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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]

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]

• 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]

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]