energy is the most reliant renewable energy source. The maintenance of wind turbines is intricate, continuous monitoring is challenging due to their elevated positions, and they are situated in rural locations. A dependable condition monitoring system is crucial for turbines working on wind. to reduce downtime and enhance output. The objective of this project is to monitor the parameters of turbine working on wind and enhance early defect identification. Sensors are employed to assess the state of the wind turbine. The utilized sensors are a temperature sensor, a vibration sensor, and a voltage sensor. Should any sensor provide an anomalous value, the data is transmitted to the IoT cloud within a matter of seconds. This project utilizes an Arduino UNO and a Wi-Fi module. The Arduino UNO gathers sensor data from several wind turbine sensors, and the Wi-Fi module transmits this information to an IoT cloud application, such as Telegram, already loaded on our mobile devices. The operation of the kit and the performance evaluation have been conducted on the suggested system.
Ma. Del Carmen Toledo-Pérez, Rodolfo Amalio Vargas-Méndez, Abraham Claudio-Sánchez
et al.
In this article, a comprehensive review of electrical microgrids is presented, emphasizing their increasing importance in the context of renewable energy integration. Microgrids, capable of operating in both grid-connected and standalone modes, offer significant potential for providing energy solutions to rural and remote communities. However, the inclusion of diverse energy sources, energy storage systems (ESSs), and varying load demands introduces challenges in control and optimization. This review focuses on hybrid microgrids, analyzing their operational scenarios and exploring various optimization strategies and control approaches for efficient energy management. By synthesizing recent advancements and highlighting key trends, this article provides a detailed understanding of the current state and future directions in hybrid microgrid systems.
Renewable energy sources and energy independence are becoming increasingly important worldwide, and reducing emissions and optimizing energy use are high on the EU’s agenda. In this context, electric and hybrid vehicles could not only be a means of transport but also an active part of the grid. This paper analyzes one year of empirical data of a hybrid vehicle using a linear programing method that allows the optimization of energy return under different settings. The aim of the study is to determine the contribution that vehicles can make to the stability of the grid and the functioning of energy communities. It also compares the distribution of energy sources used in the EU and presents the current range of V2G-capable vehicle models. The results show that hybrid vehicles can also be effective energy storage devices, especially at fleet level. V2G technology could influence the development of battery production and contribute to the expansion of secondary markets by enabling the recycling of degraded batteries for buildings or renewable energy systems. The article also summarizes the development opportunities and challenges for V2G technology, in particular its role in energy grids and sustainable transport.
Jung-Pin Lai, Yu-Ming Chang, Chieh-Huang Chen
et al.
The use of renewable energy to reduce the effects of climate change and global warming has become an increasing trend. In order to improve the prediction ability of renewable energy, various prediction techniques have been developed. The aims of this review are illustrated as follows. First, this survey attempts to provide a review and analysis of machine-learning models in renewable-energy predictions. Secondly, this study depicts procedures, including data pre-processing techniques, parameter selection algorithms, and prediction performance measurements, used in machine-learning models for renewable-energy predictions. Thirdly, the analysis of sources of renewable energy, values of the mean absolute percentage error, and values of the coefficient of determination were conducted. Finally, some possible potential opportunities for future work were provided at end of this survey.