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Optimal power flow (OPF) calculation methods are important for the power system operation and mainly focus on the deterministic power flow calculation, neglecting the impact of demand response on online security calculation of power systems with renewable energy sources. Therefore, this paper proposes an OPF calculation method that considers the uncertainties of wind power, photovoltaic (PV) power generation and demand-side response. Firstly, the research focuses on the renewable energy grid, considering the uncertainties of wind power and PV power generation, and establishes uncertainty models for wind power and PV output. Secondly, based on cloud model theory, an uncertainty model for demand response is established. According to the established models, an efficient OPF model is constructed with a linearized submodels considering multiple uncertainties. By testing on the IEEE 30-bus system as a typical example, we found the effectiveness and superiority of the proposed OPF calculation method can benefit the power system economic operation and demand side resource utilization. [ABSTRACT FROM AUTHOR]

Renewable energy is becoming more attractive as traditional fossil fuels are rapidly depleted and expensive, and their use would release pollutants. Power systems that use both wind and solar energy are more reliable and efficient than those that utilize only one energy. Hybrid renewable energy systems (HRES) are viable for remote areas operating in standalone mode. This paper aims to present the state-of-the-art research on off-grid solar-wind hybrid energy systems over the last two decades. More than 1500 published articles extracted from the Web of Science are analyzed by bibliometric methods and processed by CiteSpace to present the results with figures and tables. Productive countries and highly cited authors are identified, and hot topics with hotspot articles are shown in landscape and timeline views. Emerging trends and new developments related to techno-economic analysis and microgrids, as well as the application of HOMER software, are predicted based on the analysis of citation bursts. Furthermore, the opportunities of hybrid energy systems for sustainable development are discussed, and challenges and possible solutions are proposed. The study of this paper provides researchers with a comprehensive understanding and intuitive representation of standalone solar-wind hybrid energy systems. [ABSTRACT FROM AUTHOR]

The goal of increasing fuel efficiency and decreasing greenhouse gas (GHG) emissions has increased interest in the application of renewable energy sources and the usage of new technologies in the maritime industry. In order to implement the most suitable source, factors such as voyage duration, storage availability, and the condition of existing vessels as well as those that are still under construction should be taken into account. Propeller optimization is proposed as a long-term solution. This paper investigates the environmental aspects of propeller optimization, focusing on its potential to reduce ship vibrations fuel consumption, and, therefore, the ship's carbon footprint. The case study presents propeller optimization on a Ro-Ro passenger ship. The data collected during sea trials before and after propeller optimization will be compared. Expected fuel oil consumption will be correlated to the CO2 emission reduction. Besides propeller optimization, the paper performs a SWOT (strengths, weaknesses, opportunities, threats) analysis comparing it with solar and wind power applications on ships. [ABSTRACT FROM AUTHOR]

The hybrid system of solar and wind is very useful in your day-to-day life. The various sources generally we are using likes sun energy, wave power generation, hydropower generation, tidewater power energy, and thermal gradient energy sources. This type of energy source is readily available in the environment and can consume maximum power from the actual solar panels and windmills. In this paper, we are using solar energy by using photovoltaic cells, and wind energy by using windmills generator. Solar and wind energy are cost-effective and easy to use. It can be used in mobile phones, Laptops, Daily Home appliances, and various types of lighting loads, which can be the best energy sources for urban, and rural, areas. As day by day, the electricity consumption is increased, because the demand has been increased day by day. We have to switch to other renewable energy sources and use the electricity from these sources. Power-Voltage and Current-Voltage characteristics of solar cell panels were accomplished at different values of irradiance and temperature levels of solar panels. [ABSTRACT FROM AUTHOR]

With the large-scale growth and grid connection of intermittent renewable energy such as wind and solar, the problem of increasing renewable energy curtailment rate and system backup flexibility has become increasingly prominent. In order to solve the problem of high proportion of renewable energy scientific consumption and flexible and stable operation of energy system. We propose a flexible and economical dispatch method based on data-driven multi-regional power system. For the problem of economic dispatch of multi-area power system, a mathematical calculation model is established to satisfy the constraints of unit output, system power balance, unit ramp rate, and valve point effect, and to consider the requirement of minimizing the cost of multi-area power load comprehensively. Based on data-driven, this paper adopts an improved fruit fly optimization algorithm to quickly find the global optimal solution. The calculations are performed by IEEE6 simulation test system, and the results verify the feasibility of the proposed algorithm. The improved fruit fly optimization algorithm is compared and analyzed with other algorithms considering the quality of the obtained solutions. The results show the effectiveness and superiority of the proposed algorithm in solving multi-area economic dispatching problems in real power systems. [ABSTRACT FROM AUTHOR]

The multi‐microgrids operation can greatly improve the proportion of renewable energy integration and power reliability of rural microgrids through energy trading between adjacent microgrids. Therefore, this paper proposes an optimization method for the low‐carbon economic operation of rural microgrids which contain wind power, photovoltaic, biogas, and other common rural renewable energy sources. Using the asymmetric Nash bargaining model which can form the non‐linear energy mapping function to quantify the bargaining power of each microgrid to establish the multi‐microgrids energy trading model, and the non‐convex trading optimization problem is equivalently transformed into two convex optimization subproblems, subproblem 1 is microgrids alliance costs containing the emission costs, and subproblem 2 is cooperative benefits distribution. Then, the accelerated Alternating Direction Method of Multipliers (ADMM) method is used to solve subproblem 1 and subproblem 2, respectively, and the privacy of transaction information is also protected. The results show that: compared with the single‐microgrid operation, the combined operation of rural multi‐microgrids with biogas can greatly improve the renewable energy consumption rate, reduce carbon emission, and improve the profit income of the alliance and each microgrid. Meanwhile, the accelerated ADMM algorithm used can greatly improve the solution speed. [ABSTRACT FROM AUTHOR]

Socioeconomic growth and population increase are driving a constant global demand for energy. Renewable energy is emerging as a leading solution to minimise the use of fossil fuels. However, renewable resources are characterised by significant intermittency and unpredictability, which impact their energy production and integration into the power grid. Forecasting models are increasingly being developed to address these challenges and have become crucial as renewable energy sources are integrated in energy systems. In this paper, a comparative analysis of forecasting methods for renewable energy production is developed, focusing on photovoltaic and wind power. A review of state-of-the-art techniques is conducted to synthesise and categorise different forecasting models, taking into account climatic variables, optimisation algorithms, pre-processing techniques, and various forecasting horizons. By integrating diverse techniques such as optimisation algorithms and pre-processing methods and carefully selecting the forecast horizon, it is possible to highlight the accuracy and stability of forecasts. Overall, the ongoing development and refinement of forecasting methods are crucial to achieve a sustainable and reliable energy future. [ABSTRACT FROM AUTHOR]

Uncertainty analysis deals with the fluctuations and unpredictability of the electrical power generated from renewable resources (RRs), such as solar PV and wind energy systems. This paper gives an insight into various techniques used for the uncertainty analysis and a probabilistic Monte Carlo Simulation is applied for modelling the uncertainties concerned with RRs and electric vehicle (EV) load in the MATLAB platform. The uncertainty associated with the price sensitivity of EV charging and the state of charge of EVs is taken as a prime factor for analysis in the present work. Despite the fluctuations and unpredictability of electricity generation and consumption, the considered system ensures that the total amount of electricity supplied by solar PV, wind and grid matches the total amount of electricity demanded by EV load. Rao‐1, Rao‐2 and Rao‐3 algorithms are applied in this work to optimize the operation cost of charging stations under uncertain conditions and without any uncertainties. The results obtained without uncertainties by Rao algorithms are compared with the existing particle swarm optimisation method. In the presence of uncertainties, Rao‐1 and Rao‐2 algorithms are compared with Rao‐3 and it is found that the Rao‐3 algorithm performed better. [ABSTRACT FROM AUTHOR]

In the context of the "Dual-Carbon Strategy", the seamless integration and optimal utilization of renewable energy sources present a pressing challenge for the emerging power system. The advent of demand-side response technology offers a promising solution to this challenge. This study proposes a two-stage response control strategy for multiple DR clusters based on the specific response time characteristics of industrial and residential loads. The strategy enhances the utilization rate of wind power, harnesses the joint response capability of various types of loads on the demand side, and ensures the overall revenue of the load aggregator (LA). It underscores the importance of industrial loads in large-scale energy consumption control throughout the overall consumption response process, while residential load clusters exhibit quick response flexibility. A homogeneous energy consumption sorting unit response strategy is established from the perspective of a residential load variable-frequency air conditioning cluster unit. This strategy addresses the challenge faced by industrial electrolytic aluminum plants in coping with long-term response intervals amidst significant fluctuations in wind power consumption demand, which may lead to incomplete consumption. This study constructs a response model based on industrial and residential time-sharing tariffs, as well as the aggregator consumption penalty price, with the optimal load energy economy index serving as the evaluation criterion. A series of simulations are conducted to comprehensively evaluate the energy consumption of the two load clusters at all times and the total revenue of the aggregator in the response zone. The objective is to achieve a win–win situation for the total wind power energy consumption rate and the aggregator's economy. The results of the simulations demonstrate that the response control strategy proposed in this study enhances the overall energy consumption rate by nearly 4 percentage points compared to a single industrial cluster. The total benefit of the load aggregator can reach CNY 941,732.09. The consumption response scheduling strategy put forward in this paper bolsters wind power consumption, triggers demand response, and significantly propels the comprehensive construction and development of the dual-high power grid. [ABSTRACT FROM AUTHOR]

South Africa possesses extremely rich renewable energy resources, including solar energy and wind energy, in spite of this, South Africa traditionally relies on coal as its primary energy source. Recently, South Africa confronted with severe electricity shortage. With pressure of power shortage and the transition to low- carbon energy, South Africa has been actively promoting the development of renewable energy, aiming to diversify its energy structure. China, as the world's leading country in the development and utilization of renewable energy, has long been committed to helping South Africa achieve renewable energy development. This article reviews the cooperation progress between China and South Africa in renewable energy, and provides an in-depth analysis of the challenges currently faced by both sides. To address such challenges, it is suggested to strengthen the design of top-level cooperation mechanisms, create diverse collaboration platforms, prioritize the exchange and collaboration of scientific talents. This paper aims to provide a typical reference for deepening renewable energy cooperation between China and South Africa. [ABSTRACT FROM AUTHOR]

Utilizing renewable energy (RE) sources can provide a realistic answer to the problem of many nations' energy needs. This paper reviews the current status of using RE to produce electricity in the Kingdom of Saudi Arabia (KSA). The primary aim of the review is to identify and analyze the solar- and wind-energy utilization, problems, and future trends in KSA while taking into account all relevant aspects, associated development hurdles, and suggested solutions. All involved parties will receive useful results and conclusions from a thorough examination and analysis of the pertinent literature that can assist them in understanding the potential of solar and wind energy, considering all the linked issues. [ABSTRACT FROM AUTHOR]

Demand-side flexible load resources, such as Electric Vehicles (EVs) and Air Conditioners (ACs), offer significant potential for enhancing flexibility in the power system, thereby promoting the full integration of renewable energy. To this end, this paper proposes an optimal allocation method for demand-side flexible resources to enhance renewable energy consumption. Firstly, the adjustable flexibility of these resources is modeled based on the generalized energy storage model. Secondly, we generate random scenarios for wind, solar, and load, considering variable correlations based on non-parametric probability predictions of random variables combined with Copula function sampling. Next, we establish the optimal allocation model for demand-side flexible resources, considering the simulated operation of these random scenarios. Finally, we optimize the demand-side resource transformation plan year by year based on the growth trend forecast results of renewable energy installed capacity in Jiangsu Province from 2025 to 2031. [ABSTRACT FROM AUTHOR]

With the modernization and intelligent development of agriculture, the energy demand in rural areas continues to increase, which leads to an increased operational burden on the existing rural distribution network. Integrated energy stations (IESs), in rural areas, use renewable energy sources such as biogas, wind power, and photovoltaic as energy inputs, which can fully improve energy efficiency and help reduce the operating load and peak valley difference of rural distribution networks. In this paper, a multistage planning model is proposed for rural distribution networks with IESs based on the robust optimization method. Firstly, a rural distribution network operation framework with IESs is proposed, and a mathematical model of rural IESs is built based on the energy hub (EH). Then, the multistage robust planning model of rural distribution net-works with IESs is developed and typical scenarios of stochastic source and load are generated based on improved k-means. An iterative solution method fora two-stage robust optimization method is proposed based on the nested column constraint generation (NC&CG) algorithm. Finally, the effectiveness of the presented model and solution approach is assessed through case studies on a modified IEEE 33-node distribution network and a real 152-node distribution network. [ABSTRACT FROM AUTHOR]

The growing energy demand, the increase in its price, and the increasing pollution worldwide are considered large problems that need unusual solutions. To overcome these problems, it is necessary to search for innovative solutions using renewable resources and energy management of the energy systems. Energy management means reducing the operating, maintenance, and generation costs of the system and enhancing system performance with methods such as power loss reduction and stability enhancement and alleviating the harmful emissions to the environment. Thus, the energy management of a micro-grid has become one of the most vital aspects of the power or energy system all over the world. The objective of this paper is to optimize a multiobjective problem of a renewable energy resources (RER) based multimicrogid (MMG) system considering the output variation of the photovoltaic (PV) and wind Turbine (WT) as renewable energy resources and variation of the load demand and electricity prices. In this paper three microgrids (MGs) are connected with IEEE 33-bus distribution system each MG consists of a PV and WT. A three objective functions are modeled for this system to optimize the total annual cost, the voltage deviation and the voltage stability index (cost - performance multiobjective function). The optimization problem is solved with particle swarm optimization (PSO) technique for the system with and without RER. A comparison is carried out with two other optimization techniques, mountain gazelle optimization (MGO) and gorilla troop optimization (GTO). The results of the simulation show that the system cost is considerably reduced and the performance optimized. [ABSTRACT FROM AUTHOR]

The electrification of vehicles has emerged as a pivotal technique for addressing environmental concerns and reducing reliance on conventional fuel sources. Conversely, the best way to incorporate renewable energy into electric vehicles (EVs) is still a challenging task, particularly in enhancing the performance of EVs through efficient energy management. The transition to EVs has gained momentum as part of global efforts to mitigate environmental impacts and reduce dependence on fossil fuels. This paper proposes a hybrid method for enhancing EV performance through buck‐boost converters with renewable energy integration. The proposed technique is the joined execution of Flying Foxes Optimization (FFO) and Viscoelastic Constitutive Artificial Neural Networks (vCANNs) techniques. The proposed method's goal is to enhance the energy efficiency, minimize EV charging cost, and mitigating environmental impacts. The renewable energy sources: solar panels, fuel cells, and wind turbines, are integrated into the EV power system through buck‐boost converters. The buck‐boost converter's control signal is optimized through the FFO method. vCANNs are used to predict these control parameters. The proposed strategy is executed in MATLAB software and is compared with existing strategies. In comparison with other current approaches like particle swarm optimization, heap based optimizer, and wild horse optimize, the proposed method achieves a high efficiency of 99% and low cost of 0.05 €/KWh. [ABSTRACT FROM AUTHOR]

Virtual Inertia (VI) emulation becomes crucial to improve the stability of Hybrid Renewable Energy Systems (HRES). VI emulation needs a proper VI Controller (VIC) to adapt in various working conditions. This paper proposes the stability improvement of HRES with VIC based on optimized Fractional Order Proportional-Integral-Derivative (FOPID) with Harris Hawk Optimization (HHO). The proposed method is implemented on HRES that consists of Photovoltaic Energy Systems (PVES), Wind Energy Systems (WES), Synchronous Generator Energy Systems (SGES), and Energy Storage Unit (ESU). Based on the statistical assessment and convergence curve analysis, HHO performance in finding the optimal parameters is 31.98% to 41.62% better than the other well-known algorithms. Besides that, the VIC-FOPID-HHO gives the best stability improvement compared to basic PID and the other algorithms. In uncertain behavior of RES scenarios, the improvement by VIC-FOPID-HHO is indicated by frequency nadir reduction up 89.44%, overshoot reduction up to 98.45%, and better settling time up to 61.48%. In multi-level load shedding scenarios, the improvement by VIC-FOPID-HHO is indicated by frequency nadir reduction up 93.67%, overshoot reduction of up to 97.4%, and better settling time of up to 49.94%. [ABSTRACT FROM AUTHOR]

Unintentional islanding, defined as the inadvertent separation of distributed generators from the main grid, is a significant issue for wind power and synchronous-based microgrid. Islanding events must be detected to ensure safety and protect utility workers and equipment. The improper detection islanding may lead to power quality degradation, grid protection interference, and out-of-phase reclosing. To overcome these issues, researchers developed the islanding detection technique. This paper proposes a method, for accurately discerning between instances of islanding and those that are not islanding-related events, that employs two new criteria: (1) superimposed positive sequence impedance (SPSI) and (2) superimposed negative sequence impedance (SNSI). The islanding detection indices (IDI1 and IDI2) are calculated using the SPSI and SNSI. If the values of IDI1 and IDI2 exceed a predefined threshold, islanding is detected. The performance of the proposed approach is tested using the MATLAB/Simulink platform. The simulation results show that the proposed technique detects islanding in 20 ms at nearly zero power mismatch and under balanced conditions. Therefore, it limits unwanted tripping caused by various types of non-islanding events. The proposed technique is compared with existing schemes to demonstrate its superiority. [ABSTRACT FROM AUTHOR]

Renewable energy, sometimes called 'clean energy,' comes from natural processes or constantly renewed sources. For instance, wind and sunlight continue to move and shine, even though their availability depends on time and weather. In recent years, the high penetration of renewable energy sources, especially wind energy, has created new problems in power networks. One of the paramount concerns that we, as renewable energy specialists, must address is the inherent variability and unpredictability associated with the power generation of wind turbines. Using wind energy in microgrids with lower power and voltage levels can make them much more effective because wind energy is naturally variable. This issue specifies the need to perform a possible analysis of micro-grids that use wind energy to generate power. In this paper, the small signal stability of micro-grids will be studied under the influence of production uncertainty from wind energy. For this purpose, the method based on Gram-Charlier expansion is used as a possible numerical analysis method. Also, a method specific to micro-grids will be used to obtain the equations of the system's state, which provides excellent flexibility for modelling new components. [ABSTRACT FROM AUTHOR]

The measures needed to limit global warming pose a particular challenge to current fossil fuel exporters, who must not only decarbonise their local energy systems, but also compensate for the expected decline in fossil fuel revenues. One possibility is seen in the export of green hydrogen. Using Algeria as a case study, this paper analyses how different levels of ambition in hydrogen exports, energy efficiency and fuel switching affect the cost-optimal expansion of the power sector for a given overall emissions reduction path. Despite falling costs for photovoltaics and wind turbines, the results indicate that in countries with very low natural gas prices, such as Algeria, a fully renewable electricity system by 2050 is unlikely without appropriate policy measures. The expansion of renewable energy should therefore start early, given the high annual growth rates required, which will be reinforced by additional green hydrogen exports. In parallel, energy efficiency is a key factor as it directly mitigates CO2 emissions from fossil fuels and reduces domestic electricity demand, which could instead be used for hydrogen production. Integrating electrolysers into the power system could potentially help to reduce specific costs through load shifting. Overall, it seems advisable to analyse hydrogen exports together with local decarbonisation in order to better understand their interactions and to reduce emissions as efficiently as possible. These results and the methodology could be transferred to other countries that want to become green hydrogen exporters in the future and are therefore a useful addition for researchers and policy makers. • The required strong capacity expansion for PV and wind is challenging, but feasible. • "Energy efficiency first" benefits both the local energy transition and H2 exports. • Fully renewable power systems require a suitable political framework. • Higher hydrogen exports might help to reduce the specific power system cost. • Fossil capacity expansion must be stopped to avoid lock-ins and higher future costs. [ABSTRACT FROM AUTHOR]

The electrical energy demand is growing every day. Fossil fuel-based electrical power generation pollutes the environment. So, to fulfil the electrical energy demand, clean renewable energy sources like solar and wind are emerging as viable alternatives. But the amount of power generated from solar and wind energy sources is varying in magnitude and is intermittent in nature. So, predicting solar and wind power generation is indispensable for efficient planning and controlling of a renewable energy system. This paper aims to identify a machine learning model suitable for time-series prediction of wind power and also surveys the predominantly existing machine learning followed by the deep learning approaches. The main objective of this study is to solve a real-life problem in the renewable energy sector by accurately estimating the amount of power generation production per hour by applying machine learning techniques using historical wind power energy production data. Hence, historical wind data set with input parameters like wind speed, wind direction air temperature, pressure, and output parameters of wind power generation is considered for training and testing using mean absolute error for evaluating the prediction accuracy. Layers from simple to complex deep layers are trained and tested for prediction accuracy, i.e. baseline, linear, dense, multidense, convolution neural networks, and long short-term memory. Investigating further, LSTM and Residual LSTM prove to have higher mean absolute prediction accuracy of 0.0987 and 0.0958. This eventually enlightens the wind power forecasting models and new paths for further research applications. [ABSTRACT FROM AUTHOR]

The need for renewable energy sources is recently necessitated by attaining sustainability and climate change mitigation. Accordingly, the use of renewable energy sources has been growing rapidly during the last two decades. Yet, the potentials of renewable energy sources are generally influenced by several climatic factors that either determine the source of energy such as wind speed in the case of wind power or affect the performance of system such as the reduction in solar PV power production due to temperature increase. This highlights the need for assessing climate change impacts on renewable energy sources in the future to ensure their reliability and sustainability. This paper is intended to assess impacts of climate change on wind and solar potential energy in Egypt by the year 2065 under RCP 8.5 scenario. For this purpose, a GIS-based methodology of three main steps was applied. The results revealed that solar energy potential in Egypt is expected to be relatively less vulnerable to climate change compared to wind energy. In this respect, it was found that while wind energy potential was estimated to range ± 12%. By the year 2065 under RCP 8.5 scenario, PV module power is expected to decrease by about 1.3% on average. Such assessment can assist in developing more sustainable and flexible renewable energy policy in Egypt. [ABSTRACT FROM AUTHOR]

The need for environmentally-friendly energy generation and massive exploitation of fossil fuels are leading to the increased demand for renewable energy sources like photovoltaic (PV) and wind energy, etc. The fluctuating behaviour of PV causes various disadvantages, of which the generation of Inter-harmonics is the major problem. The production of Inter-harmonics by the load-interfacing inverters is primarily due to the Maximum Power Point Tracking (MPPT) techniques employed in PV system. The requirement of multiple perturbations in tracking peak points is the primary cause behind the inter-harmonic generation. Since solar irradiation varies throughout a specific period, there can be situations, where the operating irradiance changes abruptly from one state to the other. These abrupt changes generate enormous fluctuations in the MPPT power output, resulting in an inter-harmonics generation. This paper proposes an evolved INter-HARmonic-Elimination (INHARE) algorithm for minimizing the PV fluctuating behaviour, leading to lower voltage ripple and settling time. The proposed INHARE algorithm is examined for a 0.52 kW PV system, where the Inter-harmonics content is reduced considerably in load current for the case of varying irradiances at a constant temperature. [ABSTRACT FROM AUTHOR]

Carbon emission flow is an effective tool to obtain carbon emission distribution in power systems, which can guide the active carbon reductions of consumers through proper incentive schemes. With the increasing penetration of renewable energy, the existing single-valued carbon emission flow model based on deterministic forecasted power outputs cannot describe the impacts of renewable energy uncertainties on carbon flows. Therefore, this paper mainly focuses on the assessment of the probabilistic carbon emission flow through the power systems considering the impacts of load and renewable energy uncertainties. In this paper, the probabilistic carbon emission flow (PCEF) is innovatively proposed. With the PCEF, consumers can make better energy use plans considering their own risk appetites for carbon emission payment. Firstly, the impact factors of the PCEF are modeled, including load and renewable energy uncertainties and integrated carbon intensity of thermal plants. The uncertainties of load, wind power and photovoltaics are modeled based on a p-box method while the uncertainty of hydropower is modeled based on the multi-state model. Besides, the integrated carbon intensity model of thermal plants are proposed considering the carbon intensity variations during the operating, start-up, and shut-down stages. Furthermore, the probabilistic carbon emission evaluation framework is proposed to assess the probabilistic carbon distribution. In this framework, novel probabilistic carbon indices are defined to precisely characterize the carbon emission situation with probabilistic representations. A solution method based on an adaptive interval point estimation (AIPEM) algorithm is proposed to efficiently solve the developed evaluation problem. Tests on an IEEE-30 node system, and a real provincial power system in China validate the effectiveness and application of the proposed model. The results showed that the PCEF can be a useful tool for obtaining the probabilistic representation of carbon emission flow and guiding the consumers' active carbon reduction in power systems. • Probabilistic carbon emission flow is proposed considering multiple uncertainties. • Integrated carbon intensity model for thermal plants is developed. • A p-box model is used to describe the uncertainties of load and renewable energy. • PCEF can help consumers balance the cost and carbon risk when making decisions. [ABSTRACT FROM AUTHOR]

Wind power is a critical source of renewable energy, but the variability of wind speed can impact the efficiency and reliability of wind power generation. To improve the accuracy of wind speed prediction, this paper proposes a novel system that combines deep learning and decomposition algorithms based on ensemble optimization methods. Firstly, the paper proposes an innovative hybrid modal decomposition (HMD) method that extracts accurate features from wind speed data using Singular Value Decomposition (SVD), modal number selection method, and Variational Mode Decomposition (VMD). Secondly, the variable screening method identifies relevant factors affecting wind speed as fixed inputs. Then, the stationary test method is used to identify the sequences of different features in the mode and establish different prediction models: a bidirectional long-term short-time neural network model (Bi-LSTM) model optimized by artificial hummingbird algorithm (AHA) is used for non-stationary sequences; an ARIMA model is used for stationary sequences. Data from Sotavento and Changma wind farms are used to test this forecasting system. Simulation experiments are carried out from multiple dimensions. The results show that all error indicators are significantly improved, and the proposed prediction system is better than other comparative forecasting schemes. • The research can be practically applied in the field of wind power generation. • The research can help improve the energy utilisation of wind power generation. • A new data decomposition algorithm called HMD is proposed. • A new prediction system based on ensemble optimization idea is proposed. • Combining AHA-optimized neural networks and ARIMA to predict wind speed. [ABSTRACT FROM AUTHOR]

Nowadays, climate change is a major global societal challenge that significantly increases environmental stress. Most international organizations and policies have promoted initiatives to minimize emissions, reduce fossil fuel dependence and increase renewable energy resource integration into different sectors. An energy transformation towards more renewable systems is thus a priority. Under this scenario, the present paper describes and evaluates an alternative energy conversion matrix-based model to combine sector electrification, power generation units from renewables, and new clean technologies. The proposed green matrix-based model allows analyzing future scenarios, including electricity participation in end-use consumption and electric power generated by renewables – potentially integrated into different sectors –. The proposed model is evaluated in the state of Maine (United States). This case study is focused on decarbonizing both residential heating and transport sector through the integration of large offshore wind power plant. Results and discussion is also included in the paper, providing expected energy demand reductions and decreasing emissions through the integration of renewables. This energy transition integration case study is proposed in three road-maps with different penetration rates and time scales. The proposed green matrix-based model can be also applied to other areas and energy resources, as an alternative way to analyze and estimate renewable integration into different sectors. • Green Electrical Matrix to link electricity supply and end-use for energy transition. • Assesses renewable potential and electrification tech for end uses, e.g., transport. • Applies model to Maine, USA to show reductions in fossil fuels and emissions. • GIS and MCDM for optimal offshore wind location, maximizing electricity generation. • Short, medium, and long-term projections of renewable integration into energy system. [ABSTRACT FROM AUTHOR]

• The Prophet model is used to forecast wind and photovoltaic power generation. • An Upgraded Kernel Search Optimization (UKSO) is proposed. • A Two Time Energy Dispatch model is developed for joint scheduling. • More competitive results are offered by UKSO in dynamic power dispatch problems. Integrating renewable energy sources, such as wind and photovoltaic power generation, into the power grid is crucial for sustainable power system development and mitigating pollutant emissions. However, these sources' inherent uncertainty and randomness pose significant challenges to grid operations. Metaheuristic algorithms offer efficient solutions to optimization problems in this context and are widely employed in practice. Kernel Search Optimization (KSO) has emerged as a prominent metaheuristic algorithm due to its parameter-free nature and applicability to power dispatch problems. Nevertheless, KSO's limited local search capabilities necessitate enhancements for improved performance. This paper introduces an enhanced variant of KSO, termed Upgraded Kernel Search Optimization (UKSO), which incorporates differential evolution techniques, including mutation, crossover, and selection mechanisms, to bolster KSO's search capabilities and overall performance. The efficacy and feasibility of UKSO are evaluated through comprehensive testing using the CEC2017 benchmark. Comparative analysis demonstrates that UKSO outperforms other algorithms by achieving more optimal solutions. To address power dispatch challenges in the context of renewable energy sources, a Two-Time Energy Dispatch (TTED) model is proposed, leveraging a weighted summation approach to minimize total fuel costs and pollution emissions simultaneously. The application of the Prophet model, based on Bayesian fitting, facilitates accurate prediction of wind and photovoltaic power outputs. Through experimentation across small (8 and 14 units) and large (58 units) systems, the feasibility of UKSO and TTED is validated. In complex scenarios, UKSO demonstrates a 0.69% to 3.64% reduction in pollution emissions and a 0.99% to 1.23% decrease in economic costs compared to KSO under varying weight configurations. Furthermore, UKSO achieves a renewable energy generation utilization rate exceeding 90%, emphasizing its ability to iterate efficiently through complex problems and its efficacy in addressing renewable power dispatch challenges. These results highlight the practical significance of UKSO in optimizing power systems and provide valuable insights for future research and applications in this domain. [ABSTRACT FROM AUTHOR]

• A multi-microgrid energy real-time optimal control scheduling strategy is proposed. • Energy storage devices can actively participate in optimal energy scheduling. • Improved resilience and flexibility of energy dispatch for multiple microgrid. • Significantly reduce the number of microgrid connections to the distribution grid. • Maximising the full uptake of renewable energy in multi-microgrid systems. With the increasing prominence of the energy crisis and global climate issues, it has become an important cornerstone of the energy revolution to improve the utilization rate of renewable energy and gradually reduce the use of fossil energy. Nonetheless, the erratic fluctuations and instability of renewable energy sources such as wind and solar energy have presented certain obstacles to the effective utilization of renewable energy. In order to maximize the utilization of renewable energy, enhance its utilization efficiency, and reduce the carbon emission of power supply, this paper first proposes a real-time collaborative optimal energy scheduling and dispatching control model for multi-microgrids. Subsequently, it proposes a real-time optimal control and dispatching strategy for multi-microgrid energy based on storage collaborative. This model considers the energy storage device as an energy management controller, enabling it to participate in the energy collaborative dispatch of multi-microgrid. This enables the energy storage device in the microgrid not only to actively participate in the optimal scheduling of energy, but also to make the energy in the storage device be reasonably distributed in the multi-microgrid system, to maximize the full absorption of renewable energy in the multi-microgrid, thereby reducing the dependence of the microgrid on the distribution network, and significantly improving the elasticity and reliability of the multi-microgrid system. Finally, the feasibility and effectiveness of the proposed optimal control scheduling strategy are verified by simulation. [ABSTRACT FROM AUTHOR]

• High penetration of Renewable Energy Sources (RESs) has reduced frequency stability in modern power systems. • Use of multiple Virtual Inertial (VI) units improves frequency stability. • Coordination of multiple VI units increases energy efficiency. • A comprehensive review of VI implementation methods has been done. • Coordination of VI units has been analyzed. The modern power system is progressing from a system based on synchronous generators toward systems with high penetration of renewable energy sources (RESs) such as photovoltaic (PV) and wind power generating units which are connected to the grid through inverters. RES units will represent a significant share of the power generation in near future; therefore, the conventional approach of integrating them into the grid may lead to frequency instability. Many researchers have suggested the use of inverters with virtual inertial control methods to act as synchronous generators in the grid and maintain and increase the frequency stability. This paper presents a comprehensive overview of virtual inertial strategies and current control strategies and makes a comprehensive comparison while describing their characteristics. Then, different types of stability analyses in the presented methods are examined and examples of each are presented. In continuation and in addition to the review studies conducted in this field, methods presented with the aim of improving the virtual inertial control are carefully examined and their characteristics according to the number of resources used, the adaptivity of parameters, the use of optimization methods, the issue of coordination between several resources and the type of communication network are studied. Moreover, a comprehensive review of multiple- virtual synchronous generator (VSG) methods to develop and implement the concept of virtual inertia in weak grids are presented. Finally, a discussion of challenges and research directions is presented, particularly pointing to the integration of multiple virtual inertial units at the system level. [ABSTRACT FROM AUTHOR]

• A bi-level market-clearing mechanism to minimize the WPS is proposed. • Considering the impact of uncertainty in WPG and load demand. • Determining the optimal participation of DR in overall objectives. • The upper level is to minimize the WPS, LS, PLs, and CO 2 emissions. • The lower level is to maximize social welfare. • Studying the impact of the integration between the DR and the DTLR. Spilling has already occurred as a result of rising the penetration of intermittent renewable generation, and it is anticipated that the level of renewable energy curtailment will continue to soar. This leads to an increase in operating costs, CO 2 emissions, and not good utilization of renewable energy resources. A bi-level multi-objective optimization model is proposed in this paper to reduce wind power spillage (WPS) based on demand response (DR) and dynamic thermal line rating (DTLR). In the upper level, multiple objectives will be satisfied based on the optimal allocation and time of DR programs considering DTLR obtained in the lower level. The minimization of WPS, load shedding, power losses, and CO 2 emissions are the objectives of this level. While the lower-level aims to maximize social welfare under different scenarios and overall system constraints. Under the uncertainty of the wind power and load demand, a collection of lower-level problems that represent the market clearing conditions is used to constrain the upper-level. The effectiveness of the proposed algorithm is examined on a modified two-area IEEE 24-bus test system. Results depict that the suggested bi-level model enables considerable reductions in the WPS by up to 32.7 %. Also, there is an enhancement in load shedding, power losses, and CO 2 emissions by 28.93 %, 23.07 %, and 13.9 % respectively. Finally, the social welfare increased by up to 36.6 %. [ABSTRACT FROM AUTHOR]

As a result of the "Belt and Road" and "Going Global" policies, a growing number of Chinese power companies are expanding overseas, implementing global development strategies, and making investments in the overseas power industry. Several countries are undergoing an energy transition because of the rapid development of the world economy. This is being done to address the climate change issues that are a result of the overuse of fossil fuels. Brazil is also accelerating its pace of power transformation in its position as a major power generating country in Latin America. In addition to being the largest economy in South America, Brazil is also the country in which China has made the most investments. Additionally, Brazil is increasing its development efforts in wind power, photovoltaics, and other renewable energy sources in response to a large demand for renewable energy sources. Brazil's renewable energy sector offers a great deal of potential for investment based on solid foundations for cooperation between China and Brazil. The Grey Prediction Model was used for this research to forecast Brazil's renewable energy generation installed capacity, and the results show a positive trend in Brazil's renewable energy generation. There is a rapid growth in wind and photovoltaic power generation over the next 5 years, with growth rates reaching 50.39% and 182.99%, respectively, suggesting that there is potential for a broad range of development. Following this, the research applies factor analysis to assess investment risks associated with Brazil's renewable energy sector from 2000 to 2020. Based on the results of the study, Chinese power companies investing in Brazil's renewable energy sector face the greatest political risk, while other risks gradually decrease. To avoid political risks when investing, companies should place a high priority on preventing them. [ABSTRACT FROM AUTHOR]

• Targeting United States residential HVAC electricity load using wind energy. • Partial load targeting concept using renewable energy source. • Capacity factor and estimation analysis of wind energy potential production. • Offshore versus onshore capacity factor estimated production performance. • Heating and cooling load curtailment methodology and analysis. This paper explores the concept of partial-load targeting through presenting a technical analysis for modelling the electrical energy demand of residential HVAC systems spanning the U.S. using onshore and offshore wind turbine technology. The proposed modeling technique uses DOE backed survey data and both known and theoretical capacity factor (CF) levels to determine the amount of wind turbines required to sustain the electrical load necessary for the heating and cooling components of residential housing units within all major U.S. climate regions. To perform the analysis, annual load data sourced from the energy information administration (EIA) surveys utilizing National Renewable Energy Laboratory (NREL) benchmark simulation house parameters are calculated and compared to wind turbine CF data to determine an equivalent range of turbines required to satisfy the target load. Two production profiles based on the CF range of each region are created using onshore and offshore scenarios. These profiles are then fitted to the corresponding average annual household load for the particular climate region. An analysis on the results is then conducted to determine overall potential load reduction as well as observe characteristics between the offshore and onshore data. [ABSTRACT FROM AUTHOR]

With the high penetration of renewable generation systems in the power grid, the accurate simulation of the uncertainty in renewable energy generation is vital to the safe operation of the power system This paper proposes a novel controllable method for renewable scenario generation based on the improved VAEGAN model. The standard VAEGAN model is first improved using spectral normalization technique and the generator of GAN is trained using VAE. Then, the external and internal interpretable features in the latent space are learned as the controllable vector utilizing the principle of mutual information maximization. Finally, the renewable energy scenarios with overall features are generated using the external universal meteorological features, and renewable energy scenarios with specific features are generated by tuning along the internal interpretable feature of the controllable vector in the latent space. The proposed approach is used to produce real-time series data for renewable energy including wind and solar power. Experiments demonstrate that our method has a better performance in terms of controllable generation and enables the generation of preference patterns covering various statistical features. • A novel VAEGAN model with controllable interpretable features is proposed. • External universal meteorological features are used to generate scenarios with unknown patterns. • Internal interpretable latent features are designed to generate scenarios with specific features. • The results show a better performance of the proposed method on controllable scenario generation. [ABSTRACT FROM AUTHOR]

Introducing solar and wind power into distribution networks (DNs) is an important initiative to promote the cleanliness and low-carbonization operation of DNs. However, the source and load outputs are uncertain in DNs, which will lead to problems of inaccurate regulation and insufficient renewable energy utilization. Therefore, this study provides an efficient multi-timescale regulation strategy for DNs based on active and passive resources combined. First, the day-ahead regulation model is constructed to minimize the total operating cost in a day for DNs. It aims to determine the on-load tap changer (OLTC) and capacitor bank (CB) switching schemes and consider them as the constraints for intraday regulation by power flow unification. Then, an intraday source and load uncertainty model is considered, which aims to cope with the problems of inaccuracies in the day-ahead source and load prediction. Based on this, the objective function of the intraday regulation model is set. It aims to minimize the deviation from the total day-ahead cost and then ensure the optimal system economy and determine the intraday new energy and energy storage system (ESS) short-time action scheme. Finally, the example results show that the proposed scheme can achieve the maximum utilization of renewable energy and improve the low-carbon economic operation of the whole system. [ABSTRACT FROM AUTHOR]

Hydrogen energy storage plays an important role in improving the operation efficiency and reliability of power systems with high wind energy penetration. Hydrogen to power (HtP) system is the key link of hydrogen applications. However, the single HtP equipment is limited in power output range and efficiency. Hybrid HtP system is an important scheme to realize the performance complementary. A wider power output range can enrich the application scenarios of hydrogen energy storage and provide flexible and reliable energy scheduling in larger and more complex energy systems. In this study, first, a power system including traditional units, wind power generation and hybrid HtP system is established. Second, a bi‐layer hybrid HtP system optimal configuration model considering planning and operation is constructed. Then, a bi‐layer optimal configuration method based on the improved PSO algorithm is proposed. Finally, the optimization model and its solution method are applied to IEEE RTS‐96. The discussions based on the optimization results show that the hybrid HtP system has significant contributions in optimizing the output cost of traditional units, improving the utilization of wind energy, and reducing load shedding losses. [ABSTRACT FROM AUTHOR]

The use of renewable energy by converting it into heat is an important form of storing energy in a usable form and improving the energy supply flexibility; therefore, the electricity–heating system (EHS) can cope with load fluctuations. However, relevant research is lacking on improving the energy supply limitations by the optimal dispatch of energy flow at the typical EHS, such as the coupled CHP–heat pump–thermal storage system (CCHTS). Based on the study of the energy supply characteristics of the CCHTS for extending the energy supply limitation, this study develops an optimal dispatch method using a heat pump (HP) and the thermal storage (TS) of heating networks to improve the flexibility of the CCHTS and the accommodation capacity of renewable energy. The maximum and minimum energy supply limitation model of the CCHTS and the output power characteristic model are established. Based on the piecewise power supply constraint, the energy flow of the EHS is optimized by using the quadratic programming algorithm. The CCHTS can significantly improve the energy supply flexibility; both coupled combined heat and power (CHP) + HP and coupled CHP + TS can improve the power supply flexibility, but the enhanced effect of CHP + HP is better than that of CHP + TS. An increase of 7.6% in wind power consumption is achieved. The consumption of renewable energy increases by 17.9% in the energy flow optimization results. [ABSTRACT FROM AUTHOR]

The carbon capture‐transportation‐utilization (C‐CTU) chain strengthens the coupling between terminal energy consumption and renewable energy resources (RES), achieving carbon emission reduction in power generation sectors. However, the dynamic operation of the C‐CTU chain and the uncertainties induced by RES output pose new challenges for the low‐carbon operation. To address above challenges, the nonlinear dynamic operation model of C‐CTU chain is first proposed in this study. It is further incorporated into the day‐ahead operation scheme of the electricity‐carbon integrated system considering the stochastic nature of wind power. This scheme is treated as a two‐stage stochastic integer programming (TS‐SIP) problem with a mixed‐integer nonlinear recourse. By means of the polyhedral envelope‐based linearization method, this recourse is reformulated into its linear counterpart. To further improve the computational performance of classical decomposition algorithms, a novel Benders decomposition framework with hybrid cutting plane strategies is proposed to obtain better feasible solutions within a limited time. Simulations are conducted on two power system test cases with the C‐CTU chain. Numerical results indicate that the engagement of C‐CTU chain promotes the low‐carbon economic operation of the power system. Also, the proposed decomposition algorithm shows a superior solution capability to handle large‐scale TS‐SIP than state‐of‐the‐art commercial solvers. [ABSTRACT FROM AUTHOR]

Over the years, increasing energy demands with the growth of the population and the development of technology have caused more fossil fuel consumption. Besides, environmental pollution and climate change, which are vital importance for humanity, are encountered. In order to avoid these dangerous situations, people have started to turn to clean and renewable energy sources such as wind energy. Due to the rapid development of such situations, it is very important to obtain information on the determination of the regions where wind energy facility will be installed and the characteristics of the wind speed. Wind power estimation can be made through various statistical distributions used to explain the characteristics of wind speed data. Rayleigh, Weibull, Nakagami, Gamma, Logistic, Loglogistic, Lognormal and Burr Type XII distributions, which are frequently used in the wind energy literature, are discussed in this study and the performances of the specified distributions are compared through the data sets obtained from the stations in Istanbul from Marmara region. One of the most preferred methods in estimation problems is the maximum likelihood method, and a differential evolution algorithm is proposed for ML estimation of the parameters of the distributions examined in the study. In addition, various model selection criteria are also utilized to determine the distribution that best fits the wind speed data. [ABSTRACT FROM AUTHOR]

Amid rising energy demands in rural areas, thorough resource assessments for initiatives such as wind power are crucial. This study involves a land resource assessment for wind power generation on the rustic Sibuyan Island in the Philippines, which is currently experiencing an electricity shortage. A comprehensive overview of the island's suitability for wind energy projects is performed via evaluation and analysis using geospatial data and multi-criteria decision making (MCDM). The research results indicate that 50.44% (220.68 km2) of the island's land area is categorized as 'poorly suitable' since it considers protected areas where developments are not allowed. Only 0.08% (0.35 km2) of the island can be classified as 'marginally suitable', while 9.15% (40.73 km2), 36.64% (176.39 km2), and 0.69% (3.05 km2) are labeled as 'moderately suitable', 'suitable', and 'highly suitable', respectively. This confirms the potential for wind energy exploration on the island. Delineating the suitability levels provides a foundational framework for stakeholders that enables them to identify optimal sites for wind power, sustain the island's resources, and contribute to the renewable energy landscape of this rural location. Overall, this study, underpinned by data analysis, offers invaluable insights for decision making in wind power development, with the presented framework adaptable to other areas of interest. [ABSTRACT FROM AUTHOR]

As the building stock is projected to double before the end of the half-century and the power grid is transitions to low-carbon resources, planning new construction hand in hand with the grid and its capacity is essential. This paper presents a method that combines urban building energy modeling and local planning of renewable energy sources (RES) using an optimization framework. The objective of this model is to minimize the investment and operational cost of meeting the energy needs of a group of buildings. The framework considers two urban-scale RES technologies, photovoltaic (PV) panels and small-scale wind turbines, alongside energy storage system (ESS) units that complement building demand in case of RES unavailability. The urban buildings are modeled abstractly as "shoeboxes" using the Urban Modeling Interface (umi) software. We tested the proposed framework on a real case study in a neighborhood in Chicago, Illinois, USA. The results include estimated building energy consumption, optimal capacity of the installed power supply resources, hourly operations, and corresponding energy costs for 2030. We also imposed different levels of CO2 emissions cuts. The results demonstrate that solar PV has the most prominent role in supplying local renewables to the neighborhood, with wind power making only a small contribution. Moreover, as we imposed different CO2 emissions caps, we found that ESS plays an increasingly important role at lower CO2 emissions levels. We can achieve a significant reduction in CO2 emissions with a limited increase in cost (75% emissions reduction at a 15% increase in overall energy costs). Overall, the results highlight the importance of modeling the interactions between building energy use and electricity system capacity expansion planning. • Optimization framework combines urban building energy modeling and local planning of renewables. • Urban neighborhood buildings are modeled in an abstracted manner as "shoeboxes". • Considering local wind and solar resources along with energy storage and grid supply. • PV has the most prominent role in supplying local renewables to the neighborhood. • Substantial reduction in CO2 emissions (75%) can be achieved with limited increase in energy costs (15%). [ABSTRACT FROM AUTHOR]

• Renewables are variable and require operation flexibility. • Colombia expects more than 10 GW of solar and wind power capacity by 2030. • We quantify the flexibility requirements for the net load and ramps. • Conventional reserves must be doubled at noon. • Conventional generators must provide 7%–10% of additional ramps. Multiple factors drive the energy transition toward power systems based on renewable energies. The use of renewable energies brings new challenges for operators since they are difficult to predict. Power system flexibility is the ability to handle differences between supply and load and can be quantified to measure the effects of renewable energies on power systems. Colombia expects to triple the current solar and wind power capacity by 2030; therefore, it is essential to evaluate the flexibility of the Colombian power. This paper presents a pioneering analysis in the local context, filling a local research gap, by quantifying the flexibility requirements of the Colombian power system based on changes in the net load and its ramps and considering wind and solar generation scenarios and load projections. This analysis has not been conducted locally and can be used by policymakers to determine the country's energy transition path. The results show that renewable power impacts conventional generators. Hydro generation handles high ramp deviations well under normal conditions but faces challenges during low hydrology periods. The Colombian power system is projected to be flexible in 2030 but less so in 2050 due to overproduction and higher net load ramps. [ABSTRACT FROM AUTHOR]

The growing reliance on hydroelectric power and the risk of future droughts pose significant challenges for power systems, especially in developing countries. To address these challenges, comprehensive long-term energy planning is essential. This paper proposes an optimized electrical system for 2050, using Ecuador as a case study. For forecasting electricity demand, a Neuro-Fuzzy Adaptive Inference System is employed, utilizing real historical data. Subsequently, the EnergyPlan software constructs a long-term energy consumption model, exploring three scenarios based on Ecuador's energy potential. The first scenario represents a 'business as usual' approach, mirroring the current trend in the Ecuadorian electricity system. In contrast to the second scenario, it encompasses a broader range of renewable sources, including offshore wind, pumped storage, biomass, and geothermal energy. The third scenario extends the second one by incorporating demand response systems, such as vehicle-to-grid and hydrogen-to-grid technologies. In terms of novelty, this study highlights the innovative use of the Neuro-Fuzzy Adaptive Inference System for demand forecasting, along with a comprehensive exploration of multiple scenarios to optimize the electrical system. Research findings indicate that the integration of these new renewable energy sources not only reduces electricity import costs but also ensures surplus electricity production. Consequently, it is anticipated that the 2050 electricity system will reduce its dependence on hydroelectric energy while adopting photovoltaic and wind energy with penetration rates of 65 %, 11.2 %, and 9 %, respectively. This transition will be facilitated by a pumped storage system with a 28 % penetration rate and enhanced connectivity with neighboring countries, enabling the seamless integration of electric and hydrogen vehicles. [Display omitted] • Introducing a long-term electricity demand forecast with adaptive neuro-fuzzy inference. • An energy schedule for Ecuador in 2050 is derived from the demand forecast. • Renewable sources reduce costs, surplus electricity, and imports, enabling a 100 % renewable system. • By 2050, Ecuador's electrical system will rely on hydro, photovoltaic, wind, and pumped hydro power. • The optimized Ecuadorian electrical system will enable V2G and H2G integration in 2050. [ABSTRACT FROM AUTHOR]

As global energy demand continues to grow, the demand for renewable energy continues to rise. To meet this demand, many countries have begun to actively promote the development of offshore wind power projects. Since siting is the key to the success of offshore wind power projects, this paper aims to develop a mixed decision-making framework to address the siting of offshore wind power station (OWPS). Firstly, establish a relatively comprehensive OWPS evaluation attribute system. Secondly, the pythagorean hesitation fuzzy set (PHFS) was used to describe the evaluation information in the OWPS site selection process. It can ensure the hesitancy and fuzziness of evaluation information, and is not constrained by the sum of membership and non membership being less than 1. Thirdly, the improved SWARA method is used to calculate the weight of attributes. Finally, an extended MULTIMOORA (Multi-Objective Optimization on the basis of a Ratio Analysis plus the Full Multiplicative form) method is proposed to rank the siting alternatives for OWPS. The proposed method is applied to the study of OWPS site selection in Shandong Province, China. Through calculation, the comprehensive ranking values are G 1 = 14, G 2 = 7, G 3 = 4, G 4 = 8 and G 5 = 13. Therefore, A 3 is the best alternative. Through sensitivity analysis and comparative analysis, it was verified that the proposed model has good reliability and stability. • A new multi-attribute decision-making model is proposed. • The attribute system for site selection evaluation of OWPS is established. • Expression of evaluation information by pythagorean hesitation fuzzy set. • Pythagorean hesitation fuzzy set and SWARA are combined to calculate attribute weight. • An improved MULTIMOORA method is proposed. [ABSTRACT FROM AUTHOR]

This study presents a data-driven assisted real-time optimization model which is an innovative approach to address the challenges posed by integrating Submerged Arc Furnace (SAF) systems with renewable energy sources, specifically photovoltaic (PV) and wind power, with modern intelligent energy terminals. Specifically, the proposed method is divided into two stages. The first stage is related to data-driven prediction for addressing local time-varying renewable energy and electricity market prices with predicted information, and the second stage uses an optimization model for real-time SAF dispatch. Connections between intelligent energy terminals, demand-side devices, and load management systems are established to enhance local renewable resource utilization. Additionally, mathematical formulations of the operating resistance in SAF are explored, and deep neuron networks are employed and modified for dynamic uncertainty prediction. The proposed approach is validated through a case study involving an intelligent energy terminal with a 12.5 MVA SAF system and 12 MW capacity renewable generators in an electricity market with fluctuating prices. The findings of this research underscore the efficacy of the proposed optimization model in reducing operational costs and enhancing the utilization of localized renewable energy generation. By integrating four distinct dissatisfaction coefficients into the optimization framework, we demonstrate the model's adaptability and efficiency. The application of the optimization strategy delineated herein results in the SAF system's profitability oscillating between $111 and $416 across various time intervals, contingent upon the coefficient settings. Remarkably, an aggregate daily loss recovery amounting to $1,906.84 can be realized during the optimization period. Such outcomes not only signify considerable economic advantages but also contribute to grid stability and the diminution of renewable energy curtailment, thereby underscoring the dual benefits of economic efficiency and sustainability in energy management practices. [ABSTRACT FROM AUTHOR]

In order to explore the feasibility of a renewable hybrid energy system in highway tunnels, a scenario-coupled construction method for a highway tunnel renewable hybrid energy system is proposed. Based on this method, a tunnel on a highway in southern China serves as an example, and a renewable hybrid energy system for the highway tunnel is constructed. Using HOMER, the hybrid energy system is simulated, optimized, and analyzed, studying the characteristics of the operation of the highway tunnel hybrid renewable energy system in an off- grid mode. The optimal configuration for the hybrid energy system is proposed. The research results indicate the feasibility of constructing a highway tunnel renewable hybrid energy system by utilizing natural resources within the road area (solar energy, wind energy). The hybrid renewable energy system for this tunnel can provide 1112392 kWh of electrical energy annually, with the lowest energy cost being $0.17/kW. Furthermore, the hybrid energy system can reduce greenhouse gas emissions, contributing to the sustainable development of the environment. [ABSTRACT FROM AUTHOR]

Malta faces a significant challenge in reducing carbon emissions, with energy consumption in its 153,100 occupied residences contributing to 30% of CO2 emissions. This study focuses on a sample of an 1870s, 80 sq-m footprint, three-story residence, emblematic of similar properties facing marketability issues due to age, structure, and maintenance. The objective is to assess a techno-economic energy and CO2 abatement framework, including advanced lighting devices, appliances, photovoltaics, wind turbines, energy storage, and vehicle-to-grid possibilities. The research evaluates comfortability and calculates potential 25-year kWh reduction and cost savings for each measure. The findings demonstrate the feasibility of implementing diversified renewable and alternative energy sources in such residences. Over 25 years, approximately 250 MWh of energy could be mitigated, leading to a reduction of approximately 140 metric tons of carbon dioxide. The study emphasizes the importance of housing stock efficiency in both new construction and retrofitting, focusing on building performance for health, comfort, and living standards. While most systems are viable, further research is needed for system-wide strategy implementation, particularly in areas like energy storage and wind turbine solutions. The study concludes that adopting emerging technologies could be advantageous in minimizing system costs through innovative building-integrated designs. [ABSTRACT FROM AUTHOR]