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The use of machine learning and data-driven methods for predictive analysis of power systems offers the potential to accurately predict and manage the behavior of these systems by utilizing large volumes of data generated from various sources. These methods have gained significant attention in recent years due to their ability to handle large amounts of data and to make accurate predictions. The importance of these methods gained particular momentum with the recent transformation that the traditional power system underwent as they are morphing into the smart power grids of the future. The transition towards the smart grids that embed the high-renewables electricity systems is challenging, as the generation of electricity from renewable sources is intermittent and fluctuates with weather conditions. This transition is facilitated by the Internet of Energy (IoE) that refers to the integration of advanced digital technologies such as the Internet of Things (IoT), blockchain, and artificial intelligence (AI) into the electricity systems. It has been further enhanced by the digitalization caused by the COVID-19 pandemic that also affected the energy and power sector. Our review paper explores the prospects and challenges of using machine learning and data-driven methods in power systems and provides an overview of the ways in which the predictive analysis for constructing these systems can be applied in order to make them more efficient. The paper begins with the description of the power system and the role of the predictive analysis in power system operations. Next, the paper discusses the use of machine learning and data-driven methods for predictive analysis in power systems, including their benefits and limitations. In addition, the paper reviews the existing literature on this topic and highlights the various methods that have been used for predictive analysis of power systems. Furthermore, it identifies the challenges and opportunities associated with using these methods in power systems. The challenges of using these methods, such as data quality and availability, are also discussed. Finally, the review concludes with a discussion of recommendations for further research on the application of machine learning and data-driven methods for the predictive analysis in the future smart grid-driven power systems powered by the IoE. [ABSTRACT FROM AUTHOR]

The transformation of the energy sector, based on the development of low-carbon technologies, is essential to achieve climate neutrality. The Life Cycle Assessment (LCA) is a powerful methodology for assessing the environmental impact of energy technologies, which proves to be a useful tool for policy makers. The paper is a review of the main LCA studies of power generation systems performed over the past ten years aiming at comparing the energy technologies to identify those with the lowest impact on the environment, evaluated in terms of gCO2eq/kWh emissions. Screening criteria were established to include only studies of the highest qualitative significance. The authors decided to assign greater weight to emission values reported in more recent studies. For nuclear and renewable energy technologies, most of the emissions are related to the pre-operational phases. Notably, both nuclear and wind technologies, along with other renewable sources throughout their entire life cycle, exhibit significantly lower and less variable emissions compared with conventional gas- and coal-fired technologies. [ABSTRACT FROM AUTHOR]

Solar energy for water pumping is a possible alternative to conventional electricity and diesel-based pumping systems, particularly given the current electricity shortage and the high cost of diesel. The literature survey includes a comparison between previous studies of pumping systems using photovoltaic cells, and the extent of the influence of external factors such as radiation intensity and temperature on the efficiency of the system. Additionally, the use of water storage to generate electrical energy through potential energy by means of hydraulic generators and the effect of the amount of flow and height on the amount of energy generated, as well as the types of hydraulic generators are discussed in this paper. Nowadays, solar power is a major contributor to the world's electrical energy supply, either by generating electrical energy directly from solar cells or through water storage, which will be covered in this review. When compared to electricity or diesel-powered systems, solar water pumping is more cost-effective for irrigation and water supply in rural, urban, and remote areas. This paper also highlights the challenges that must be overcome to develop high-quality, long-lasting solar power technology for future use. [ABSTRACT FROM AUTHOR]

Summary: In light of the increasing share of renewable energy sources (RES) in electricity generation, the research community is directing their attention toward developing highly efficient DC‐DC high‐gain step‐up converters. These converters are designed to exhibit reduced voltage stress on the switching devices (semiconductors). This paper presents a comprehensive study of a switch voltage stress‐reduced step‐up converter that offers a range of additional features, including the availability of a common ground point, continuous input current, simple two‐mode continuous conduction mode (CCM) operation, wide duty cycle range, and quadratic boosting factor. To achieve the aforementioned features, the converter utilizes two switches with the same operating modes, eliminating the need for complex switching circuitry. Operational modes, steady‐state analysis, and design considerations have been presented in detail. Comparative analysis indicates that the voltage conversion ratio per number of devices in the proposed topology is significantly higher than that of similar topologies. In addition, the voltage stress of switches is considerably less than the topologies addressed in the literature. The study further includes detailed mathematical and experimental analyses, small signal analysis, and hardware implementation to support the theoretical findings. The converter's efficiency has been thoroughly analyzed along with thermal analysis to ensure its reliability. [ABSTRACT FROM AUTHOR]

To overcome its reliance on burning fossil fuels for energy generation and water desalination, Kuwait has pioneered research and cutting-edge projects in renewable energy since the 1980s. This paper examines the power sector in Kuwait and emphasizes the government’s keenness to diversify the country’s electric power supply. It provides a comprehensive overview of Kuwait’s efforts, to emerge as a leading example in the region, fostering collaboration and matchmaking within renewable energy industries. The country’s goal is to meet 15% of the demand for power from renewable energy by 2030, including a reduction in energy use in the building sector by 12% and a decrease in CO2 emissions by 33%. The country’s initiatives revolve around harnessing renewable sources, particularly emphasizing the exploration of solar photovoltaic (PV) systems. Challenges and opportunities are presented to provide information for international institutions, technology companies, designers, researchers, and decision makers, aiming to increase the effectiveness and efficiency of PV project implementation in the country and the region. [ABSTRACT FROM AUTHOR]

A dependable and cost-effective energy distribution scheme is the need in the current scenario of distributed generation of electricity. In the recent past, different economic dispatch techniques are proposed to address this challenge of cost-effectiveness. The fossil fuels issue of pollutant emission and the limited stock has pushed the research community to focus on the direction of renewable energy generation. The aim of this paper is to present a multi-objective cost-emission-based dynamic economic dispatch (MOCEDED) algorithm, which includes thermal, solar, and wind-based power plants. Moreover, the uncertainty of solar and wind power in terms of curtailment is also included in the formulation of the algorithm. The formulated objective function is quadratic constrained programming, which is implemented in General Algebraic Modeling System software. Two paradigms, i.e. only cost-based DED and MOCEDED algorithm, have been analyzed. The outcome of the proposed MOCEDED algorithm shows more promising results than cost-based DED. [ABSTRACT FROM AUTHOR]

Renewable energy sources are a promising hope for avoiding many environmental and economic problems such as the problems of climate change and environmental pollution resulting from fossil fuels combustion, in addition to the problems of oil derivatives high prices and their absence in many countries, which in turn affected the rise in electricity prices. Yemen is considered one of the countries most affected by electricity prices rise due to lack of oil derivatives as a result of the ongoing wars in Yemen. This paper presents a technical and economic study of renewable energy sources for producing and storing electricity. It gives a clear scientific and economic vision for implementation of these projects in one of the Yemeni islands, Socotra. This study has proven the high efficiency of energy sources in this region, which encourages their use to produce electricity to cover the region needs at low prices compared to the current prices of electricity in Yemen., where the cost of electricity from renewable energy sources ranges between 0.073 to 0.25 $ / kWh. The study also provides an assessment of the expected decline in electricity prices until 2030. It should be noted that this study can be applied to many coastal cities and other islands in Yemen at a cost close to this cost. [ABSTRACT FROM AUTHOR]

Solar power occupies a significant position among global renewable energy sources due to its abundant energy potential. Consequently, its contribution to electricity generation is steadily increasing. However, obtaining peak efficiency from fixed solar photovoltaic (PV) panels is a formidable task due to their limited ability to consistently tap into solar energy. To tackle this issue and mitigate energy efficiency losses, the utilization of solar tracking systems has emerged as an exceptionally effective solution. These systems enable continuous adjustment of the panels' position to align with the sun's trajectory, optimizing energy absorption and enhancing overall performance. This paper presents the performance and cost analysis of three distinct solar panel tracking systems, namely, a fixed system, a single-axis system, and a dual-axis system. The systems are operated under identical coordinates and conditions. The production data are collected over a period of 15 days for comparative analysis. The tracking movements of the systems are controlled using Arduino. The mechanical components are specifically designed for the establishment of each system. The findings of this study indicate that both single-axis and dual-axis solar tracking systems outperformed fixed systems in terms of power generation. The single-axis system demonstrated a 24.367% increase in power production, while the dual-axis system showed a 32.247% increase compared to the fixed system. Moreover, a cost analysis was carried out considering the installation expenses and power production data of the three systems. It was determined that the single-axis tracking system achieved payback in 0.39 years less compared to the fixed system, while the dual-axis system achieved payback in 1.48 years less compared to the fixed system. Overall, this study underscores the advantages of implementing solar tracking systems, particularly in the single-axis and dual-axis configurations, as they contribute to higher power generation and cost-effectiveness compared to fixed systems. [ABSTRACT FROM AUTHOR]

Decarbonization of the power sector in China is an essential aspect of the energy transition process to achieve carbon neutrality. The power sector accounts for approximately 40% of China's total CO2 emissions. Accordingly, collaborative optimization in power generation expansion planning (GEP) simultaneously considering economic, environmental, and technological concerns as carbon emissions is necessary. This paper proposes a collaborative mixedinteger linear programming optimization approach for GEP. This minimizes the power system's operating cost to resolve emission concerns considering energy development strategies, flexible generation, and resource limitations constraints. This research further analyzes the advantages and disadvantages of current GEP techniques. Results show that the main determinants of new investment decisions are carbon emissions, reserve margins, resource availability, fuel consumption, and fuel price. The proposed optimization method is simulated and validated based on China's power system data. Finally, this study provides policy recommendations on the flexible management of traditional power sources, the market-oriented mechanism of new energy sources, and the integration of new technology to support the attainment of carbon-neutral targets in the current energy transition process. [ABSTRACT FROM AUTHOR]

The feasibility and potential assessment (PA) of solar PV energy is one of the key factors in identifying the most promising areas for the installation of solar PV stations. It determines the useful energy generated in the given area. This paper assesses the solar energy distribution and PA in the North Shewa administration zone. Based on the data collected and analysis made, it is found that more than 80% of the North Shewa areas are suitable for the solar energy generation for off-grid and on-grid systems. Hence, the solar potential of the North Shewa zone completely fulfills the standards of sunshine, solar radiation, and temperature. That is, most of the areas have solar radiation of 5.2 kWh/m2, and daily sunshine is greater than 7 h. The maximum energy production is found in December in Shewa Robit, Mehal Meda, Eneware, Debre Berhan, Alem Ketema, and Sela Dengay with 175.35 kWh, 188.18 kWh, 180.78 kWh, 189.54 kWh, 175.78 kWh, and 189.63 kWh, respectively. This is a good opportunity for investors to invest in solar PV electricity generation. It will solve the issue of electricity supply to the community, which currently relies on wood and fossil fuels. It also highlights the positive opportunities for the future implementation of solar energy. [ABSTRACT FROM AUTHOR]

Real-time hybrid simulation (RTHS) divides a structural system into an analytical and experimental substructure. The former is based on a well-established analytical model while the latter consists of a physical model in the laboratory, for which there is not a well-established analytical model. This paper extends real-time hybrid simulation to monopile-type Offshore Wind Turbines (OWTs) to enable the investigation of their behavior considering the response of pile foundations under operational and more severe conditions. The embedded foundation and surrounding soil of the OWT are modeled physically in a soil box in the laboratory while the remaining parts of the system and loading are modeled analytically. The program OpenFAST, developed by the National Renewable Energy Laboratory (NREL), is linked to the RTHS coordinator to determine the hydrodynamic and aerodynamic loads acting on the OWT, along with modeling the dynamics of the electric power generation equipment and associated controller for the OWT. The RTHS framework along with its initial implementation and validation are described in this paper. RTHSs of a 5 MW OWT subjected to operational and more severe conditions are performed to experimentally validate the framework. The framework offers a realistic approach to investigate the behavior of OWT structures supported on monopiles. This approach accounts for the coupled response of the OWT structure with its foundation, while experimentally capturing the nonlinearities of the soil-foundation interaction in real-time. • RTHS is extended to offshore wind turbine monopile structures. • OpenFAST software is modified and integrated into a RTHS framework. • Validated RTHS framework that incorporates the complete system into the simulation. • RTHS enables experimental assessment of offshore wind turbine foundation behavior. • RTHS computational challenges of offshore wind turbine structures are presented. [ABSTRACT FROM AUTHOR]

Renewable Energy Communities (RECs) are emerging as an effective concept and model to empower the active participation of citizens in the energy transition, not only as energy consumers but also as promoters of environmentally friendly energy generation solutions, particularly through the use of photovoltaic panels. This paper aims to contribute to the management and optimization of individual and community Distributed Energy Resources (DER). The solution follows a price and source-based REC management program, in which consumers' day-ahead flexible loads (Flex Offers) are shifted according to electricity generation availability, prices, and personal preferences, to balance the grid and incentivize user participation. The heuristic approach used in the proposed algorithms allows for the optimization of energy resources in a distributed edge-and-fog approach with a low computational overhead. The simulations performed using real-world energy consumption and flexibility data of a REC with 50 dwellings show an average cost reduction, taking into consideration all the seasons of the year, of 6.5%, with a peak of 12.2% reduction in the summer, and an average increase of 32.6% in individual self-consumption. In addition, the case study demonstrates promising results regarding grid load balancing and the introduction of intra-community energy trading. [ABSTRACT FROM AUTHOR]

The global drive to achieve the climate change mitigation targets set during the Paris Agreement and COP26 necessitates sustainable electricity generation from renewable energy sources. However, renewable energy systems face economic challenges due to unattractive feed‐in‐tariffs (FiT). This paper presents a mathematical programming model to optimise the participation and electricity generation by biomass power plants in a FiT programme. A disjunctive fuzzy optimisation approach is adopted in this work due to the presence of several biomass power plants, thus resulting in a multi‐objective optimisation. The newly developed mixed‐integer linear programming (MILP) model maximises the overall satisfaction extent of biomass power plants in power generation. The optimisation model ensures the satisfaction of all biomass power plants' technical and economic constraints. A base case study of seven power plants utilising various palm biomass is used to demonstrate the disjunctive fuzzy optimisation approach for the MILP model. This is followed by a scenario investigating the impact of demand variation. The optimisation of the case studies favours the utilisation of empty fruit bunch (EFB), palm oil mill effluent (POME), and palm mesocarp fibre (PMF) for sustainable electricity generation. Palm kernel shell (PKS) is the least preferred palm biomass due to its lowest net availability of useful energy from biomass combustion. This work provides a decision tool for the selection of biomass type during electricity generation. The MILP model in this work may be readily extended for any number of power plants, handling a variety of biomass feedstocks. [ABSTRACT FROM AUTHOR]

Green technology or renewable energy system (RES) is an advancement of the electricity generation which creates pollution free environment. Since last 15–20 years, renewable power plant works in the traditional way over the world and needs technology enhancement. This paper presents a complete review of application of recent advanced technology in the field of RES. The paper analyses and discusses the potential application of artificial intelligence, the internet of things, cloud computing, big data, blockchain and game theory in the area of RESs such as solar energy, wind energy, biomass energy, etc. At the end of this paper, the ordinal assessment of application of recent technology for different objectives of RES is presented. [ABSTRACT FROM AUTHOR]

In the era of high energy demand caused by rapid population growth, renewable energy is required in the energy mix for the sustainability of both human and industrial development. This paper investigates the potential of extracting heat energy from abandoned oil and gas wells for electricity production. Four wells located in the Niger Delta were considered. Each well has its properties and steam flow rates. The geothermal reservoir was modeled as a liquid-vapor-dominated system at in situ reservoir conditions. The fluid temperature in the reservoir was determined to be 170oC in the reservoir. Equations describing geothermal resource potentials, energy flow in a wellbore, and gross electrical power production using turbines were presented. A Single-flash turbine system was used because steam was produced in the reservoir. MATLAB software was used in the simulation of the heat transfer process. The results showed that the geothermal energy corresponding to the reservoir considered was 2.45 x 1012 Joules. The electricity production result showed that there was a reduction in gross electrical power output due to an increase in sink temperature, while the relationship between gross electrical power output and separator temperature was such that there was an initial increase in gross electrical power output due to an increase in separator pressure until an optimum point was reached wherein further increase in separator pressure progressively decreased the value of the gross electrical power output. The optimum separator temperatures for well 1, well 2, well 3, and well 4 were 120oC, 120oC, 110oC, and 105oC respectively at a sink temperature of 45oC. Because of the high reservoir pressure used, the separator pressure was higher than the actual reservoir pressure. Proper understanding of heat flow which enables accurate estimation of heat production is necessary to maximize heat extraction from the reservoir. The simulation conducted reveals that geothermal resources holds great potentials for electrical energy generation and should be put to developmental use in Nigeria as a means to increase the total power generation and increase the portion of renewables in Nigeria energy value chain. [ABSTRACT FROM AUTHOR]

In recent years, one of the key postulates in the European Union's policy has become the development of renewable energy sources. In order to achieve the desired synergy effect, the idea of combining two selected sources of energy appeared. This article presents a technical and economic analysis of a hybrid connection of a ground source heat pump with a photovoltaic installation. Taking into account the heat demand of the building, a ground heat pump with a catalog nominal heating power of 25 kW was selected. This article presents the problem of the economic profitability of using a hybrid combination of a heat pump and photovoltaic panels in domestic hot water and central heating systems. The justification for the use of such heat sources in these installations is due to global trends and the gradual departure from conventional energy sources such as oil or gas boilers. This paper presents the economic and ecological results of using the pump heat connected together with photovoltaic panels. In the economic analysis, with the assumed installation costs related to the use of the considered heat pump and PV, two parameters commonly used in the investment analysis (static and dynamic) were used, namely, the simple payback period and the net present value of the investment. For the adopted assumptions, the usable area of the facility and the number of years of use were indicated, at which the investment in question is competitive with other alternative investment interest methods and will start to bring tangible benefits. The performed analysis also has measurable environmental benefits in the form of a reduction in carbon dioxide emissions at the level of 2893 kg/year into the atmosphere. The presented solution will help future investors understand the investment profitability mechanism for their households. [ABSTRACT FROM AUTHOR]

This paper presents an inerter-integrated wave energy converter (WEC) for vibration mitigation of offshore floating structures. The proposed WEC complies a floating buoy and an upper structure placed over the floating buoy connected by a spring and a ball screw with a rotational mass, which converts linear motion to rotational one, and the ball screw is connected to a rotating generator. As the two bodies move separately, the inertial force proportional to the relative acceleration of the two bodies called inerter is produced. This mechanism, similar to passive vibration reduction systems in automobiles and civil structures, not only produces electric power but also mitigates the vibration induced by the wave disturbances acting on the floating buoy. In this research, its analytical model was developed based on the two-degree-of-freedom dynamic model and the circuit equation of the generator. Numerical studies considering only the heave direction showed that the proposed WEC can achieve electric power generation and vibration mitigation of the upper structure at the same time. Moreover, to verify the efficacy experimentally, a small-scaled prototype was designed, and wave flume testing was conducted, which demonstrated a response reduction of more than 40% compared to the device without the proposed mechanism. • An WEC with inerter is proposed for vibration mitigation of floating structures. • The WEC achieves both vibration suppression and power generation simultaneously. • An analytical model is developed to evaluate the performance numerically. • Wave flume testing is conducted to validate the concept of the proposed WEC. [ABSTRACT FROM AUTHOR]

Conventional petrol vehicles emit a substantial quantity of greenhouse gases, leading to increasingly serious global warming problems. The expansion and development of renewable power generation technologies is conducive in promoting the use of electric vehicles, which are more environmentally friendly. This paper proposes a multi-objective power expansion model considering renewable energy policy objectives. The model regards the problem as a multi-period optimisation task, taking the newly installed capacity and the power generation capacity of each power generation technology as decision variables, and simulating the uncertain factors in the planning process using Geometric Brownian Motion and Monte Carlo approaches. The optimisation objective of the model is to minimise expected costs, reduce risk and environmental impacts, and incorporate changing policy objectives into the constraints to meet policy makers' expectations for renewable energy development. Then, a decentralised target search-based multi-objective evolutionary algorithm is proposed to solve the model. Its effectiveness is verified by a numerical example using real data from the Chinese power system. The experimental results show that the proposed algorithm exhibits improved performance compared with benchmark algorithms and provides high quality and diverse Pareto-optimal solutions to decision makers. Finally, the optimal plans for power expansion and generation mix under different preferences and policy objectives are discussed and corresponding recommendations are made. [Display omitted] • Planning for the expansion of power generation under uncertainties is determined. • Strategies for the expansion of power generation towards renewable energy are optimised. • A novel multi-objective evolutionary algorithm with a constraint handling method is developed. • The impact of multiple renewable energy policies on power generation expansion planning is discussed. [ABSTRACT FROM AUTHOR]

This paper investigates the challenges isolated islands face in transitioning from fossil fuel-based electricity generation to renewable energy sources. The Canary Islands serve as a case study, where photovoltaic and wind power are the primary renewables, but their variability requires a deep techno-economic analysis. The island's energy demand is predicted to rise by 100% due to economic growth, electrification and electric vehicles. However, implementing renewable systems encounters obstacles, such as limited suitable sites and protected areas. The study uses Wilks' methodology and Monte Carlo sampling to explore 59 combinations of randomly selected inputs of the uncertain variables, aiming for a 95/95% coverage and confidence level in the results. In most cases, they experience energy shortages, failing to meet electric demand. Even though a new generation mix appears to cover demand under all circumstances, the uncertainty unveils a different reality, leading to an approximate 25% increase in system costs. Surpluses in energy generation, while seemingly positive, can pose challenges. The new system's Levelized Cost of Energy increases from around 14 to 17c€/kWh. These cost increases are contingent upon future performance and the variability of uncertain parameters, leading to excesses ranging from slightly below 25% to over 40%. • Demand coverage scenario designed to meet electric demand through renewable sources. • Uncertainty analysis of a scenario for the complete electrification of the economy. • Best Estimate Plus Uncertainty analysis implementing the Wilks' methodology. • Resizing the system to cover the demand with 95% coverage and confidence levels. • Differences in using deterministic and stochastic methods for electric demand coverage. [ABSTRACT FROM AUTHOR]

The share of wind-based electricity generation is gradually increasing in the world energy market. Wind energy can reduce dependency on fossil fuels, as the result being attributed to a decrease in global warming. This paper discusses and reviews the basic principle parameters that affect the performance of wind turbines. An overview presents the introduction and the background of energy consumption, following the order of the elaboration of wind turbines, including mathematical models, categories of wind turbines were critically discussed. Moreover, it also focuses on materials that are commonly considered for wind turbine manufacturing, and the process used to recycle them. The scale of recycling methods for fiberglass and thermoplastic is presented in the respective section. Various parameters that reduce the function of wind turbines are explained in depth. This review also discusses various environmental impacts of wind turbines. Future research studies are suggested in the conclusion section. [ABSTRACT FROM AUTHOR]

Climate change motivated by human activities constitutes one of the main challenges of this century. To cut carbon emissions in order to mitigate carbon's dangerous effects, the current energy generation mix should be shifted to renewable sources. The main drawback of these technologies is their intermittency, which will require energy storage systems to be fully integrated into the generation mix, allowing them to be more controllable. In recent years, great progress to develop an effective and economically feasible energy storage systems, particularly motivated by the recent rise of demand for electric transportation, has been made. Lithium-ion (Li-ion) battery prices have fallen near 90% over the past decade, making possible the affordability of electric vehicles and transforming the economics of renewable energy. In this work, a study on storage capacity demand previously presented as conference paper is expanded, including a deep analysis of the Spanish generation mix, the evaluation of the energy storage requirements for different low-carbon and carbon-free scenarios in Mainland Spain, and the calculation of the CO2 emissions' reduction and the associated storage costs. [ABSTRACT FROM AUTHOR]

This paper introduces the modeling and control of split-shaft drivetrains where the system's inertia is adjusted to store the energy. Accordingly, a flywheel is mechanically coupled with the rotor of a doubly fed induction generator. The generator is driven by a split-shaft drivetrain that decouples the turbine's shaft from the shaft of the generator to provide independent control of their angular velocities. Hence, the turbine controller can track the point of maximum power while the generator controller can adjust the generator speed. Accordingly, the flywheel, which is directly connected to the shaft of the generator, is charged and discharged by controlling the generator speed. In this process, the flywheel can modify the electric power generation of the generator on-demand. Since the drivetrain is a split-shaft, the turbine speed is not affected by this energy storing process. This improves the quality of injected power to the grid. The structure of the flywheel energy storage can be simplified by removing its dedicated motor/generator and the power electronics driver. This significant modification can only occur in the split-shaft drivetrain. Two separate supervisory controllers are developed in the form of fuzzy logic regulators to generate a real-time output power reference. Furthermore, small-signal models are developed to analyze and improve the maximum power point tracking controller. Extensive simulation results demonstrate the feasibility of such a system and its improved quality of power generation. [ABSTRACT FROM AUTHOR]

Penetration of renewable energy resources into grid is necessary to meet the elevated demand of electricity. In view of this penetration of solar and wind power increasing immensely across the globe. Solar energy is widely expanding in terms of generation and capacity addition due its better predictability over wind energy. Electricity pricing is one of the important aspects for power system planning and it felicitates information for the electricity bidder for accurate electricity generation and resource allocation. The important task is to forecast the electricity price accurately in grid interactive environment. This task is tedious in renewable integrated market due to intermittency issue. In this paper, investigation has been done on the effect of solar energy generation on electricity price forecasting. Different state of the art Machine learning (ML) models have been applied and compared with LSTM model for electricity price forecasting and the evaluation of the impact of solar energy generation on electricity price has been done. During the investigation it was found from the results that the LSTM model outperform all other models and impact of solar energy generation on electricity price is evaluated using forecasting metrics. The forecasted electricity price considering the factor of solar energy generation was lower as compared with the forecast without solar energy generation. The reliability test of the MAPE values has been performed by calculating confidence interval for proposed model. [ABSTRACT FROM AUTHOR]

• This paper presents the Survey of AI Methods for Renewable Energy Forecasting. • Taxonomy of AI for solar and wind renewable energy is presented. • Combinations of ML and metaheuristic methods for renewable energy are discussed. • Prospects of improved metaheuristic algorithms for renewable energy forecasting are presented. The efforts to revolutionize electric power generation and produce clean and sustainable electricity have led to the exploration of renewable energy systems (RES). This form of energy is replenished and cost-effective in terms of production and maintenance. However, RES, such as solar and wind energies, is intermittent; this is one of the drawbacks of its usage. In order to overcome this limitation, studies have been undertaken to forecast its availability and power output. The current trending method of forecasting availability and the power generated by the RES is the artificial intelligence (AI) method. However, with all its potential, traditional AI, such as Artificial Neural Network (ANN), Support Vector Machine (SVM) and many more, does not have it all. Because of this, metaheuristic algorithms are being explored as optimization techniques to increase the performance accuracy of these AI forecasting methods and overcome some challenges of traditional AI models. This study presents an insightful survey of AI (traditional AI and metaheuristic) methods explored to forecast the availability of solar and wind renewable energy systems. A survey of the existing surveyed literature was presented. The taxonomy of the explored AI in RES was formulated, and the theoretical backgrounds of some of the traditional AI algorithms were presented. Also, the various forms of metaheuristic algorithms and the improved versions applied to optimize classical AI methods to forecast RES systems' availability and power output were surveyed. A conceptual framework of the hybrid AI application in RES was formulated. Finally, the survey discussion, insight, challenges of the existing models and future directions were presented. [ABSTRACT FROM AUTHOR]

Despite the importance of cost of capital and expected returns from investing on renewable electricity generation in Africa on one hand and the implications of such investments on economic, social and environmental factors on the other. These concepts are not well understood by non-finance and finance actors respectively. Studies that analyze renewable energy-growth nexus at regional and global level are lacking. Instead, existing studies emphasize on individual countries or panel of countries. The paper undertakes comparisons of costs and returns of selected renewable energy technologies in Africa and presents estimates of its impact on Africa's economic growth between 1990 and 2014. Even with abundant renewable resources and well performing technologies measured using levelized cost of electricity and expected internal rate of return, investment on renewable electricity generation is quite low. Findings from Autoregressive Distributed Lag approach and cointegration established a long run relationship among hydro power generation, carbon emissions and electricity consumption. Error Correction Model results suggest that investment in renewable generations excluding hydro power generation is boosted by economic growth, so long as development of such renewables corresponds with lesser carbon emissions. To accelerate renewable development, SSA countries will need to deploy initiatives that enhances national economic growth. [ABSTRACT FROM AUTHOR]

According to statistical reports, thermal power plants have long played a critical role in supplying electricity using fossil fuels. However, due to the high investment and operation costs of these power plants and their destructive effects on the environment, renewable energy sources (RESs) in power networks have been considered an effective alternative to traditional power plants. Optimal scheduling of microgrid in smart grids has a significant impact on reducing energy consumption, environmental pollutants and end-user's energy prices. In this paper, the influence of plug-in hybrid electric vehicles (PHEVs) charging on microgrids' optimal operation is evaluated. To assess the behaviour of PHEVs, three various charging patterns consisting of uncontrolled, controlled and smart charging methods are considered. Uncertainties due to forecast error in the PHEVs, loads, prices and renewable source output power are also considered in microgrid modelling energy management. To deal with the optimisation scheduling of microgrid considering uncertainty, a modified harmony search (MHS) algorithm is used. The suggested scheme is validated using simulations and without the PHEV charging effects compared with the conventional methods. [ABSTRACT FROM AUTHOR]

Considering the broad range of applications, efficient retrieval and storing electrical energy methods are still challenging. Besides the load variations, the ever‐increasing intermittent renewable energy penetration into the grid system has witnessed the system complexities. In off‐grid applications, energy storage can balance electricity consumption and electricity generation to avoid voltage and frequency deviations. This research paper focuses on the energy management of an off‐grid climate refuge system used for hot and arid locations with a system comparison for two routes of different storage techniques, namely flywheels and a lithium‐ion battery. The proposed system can generate its power from photovoltaics and provide cooling and other auxiliaries through vapor compression cycle and water misting units with an operation of about 16 hours (ie, from 7:00 am to 11:00 pm) on the weekdays and 12 hours on the weekends. A comparison of levelized costs is conducted for the evaluation using HOMER software. The annual energy production by solar photovoltaics for the proposed system is 20 MWh, and the annual consumption is 16 MWh. The photovoltaic‐battery storage system has shown the lowest cost of electricity, corresponding to 0.761 $/kWh, and net present cost of $66 238 and is optimum in all sensitivity analysis cases. [ABSTRACT FROM AUTHOR]

To moderate global warming, conventional fossil fuels are depleted. As the population increased with the rising standard of living and industrial growth, the global environment is affected and cause the greenhouse gases occurrence, which are frequently increased by unlimited use of fossil fuels. The generation of electric power loads increases the power demand on the basics of modern power technology development. Several benefits can be attained by installing the distribution generation with the quality and reliability of power delivered. However, the global energy problem can be resolved by renewable energy sources as an alternative energy generation. Technological developments in the last decade have increased the use of renewable energy sources. In worldwide, several renewable energy sources are used to attain their own power demand. The photovoltaic (PV) generation is the essential renewable energy source to serve the increasing electrical loads. The fastest-growing PV system has the naturally available energy sources of robust evolution with elegant benefits. The foremost objective of this paper is to examine the performance of the PV system with various Maximum Power Point Tracking (MPPT) algorithms. The solar irradiance and temperature make it complex to track the MPPT of PV systems. This review is about various MPPT algorithms like online, offline, and hybrid methods. The selected algorithms from each discussion are simulated in MATLAB/Simulink environment to match their performance in footings of the dynamic response and efficiency of the PV system under the variations of solar irradiance and temperature. An explanation and discussion of the PV system are achieved with the study of different types of MPPT algorithms of PV systems. [ABSTRACT FROM AUTHOR]

Solar energy contributes a major share in sustainable energy-based electricity generation. The solar power transfer system (SPTS) has emerged to integrate solar energy into the distribution network. Moreover, the adverse effects and exhaustible nature of fossil fuels have made the control and design of the SPTS significant. This paper presents a novel grid integration control, named frequency adaptive generalized integral (FAGI) algorithm for a three-phase four-wire SPTS to compensate the reactive power, harmonics and DC offset demands of the sinks connected at the common point, and also eliminates the grid neutral current along with maximum PV array power injection to the grid. The narrow bandwidth bandpass filter property of the FAGI algorithm with adaptive corner frequency improves the fundamental component quality without compromising the response time at adverse operating conditions. The two-stage SPTS uses a DC–DC boost converter for peak power extraction. An enhanced incremental conductance (EIC) algorithm is utilized for its control, which gives simple and dynamic performance over other conventional maximum power point tracking techniques. The system control is developed to meet the load requirements, even at zero irradiance by importing the active power from the grid. Therefore, the major objectives of this work are extracting maximum power, meeting the load demands, and maintaining the grid power quality at any operating conditions. Comparison of FAGI with other common controls, is carried out and the results are produced. The system performances are analyzed at PV-side, load-side, and grid-side disturbances in the MATLAB/Simulink model, and examined the grid power quality compliance with the IEEE-519 standard. An experimental prototype of the system is developed in the laboratory and tested rigorously for multiple dynamic scenarios for validating the reported claims. • Novel frequency adaptive generalized integral control for grid integration. • Compensates load reactive power, unbalanced currents, harmonics and neutral current. • FAGI enhances dynamic response at fluctuating loads and rejects input DC offset. • System has bi-directional power transfer capability at the grid coupling point. • Performance confirmed from experimental results on developed prototype. [ABSTRACT FROM AUTHOR]

Renewables around the globe, provide fuel diversification, energy security and produce little or no emissions during the electricity generation. The unavailability of rotating masses prohibits on-grid renewable energy generation systems from providing inertia during a frequency disruption. This would be extremely unsettling and impactful when the proportion of energy in the grid due to renewables is high. India currently, has a 28.04% share of on-grid renewable energy. The grid infrastructure systems of India would face severe challenges when the share of on-grid renewables leaps past 50% in the future. Thus, this paper primarily addresses the repercussions of increasing share of on-grid renewables in terms of inertia and frequency response in India. The energy scenario of 2040 in India under 80% proliferation of on-grid renewables has been well reviewed in this paper. The novelty of this paper is the narrative literature review of the Indian Electricity Grid Code (IEGC) on frequency response which is unique and exclusive to this manuscript. It demonstrates the current strategies prevalent in the grid system of India towards frequency response and inertia support. This paper also explains the diverse control strategies and energy storage technologies required for adoption with the on-grid renewables in India for frequency response. The other novelty in this paper is the insight about the inertia lost due to an on-grid 100-kW p solar photovoltaic system to explain the readers about the impact when the integration is hundreds to thousands of kilowatts. • Share of renewables in the Indian grid network is 28.04% (113.226 GW) as of 2022. • India aspires to achieve 54% share of on-grid renewables by 2030 and 80% by 2040. • Indian Electricity Grid Code indicates need for expansion in active power reserves. • India requires diverse control strategies and energy storages for inertia support. • On-grid 100-kW p solar photovoltaic system loses inertia of 100-kW for 4.44 s. [ABSTRACT FROM AUTHOR]

Capacity expansion models for the power sector are used to project future decisions over the coming decades by simulating investment and operation decisions for the use of electricity. Due to model performance constraints, these models typically do not explicitly simulate every hour within a year, but instead simulate representative time segments (groups of hours). This paper evaluates different approaches for selecting time segments across three methods: sequential, categorical, and clustering, across a wide range of time-segment quantities, for a total of 204 temporal profiles. To measure the performance of each profile's ability to accurately represent data, the root-mean-square-error of each profile's time segments are compared to the data's original hourly data. The temporal alignment across regions is also measured (i.e., how often windy days align across regions). Different spatial resolutions were applied for a subset of the temporal selection methods to investigate the impact spatial resolution has on performance. This paper provides a framework for measuring the value of different temporal selection methods and of adding more granular data to energy system models. Overall, multi-criteria clustering yields the lowest root-mean-square-error across all datasets evaluated and provides a holistic view of the intertwined relationships between renewable generation and electricity demand. • Temporal selection methods analyzed across three commonly used techniques. • Measured performance of representing model data within representative hours. • Measured temporal performance across electric load and wind and solar generation. • Clustering techniques performed better than sequential or categorical techniques. [ABSTRACT FROM AUTHOR]

In this paper we investigate the system integration costs for wind and river type hydropower generation in the Turkish electricity market, which mainly covers system imbalance costs, congestion management costs and ancillary services costs. The paper also shows that reflecting the imbalance costs to the renewable generators themselves reduced their generation forecast errors drastically. By using an Autoregressive Distributed Lag (ARDL) model, we find that wind and river type hydro generations cause significant imbalances in the Turkish electrical system. On average 1 MWh hydro and wind generation increases imbalance costs by 12.04 TL ($2.10) and 4.03 TL ($0.7), respectively. Most of the renewable imbalance effect stems from the congestion between the main consumption areas and the generation zones of river-type hydro. These costs are socialized and reflected to all consumers through transmission tariffs. It seems reasonable to reflect the congestions costs to the ones causing them which will create the necessary incentives to curb them. Defining new price zones that consider the persistent congestions in the system is recommended for this problem as it is a market-friendly and efficient solution. • The research examine the system integration costs of river-type hydro and wind generation. • These generations caused significant imbalances between 2012 and 2019 in Turkey. • They raised imbalance costs by 12,0 TL ($2.1) and 4,0 TL ($0.7) per MWh respectively. • Most renewable imbalance stems from the congestions due to river type generation. • New price zones should be defined considering congestions for this problem. [ABSTRACT FROM AUTHOR]

This paper presents two different scenarios for the energy system of the Netherlands that achieve the Dutch government's national target of near net-zero greenhouse gas emissions in 2050. Using the system optimisation model OPERA, the authors have analysed the technology, sector and cost implications of the assumptions underlying these scenarios. While the roles of a number of key energy technology and emission mitigation options are strongly dependent on the scenario and cost assumptions, the analysis yields several common elements that appear in both scenarios and that consistently appear under differing cost assumptions. For example, one of the main options for the decarbonisation of the Dutch energy system is electrification of energy use in end-use sectors and for the production of renewable hydrogen with electrolysers. As a result the level of electricity generation in 2050 will be three to four times higher than present generation levels. Ultimately, renewable energy – particularly from wind turbines and solar panels – is projected to account for the vast majority of electricity generation, around 99% in 2050. Imbalances between supply and demand resulting from this variable renewable electricity production can be managed via flexibility options, including demand response and energy storage. Hydrogen also becomes an important energy carrier, notably for transportation and in industry. If import prices are lower than costs of domestic production from natural gas with CCS or through electrolysis from renewable electricity (2.4–2.7 €/kgH 2), the use of hydrogen increases, especially in the built environment. • We explore two scenarios for a near climate-neutral energy system in the Netherlands. • With the OPERA model we determine their technology, sector and cost implications. • The electricity share in primary energy increases from 19% today to 41–71% in 2050. • Hydrogen becomes another important energy carrier, notably in transport and industry. • Biomass use increases, mostly as fuel in transport and industry. [ABSTRACT FROM AUTHOR]

This paper presents two different scenarios for the energy system of the Netherlands that achieve the Dutch government's national target of near net-zero greenhouse gas emissions in 2050. Using the system optimisation model OPERA, the authors have analysed the technology, sector and cost implications of the assumptions underlying these scenarios. While the roles of a number of key energy technology and emission mitigation options are strongly dependent on the scenario and cost assumptions, the analysis yields several common elements that appear in both scenarios and that consistently appear under differing cost assumptions. For example, one of the main options for the decarbonisation of the Dutch energy system is electrification of energy use in end-use sectors and for the production of renewable hydrogen with electrolysers. As a result the level of electricity generation in 2050 will be three to four times higher than present generation levels. Ultimately, renewable energy – particularly from wind turbines and solar panels – is projected to account for the vast majority of electricity generation, around 99% in 2050. Imbalances between supply and demand resulting from this variable renewable electricity production can be managed via flexibility options, including demand response and energy storage. Hydrogen also becomes an important energy carrier, notably for transportation and in industry. If import prices are lower than costs of domestic production from natural gas with CCS or through electrolysis from renewable electricity (2.4–2.7 €/kgH 2), the use of hydrogen increases, especially in the built environment. • We explore two scenarios for a near climate-neutral energy system in the Netherlands. • With the OPERA model we determine their technology, sector and cost implications. • The electricity share in primary energy increases from 19% today to 41–71% in 2050. • Hydrogen becomes another important energy carrier, notably in transport and industry. • Biomass use increases, mostly as fuel in transport and industry. [ABSTRACT FROM AUTHOR]

The research on microbial fuel cells (MFCs) is rising tremendously but its commercialization is restricted by several microbiological, material, and economic constraints. Hence, a systematic assessment of the research articles published previously focusing on potential upcoming directions in this field is necessary. A detailed multi-perspective analysis of various techniques for enhancing the efficiency of MFC in terms of electric power production is presented in this paper. A brief discussion on the central aspects of different issues are preceded by an extensive analysis of the strategies that can be introduced to optimize power generation and reduce energy losses. Various applications of MFCs in a broad spectrum ranging from biomedical to underwater monitoring rather than electricity production and wastewater treatment are also presented followed by relevant possible case studies. Mathematical modeling is used to understand the concepts that cannot be understood experimentally. These methods relate electrode geometries to microbiological reactions occurring inside the MFC chamber, which explains the system's behavior and can be improved. Finally, directions for future research in the field of MFCs have been suggested. This article can be beneficial for engineers and researchers concerned about the challenges faced in the application of MFC. [Display omitted] • Basic principles and mathematical modeling of MFC technology are discussed. • Limitations in various electrode and membrane materials are outlined. • Mass transfer limitations as well the ways to overcome are provided. • All potential applications of MFC technology are included with case studies. • Need for MFC standardization to indicate power performance is explored. [ABSTRACT FROM AUTHOR]

Concerned with the increasing greenhouse gases in the atmosphere due to fossil fuels, the entire world is focusing on electricity generation through renewable energy resources. The most advantageous aspect of the distributed renewable sources is to provide the electricity to remote, scattered and the deprived rural areas by developing the hybrid power system at the smaller scale where power transmission through grid extension is not viable due to some economical, technical or environmental constraints for building new transmission lines. An accurate and adequate control strategy becomes inevitable to uphold the smooth operation by restraining the frequency and voltage deviation within its limit ensuring the highest degree of reliability of hybrid power system to provide an adequate power quality. In this paper, a comprehensive review of different control strategies adopted in isolated and interconnected multi-area hybrid power systems is presented. [ABSTRACT FROM AUTHOR]

Greenhouse gas emissions from the burning of fossil fuels are one of the main causes of anthropogenic climate change. Large-scale deployment of renewable energy can play an immense role in transforming the global energy system and mitigating the emissions. This paper describes the development of a novel framework called MArket Penetration ModeLing of Renewable Energy Technologies in Electric Power Sector (MAPLET-PS). MAPLET-PS assesses the impacts of policy measures such as carbon price and financial incentives on the adoption of renewable energy technologies. The framework was used to develop a case study for the electric power sector of Alberta, a fossil-dominated province in Western Canada. The results show that implementing a carbon price on fossil fuel electric power sources and incentives for renewable energy, along with the phase-out of coal-fired electricity generation, can mitigate 29% of Alberta's electricity sector 2020 GHG emissions by 2050 and reduce GHG emissions from 46.5 Mt of CO 2 eq. in 2020 to 23.6 and 29.1 Mt of CO 2 eq. per year in 2030 and 2050, respectively, in Alberta. Moreover, these changes can increase the share of renewable energies from 12.5% in 2018 to 30% in 2050. These rates can be achieved by implementing a carbon price along with a 1000 $ incentive per kW new capacity development and 70 $ incentive per MWh electric power generation from renewable sources, from 2021 to 2025, primarily from wind turbines. • The analysis focuses on renewable electric power generation technologies. • A combined market penetration model is developed. • Electric power generation mix is modeled from 2020 to 2050. • Effects of carbon price and financial incentive on generation mix are analyzed. • Annual mitigation potential of greenhouse gases is evaluated. [ABSTRACT FROM AUTHOR]

• Comprehensive thermo-economic assessments of small-scale solar ORC systems are presented. • Various solar collectors, ORC configurations, expanders and fluids are considered. • Evacuated collectors, subcritical recuperated cycle, piston expander, isobutane are preferred. • Highest electricity generation and thermal efficiency are 73 kWh/year/m2 and 5.5%. • Levelised cost and payback as low as 0.35 $/kWh and 16 years in high solar-resource areas. In this paper, results from comprehensive thermoeconomic assessments of small-scale solar organic Rankine cycle (ORC) systems are presented based on weather data in London, UK, which is taken as representative of a temperate climate with modest temperature changes, mild winters and moderate summers. The assessments consider a range of: (i) solar collector types (flat-plate, evacuated-tube, and evacuated flat-plate collectors); (ii) power cycle configurations (basic/recuperative, partial/full evaporating, and subcritical/transcritical cycles); (iii) expander types (scroll, screw, and piston) and designs; and (iv) a set of suitable working fluids. All possible solar-ORC system designs are optimised by considering simultaneously key parameters in the solar field and in the power cycle in order to obtain the highest electricity generation, from which the best-performing systems are identified. Selected designs are then subjected to detailed, annual simulations considering the systems' operation, explicitly considering off-design performance under actual varying weather conditions. The results indicate that, among all investigated designs, solar-ORC systems based on the subcritical recuperative ORC (SRORC), evacuated flat-plate collectors (EFPCs), a piston expander, and isobutane as the working fluid outperforms all the other system designs on thermodynamic performance, whilst having the highest annual electricity generation of 1,100 kW·h/year (73 kW·h/year/m2) and an overall thermal efficiency of 5.5%. This system also leads to the best economic performance with a levelised cost of energy (LCOE) of ~1 $/kW·h. Apart from the specific weather data used for these detailed system simulations, this study also proceeds to consider a wider range of climates associated with other global regions by varying the solar resource available to the system. Interestingly, it is found that the optimal solar-ORC system design remains unchanged for different conditions, however, the LCOE can drop below 0.35 $/kW·h and payback times can be shorter than 16 years in high solar-resource regions, even in the absence of incentives that would otherwise lead to even better economic performance. This work complements previous efforts in the literature by considering the full design and operational features of solar-ORC systems, thereby providing valuable guidance for selecting appropriate cycle configurations, components, working fluids and other characteristics and, for the first time, presents a comprehensive comparison of such systems in small-scale applications. [ABSTRACT FROM AUTHOR]

• The European Union trends create an opportunity for geothermal power plants. • The combine analysis of heat source, Organic Rankine Cycle and turbine were done. • The binary geothermal power plant can obtain approximately 2 MW of power output. • The parameters of a low-temperature heat source for Polish conditions are determined. • Designed the two-stage axial turbine reached the internal efficiency of 79%. In recent years, pressure has been growing to increase the share of renewable energy sources in electricity generation, which may offer opportunities for the development of geothermal energy in regions that have been so far considered unprofitable in this respect. One such country currently undergoing an energy transition is Poland in which low-temperature geothermal resources are currently used for district heating and recreation purposes. Nevertheless, research on the possibilities of using geothermal energy for electricity production is ongoing. The objective of this paper was to perform a comprehensive analysis of a binary power plant construction in relation to low-temperature petro-geothermal resources. The potential binary power plant is located in the area characterized by temperature of 120 °C at depths of 5000 m. About half of Poland's area, especially the regions of western and central Poland, has these characteristics. It was assumed that brine at volume flow rate of 400 m3/h is a heat source for the Organic Rankine Cycle with isobutane as a working medium. The thermal efficiency based on the First Law of Thermodynamics and the power output were estimated at 10.5% and 1.79 MW e , respectively. In addition, the thermal efficiency based on the Second Law of Thermodynamics was calculated at 29.0%. For the calculated cycle parameters, a preliminary design of a two-stage axial turbine was constructed. All results were compared to the other binary power plants and they confirm that establishing the binary power plant in Poland would be thermodynamically justified. The main novelty of the present work is the combination of three issues, namely the selection of the low- temperature heat source, the design and analysis of the cycle together with a turbine adapted to these conditions. [ABSTRACT FROM AUTHOR]

Olive sector residues could contribute to increased use of renewable energies, especially in those areas where the olive crop is produced. This paper determines the maximum amount of electrical and thermal energy which could be obtained in Andalusia from olive sector residues, and the economic value that could be obtained from these energy uses. For this, the current data on installed power and electrical and thermal generation are compared with the calculated potential data which would be obtained at full capacity. The results show there is a 69.23% wastage of olive sector residues for energy purposes. Thus, using the resources at full capacity, 3.9 million tons of biomass per year could be obtained for energy purposes. Currently, only 2 million tons of biomass are used for energy purposes. This wastage translates into a below potential generation of electrical and thermal energy. The full use of these residues would allow 83.9% and 64.9% higher generation of electrical and thermal energy, respectively. The results obtained also show that the economic value of olive sector biomass is higher than the market price value, with the average values for each use being 248.20 €/t for domestic thermal use, 165.04 €/t for electrical use and 139.50 €/t for industrial thermal use. Thus, it is considered feasible that the olive sector biomass could be used to a greater degree than at present. In this sense, it is recommended that more electricity generation plants and thermal-generating systems be put into operation. • There is a 69.2% wastage of olive sector residues that could be used for energy purposes. • The full use of olive by-products would account for 6.2% of renewable electricity. • The full use of olive by-products would account for 26.7% of thermal generation. • Better use of residues would promote the start-up of 55 and 1762 electricity and thermal plants, respectively. • The economic value of olive sector biomass allows the value chain to be covered. [ABSTRACT FROM AUTHOR]

[Display omitted] • Tighter EU ETS target (−63% instead of −43% in 2030) speeds up transformation by 3–17 years. • Renewable share >74% in 2030, EU-wide coal phase-out almost completed by 2030. • Tighter target decreases cumulative emissions by 54%, increases costs by only 5%. • Carbon prices increase to 129EUR/tCO 2 in 2030 under ambitious ETS target. • Unavailability of fossil CCS and/or nuclear does not affect results. The EU Green Deal calls for climate neutrality by 2050 and emission reductions of 50–55% in 2030 in comparison to 1990. Achieving these reductions requires a substantial tightening of the regulations of the EU emissions trading system (EU ETS). This paper explores how the power sector would have to change in reaction to a tighter EU ETS target, and analyses the technological and economic implications. To cover the major ETS sectors, we combine a detailed power sector model with a marginal-abatement cost curve representation of industry emission abatement. We find that tightening the target would speed up the transformation by 3–17 years for different parts of the electricity system, with renewables contributing 74% of the electricity in 2030, EU-wide coal use almost completely phased-out by 2030 instead of 2045, and zero electricity generation emissions reached by 2040. Carbon prices within the EU ETS would more than triple to 129€/tCO 2 in 2030, reducing cumulated power sector emissions from 2017 to 2057 by 54% compared to a scenario with the current target. This transformation would come at limited costs: total discounted power system costs would only increase by 5%. We test our findings against a number of sensitivities: an increased electricity demand, which might arise from sector coupling, increases deployment of wind and solar and prolongs gas usage. Not allowing transmission expansion beyond 2020 levels shifts investments from wind to PV, hydrogen and batteries, and increases total system costs by 3%. Finally, the unavailability of fossil carbon capture and storage (CCS) or further nuclear investments does not impact results. Unavailability of bioenergy-based CCS (BECCS) has a visible impact (18% increase) on cumulated power sector emissions, thus shifting more of the mitigation burden to the industry sector, but does not increase electricity prices or total system costs (<1% increase). [ABSTRACT FROM AUTHOR]