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Renewable energy resources pose the issue of intermittency and variability, leading to the "duck curve" problem. The electrification of the transportation sector, specifically the electric vehicle (EV), exacerbates this issue as charging is concentrated during evening hours when renewable energy generation decreases. This study addresses these challenges by analyzing the heterogeneous charging preferences of EV drivers using a discrete choice experiment and latent class logit model. By segmenting drivers into three distinct groups based on their sensitivity to charging costs, time, and station distance, this study reveals significant variations in charging behaviors. Using Jeju Island, South Korea, as a testbed, the impact of different time-of-use (TOU) tariff designs and infrastructure expansion scenarios on the power grid are simulated. The findings indicate that tailored TOU tariffs and strategic deployment of charging stations can reduce renewable energy curtailment by 11.3% and gas-fired power generation by 3.8% by 2030. These findings emphasize the importance of incorporating driver behavior insights into energy policy design to enhance grid stability and optimize renewable energy integration. This study provides valuable policy recommendations for achieving carbon neutrality through effective EV charging management and infrastructure planning. • Tailored EV TOU tariffs can mitigate curtailments and 'duck curve' issue. • Understanding heterogeneous EV charging behavior is important for designing TOU. • Strategic EV charging infrastructure is required for the TOU design effectiveness. • EV TOU could reduce curtailments by 11.3% in Jeju Island, South Korea, by 2030. [ABSTRACT FROM AUTHOR]

The fast-increasing applications of renewable energy and electric vehicles (EVs) pose significant challenges to the safe and economic operation of the power grid. However, through Virtual Power Plant (VPP) technology, EV fleets can be aggregated into large-scale EV generalized energy storage systems, providing scarce dispatchable flexible resources for the future power system. Rapid and accurate prediction of the Aggregated Schedulable Capacity of EV (EVASC) fleets is a crucial technology for VPP with Electric Vehicles (EV-VPP) to participate in various ancillary services of the power system. In this paper, orienting for various dispatch scenarios in the power system, a multi-time scale prediction model of the EVASC for EV-VPPs is presented based on the enhanced Prophet algorithm combining a light gradient boosting machine (LGBM) algorithm. The enhanced Prophet-LGBM algorithm is initially proposed here by incorporating the LGBM algorithm into the traditional Prophet algorithm in series and adding extra sequential features in its input, such as temperature, weather, and alike. In this way, the problem of overfitting and low capability in addressing additional input features in the traditional Prophet is overcome. The proposed enhanced Prophet-LGBM-based prediction method of the EVASC for EV-VPPs is validated using 1.8 million actual charging records of over 4000 charging piles for half a year at the provincial level. The 30-day simulation results of the day ahead and intraday predictions of the EVASC for EV-VPPs show that much higher performances in the prediction accuracy can be achieved, specifically in holidays. • An electric vehicle schedulable capacity forecasting model is presented. • An enhanced Prophet plus a light gradient boosting machine is proposed for the model. • The model can forecast the multi-time scale electric vehicle schedulable capacity. • The model is validated using 1.8 million charging records of 4000 piles • The prediction simulations show much higher accuracy, specifically in holidays. [ABSTRACT FROM AUTHOR]

With the global energy supply shortage and increasingly severe global environmental problems, lignocellulosic biomass is seen as a potential renewable energy source to replace traditional fossil fuels. Corn stover, an abundant renewable lignocellulosic biomass, has good application potential in biofuel production. This study innovatively proposes a polygeneration system that produces dimethyl ether (DME) and power simultaneously. The system integrates biomass gasification, supercritical CO 2 Brayton cycle (S-CO 2) power generation, and water gas shift (WGS), effectively converting corn straw into DME while generating electricity. Additionally, the carbon dioxide (CO 2) produced in the system is captured using monoethanolamine chemical absorption methods. Perform sensitivity analysis on the system, analyze the impact of multiple factors on the product DME, and optimize various parameters. In addition, the system's energy, exergy, and economic efficiency were calculated, and the system's thermal performance and economic benefits were analyzed. The results show that the system's energy efficiency is 49.54%, and the exergy efficiency is 41.12%. The system has significant economic benefits, with the ability to recover initial investment within 7.12 years and achieve a net present value of approximately $21.30 million over a 20-year lifespan. [Display omitted] • A novel power and DME polygeneration system is proposed. • The system integrates biomass gasification and carbon capture. • Sensitivity analysis is performed to optimize operation parameters. • An energy, exergy, and economic analysis are conducted on the system. • The system has thermal efficiency of 49.54% and exergy efficiency of 41.12%. [ABSTRACT FROM AUTHOR]

Methanol and ammonia emerge as two of the most important energy carriers in a new decarbonized society. In this work, a systematic assessment of the power generation based on these chemicals is performed using two different alternatives: direct utilization as green fuels in fuel cells or as carriers for hydrogen. Despite the need for a previous stage for hydrogen production, the use of these chemicals as hydrogen carriers demonstrates higher efficiencies (around 40%), mainly due to the higher degree of maturity of the hydrogen fuel cells. This is reflected in the cost of electricity for the different alternatives with around 700 €/MWh for hydrogen carrier options and about 1200 €/MWh for the direct utilization as green fuels. Compared to hydrogen, the use of methanol or ammonia has a higher electricity production cost. However, future improvements in the efficiency of fuel cell units could convert these fuels is competitive options. In addition, for different scenarios combining transportation and power generation, methanol and ammonia emerge as technically and economically feasible alternatives, especially for distances over 3000 km. Consequently, both hold a pivotal role in addressing the challenges associated with hydrogen within a future energy systems characterized by high renewable penetration. • A techno-economic evaluation of using methanol/ammonia in fuel cells is performed. • The use of methanol/ammonia as hydrogen carriers is the preferred option. • The cost of electricity for the best alternatives ranges around 600–800 €/MWh. • To achieve competitive levels, the efficiency of direct fuel cells must be doubled. • For distances greater than 3000 km, methanol and ammonia show great potential. [ABSTRACT FROM AUTHOR]

Currently, liquid hydrogen is the optimal way to store hydrogen, and many studies have combined it with solar energy to address the issues of low efficiency and high energy consumption during hydrogen liquefaction. However, these renewable energy sources are highly dependent on the environment and can suffer from an unstable supply. In this study, a new hydrogen liquefaction process based on solar energy utilizing a solar heat pump system is proposed to solve these problems. An absorption refrigeration system (ARS) is added to the mixed refrigerant pre-cooling and Joule-Brayton deep cooling to cool the compressor output stream during the hydrogen liquefaction process. In addition, a solar-based heat pump system to supply stable heat flow to the ARS is proposed for the first time, and a dual-circuit organic Rankine cycle to recover the residual heat from solar energy in the low-temperature and high-temperature circuits of the heat pump system (HPS) is introduced, which can be used to replenish the electric power of the compressor and other equipment in the liquefaction process. The excess cold from the hydrogen liquefaction process is used for carbon dioxide liquefaction. The process is simulated using HYSYS software, and a genetic algorithm (GA) is used for system optimization. The results show that the production of LH 2 is 98 tons per day with specific energy consumption (SEC), coefficient of performance (COP), and exergy efficiency of 5.633 kWh/kg LH2 , 0.217, and 53.15%, respectively; the proposed system is approximately 13% more energy efficient than the reference process. The overall liquefaction cost of the system is 22.456 M$, and the unit liquefaction cost is 0.624$/kg LH2. The energy savings and reductions achieved by converting unstable solar energy into a stable heat source for the hydrogen liquefaction process can serve as a prime reference for subsequent designs. • Hydrogen liquefaction process under solar heat pump system was established. • Heat pump circulate was added to convert solar energy into a stable heat source. • Double-circuit organic Rankine cycle was developed to utilize solar waste heat. • Hydrogen liquefaction cold energy was used for carbon dioxide liquefaction. [ABSTRACT FROM AUTHOR]

Unitized Regenerative Fuel Cells (URFCs) are a promising technology that utilizes renewable energy sources to efficiently convert them into electricity while offering potential for renewable energy storage. These cells facilitate the conversion between electrical and chemical energy, enabling processes such as electrolysis and hydrogen gas synthesis from water. This study aims to propose a more efficient and stable mass transfer solution for URFCs through the integrated optimization of flow field structure and porous transport layer configuration. Leveraging Taguchi orthogonal design, Pearson correlation coefficient, contour analysis, multi-criteria decision making, and the integration of artificial neural networks with non-dominated sorting genetic algorithm-III, an optimized selection is performed. Results indicate that the optimized structure exhibits improved performance in both electrolytic cell (EC) and fuel cell (FC) modes. At a current density of 1.5 A/cm2, compared to traditional structures, the voltage decreases by 8.2 mV in the EC mode. In the FC mode at a current density of 1.0 A/cm2, performance improves by 5.748%, and URFC round-trip efficiency increases by 6.183%. The assessment of mass transfer capability reveals that the optimized structure promotes gas transfer processes in different modes, leading to significant overall performance enhancement of URFC. These findings provide valuable guidance for the enhanced performance of URFC. • A three-dimensional two-phase non-isothermal model is established for URFC. • Pearson correlation coefficient is employed to analyze the correlation of optimized variables. • Multi-criteria decision making and ANN + NSGA-III are applied to the integrated optimization for URFC. • Alternative 2 configuration resulted in a 6.183% increase in URFC's round-trip efficiency. [ABSTRACT FROM AUTHOR]

To reduce carbon emissions, logistics companies take measures to achieve the climate neutrality target to reach "Green Logistics." As one of the valuable measures, gradually replacing present fossil fuel vehicles with electric vehicles for parcel delivery and linehaul transportation is scheduled at the terminals of logistics companies. The charging load of a fully electrified vehicle fleet challenges the terminal power system. The integrated optimization of sizing and energy management strategy is employed to design, model, optimize, and compare capacity expansion plans involving different combinations and various energy elements. These plans include upgrading the grid connection through the addition of a new transformer and incorporating renewable energy sources and/or energy storage sources. The evaluation encompasses both technical and economic considerations. The effects of different prices of electricity and components, energy management strategy, and uncertainty of power generation and load demand on the choice of optimal solutions, corresponding component sizes, and overall cost were compared and analyzed. The economic feasibility of upgrading the grid connection is demonstrated to be superior compared to utilizing energy storage sources. • The terminal electrification of a logistics company with an electric vehicle fleet • A techno-economic assessment of capacity expansion planning for the terminal • Optimal solutions with renewable energy, energy storage, and grid connection • Sensitivity and uncertainty analyses with prices, strategies, generation, and load • Specified electricity tariffs and policy support for electricity costs in Sweden [ABSTRACT FROM AUTHOR]

Demand response (DR) is a promising solution to unlock the flexibility of numerous customers with the rising proliferation of renewable energy (RE) in power system. However, the current demand response mechanisms face the challenge of low customers participation. The main reason is the significant diversity in customers' energy consumption characteristics, while existing mechanisms often provide a uniform DR signal without personalized guidance. To address these gaps, this paper proposes a novel personalized DR scheme based on sub-customer directrix load (SCDL) that considers the diversities of the customers' energy consumption characteristics. Firstly, the concept of SCDL is proposed, which decomposes the unified customer directrix load (CDL) into multiple SCDLs considering the energy consumption characteristics of massive customers. Subsequently, the formulation method of SCDL is described. The extraction of energy consumption characteristics is emphasized to ensure the reduction of regulation cost under the personalized guidance of SCDL. Furthermore, the collaborative interaction between the load aggregator layer and the customer layer is depicted by the bi-layer optimization model to achieve the optimal response effect. Finally, a multidimensional portrait method is designed to measure customers' performance in DR programs. Test systems comprising diverse DR customers are generated using the real-world data from Open Energy Information. The simulation results validate the effectiveness of the proposed personalized DR mechanism which considerably reduces the customers' regulation cost and significantly improves the DR effect. • Proposing a novel concept of sub-CDL by decomposing the unified CDL. • Designing a personalized demand response (DR) mechanism based on the sub-CDL. • Customers select a sub-CDL in the DR according to their energy consumption characteristics. • Tailoring a bi-layer optimization-based sub-CDL formulation method to promote the demand response effect. • Presenting a multidimensional portrait method to measure the demand response performance of customers. [ABSTRACT FROM AUTHOR]

With the rapid depletion of fossil fuels and its detrimental environmental concerns, renewable energy sources (RES) have been widely adopted in the ship power system for improving the energy flexibility at the seaport and making it easier to create a green maritime transit corridor. However, the problem of load frequency control in RES-based micro-grid systems is inescapable because of its low inertia, sporadic characteristics of RES and random load demand. Therefore, this paper presents a novel fractional order proportional integral-one plus tilt-derivative PIλ-(1 + TD) cascade controller for frequency regulation of seaport hybrid micro-grid (SHMG) system consisting of diverse RES including bio-diesel generator, fuel cell, organic Rankine cycle-based solar thermal power, wind turbine generator, sea wave energy and energy storage system. This study introduces a flow direction algorithm (FDA) to optimize the suggested hybrid cascaded PIλ-(1 + TD) controller with integral time square error serving as the objective function. The dominance of the FDA-optimized PIλ-(1 + TD) controller is proved over proportional-integral-derivative (PID), fractional-order PID (FOPID), tilt-integral-derivative (TID) and cascaded PI-PD control scheme by examining the dynamic behavior of SHMG when a variety of sea-shore loads are used as significant disruption to the considered system. Apart from a multi-step variation of load and renewable energy generation, various realistic scenarios such as random load variations, wind speed fluctuations, solar irradiance changes, availability uncertainty of RES and generating/storage units, communication time-delay and ± 30% model perturbations are also considered to analyze the efficacy and resiliency of PIλ-(1 + TD) controller. The hybrid electrical energy source, flywheel and plug-in hybrid electric vehicles are used to support the SHMG for meeting the transient and steady-state power need. Heat pumps and freezers are also considered in SHMG to further advance the system's performance. The advocated PIλ-(1 + TD) controller achieves a substantial reduction of almost 30% to 38% in settling time, 16% to 27% in undershoot and 76% to 87% in overshoot compared to contemporary PID, FOPID, TID and cascaded PI-PD controllers. Furthermore, the robustness of the proposed frequency control strategy is also validated when SHMG is integrated into the main grid. Comparative analyses of signal strength induced by different controllers are also worked out to ensure fair comparison and prevent actuator saturation. Finally, the Bode-based relative stability of the proposed controller is analyzed compared to its contemporaries. • FDA-tuned hybrid cascaded PIλ-(1 + TD) frequency controller for seaport hybrid μG powered by renewable energies • Interaction of seaport micro-grid to the sea/shore side and utility grid. • Exceptional dynamic performance across various operational scenarios. • ESS and controllable loads contribute significantly in frequency control. • Enhanced system stability while ensuring the optimum utilization of RES. [ABSTRACT FROM AUTHOR]

DC microgrids (MGs) have gained significant attention due to their ability to complement renewable energy generation. In order to improve the operational stability of DC MGs, a predefined-time-based secondary controller is proposed to restore voltage drops caused by droop control and achieve current allocation among agents according to expected ratios. To conserve communication resources, the communication topology is established as a directed network. Each distributed generator (DG) receives voltage and current information from neighboring devices and uses its own current and voltage regulators to achieve control objectives. Compared to existing controllers, the convergence time upper bound of the proposed approach is independent of the initial value and can be directly adjusted as a parameter. The stability of the proposed control strategy is then verified using Lyapunov theory. Finally, hardware-in-loop test results are conducted to validate the performance of the control strategy, and its superiority is demonstrated through comparison with existing control methods. • The upper limit of the convergence time can be predefined as an input parameter. • A proportional consensus is achieved for the current with a directed topology. • The effectiveness is validated by using the RT-LAB experimental platform. [ABSTRACT FROM AUTHOR]

The study investigates the impact of environmental regulations, specifically environmental taxes, on energy consumption, production gaps, and energy security risk in highly energy-intensive countries from 1995 to 2018. Using panel quantile regression analysis, the research finds that environmental regulations significantly decrease the energy consumption-production gap. Moreover, these regulations directly and indirectly mitigate energy security risks by promoting energy transition and increasing the contribution of renewable energy resources. The study concludes by recommending the implementation of an eco-regulatory policy framework to minimize energy-related risks, highlighting the potential of regulatory measures in achieving a balance between energy needs for development and environmental sustainability. • Energy Gap as a problem for emerging economies • Influence of environmental regulations to overcome energy gap. • Energy security risk as a complex problem • Environmental Regulations to overcome energy security risk. • Possible channels for ER to overcome energy security risk. [ABSTRACT FROM AUTHOR]

This study examines the impact of feed-in tariffs (FITs) on promoting investment in renewable energy (RE) in Southeast Asia. Using firm-level data from six Southeast Asian economies from 2012 to 2021, we find robust evidence that FITs substantially boosted firms' investment in RE. Moreover, the impact of FITs on RE investment in Southeast Asian economies varies across different RE technologies and firm characteristics. We find that the significant and positive impact of FITs on RE is largely driven by solar FITs on promoting investment in solar RE, but such effects are insignificant for other RE technologies including wind and biomass, geothermal, and hydro. Further, the impact of FITs on RE investments is more pronounced among younger and smaller firms than in older and larger firms. Our empirical evidence has implications for policy makers to design FITs in a cost-effective manner to promote more private sector investment in RE. • We use firm-level data from Southeast Asia 2012–2021 to study the impact of FIT; • FIT substantially boost firms' investments in RE; • FIT promote investments in solar; • Impacts are insignificant for wind and biomass, geothermal, and hydro; • FIT promotes investments by younger and smaller firms. [ABSTRACT FROM AUTHOR]

The high penetration of renewable energy sources (RES) in power generation has driven demand for advanced integrated energy management systems (IEMS). In this study, to address the challenges of insufficient adaptability to dynamic supply–demand, a multi-type energy IEMS combining compressed air energy storage (CAES) and a battery energy storage system (BESS) is proposed, which operates under a multi-mode energy storage (MES) mechanism with rapid response, long-term balance, and synergic adjustment modes. To address the complexity of sequential decisions, an option-critic based twin delayed deep deterministic policy gradient (OCTD3) algorithm is firstly proposed within the hierarchical reinforcement learning (HRL) framework, enhancing efficiency through encapsulation of subtasks within "options". Additionally, model precision is refined by fitting the electricity–gas–heat conversion dynamics of CAES under off-design conditions. Dispatch tasks are modeled as an option-based Semi-Markov Decision Process (SMDP) and optimized by the OCTD3 to improve the power fluctuations index (PFI), comprehensive costs index (CCI), and system response synergy index (SRSI). Comparative simulations reveal that the MES mechanism boosts SRSI by 91.8%, showcasing high adaptability to varied supply–demand scenarios. The OCTD3 algorithm develops five hybrid strategies for CAES–BESS across three modes, effectively cutting costs by reducing electricity purchases and fluctuations expenses, and lowering PFI by 42.2% through balancing peak–valley loads and swiftly responding to transient shifts. [Display omitted] • A multi-type energy IEMS integrats a multi-mode CAES/BESS hybrid mechanism. • Three indices evaluate the stability, cost-effectiveness, and responsiveness. • Dispatch tasks featuring discrete events are modeled as an option-based SMDP. • Hierarchical OCTD3 algorithm enhances efficiency with encapsulated subtasks. • Mechanism boosts SRSI by 91.8% and algorithm decreases PFI by 42.2%. [ABSTRACT FROM AUTHOR]

This paper estimates how electricity price divergence within Sweden has affected incentives to invest in photovoltaic (PV) generation between 2016 and 2022 based on a synthetic control approach. Sweden is chosen as the research subject since it is the only member of the European Union with multiple bidding zones (BZ) that faces dramatic price divergence between low-tariff BZ in Northern and high-tariff BZ in Southern Sweden since 2020. The results indicate that PV uptake in municipalities located north of the BZ border is reduced by 40.9%–48% compared to their Southern counterparts, reinforcing existing imbalances in PV uptake and impeding renewable energy investments in the low-tariff BZ. While this is less of a concern in Northern Sweden were additional potential for PV generation is limited and most electricity is sourced from hydropower, these findings have important implication for countries with pre-existing imbalances or low shares of renewables in their electricity mix such as Germany and the Czech Republic. Since investments are channeled to high-tariff BZ, capacities in low-tariff areas are underutilized which might hinder a successful transition towards renewable generation. Hence, unused potential fore renewable generation should be carefully considered when designing bidding zone borders. • Exploiting electricity price divergence across Swedish bidding zones. • PV investment is 40.9%–48% lower in low-tariff areas. • Application of a synthetic control approach. • Under-investment in solar generation can impede decarbonization. • Policy makers need to subsidize investment in solar generation in low-tariff bidding zones. [ABSTRACT FROM AUTHOR]

With the release of the '2050 Net Zero Emissions from Shipping' by the International Maritime Organization (IMO) and China 'Carbon Neutrality' policy, it is imperative to develop renewable energy ships. River-sea going renewable energy ships have high economic benefits and low emissions. They can sail in both rivers and seas without other ships and barges. To realize the economical and low-carbon operation of river-sea going renewable energy ships, a variable time-scale power scheduling strategy is proposed. A new model of all-electric propulsion power is firstly proposed. The model focuses on the multi-variables coupling variations of a ship propulsion process, such as resistance, motion state, navigation speed, deadweight and so on. A voyage-power hierarchical framework which is based on a stochastic model predictive control (SMPC) method is developed to optimize the scheduling scheme. A novel variable time-scale control mechanism in view of uncertainty fluctuation quantization is proposed. Results show that the energy consumption of the proposed all-electric propulsion power computational model is reduced by 52.6%, compared with the traditional computational model. The operation cost and carbon emission which are determined by the proposed method is reduced by 21.6% and 35.9%, respectively. [Display omitted] • A new multi-variable coupling model of AEP power is proposed to improve the calculation accuracy of a river-sea going ship. • A voyage-power hierarchical scheduling framework is proposed by an improved stochastic model predictive control (SMPC) method. • A novel variable time-scale control mechanism in view of uncertainty fluctuation quantization is proposed. • Results show that the proposed AEP power model can improve the accuracy and economy of a ship power scheduling. [ABSTRACT FROM AUTHOR]

With the rapid advancement of distributed energy resources (DERs), artificial intelligence, and smart meter technologies, traditional consumers are undergoing a paradigm shift towards 'prosumers'. In this context, peer-to-peer (P2P) energy trading emerges as an effective approach to enhance local energy utilization. Nevertheless, the inherent intermittency and forecasting challenges associated with renewable energy resources may magnify uncertainties in the markets, and pose a potential threat to destabilize the markets. To address this challenge, this paper presents a method to assess the economic impacts of forecasting errors and introduces a metric, the bill deviation index. Additionally, the consequences of forecasting errors on market outcomes are examined based on the mathematical model of three different pricing mechanisms. Our findings indicate that forecasting errors can lead to significant financial discrepancies, the magnitude of which is closely related to the pricing mechanisms and their dependency on energy quantity. The paper further underscores the role of variability in clearing price, balancing cost, and the supply–demand relationship in determining the economic fallout of forecasting errors. It concludes by providing insights for managing energy trading in markets marked by high forecasting errors and suggests strategies to mitigate the associated economic risks. • An economic impact assessment method considering pricing calculation principles is proposed. • The influence of forecasting errors on market outcomes is analyzed through three pricing mechanisms. • A P2P energy trading model is developed to evaluate crucial variables under various market conditions. [ABSTRACT FROM AUTHOR]

The increasing integration of renewables poses great challenges to the power system planning problem, especially, the power outputs of renewable generation show the inherent characteristics of long- and short-term chronological variability and uncertainty, which puts higher requirements on the flexibility of the planning strategy. While incorporating unit commitment (UC) constraints considering reserve capacity into planning models has been widely investigated to address renewable energy generation volatility, the re-dispatch of generation units to balance short-term forecasting errors in renewable energy is generally ignored. In this paper, we propose a novel expansion planning model that integrates operational flexibility constraints (EP-OFLX). This model aims to address both long- and short-term chronological variability and uncertainty, optimizing investment decisions for both generation and transmission facilities to achieve the desired level of renewable energy penetration. These constraints include clustered unit commitment constraints to accommodate the variability of renewables and robust re-dispatch operating constraints to handle the uncertainty of renewable generation. A case study is conducted on a modified IEEE RTS-79 system to demonstrate the effectiveness of the proposed method and verify the scalability of the EP-OFLX model. Additionally, the impacts of renewable uncertainty on flexible resource planning are analyzed. • The re-dispatch behaviors of generation units for balancing the short-term forecasting errors of renewable are considered. • The chronologically arranged daily/weekly representative scenarios are employed to characterize long-term variability in renewable energy. • A set of operational flexibility constraints is constructed to accommodate the variability and uncertainty of renewable generation. • A novel expansion planning model incorporating operational flexibility constraints is proposed. [ABSTRACT FROM AUTHOR]

Accurately assessing complementarity is a foundational work to the hydro-wind-solar hybrid energy system planning and dispatching. However, the existing complementary assessment indexes such as Pearson, Kendall, Spearman coefficient, or other improved indexes, suffer from limitations such as being applicable only to two-dimensional objects, failing to simultaneously capture the fluctuating trend correlations thought the whole evaluation periods and the ramp correlations in the adjacent time periods, and ignoring the impact of the fluctuation amplitudes. To address these issues, we propose a novel complementary assessment metric based on vine structure and rank correlation analysis. Vine structure enables the inclusion of multidimensional assessment object and rank correlation analysis takes into account both the fluctuating trend correlations thought the whole evaluation periods and the ramp correlations in the adjacent time periods. Meanwhile, the introduction of the discount factor allows fluctuation amplitudes impact to be included in the evaluation system. Comparisons based on several typical scenarios verify that the proposed metric can reflect the complementarity between data series more accurately. Subsequently, taking a national-level hydro-wind-solar hybrid energy base in China as the case study, result shows: (1) hydro-wind-solar shows different complementarity at different time scales and significantly better on monthly scales than on annually, daily and hourly time scales; (2) the optimal ratio of wind-solar installed capacity is determined by combining multiple time scales based on the proposed metric. • Challenges encountered in the evaluation of complementarity with existing complementarity indexes were analyzed. • A novel metric is proposed for evaluating object dimension self-adaptation energy complementarity. • The complementarity of the integrated hydro-wind-solar energy base on the lower Jinsha River in China was studied. [ABSTRACT FROM AUTHOR]

Space cooling constitutes >10% of worldwide electricity consumption and is anticipated to rise swiftly due to intensified heatwaves under emerging climate change. The escalating electricity demand for cooling services will challenge already stressed power grids, especially during peak times of demand. To address this, the adoption of demand response to adjust building energy use on the end-user side becomes increasingly important to adapt future smart buildings with rapidly growing renewable energy sources. However, existing demand response strategies predominantly explore sensible cooling energy as flexible building load while neglecting latent cooling energy, which constitutes significant portions of total energy use of buildings in humid climates. Hence, this paper aims to evaluate the demand response potential by adjusting latent cooling energy through ventilation control for typical medium commercial office buildings in four representative cities across different humid climate zones, i.e. , Miami, Huston, Atlanta, and New York in the United States (US). As the first step, the sensible heat ratio, defined as sensible cooling load to total building load (involving both sensible and latent load), in different humid climates are calculated. Subsequently, the strategy to adjust building latent load through ventilation control (LLVC) is explored and implemented for demand response considering the balance of energy shifting, indoor air quality, and energy cost. Results reveal that adjusting building ventilation is capable of achieving 30%–40% Heating, Ventilation, and Air-conditioning (HVAC) cooling demand flexibility during HVAC operation while among this, the latent cooling energy contributes 56% ∼ 66.4% to the overall demand flexibility. This work provides a feasible way to improve electricity grid flexibility in humid climates, emphasizing the significant role of adjusting latent cooling energy in building demand response. • Utilize EnergyPlus to evaluate the latent cooling load of commercial buildings in humid climate zones. • Propose a latent load adjustment strategy through ventilation control for building energy demand response. • Compare the flexibility of sensible to latent cooling load by adjusting setpoint of air-conditioning. [ABSTRACT FROM AUTHOR]

Wind energy is one of the renewable energy sources whose production capacity has significantly increased in recent years due to the rapid technology development. However, the utilization of this energy faces challenges such as site selection, wake phenomenon, windfarm layout, and its cost-effectiveness. In this regard, the present study provides a global framework for evaluating wind energy considering these challenges. This framework consists of three phases, where in the first phase, the most suitable location for windfarm is evaluated from twelve sustainable aspects. Multi-criteria decision-making(Z -MARCOS,Z-ARAS,Z-TOPSIS,Z-EDAS,Z-WSM, and Z-COPRAS) with Z-number theory are employed to select best site under uncertainty and reliability. Then, in the second phase, the wake phenomenon in the windfarm is evaluated and windfarm layout are optimized by conventional and adaptive genetic algorithms. Finally, in the third phase, the performance of the windfarm is assessed using 7E indicators. Iran is considered as a case study. The results showed constructing windfarm in Khorasan is the most suitable, leading to the production of 263GWh/yr with exergy(energy) efficiency of 49(43) % and electricity cost (payback period) of 53 $/MWh (13 years), which is cost-effective. In general, the proposed framework provides a comprehensive evaluation and can serve as a framework for windfarms worldwide. • A novel framework for assessment windfarm development is introduced. • Six MCDM methods along with Z theory is considered for windfarm site selection. • Windfarm layout optimized by conventional and adaptive genetic algorithm. • Energy, Exergy, Economic, Environmental and its combination is used for feasibility. [ABSTRACT FROM AUTHOR]

Renewable clean energy sources, such as wind, solar, and wave energy, are currently gaining prominence and are being extensively researched. Given the escalating significance of clean energy, hybrid systems have gradually become an effective solution to address energy and environmental issues. In order to maximize the advantages of wind, wave energy, and photovoltaic (PV), this paper proposes a hybrid wind-wave-PV system (HWWPS) by combining wind turbines, PV panels, and wave energy converter (WEC) to achieve higher energy production and efficiency. To further enhance power output, this paper focuses on the influence of system spatial array optimization on power output and adopts an algorithm to establish a strategic layout. Through the integration of the chaos algorithm, exponential asynchronous factor, and the sine-cosine mechanism, the original slime mould algorithm (SMA) algorithm is enhanced to the exponential slime mould algorithm (ESMA), which has better optimization capability and is particularly suitable for determining the optimal power configuration. Simulations are conducted on hybrid systems consisting of three, seven, and thirteen buoys, respectively, which compare the ESMA are compared with the other six algorithms. The results verify the advantages of ESMA over the other algorithms, and demonstrates the superiority becomes more prominent as the system size increases. • A new hybrid wind-wave-PV system is established for the first time; • An exponential slime mould algorithm is proposed; • The comparison of the six algorithms highlights the ability of the exponential slime mould algorithm to optimize the array layout of new hybrid wind-wave -PV system. [ABSTRACT FROM AUTHOR]

The developmental trajectory of provincial power grids in alignment with dual‑carbon goals requires the systematical prediction of China's provincial power grids. In this study, a comprehensive electricity production optimization model covering all 31 provinces in mainland China is developed to simulate and optimize the provincial operation of the power sector for 2020–2060 under a reference scenario and two renewable energy development scenarios. Then, dynamic, province-specific, and yearly grid CO 2 emission factors and relative direct and indirect emissions from both the producer and consumer sides are evaluated. We find significant increases in renewable energy installed capacity and power generation, with yearly growth rates of 6.40% and 5.29%, respectively, from 2020 to 2060. Solar and wind power generation will contribute the most to the national installed capacity and power generation (79.03% and 56.03%, respectively, in 2060). Notably, Inner Mongolia is projected to represent the majority of national solar and wind power generation, with the largest mitigation potential, but the highest grid CO 2 emission factor. Substantial reductions in grid CO 2 emission factors (by 12.54%) and associated CO 2 emissions (by 19.73%) are linked to renewable energy development in the power sector. Our results help highlight the effectiveness of renewable energy development and facilitate future provincial policymaking. [Display omitted] • Provincial grid emission factors in China from 2020 to 2060 are estimated for the first time. • A comprehensive analysis considering production structure optimization and provincial grid CO 2 emission factors under provincial dual-carbon goals is innovatively proposed. • A systematic optimization model is established to explore the long-term pathways of the provincial power sector under dual-carbon goals. • A full-scale (all provinces in mainland) research with high spatial resolution (provincial level) has been developed. • Long-term dynamic development paths of the electricity sector in all provinces between 2020 and 2060 have been depicted. [ABSTRACT FROM AUTHOR]

Power systems need flexible resources to support renewable energy integration, where electric vehicles (EVs) provide a prospective solution. This study utilizes a charging station operator as a spatial aggregator to evaluate load flexibility of fast-charging stations (FCSs) with price incentives. First, FCSs' load flexibility is related with drivers' charging station choice uncertainty. Therefore, this uncertainty is analyzed by the logistic regression model and real-world data is utilized to obtain the model parameters and clarify drivers' choice preferences. Second, the price-varying load-transfer probabilities and spatial correlation parameters among FCSs are analyzed. Based on them, the probability theory is employed to obtain the spatially correlated probability distribution of FCS loads with price incentives. Third, the evaluation model of FCSs' load flexibility with real characteristics is established with drivers' choice uncertainty and spatial correlation as the basis of building a future general virtual power plant that considers spatial correlations, considering that the flexibility range varies with the response probability in a single FCS and that the price-varying spatial correlation between multiple FCSs flexibility. Case studies validate the effectiveness of the proposed method. The results validate the driver preferences obtained from real-world data, the price-varying spatial correlation of FCSs for different types of drivers, and the feasibility of grid congestion alleviation with obtained FCSs' load flexibility. • Drivers' charging station choice uncertainty is analyzed using real-world data. • Price-varying load-transfer probabilities among FCSs are analyzed. • The spatially correlated probability distribution for FCS loads is obtained. • The evaluation model of FCSs' load flexibility is established. [ABSTRACT FROM AUTHOR]

The increasing integration of renewable energy sources with power systems has led to greater uncertainties, heightening the need for more flexible services. Demand-side resources can provide significant flexibility cost-effectively, making it crucial to design new business models to harness their flexible capacities. Hence, this paper introduces a three-stage techno-economic framework designed to enhance the exploitation of flexible capacities from demand-side resources such as Internet-of-Things (IoT)-Enabled Appliances (IEAs), Battery-Integrated Rooftop Solar Panels (BIRSPs), and Electric Vehicle (EV) smart charging systems in the local flexibility market. The proposed framework operates in three stages. First, smart homes with IEAs, BIRSPs, and smart charging systems assess their flexibility and report it to their Microgrid (MG) operators. Next, MG operators optimize strategies to maximize market returns and report capacities to the Distribution System Operator (DSO). Finally, the DSO manages flexibility transactions based on MG inputs. A Stackelberg game theory model using a gradient-based method updates the coupling variables between market parties, ensuring convergence in the second and third stages within a decentralized space with limited information sharing. Implemented on a modified IEEE 69-node distribution system, the model fully utilizes the potential of smart homes, including IEAs, BIRSPs, and smart charging systems in the local flexibility market. It reduces the Upstream Network (UN) share in the local flexibility market by 56.86% and decreases DSO costs for energy balancing by 22.36%. • Multi-stage model for releasing IoT-enabled smart home capacities in flexibility markets • Gradient-based Stackelberg game boosts market convergence and limits info sharing • Game theory sets market prices based on all parties' scheduling strategies • Reducing costs by releasing all demand-side capacities into the local flexibility market [ABSTRACT FROM AUTHOR]

Addressing renewable energy (RE) curtailment in power systems necessitates a comprehensive strategy leveraging peak regulation resources from both the power and load sides. On the power side, deep peak shaving of thermal power plants can mitigate surplus electricity during periods of high RE production. On the load side, energy-intensive industrial loads, characterized by large capacity and rapid adjustability, can respond effectively to energy deficits when RE is low. To enhance the peak regulation capacity for optimal RE accommodation, this paper proposes a collaborative optimization method combining electrolytic aluminum load (EAL) regulation with thermal power deep peak shaving (DPS). The study is initiated by developing a sophisticated peak regulation model for the electrolytic aluminum load (EAL), considering production characteristics, cost features, and safety constraints. Subsequently, a collaborative optimization model is formulated, integrating EAL regulation with thermal power deep peak shaving (DPS), aiming to minimize societal peak regulation costs (PRC). Applying this model to a practical regional grid in Yunnan Province reveals substantial reductions in RE curtailment rates, effective minimization of total social PRC, and enhanced operational economics of thermal power DPR under varying wind power scenarios. Notably, the proposed approach requires only minor adjustments to the EAL, ensuring production safety is maintained. This research provides valuable insights for the seamless integration of EAL into grid peak regulation services. • A refined peak regulation model of the electrolytic aluminum load (EAL) is established. • The segmented cost model of the EAL is constructed. • Collaborative optimization integrating EAL regulation and thermal power deep peak shaving. • The effectiveness of collaborative optimization under various RE scenarios is proved. • The influence of EAL regulation space on the social peak regulation effect is discussed. [ABSTRACT FROM AUTHOR]

Hybrid energy storage system (HESS) is an ESS integrated with renewable energy source (RES), allowing PV owners to participate in the electricity market. By investing in HESS, PV owners can yield additional revenue by providing services to system operators, such as avoiding and delaying transmission and distribution network investment, relieving grid congestion, and providing ancillary services (AS). However, in some jurisdictions, the high installation expenses of HESS make the investment by PV owners economically unviable. In this paper, a business model is proposed to improve the investment value of HESS, and a mixed-integer linear programming (MILP) optimization problem is modelled to calculate the optimal capacity of HESS that maximizes the PV owner's profit. The simulation results of the proposed model were used to assess economic feasibility through internal rate of return (IRR) and levelized cost of energy (LCOE). Based on economic evaluations, the appropriate level of capacity incentive needed to motivate investment in HESS was identified. The simulation used the historical system marginal price (SMP) of Jeju-island, Korea, and the PV generation patterns of various regions. The Monte-Carlo method was used to obtain results that are robust to the regional and temporal uncertainty of RES. Simulation results using the 2021 SMP data indicate that at a capacity incentive level betweem 29 and 31%, the NPV, IRR, and LCOE meet the required thresholds, demonstrating the economic viability of HESS investment. In scenarios with negative pricing, HESS investment is feasible without providing a capacity incentive. The IRR derived from the simulation is above the cost of equity, and the LCOE is below the maximum LCOE of unsubsidized gas peak resources confirming that the level of capacity incentives is appropriate. • Energy storage system is a key resource for integrating renewable energy sources • Proposals of various business model for photovoltaic owner to obtain revenue from energy storage system • Determination of the optimal hybrid energy storage system capacity considering the value of grid service • Economic evaluation to establish the appropriate capacity incentive level [ABSTRACT FROM AUTHOR]

This study developed a methodological approach for long-term electricity demand forecasting and applied it to the electricity demand in Cuba, which is crucial for transitioning from a fossil fuel-dependent system to renewable energy sources. The methodology employs enhanced complete ensemble empirical mode decomposition with adaptive noise (ECEEMDAN) applied for obtaining long-term trends from historical electricity usage data decomposition, combined with a long short-term memory (LSTM) deep learning model for prediction. Comprehensive datasets, including historical electricity consumption, economic indicators, and demographic data, are utilized in the analysis. Monte Carlo simulations, then, are integrated to address uncertainties in prediction and explore 50 different scenarios of future electricity demand. The study forecasts varying scenarios for the energy demand of Cuba by 2050, with the extreme low scenario projecting a decrease of up to 7.9% compared to the 2019 level. This research offers a groundbreaking framework specifically designed to aid Cuba's energy sector stakeholders in informed decision-making during this critical energy transition. The adaptability of the methodology makes it applicable for long-term projections in various sectors, offering a reliable tool for global decision makers. • Novel integration of ECEEMDAN with LSTM for enhanced electricity demand forecasting. • Incorporates uncertainty modeling for realistic long-term electricity demand scenarios. • Employs a multivariate dataset for comprehensive energy context analysis. • Provides strategic insights for energy planning and policy in transitional contexts. • Addresses crucial energy transition challenges in developing countries like Cuba. [ABSTRACT FROM AUTHOR]

The introduction of renewable energy introduces operational challenges for the modern multi-energy system. To accommodate the uncertainty, this paper proposes an online probabilistic energy flow for the hydrogen-power-heat system. First, a hydrogen-power-heat system model is established to depict the interdependency of multiple energy, in which hydrogen fuel cells interconnect hydrogen, power, and heat systems by converting the hydrogen into power and heat. The probabilistic energy flow is further built considering the uncertain wind power. An efficient algorithm based on multi-parametric programming (MPP) is proposed to enhance the computational performance of probabilistic multi-energy flow for online applications. The parameter region of the probabilistic multi-energy flow in MPP model is divided into multiple critical regions (CR). Each CR reveals a unique mapping relationship from uncertain wind power to system optimal solutions. Probabilistic energy flow could be quickly calculated based on mapping relationship rather than solving optimization problem. Therefore, the proposed method could be applied in online energy flow calculation. Numerical results tested on a 6–6-8 system and an improved 40–118 − 13 system validate the accuracy and efficiency of the proposed method in probabilistic energy flow calculation. • Online rapid computational method for probabilistic energy flow is proposed for hydrogen-power-heat system. • Parameter hyperspace of uncertain wind power in multi-parametric programming is divided into a set of critical regions. • Mapping relationship is applied to quickly obtain optimal solution. • Two test systems are provided to validate effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Wave energy and ocean current energy are considered stable, reliable, and highly predictable renewable energy sources. The development of ocean current energy conversion technology is crucial in addressing power shortages. However, the low velocity of most ocean currents worldwide, typically less than 1.5 m/s, poses a challenge for traditional ocean current energy capture devices. Current estimations of power generation from underwater devices often significantly differ from actual results. This study presents an eel-type power generation device designed for underwater use, investigates the design and optimization of a hydraulic power take-off (PTO) system suitable for such devices, and examines key components of the hydraulic PTO system like variable hydraulic motors and generators. Analytical research was conducted to understand its operational characteristics, with a focus on components such as the accumulator, speed control valve, and energy storage flywheel. The article also delves into the hydrodynamic and energy conversion characteristics of the device under shallow water wave and current conditions, enhancing the hydraulic PTO system model and establishing an integrated calculation model for real-time data transmission during the calculation process. Through evaluations of pitch angle and power generation under varying sea conditions, the study explores the impact of hydraulic motor displacement and damping coefficient on the hydrodynamic and power generation characteristics of the device, further validated through sea trials. The study findings indicate a high level of parameter adaptation among the components of the hydraulic system. The addition of an accumulator to the system results in smoother output response curves, suggesting that the accumulator absorbs impacts and stabilizes the hydraulic power transmission system. The proposed comprehensive calculation method enables a more precise prediction of the performance of the eel power generation device in real marine environments, capturing its movement behavior and power generation characteristics. Adjusting the motor displacement or damping coefficient under specific conditions can optimize the total damping of the hydraulic PTO system for maximum power output. The optimal power point of the hydraulic power generation system varies for different sea conditions, with a potential power generation efficiency of up to 80.3% under certain circumstances. • An eel power generation device suitable for deep-sea micro-current sea condition and wave-current combined sea condition • Design and optimal Control of power take-off (PTO) system • The integrated calculation of hydrodynamic and energy conversion is studied. • Comparison of the results of sea test and integrated calculation. [ABSTRACT FROM AUTHOR]

The agricultural limitations in Jordan are due to scarce water resources and are further exacerbated by the arid and hyper-arid climatic conditions. Agrivoltaic systems are the co-location of electricity and agricultural production, optimizing the interplay between the two systems for maximum total production per land area unit. This study investigates the technical potential of agrivoltaic systems in Jordan using geospatial analysis and open data sources and the extent to which they can contribute to the water-energy-food nexus, improving water, food, and energy security while serving national sustainable development goals. The analysis calculated agrivoltaic-suitable zones totaling 9.5% of the country's land area. Further estimations indicate that covering only half of the current irrigated summer tomato fields with PV paneling will fulfill the 50% renewable energy (RE) generated electricity target and potentially save between 4 and 8.6% of the total water budget of the country. • Agrivoltaic (AV): co-locating solar power & crops. 9.5% of Jordan has AV potential. • AVs for energy security: Meeting Jordan's local electricity targets (RE goals). • AVs for water security: Saving a significant portion of Jordan's annual water budget. • Example: 50% tomato fields with AVs can meet 50% RE target & save 4-8.6% water budget. • More research needed: Economic & market potential of AVs in Jordan. [ABSTRACT FROM AUTHOR]

During the development and deployment of distributed renewable energy, enhancing residents' cognitive preferences towards renewable energy can effectively mitigate resistance. To examine the effect of residents' cognitive preferences on the development of distributed renewable energy, we first qualitatively and quantitatively analyzed residents' cognitive preferences through literature review and big data analysis. Subsequently, a two-way fixed effects model with time and region were utilized to test the relationship between residents' cognitive preferences and distributed renewable energy. However, the selected sample may lead to biased estimation results due to omitted variable and self-selection bias. To ensure the reliability of the results, we used the instrumental variable method and the Heckman correction method to eliminate these biases. Finally, we used the spatial autoregressive model to explore the spatial interaction relationship between surrounding cognitive activities and residents' cognitive preferences. The results show that residents' cognitive preferences significantly promote the development of distributed renewable energy. Moreover, residents living in areas with higher levels of air pollution, income, education, and informatization are more receptive to the development of distributed renewable energy. Additionally, we found a "herd effect" in residents' cognitive preferences in neighbouring regions, which is limited to adjacent areas. These findings provide an important reference for the government to effectively guide residents' cognition to form the "herd effect", and then promote the large-scale development of distributed renewable energy. • We use qualitative analysis methods to clarify what is residents' cognitive preferences (RCPs) for the renewable energy. • We quantified the RCPs using the residents' Baidu search index data. • We test the role of RCPs in the development of distributed renewable energy by constructed a regional quarterly dataset. • We explore the interaction between the surrounding cognitive environment and RCPs. [ABSTRACT FROM AUTHOR]

Recommender systems play a critical role in optimizing building energy consumption by providing personalized advice based on data analytics and user preferences. However, the literature highlights the need for systems that can justify their recommendations, as many of these systems use non-transparent machine-learning techniques. This research introduces two distinct types of indicators with three main goals: to identify patterns of flexible consumption behavior using transparent and straightforward methods suitable for remote decision support systems, thereby eliminating the need for extensive databases; to evaluate the feasibility of installing solar panels on building facades, rooftops, and structures using high-resolution 3D models; and to enhance understanding through a quantitative assessment of the feasibility and suitability of integrating renewable energy sources, particularly photovoltaic systems. Flexible prosumers are scored by assessing their energy consumption level, consistency, and variability through the Flexible Consumption Indicators. Topology Indicators perform a quantitative assessment of the feasibility of support surfaces for installing photovoltaic panels, taking into account rooftop pitch angles, orientations, and surrounding and internal structures, identifying those areas exposed to sufficient levels of irradiation. This study, which uses actual consumption profiles and similar households' buildings 3D models, demonstrates how the proposed indicators can aid identifying users with flexible consumption profiles that reside in buildings compatible with renewable energy sources, aiding in decision-making process within the energy transition. • Determine flexible end-users based on energy level, consistency and variability. • Rank the topology based on high resolution 3D models to assess radiation levels based on angle, orientation and structural singularities. • Combination of both indicators through and hybrid approach • Assessment of suitability and feasibility of potential end-users with PV energy sources. [ABSTRACT FROM AUTHOR]

A current challenge in energy system modelling is integrating high temporal and spatial resolution with a detailed representation of technologies and sector coupling within a single model. This paper presents a pioneering sectoral optimisation approach for transformation pathways in the German federal states. Spatial resolution in the energy system model REMod is increased and the model is extended from a single-node to a multi-node model for Germany by integrating energy exchanges between regions into a simulation-based optimisation. The approach highlights the challenge of adjusting energy distribution to address the mismatch between high final energy demand in some states and the potential for renewable energy resources in others. The extended model was compared with the results of the single-node model with the same boundary conditions. The comparison showed that increasing the spatial resolution did not significantly affect the overall cost of energy system transformation. However, the composition of the energy system changes, leading to approximately 6% more renewable capacity being installed in a scenario with ten regions. The results for the German federal states indicate a concentration of onshore wind development in the northern federal states with higher full load hours, especially in Lower Saxony/Bremen with 36 GW and Schleswig-Holstein/Hamburg/Mecklenburg-Vorpommern with 38 GW in 2045. The expansion of photovoltaic capacity in densely populated states such as North Rhine-Westphalia (73 GW), Bavaria (71 GW), and Baden-Württemberg (54 GW) reflects the need to meet growing electricity demand. The solar photovoltaic to wind power ratio varies between the federal states and influences the choice of flexibility options, including power-to-x technologies or gas and hydrogen turbines. By 2045, Germany's primary energy supply will shift to states with abundant renewable resources. Spatial flexibility is essential to balance electricity demand and solar and wind potential, mainly through state interconnections. • Sector-coupled energy system model with high temporal and spatial resolution developed. • Simulation-based optimisation as alternative to linear optimisation techniques. • Transformation path on federal states level for Germany modelled. • Transmission grid expansion is needed to transmit renewable energy from Northern to Southern Germany. [ABSTRACT FROM AUTHOR]

Environmental concerns and the need for renewable resources in energy production increase the interest of researchers in the subject of the biomass supply chain. The widespread use of biomass requires the need for design and optimization of the biomass supply chain network for real-life conditions. The complex and large-scale nature of the biomass supply chain leads to the existence of uncertainties that introduce several disruptions. Ignoring disruptions in the decision-making process makes the network design sensitive and vulnerable. The concept of resilient network design gives the biomass supply chain the ability to quickly adapt under changing conditions and maintain flow in cases of disruption. This study aims to develop a resilient biogas supply chain network design considering disruptive risks. A scenario-based mixed-integer linear programming model is developed under disruptions. Against both supply disruptions and facility disruptions, three resilience strategies are implemented: (1) multi-sourcing, (2) coverage distance, and (3) backup assignment strategy. To analyze and validate the effects of each proposed resilience strategy, comparative analyses are conducted considering several cases with different assumptions. For comparative analyses, the developed scenario-based optimization model is applied in the form of different model adjustments that incorporate the proposed resilience strategies. The applicability of the proposed resilience strategies is validated by analyzing their impact on the network structure, efficiency, production outputs, and economic performance of the supply chain. The impact of resilience strategies is reported and analyzed to make better inferences and contribute to managerial insights. Comparative results highlight that resilience strategies lead to improvement in different performance measures of the supply chain. • This study develops a resilient biogas supply chain network design. • Both supply and facility disruptions are considered. • A scenario-based optimization model is proposed. • Multi-sourcing, coverage, and backup facility resilience strategies are implemented. • Impact of resiliency strategies on BSCND model are analyzed. [ABSTRACT FROM AUTHOR]

Electricity is fundamental to the development of national economies and societies, reliant on accurate power load forecasting for its stable supply. Ultra-short-term power load forecasting analyzes historical power load data to predict load changes within the next hour. This forecasting is crucial for achieving efficient power dispatching, improving emergency management, and ensuring the stable operation of the power system. However, with the increasingly widespread application of renewable energy, its inherent intermittency exacerbates the complexity and randomness of power loads, posing a challenge for models to accurately capture data features. In addressing this challenge, the study presents a novel method for feature extraction from time series data, aimed at enhancing the accuracy of power load forecasting. By analyzing trend, periodicities, and randomness, it simplifies complex time series data into several stable data features, significantly reducing noise-induced errors and enhancing the identification and understanding of power data features. Moreover, this study applies the feature extraction method to five prevalent deep learning models. Experimental results show that the deep learning models using this feature extraction method reduces the mean absolute percentage error by an average of 54.6905%, 42.6654%, and 51.3868% on datasets from three different substations in China. These results not only affirm the method's efficacy in forecasting power load but also provide new technical foundations for the reliable functioning of future power systems. • A new framework for minute-level ultra short term power load prediction is developed. • A new feature extraction method for time series is proposed. • The method simultaneously considers the complexity and randomness of power load data. • Minute-level power load prediction within an acceptable time frame is achievable. [ABSTRACT FROM AUTHOR]

Bioenergy is a renewable energy alternative to fossil fuels, offering a means to reduce GHG emissions and support sustainable development in rural areas. Türkiye possesses diverse biomass resources, well-suited for electricity generation. By comprehensive data collection, we projected the long-term potential of each biomass type and assessed its contribution to the country's clean energy goals. Our analysis encompassed the potential of various sustainable biomass resources, including animal manure, municipal organic waste, sewage sludge, crop residues and firewood. In the modeled reference scenario following the country's energy targets, the share of renewables in electricity generation increases to 73% and with more ambitious biomass utilization, this share could be elevated to 82% by 2050, potentially reducing GHG emissions by 96.6 million ton of CO 2 eq. The findings of this study provide valuable insights for policymakers, demonstrating the extent to which sustainable bioenergy can contribute to shaping the electricity supply mix and influence long-term energy goals. It also helps in identifying crucial feedstock types for substantial bioenergy production, thus contributing to the progress of decarbonized development. • In the modeled reference scenario aligned with Türkiye's energy targets, the share of renewables in electricity generation increases to 73% and more ambitious biomass utilization could elevate this to 82% by 2050. • Bioelectricity has the capacity to meet up to 16% of the country's total electricity demand by 2050. • Applying the highest bioenergy demand scenario can avoid 96.6 million tons of CO2 equivalent over the projection period, contributing to a substantial 73% reduction relative to base year emissions. [ABSTRACT FROM AUTHOR]

Gas–electricity integrated energy systems (GE-IES) offers a promising solution for enhancing energy efficiency and accommodating renewable energy sources. Accurate dynamic simulation is essential for optimizing and controlling GE-IES. However, the presence of various local controllers introduces prominent discrete characteristics, posing challenges for the dynamic simulation of the GE-IES. This paper investigates the dynamic simulation method in GE-IES with discrete characteristics. Firstly, we propose an adaptive step size simulation method based on quantized state system theory. This method maintains the event-driven characteristics of the quantized state integration algorithms, while enhancing computational speed through adaptive step size adjustments. Secondly, we establish an event-driven simulation framework that facilitates interactions of different subsystems during the dynamic simulation, improving the compatibility with various models and solving algorithms. Finally, we validate the accuracy, efficiency, and scalability of the proposed method and the framework using two typical GE-IES cases with different scales. Simulation results demonstrate the effectiveness on the dynamic simulation of GE-IES and highlight the feasibility of natural gas networks in consuming and storing surplus renewable energy. • An adaptive step size, 2nd/3rd-order quantized state system simulation method is proposed. • A comprehensive event-driven simulation framework incorporating both state-discretized and time-discretized methods is established. • Improved performance is achieved in simulating gas–electricity integrated energy systems with discrete characteristics. • The effectiveness in analyzing operation strategy and fault dynamics is verified. [ABSTRACT FROM AUTHOR]

To achieve green and low-carbon development in the logistics industry, logistics operators are promoting the electrification of logistics fleets, which imposes requirements for well-developed charging facilities and integrated renewable energy sources. Due to the specific characteristics of logistics activities, which are different from those of normal vehicles, the infrastructure planning for logistics delivery needs to be considered in coordination with the logistics system's operation. The coupling of planning and operation poses challenges for decision-making. This paper presents a planning–operation coupling optimization framework for low-carbon logistics delivery. The planning level optimizes the location and capacity of charging facilities, photovoltaic (PV), and energy storage systems (ESSs) based on the idea of charging demand matching. The operation level uses deep reinforcement learning (DRL) to simulate the logistics fleet's action patterns, optimize routes and charging behaviors, and extract charging demands. Benefiting from the advantages of DRL, planning and operation can interact well through charging demands, mutual coupling, and iterative adjustment. Numerical experiments based on real-world data show that the proposed framework effectively reduces charging costs and carbon emissions and also provides a benchmark for the cost evaluation of carbon reduction in logistics delivery activities. • A joint planning–operation framework suits the properties of logistics delivery. • The infrastructure planning model adopts the idea of charging demand matching. • DRL is used to extract charging demands and interact with the planning model. • Numerical experiments with real-world city data validate the method's effectiveness. [ABSTRACT FROM AUTHOR]

The technology of Pumped Heat Energy Storage allows for the storage of large amounts of electrical energy, which is currently crucial due to the worldwide use of renewable energy sources (intrinsic and highly variable). In these systems, electrical energy is used to feed a heat pump cycle (charging phase) that exchanges heat with two thermal reservoirs, storing part of the input energy in the form of thermal energy. This energy is then supplied to the grid through a power cycle (discharging phase) when needed. This article presents a zero-dimensional model of a Pumped Heat Energy Storage system that uses a numerical zero-dimensional model for the reciprocating machines and an analytical model for the heat exchangers. By feeding the model with the operating point data (rotational speed of the reciprocating machines, pressure at a point in the system, fluid temperatures in the reservoirs, and mass flows of the thermal fluids circulating through the heat exchangers) and the geometric dimensions of all the machines, the steady-state evolution of the system can be obtained as result and also the powers of the reciprocating machines and heat exchanged with the thermal reservoirs, circulating mass flow of the working fluid, inlet and outlet temperatures and pressures of each machine, and the round-trip efficiency of a certain configuration, among others. Evaluating the coupling of the machines in a particular setup an overall efficiency of around 36% was calculated for the condition with the maximum local performance coefficient. Analysing the dependency between the stages of charge and discharge an operating condition that improved this value up to an efficiency of approximately 40% was obtained. Both values are conditioned for the use of molten salt, particularly its high melting temperature, which imposes higher temperatures at the expander inlet thus decreasing the overall efficiency of the system. • Advanced Modelling for PHES : Zero-dimensional model for comprehensive analysis. • Tailored Efficiency Analysis : Precise efficiencies for system configurations. • Optimal Operation Insights : Beyond COP for higher system efficiency. • Renewable Energy Integration : Valuable insights for sustainable energy systems. [ABSTRACT FROM AUTHOR]

This study measures the carbon dioxide (CO2) emissions into six-factor effects, including carbon trade intensity, trade of fossil fuels, fossil fuel intensity, renewable sources productivity, electricity financial, and financial development for Pakistan from 1992 to 2021, using decomposition, decoupling, mitigation rate of CO2 emissions and prediction analysis. The empirical findings show that the main factor leading to CO2 emissions is fuel intensity, while renewable productivity is the factor that mitigates CO2 emission during the period, with a contribution of 23.8%. During the study period, expansive negative decoupling, weak decoupling, expansive coupling, and strong decoupling were determined, however, expansive negative decoupling was at its maximum period. The outcomes confirm that CO2 emissions identify an expansive negative decoupling with economic growth in the current index, while expansive coupling and strong decoupling provided negative emissions only during 2010–2012 and 2019–2020 due to energy substitution and epidemic situations. The mitigation rate of carbon emissions was observed in 1997–2001, 2002–2006, and 2007–2011 due to an increase in the share of renewable sources. The prediction model presents that economic and fossil fuel trade are the major concerns; the CO2 emissions would increase by 68.63 Mt. in 2041, which could be reduced by renewable resources and technologies. • Energy transition, carbon trade and electricity generation valued under 6 factors. • Decomposition, decoupling and mitigation potential methods are used. • Renewable productivity mitigates CO2 emission while fuel intensity rises CO2. • With four decouplings, expansive negative decoupling was at maximum period. • CO2 emissions would increase by 68.63 Mt. in 2041 which could be reduced by RETs. [ABSTRACT FROM AUTHOR]

The increasing adoption of renewable energy sources (RESs) in public power grids has led to a demand for more intelligent energy management systems (EMSs) in large-scale buildings. A common approach for controlling EMSs for buildings is Model Predictive Control (MPC). For large-scale buildings, hierarchical MPC schemes have been proposed, offering the advantage of scalability through problem decomposition into multiple layers. However, hierarchical schemes often suffer from information mismatch due to information asymmetry between layers, leading to suboptimal control performance. This issue is worsened by model errors inherent to the models underlying the MPC controllers. To address these challenges, we propose a hierarchical MPC approach, which includes data-driven error compensation. Additionally, to mitigate information mismatch, a one-iteration communication step is introduced between the hierarchical layers. The proposed approach comprises two layers: an aggregator layer that controls overall energy flows of the building, and a distributor layer that allocates thermal energy to individual temperature zones. The distributor may request additional thermal budget by providing the aggregator with an otherwise expected performance loss, which it can trade off accordingly. The approach is evaluated in a software-in-the-loop (SiL) simulation using a physics-based digital twin model of a multi-zone commercial building, showing notable improvements in overall control performance in comparison to a naive hierarchical baseline and similar performance to a monolithic baseline. • New hierarchical MPC for energy management of a multi-zone commercial building. • One-iteration communication step between layers to mitigate information asymmetry. • Incorporation of data-driven error compensation of heat influences. • Evaluation using real-world data and physics-based digital twin simulation. [ABSTRACT FROM AUTHOR]

The growing significance of renewable energy sources within the electricity market, along with the need to tap into lower head and capacity sites towards achieving recent sustainability goals, has highlighted the need for the development of hydro turbines and reversible machinery that exhibit enhanced performance across a broad operating envelope. This need is further compounded by the rising importance and strictness of environmental regulations, which necessitates the design of turbomachinery with improved environmental characteristics. In this context, Deriaz turbomachines emerge as a promising solution due to their numerous potential benefits. Notably, they can function as reversible pump-turbines, with remarkable hydraulic efficiency across a wide spectrum of operational loads. In this study, a numerical approach aimed at optimizing the design of a Deriaz turbine, in order to achieve high energy efficiency and improved fish-friendly behaviour in both pump and turbine operational modes, is introduced. Using a previously developed versatile design tool, coupled to numerical simulation and optimization tools, a new Deriaz reversible pump-turbine has been designed. Through a rigorous design process, numerous geometry variations have been analysed with the aim of improving the operational characteristics and environmental performance of the machine. The final geometry selected through the optimization procedure, was manufactured, and tested in both turbine and pump operating modes. By comparing the numerical to the experimental results, a remarkable agreement was observed particularly in turbine operation, thereby validating the design methodology and highlighting the efficacy of Deriaz turbines as a means to accomplish net-zero emissions goals. • A numerical approach for optimum design of mixed flow reversible turbines is introduced. • The new machine exhibits high energy efficiency and improved fish-friendly behaviour. • Fewer blades of optimal design can be a successful proposition for this machine type. • A scaled model was manufactured and tested in the Laboratory for both operation modes. • Numerical and experimental results agreement validates the new design methodology. [ABSTRACT FROM AUTHOR]

Battery energy storage systems (BESS) can complement the variability of local renewable energy sources. However, existing research focuses on the design of BESS for electricity systems, mainly neglecting interaction with other energy vectors, e.g., the thermal vector. This study investigates the impact of explicitly modelling the thermal vector on the optimal design of BESS within local multi-energy systems. A holistic problem, including the nonlinear representation of the AC power flow, was developed within a non-convex mixed integer quadratically constrained program formulation. Two modelling approaches were employed: the explicit modelling of the thermal vector, and its implicit consideration within an all-electric demand model. These approaches were applied to investigate the impact of neglecting the thermal vector on the optimal BESS design in two real-world case studies. A constant and a time-varying electricity tariff, and three different solar irradiance scenarios were investigated. The results show significant BESS oversizing, higher annual costs and higher global warming impact when neglecting the explicit model of the thermal vector, both within a building and a local energy community. A time-varying electricity tariff enhances the BESS oversizing, with up to 20.5% oversizing for the BESS for a high solar irradiance scenario. Moreover, the annual costs of the all-electric demand model are around 8% higher compared to the explicit multi-energy model. Our findings clearly state the importance of explicitly modelling the coupled thermal vector during the sizing of electrical storage systems. • Integrated multi-energy system modelling is crucial for optimal battery design. • Holistic problem formulation including the AC power grid and the heating network. • Significant impact of explicit thermal domain modelling on electrical storage sizing. • All-electric demand modelling leads to significant battery oversizing. [ABSTRACT FROM AUTHOR]

Photovoltaic (PV) distributed generators (DGs) are inherently stochastic and have low inertia owing to their weather dependence and connection to an inverter. Frequency regulation presents a significant challenge for the high penetration of PV-DGs to microgrids. Recently, a virtual synchronous generator (VSG) has been proposed to enhance the inertia of microgrids because it mimics the behavior of synchronous generators. However, few studies have utilized VSG-based DGs for secondary frequency regulation owing to their stochastic power output. In this study, a model predictive control scheme integrated with two-layer moving-horizon estimation (TL-MHE) observer is proposed and applied to the secondary frequency control of a PV high-penetration microgrid. The local observer estimates the power disturbances in each PV-DGs, whereas the centralized observer estimates the system states and load disturbances. The two-layer observer design decouples multiple observations and reduce the order of the observation equations. Compared with conventional centralized observer-based controllers, the proposed scheme can obtain better observation results. The model prediction controller optimizes the restorative power from each DG in real time to minimize frequency fluctuations, thus affording better control performance. Simulation results show that the proposed TL-MHE method can accelerate the system frequency recovery, reduce frequency- fluctuations peaks, and reduce frequency deviation by more than 80% compared with the conventional method. • A model predictive control scheme integrated with two-layer moving-horizon estimation observer is proposed. • The two-layer observer design decouples multiple observations and reduce the order of the observation equations. • We use deloading virtual synchronous generator control to make the renewable energy participating in the secondary frequency regulation. • The proposed method overcomes the stochasticity of each generating unit and load. [ABSTRACT FROM AUTHOR]

The energy system and its energy performance of R-CELLS, a residential zero energy building from team Tianjin U+ in the Solar Decathlon China 2022, is introduced in this paper. When designing and constructing the R-CELLS energy system, two sets of challenges are encountered. Firstly, from an architectural perspective, the limited construction timeframe (only 21 days) and constrained space for housing the equipment, also ease of maintenance should be taken into account. Secondly, from an operational perspective, a constrained period for tests before the competition, a large number of devices dependent on electricity, heat and hot water supply should be considered. In consideration of the ease of assembly and maintenance of the energy system components and the aesthetic requirements of R-CELLS, an improved "photovoltaics, energy storage, direct current, flexibility" based energy system is utilized in R-CELLS. The R-CELLS energy system integrates various renewable energy sources (building integrated photovoltaics, photovoltaic-thermal, and building integrated wind turbines). Furthermore, it incorporates energy storage systems such as battery energy storage system, hot water tank, and phase change material thermal storage. In order to satisfy user requirements in grid-connected, off-grid and outage scenarios, including electricity, heat and domestic hot water, a tri-period energy management strategy based on optimized scheduling and rule-based control is proposed to achieve weekly, daily, and hourly net zero goals. Furthermore, the energy performance of R-CELLS is simulated for a whole year, including typical days in winter and summer, as well as the competition period. During the Solar Decathlon China 2022 competition, R-CELLS was subjected to testing by the committee. The results demonstrated that R-CELLS can be operated off-grid and with zero energy consumption, while meeting the test requirements for indoor environment, heating and cooling, and home life. • An improved PEDF based energy system of residential ZEB is utilized in R-CELLS, the champion competition entry from team Tianjin U+ in the Solar Decathlon China 2022. • A three-period EMS based on optimized scheduling and rule-based control is proposed. • Simulation data demonstrate that R-CELLS meets the ZEB standards. • The actual operation during the competition validates the reliable performance of the energy system, the effectiveness of the EMS, and the fulfillment of various energy needs of the residents. [ABSTRACT FROM AUTHOR]

The article explores the integration of a methanol production system with a modern combined cycle power plant utilizing oxy-combustion technology, resulting in enhanced efficiency for both systems (synergy effect). The integration involves various components like a 90 MW wind farm, hydrogen generators, and methanol production units. Notably, CO 2 preparation and compression for the methanol reactor within the power plant optimize methanol production and reduce energy consumption in the oxygen separation unit by supplementing oxygen through electrolysis. Utilizing oxygen from electrolysis also decreases energy consumption in the gas turbine's oxidant compressor. Additionally, compressing CO 2 to lower pressures reduces energy consumption in the CCU installation. The article presents a methodology for quantifying energy efficiency for both the methanol production unit and the combined cycle power plant. Integration leads to significant efficiency increases, particularly for low-power generation setups and with greater contributions from renewable energy sources. For instance, in scenarios involving a gas turbine with 7.5 MW capacity, efficiency gains range from 0.8 to nearly 6 percentage points with multiple wind farms. This study is the first of its kind to demonstrate the synergistic advantages of directly integrating an oxy-combined cycle power plant with a methanol production facility. The integration results in heightened efficiency for both the power plant and the methanol production setup, contributing significantly to the advancement of hydrogen technology and the green deal initiative. The unique approach of utilizing hydrogen from electrolysis setups and CO 2 derived from exhaust gases of power plants to generate liquid methanol, a superior energy carrier, is a significant contribution to the existing body of literature. The demonstrated synergistic outcomes of integrating a methanol production facility with combined cycle power plants stem from augmented unit efficiencies, overall installation improvements, and the amelioration of the ecological attributes of the power plant. The innovative character of this research endeavor is underscored by the fact that no existing installation worldwide currently pairs methanol production with a combined cycle power plant. [Display omitted] • Mutual synergy: Methanol system integrated with oxy Combined Cycle Power Plant • Efficiency gains across power plant, methanol unit, and setup • CO 2 prep and compression optimize methanol, reduce Air Separation Unit energy • Oxygen supplementation lowers gas turbine compressor energy • Lower CO 2 mass flow cuts energy use in Carbon dioxide Compression Unit installation [ABSTRACT FROM AUTHOR]

The construction of a virtual power plant (VPP) represents a powerful approach for harnessing the adjustable capacity of flexible resources. VPP actively participates in transmission network dispatching by aggregating these flexible resources. However, existing practical implementations and research cannot describe the influence of renewable energy's inherent unpredictability on the dispatching boundaries of VPPs. This shortfall leads to a lack of organic coordination between flexible resources and the power transmission network. Therefore, this paper suggests a way to characterize the dispatching boundaries of VPPs based on the concept of flexible operation region (FOR), aiming to better incorporate the flexible resources of decentralized access and lessen the issue of insufficient computing power caused by unified optimization. Initially, an analytical model for the VPP's FOR is presented, followed by dimensionality reduction of the FOR to the VPP's gateway. Subsequently, participation in transmission network scheduling is facilitated using an equivalent dimensionality reduction model. Based on standard example testing, the proposed method's effectiveness and accuracy have been verified in this paper. • This paper proposes a method to characterize the dispatching boundary of VPP based on the FOR. • Constructs the static voltage security region (SVSR) related to each node. • Thermal security region (THSR) related to each branch in the cluster in the power injection space. • This paper proposes a strategy to identify and eliminate the ineffective constraint • The boundary construction method of VPP is applied to the cooperative optimal dispatching. [ABSTRACT FROM AUTHOR]

The rapid development of cyber-physical power system has highlighted the pressing issue of cyber attacks (CAs) faced by large-scale renewable energy power systems (LREPS). The injection of false data attacks on critical electrical equipment by attackers can lead to abnormal system frequencies, triggering cascade failures and causing widespread blackouts. This paper introduces a tri-level defense model specifically crafted for LREPS, comprising an upper, middle, and lower level (defender-attacker-operator). By promoting collaborative decision-making among these agents, the model aims to mitigate the rate of change of frequency (RoCoF) and effectively withstand CAs. The upper-level employs an economic-inertia optimization strategy, scheduling generator units to maximize system inertia while minimizing generation costs. By leveraging the strong duality theorem, the tri-level model is decomposed into a bi-level model consisting of a master problem and a subproblem, solved using the Benders decomposition method. Furthermore, this paper also considers augmenting the virtual inertia of renewable energy units when defense resources are insufficient, ensuring RoCoF remains within limits after CAs. The effectiveness and superiority of the proposed model and strategy are validated through case studies based on the improved IEEE 118-bus system. The results demonstrate that the model can reduce load shedding caused by the worst-case CAs, enhance system inertia, lower RoCoF , and improve the stability of LREPS. • Propose a defense model against frequency disturbances caused by cyber attacks. • Minimize load shedding under worst-case attack. • Enhance system inertia through economic-inertia optimization. • Model significantly reduces rate of change of frequency. [ABSTRACT FROM AUTHOR]

Moving towards carbon-neutral societies, both nuclear and renewable energy can potentially supply CO 2 -free electricity. While the cost of renewable energy has decreased significantly, the cost of nuclear has, however, increased in the past decades and now in general exceeds the cost of renewables. However, one cannot compare directly the per unit cost of electricity since temporal behavior in the electricity production differs substantially between the two groups of technologies. Nuclear power inherently aims to provide a constant base load supply of electricity, while renewables generally depend on weather patterns. Thus, the two have different requirements and impact the overall system costs differently regarding flexibility and system design. Focusing on the case of Denmark, this article investigates a future fully sector-coupled energy system in a carbon-neutral society and compares the operation and costs of renewables and nuclear-based energy systems. The study finds that investments in flexibility in the electricity supply are needed in both systems due to the constant production pattern of nuclear and the variability of renewable energy sources. However, the scenario with high nuclear implementation is 1.2 billion EUR more expensive annually compared to a scenario only based on renewables, with all systems completely balancing supply and demand across all energy sectors in every hour. For nuclear power to be cost competitive with renewables an investment cost of 1.55 MEUR/MW must be achieved, which is substantially below any cost projection for nuclear power. • Both renewable energy and nuclear power benefits from sector coupling. • Hourly modelling is required to capture system effects. • Nuclear systems require less flexibility capacity than renewable only systems. • A renewable energy system is cheaper than a nuclear based system. • Lower flexibility costs do not offset the high investment costs in nuclear energy. [ABSTRACT FROM AUTHOR]

The dispatchable region (DR) describes the ability of a power system to adapt to the variability of distributed renewable energy (DRE) generation. Switchable devices such as soft open points (SOPs) and tie switches enable the AC–DC distribution network to flexibly cope with DRE power fluctuations. Currently, research on DRs seldom considers the effects of SOPs and reconfiguration with tie switches in AC–DC distribution networks, which prevents accurate evaluation of DR for this type of network. To bridge this gap, this paper formulates DRs for DRE generation in an AC–DC distribution network considering SOPs and reconfiguration with tie switches. The original model for deriving DRs is typically nonconvex, and the DR boundaries are intractable because of nonlinear power flow equations and binary variables of tie switches. Accordingly, this paper employs the outer polyhedral approximation and convex hull relaxation to approximate the original DR. An efficient adaptive constraint generation algorithm is developed to search for boundaries of the approximated DR. The numerical results show that the proposed model can effectively adapt to the DR of reconfigurable AC–DC distribution networks with SOPs and verify the accuracy and computational efficiency of the proposed method. • DRs of DRE generation are formulated in AC–DC distribution networks considering SOPs and tie switches. • Power flow equations and reconfiguration constraints in the AC-DC network are handled using polyhedral approximation and convex hull relaxation. • The accuracy and computational performance of the proposed method is verified. [ABSTRACT FROM AUTHOR]