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With the surging energy price and environmental concerns, measures to improve energy efficiency have attracted increasing concerns of the manufacture sector, especially energy-intensive manufacturing industries such as tissue paper mills. Energy-efficiency scheduling, as a novel energy-efficient method, has attracted the attention of an increasing number of researchers in recent years. Drying process is the most energy-intensive production process in tissue paper mills, which has a great energy-saving potential. This paper aims to reduce the energy costs for the tissue paper mill, consisting of processing energy cost and set-up energy cost, through integrating drying process optimization with energy-efficient scheduling. First, the energy cost model and the scheduling model were built. Then, the energy cost of the drying process of every job in a given scheduling problem was optimized using particle swarm optimization (PSO). Afterwards, the energy cost was further optimized using energy-efficiency scheduling. In addition, a hybrid non-dominated sorting genetic algorithm II (NSGA-II) was utilized to solve the energy-efficiency scheduling problem. Finally, several real scheduling problems from a real tissue paper mill were addressed using the proposed approach to demonstrate its effectiveness in energy saving. The experiment result showed that there is a great energy-saving potential in the drying process, accounting for up to 12.53% of the total energy consumption. Moreover, the maximum energy saving ratio of the proposed approach could reach 9.03%. On the whole, the proposed approach can provide a new energy-saving method for tissue paper mills or other manufacturing industries. [ABSTRACT FROM AUTHOR]

The pulp and paper industry is a high energy consuming and polluting sector, and carbon emissions emitted from this sector are worthy of attention. This article, based upon an analysis of China’s Pulp and Paper Industry (CPPI), provides estimates of each of the following: carbon emissions from energy consumption, pre-treatment sector, combustion of condensed black liquor, and methane emitted from incomplete aerobic digestion during sewage treatment of CPPI. During the study period (2005–2012), total CO 2 emissions ranged from 126.0 Mt to 155.4 Mt. Energy consumption was estimated to be the largest source of carbon emissions, however, due to the application of the local emission inventory rather than the IPCC inventory, energy consumption decreased by 4.7%, a lower percentage than was calculated in a previous study. According to this study’s estimation, the emissions caused by the recovery of biomass energy contributed 26–29% of the total CO 2 emissions. CH 4 generated from sewage treatment accounted for 9–11% of the total carbon emissions. The CO 2 intensity dropped during the study period, which reflected the improvement of energy efficiency in the pulp and paper industry. The outcome of this study provides not only detailed information about CPPI’s carbon emissions, but also a calculation framework for studying carbon emissions from pulp and paper sector in the other regions. It suggests that the local carbon emissions inventory should be used for estimating carbon emissions and to reduce the fossil fuel energy, increase energy recovery from biomass, and that promoting cleaner production is essential to achieve a low carbon development of the pulp and paper industry. [ABSTRACT FROM AUTHOR]

In an era of increasing energy demands and environmental concerns, optimizing energy consumption within buildings is crucial. Despite the vast improvements in HVAC and lighting systems, plug loads remain an under-studied area for enhancing building energy efficiency. This paper studies smart plug active operating mode detection, plug-level load forecasting, and plug scheduling methodologies. This research leverages a unique dataset from the University of California, San Diego, consisting of readings from over 150 smart plugs in several office buildings for more than a year, notably during the post-Covid era. This dataset is made publicly available. A comprehensive literature review on plug, i.e. , appliances operating mode detection is presented. Novel unsupervised learning approaches are applied to identify plug operating modes. A pipeline integrating the detected modes with forecasting and scheduling is developed, aiming at building energy consumption reduction. Our findings offer valuable insights and promising results into smart plug management for energy-efficient buildings. • A novel year-long dataset of power readings from 169 office smart plugs is introduced. • The first review of plug load mode detection methods and terminology is presented. • The paper proposes an unsupervised method to identify plugs load operating modes. • The proposed smart plug scheduling pipeline reduces energy consumption by 20%. [ABSTRACT FROM AUTHOR]

Under the background of green certificate trading (GCT)‑carbon emission trading (CET) market, the application of P2P trading in regional integrated energy system (RIES) has not been fully explored, and it is urgent to design a reasonable prosumer P2P trading mechanism to adapt to the development of diversified electricity market. Therefore, this paper proposes a prosumer P2P cooperative trading model based on generalized Nash bargaining. Specifically, based on the differential modeling of various types of prosumers, this paper constructs a prosumer P2P cooperative trading model based on generalized Nash bargaining theory, and quantifies the bargaining power of prosumers through the contribution value of interactive operation. The GCT-CET collaborative mechanism is introduced to promote the consumption of distributed energy by transforming carbon quotas through green certificates. In addition, this paper designs a warm start and adaptive step alternating direction multiplier method decentralized algorithm (WAS-ADMM) to improve the solution efficiency and protect the trading privacy of prosumers. Through a case study, it analyzes the impact of prosumers' P2P cooperative trading on the consumption of distributed energy, prosumers' GCT-CET costs and comprehensive costs in the context of GCT and CET market. The simulation results show that the proposed trading model reduces the GCT-CET costs of the prosumers' alliance by about 24.98%. • A P2P cooperative trading model of integrated energy is constructed based on GNB. • A GCT-CET collaborative mechanism is proposed to promote the consumption of renewable energy. • An improved decentralized algorithm based on WAS-ADMM is designed to solve the P2P trading model. [ABSTRACT FROM AUTHOR]

The accelerating urbanization, rapid industrial development, and excessive consumption of fossil fuels pose survival challenges such as energy depletion and environmental degradation for humanity. In the context of industrial park development, constructing a low-carbon energy system, increasing the proportion of renewable energy, enhancing energy-level matching, and utilizing heat pumps for deep waste heat recovery are effective approaches to reduce fossil energy consumption and alleviate environmental pressures. Addressing issues such as diverse thermal flows in industrial sites, complex electricity-heat-cooling energy demands, and unclear industrial-building energy coupling mechanisms, this paper proposes a two-stage planning framework, comprising the following five steps: (1) constructing a waste heat utilization system centered around heat pumps, (2) establishing a mechanism for matching heat sources, equipment, and thermal sinks, (3) establishing an energy conversion model and energy quality quantification system, (4) optimizing waste heat utilization path allocation, and (5) conducting a system energy flow analysis. Case studies demonstrate that the proposed system achieves optimized matching of multiple heat sources and sinks in industrial and building scenarios through thermal integration, meeting diverse energy demands in the park, including electricity, cooling, and multi-grade heat. After coupling with traditional steam pipeline networks, the annual total cost and annual steam power consumption decrease by 33% and 39% respectively, with a waste heat contribution rate of up to 26% and a renewable energy penetration rate of up to 25%, and a waste heat recovery system payback period of 6 years. By establishing an energy quality quantification system and conducting multi-objective optimization considering losses and economic costs, this paper provides Pareto frontier solutions, offering references for engineering proposals. The proposed networked waste heat recovery system is characterized by low energy consumption and high economic efficiency, effectively integrating the energy characteristics of industrial parks and demonstrating engineering applicability. [Display omitted] • Focusing on industrial-building energy conjugation issues • Proposing a low-carbon energy system planning framework for industrial parks • Establishing a networked matching mechanism for cold and hot flow streams • Short system recovery cycle, good economic performance • High contribution rates of waste heat and renewable energy, evident low-carbon effect [ABSTRACT FROM AUTHOR]

The objective of this paper is to optimize the energy consumption and performance of fuel cell electric bicycles (FCEBs) under specific key input parameters. The paper applied an integrated method that includes an artificial neural network (ANN) and genetic algorithm (GA) to forecast and identify an optimal performance and energy consumption of FCEBs. The simulation model of FCEBs is established and simulated in MATLAB-Simulink environment to generate 1000 data points, that are used for training, validating, testing artificial neural network, the ANN architecture containing five input neurons, two hidden neurons and two output neurons, respectively. Furthermore, the GA is integrated to find the maximum performance and energy consumption once the ANN has exactly been trained. The study found that the FCEB configuration can achieve an effective performance at 30.3 km/h with required power of 210.4 W under speed level_5, radius of wheel 0.39 m, frontal area 0.423 m2, slope grade 0%. In order to validate and verify the simulated results, the experimental approach method was conducted in the same condition. The experimental results fit well with the simulated results in the same initial input parameters. • A method integrating the artificial neural network with genetic algorithm is applied. • The consumed power and fuel cell voltage are optimized by combining artificial neural network and genetic algorithm (ANN-GA). • The effective performance area of fuel cell electric bicycle under various a structure and operating parameters is identified. • Deep-learning results reflect major physical mechanism of fuel cell electric bicycle operation. [ABSTRACT FROM AUTHOR]

As the renewable energy consumption responsibility weight system is gradually implemented in various provinces, there is a closer intersection between the excess consumption and green certificates trading. Existing trading platforms can only meet the needs of green certificates trading. This single-platform solution is no longer sufficient to meet the trading requirements under this system. Consequently, this paper proposes a excess consumption and green certificates trading platform based on blockchain technology. This platform utilizes the decentralized, open, and anonymous characteristics of blockchain technology and rely on its smart contract capabilities to simultaneously meet the comprehensive trading needs of surplus consumption and green certificates trading through combining user-initiated transactions with platform-intelligent recommendations. It adapts to the complex excess consumption and green certificates trading market that has emerged following the issuance of the renewable energy consumption responsibility weighting policy. To conduct simulated transactions, 16 groups of buyers simulate a total of 215,792 excess consumption vouchers and 143,861 green certificates in this paper. The transaction results all meet the expected transaction scenarios under the conditions of smart recommendation, demonstrating the platform's transaction feasibility. [Display omitted] • Design a blockchain-based smart contract solution for volume consumption trading and green certificate trading. • Build a database containing 215,792 excess consumption vouchers and 143,861 green power certificates. • Simulate the purchase of 160 buyers by establish a transaction platform. • Analyzed the application direction of the data produced by the trading platform [ABSTRACT FROM AUTHOR]

The motivation of this paper is to investigate the resistance of countries' energy research and development (RD&D) expenditures to random shocks. The analysis includes the five countries (France, Germany, Japan, the United States, and the United Kingdom) that are the biggest investors in RD&D in fossil fuels, renewables, energy efficiency, and nuclear energy. Thus, economies will be able to take precautions against global shocks in producing sustainable energy policies. To achieve this aim, this paper uses the artificial neural network approach, which uses a distributed computing model that can store and generalize data after a learning period. Empirical results widely depend on countries and distinct energy technology policy fields. The key findings provide evidence that RD&D expenditures, except for renewables, do not tend to mean revert. The economies of Japan, Germany, and the United States should make more renewable investments to reduce the resistance of renewable energy sources to shocks and thus produce policies for both environmental sustainability and energy security. The economies of France and the United Kingdom can ensure energy security by continuing their sustainable energy policies. • Applied SDG policies focused on energy technology RD&D are presented. • The stochastic characteristics of energy technology investments are examined. • Carbon-reducing policies are recommended for the top five economies. • Energy technologies are perused through artificial intelligence approach. [ABSTRACT FROM AUTHOR]

CaO-CaCO 3 thermochemical energy storage is a promising technology for solar energy utilization and storage. Alkaline papermaking waste (APW) from paper mills, which is mainly composed of CaCO 3. Herein, TiO 2 /MnFe 2 O 4 co-modified APW was synthesized. The energy storage capacity of TiO 2 /MnFe 2 O 4 co-modified APW was studied during CaO-CaCO 3 thermochemical energy storage cycles in a duel fixed bed reactor. TiO 2 /MnFe 2 O 4 co-modified APW mainly consists of CaO, MgO, CaTiO 3 , CaMnO 3 , and Ca 2 Fe 2 O 5. The introductions of TiO 2 and MnFe 2 O 4 significantly improve the cyclic stability and optical absorption property of APW in 30 cycles. The co-modified APW (the mass ratio for CaO:TiO 2 :MnFe 2 O 4 = 100:5:5) possesses high sintering resistance, energy storage, and optical absorption capacities in 30 cycles. The energy storage conversion and capacity of the co-modified APW are 0.72 and 2288.57 kJ/kg after 30 cycles, respectively. The optical absorptance of the co-modified APW reaches 49%, which is 9.25 times as high as that of APW. CaTiO 3 and CaMnO 3 effectively mitigate the sintering of CaO grains. CaMnO 3 and Ca 2 Fe 2 O 5 significantly increase the optical absorptance of APW. The co-modified APW possesses stronger basicity and more oxygen vacancies, which is favourable for the carbonation reaction. Thus, TiO 2 /MnFe 2 O 4 co-modified APW is a good energy storage material. • TiO 2 /MnFe 2 O 4 co-modified papermaking waste is used for CaO-CaCO 3 energy storage. • The composite mainly consists of CaO, MgO, CaTiO 3 , CaMnO 3 , and Ca 2 Fe 2 O 5. • The composite has high and stable energy storage and optical absorption capacities. • The energy storage density of the composite is 2288.57 kJ/kg after 30 cycles. • The composite has high sintering resistance, stronger basicity, and oxygen vacancy. [ABSTRACT FROM AUTHOR]

In recent decades, the use of fossil fuels has had a harsh impact on the climate, and the frequency and intensity of various extreme weather events have increased, which has triggered an increased the use of renewable energy sources and reflection on technological advances. Many countries are trying to promote renewable energy to reduce their impact on climate change, but the impact of national energy policies in the climate risk framework remains poorly understood. Based on the panel data of 84 developed/developing countries around the world from 2006 to 2019, this paper studies and analyzes the impact of renewable energy use on climate risk. The study found that the increase in the proportion of renewable energy use can reduce the climate risk, and this result is still robust after undergoing robustness tests such as changing variables, samples, and research methods. The findings offer an important solution for European countries, where climate risks are most acute. The heterogeneity analysis shows that the higher the level of economic development, population density and the lower the level of incorruptibility, the weaker the inhibitory effect of renewable energy use on climate risk. The mechanism analysis also shows that the increase in the proportion of renewable energy use can reduce climate risk by reducing carbon dioxide (CO 2), methane (CH 4), nitrogen monoxide (NO), PM2.5 emissions, and the proportion of fossil fuel use, but it does not reduce climate risk by increasing biodiversity. In particular, we find that technological progress has not played a role in the use of renewable energy to reduce climate risk, breaking the stereotype. The policy recommendation of this paper is that under the increasing pressure of climate change, the transition to clean energy is an indispensable option, and the need to accelerate the energy transition becomes more urgent. There is a greater need to further protect biodiversity and enhance technological progress. • The increase in the proportion of renewable energy use can reduce the climate risk. • The mechanism is to reduce CO 2 , CH 4 , NO, PM2.5 emissions and fossil fuels use. • The use of renewable energy cannot reduce climate risks by increasing biodiversity. • The impact of renewable energy use on climate risk is heterogeneous. • Technological progress has not played a role in the use of renewable energy to reduce climate risk. [ABSTRACT FROM AUTHOR]

Extant research has highlighted the pros and cons of the use of (a) competition and (b) positive (peer) pressure as two mechanisms for invoking peer effects to bring about long-term behavior change. However, these mechanisms have been studied separately and in different contexts. In this paper, we compare the differential impacts of competition versus positive pressure-induced pro-environmental behavior change in the same target population. This study spans a seven-year horizon between 2013 and 2019 (pre-COVID) during which the workforce of about 2500 employees in the Stephan P. Clark Government Center in Miami, Florida, was split into four groups - one control and three treatment groups. Each treatment group was subjected to (a) a healthy inter-group competition through tracking, benchmarking, and comparing monthly group level electricity saving, and (b) positive pressure through the appointment of champions who encouraged pro-environmental behavior among their group members. Studying these mechanisms concurrently in the same population allows for a nuanced comparison of their differential impacts on pro-environmental behavior change. • In this paper, we compare the differential impacts of competition-induced spillover and supervision-induced peer pressure in affecting pro-environmental behavior change in the same target over a seven-year analysis horizon. • The study spans 2013-2019 during which the workforce of about 2500 employees in a building was split into four groups (one control and three treatment) and was subjected to both peer pressure and healthy competition. • The study spans 2013-2019 during which the workforce of about 2500 employees in a building was split into four groups (one control and three treatment) and was subjected to both peer pressure and healthy competition. • These results have implications for the design of pro-environment campaigns that could leverage peer effects through fostering an environment of intragroup pride and healthy inter-group rivalry sprinkled with intermittent supervisory interventions for making long-term impacts. [ABSTRACT FROM AUTHOR]

The energy transition towards sustainable resources is more urgent than ever given the environmental and geopolitical challenges. Being one of the major energy users, cities need to understand their energy sector to accomplish its transition, by means of data. However, data are not easily accessible and have their own challenges. This paper presents a joint effort between researchers, city representatives and industry to provide an urban system service that supports research, accelerates urban innovation, and involves the community. An energy data portal, "NRGYHUB", has been developed, where hourly data from thousands of energy meters are available. These meters were collected from neighborhoods in the city of Västerås, Sweden, and they measure electrical and heating energy. In addition, the data are complemented by geometrical and non-geometrical information of the buildings, as well as demographic statistics of the areas. The paper describes the process of data collection, preprocessing, and visualization, in addition to the main challenges and limitations of the project. This dataset can be used for energy use benchmarking, prediction, and analysis. • The creation and implementation of an energy data portal is detailed and made available on nrgyhub.mdu.se. • Legal and licensing issues in data sharing are explored, offering insights for those interested in Sweden and beyond • By showcasing real-world problems, the impact and role of this data sharing initiative are demonstrated. [ABSTRACT FROM AUTHOR]

An effective way to promote the consumption of renewable energy resource generation is by forming a joint power supply system between photovoltaic (PV) power plants widely distributed along a river basin and large-scale cascade hydropower plants, using shared transmission lines to transmit power to the power grid system. However, renewable energy resource generation has significant prediction uncertainty and can flood into the power grid, which is detrimental to its safe and stable operation. To solve the above issues, this paper proposes an improved interval optimization method to model the uncertainties of the inflow runoff of a cascade hydropower plant and the photovoltaic power output. On this basis, a medium- and long-term interval optimization scheduling model is established for a cascade hydro-PV complementary system based on extreme scenarios. This model does not need to obtain an accurate probability density distribution for renewable energy, and can realize the overall optimization of runoff-PV uncertainty scenarios within a certain range, taking into account the economy and robustness of system operation. In order to ensure the solution efficiency, the original model is transformed into a mixed integer linear programming (MILP) model by a variety of deterministic transformation methods and linearization techniques. The case study shows that: 1) Under the premise that the system power output is relatively stable, the power curtailment of the interval optimized scheduling scheme proposed in this paper is reduced by 47.5% compared with the robust scheduling scheme. The ability to cope with extreme scenarios is also significantly improved compared with the deterministic scheduling scheme. 2) There is a game relationship between the robustness and economy of the obtained scheduling scheme. When the fluctuation interval width of random variables increases from 20% to 30% and 40%, the corresponding total power generation decreases by 2.4% and 4.7%, respectively. 3) A large penalty factor is conducive to reducing the amount of abandoned hydropower, but in general, the penalty factor and ecological flow level have no obvious influence on the dispatching results, and the values of both can be reasonably determined according to the actual operation requirements. • An improved interval optimization method is proposed to deal with the uncertainty of inflow runoff and PV power output. • A MILP model for the medium- and long-term operation of a cascade hydro-PV complementary system based on extreme scenarios is established. • The resulting scheduling scheme achieves balance between economy and robustness. • The influence of penalty factor and ecological flow on the dispatching results is not obvious. [ABSTRACT FROM AUTHOR]

Forecasting energy demand has become increasingly important due to technological advances, especially new power systems and population growth. Accurate predictions of energy demand and supply are crucial for academics and policymakers. Also, energy consumption is non-stationary and dynamic in time, requiring an adaptive forecasting algorithm. This paper presents a new adaptive hybrid approach for energy time series forecasting using statistical methods. Some of the most popular energy demand time series applications are used to demonstrate the superiority of the proposed algorithm, which covers a wide range of critical energy demand forecasting situations at various scales. The paper's objectives are presented in three phases. In Phase I, ARFIMA is used to forecast the entire original time series as a statistical inspector and later for comparison. Although ARFIMA's fractionality feature helps to capture long and short-term memory behavior in time series, to be more adaptive and become close to a nonlinear algorithm that can handle non-stationarity, and Gaussian and non-Gaussian time series closely, a novel dynamic statistical structural break detection framework is developed, and used in phase II to identify the time series' change points and associated time indexes. Since time series are partitioned based on structural change detections, they approach one in terms of the Hurst exponent and exhibit pure long memory, which is ideal for ARFIMA modeling. Phase III captures non-stationary statistical properties and memory characteristics by applying adaptive ARFIMA modeling to segmented time series. Finally, the predicted partitions are concatenated. Various performance assessment metrics, e.g., Mean Absolute Percentage Error (MAPE), tested on the final achieved results show that the proposed adaptive algorithm outperforms the forecasting capabilities of the existing algorithms on the same energy demand time series used in the current paper. MAPE of the proposed approach for the four utilized real-life cases are 1.72%, 1.35%, 0.22%, and 4.4%, respectively. Therefore, the proposed model is a satisfactory method for energy demand forecasting due to its high accuracy. • A novel adaptive hybrid algorithm is developed for energy demand forecasting. • The prediction system uses ARFIMA and a novel dynamic structural break detection framework. • The adaptive prediction method can forecast energy demand with high accuracy. • The structural break detection works dynamically, allowing ARFIMA to be adaptive. • The prediction method captures the energy time series' nonstationary statistical features. [ABSTRACT FROM AUTHOR]

Shared energy storage is widely recognized as an energy hub for the coordinated operation of regional integrated energy systems (RIESs). Multi-energy systems (MESs) share centralized energy storage to store excess renewable energy sources (RESs). This paper proposes a novel energy-sharing framework considering hydrogen trading, which is suitable for residential, industrial, and commercial RIES structures. Shared energy storage operator (SESO) promotes hydrogen energy transactions by formulating time-of-use (TOU) hydrogen prices. The proposed hydrogen energy trading method can be regarded as a master-client structure, and a hierarchical optimal scheduling method based on the Stackelberg game relationship is designed. Finally, a case study is performed based on an optimal scheduling algorithm combining particle swarm optimization (PSO) and mixed integer linear programming (MILP). The cases show the electricity‑hydrogen shared energy storage mechanism in RIESs can improve the RESs utilization rate and effectively reduce the operating costs of each system. Moreover, compared with RIESs with a single centralized electric energy storage, the TOU hydrogen price mechanism can further lower the energy prices and improve the economy of the RIES. The cases show that hydrogen energy sharing reduces the operating cost of RIES by 9.96% and increases the regional energy utilization rate by 2.97%. And the shared energy storage business model can satisfy the economy of both SESO and RIES. It confirms the rationality of the electric hydrogen shared energy storage project. • A sharing energy storage mechanism in multi-energy systems is presented in this paper. • The hydrogen trading methods based on time-of-use hydrogen prices are considered in this paper. • A hierarchical optimal scheduling method based on the Stackelberg game is designed. [ABSTRACT FROM AUTHOR]

This paper proposes a novel method for integrating renewable energy sources (RES) and power-to-gas (P2G) technologies to optimize energy utilization efficiency as RES capacity increases. The method introduces the potential RES penetration index (PRPI) to select the appropriate capacity of P2G facilities for improved RES acceptance rates. In order to establish input constraints for RES and P2G facilities, this study determines the saturation point of RES (R-SP) and the saturation point of P2G (PG-SP). Additionally, the paper introduces the marginal capacity rate of RES to P2G (MCRG) as a useful tool for evaluating the adequacy of planned facilities. This paper's index is limited to PV as a renewable energy source. By incorporating PRPI into the input plan of P2G facilities, the proposed method improves PV's acceptance rate, contributes to facilities' economic planning, and streamlines optimization complexity. Previous studies have demonstrated the potential for P2G technology to optimize the integration of PV into the grid. However, the proposed method considers the PV's acceptance rate and provides system planners with necessary information on RES and P2G facility capacity. The method proposed in this paper can be extended to include other RES. • Novel Index for PV-P2G integration (PRPI). • Enhancing Acceptance rate of PV applying PRPI • Reduced optimization complexity. • Adaptable to various renewables. [ABSTRACT FROM AUTHOR]

The use of ejectors in transcritical CO 2 refrigeration systems is of key significance for improving system performance, and the investigation of ejectors is particularly important. In this paper, a CO 2 two-phase numerical model considering non-equilibrium phase change is established to investigate the two-phase flow characteristics and entrainment performance in the ejector under different operating conditions. In particular, this study is devoted to the trade-off analysis of internal mass transfer and energy utilization efficiency. After a series of tests, the accuracy of the numerical model has been validated, and with the increase of primary inlet pressure, the entrainment performance first increases and then decreases, while the entrainment performance is greatly improved with the increase of primary temperature. There is a critical back pressure, beyond which the entrainment performance drops rapidly. If the pressure reaches 8.46 MPa and the corresponding temperature is set at 303.7 K, the efficiency of the ejector is 27%. When the temperature rises to 313 K, the efficiency increases to 31%, and the ratio of exergy destruction decreases from 48.3% to 10.2%. However, when the back pressure drops to 4.0 MPa, the working efficiency is only 1.8%, which fails to work normally. Effectively balancing the inlet conditions with energy utilization is a key strategy for optimizing ejector use. • CO 2 two-phase model using phase change for CO 2 ejector in refrigeration systems. • Two-phase flow behavior and entrainment performance of the ejector are analyzed. • Exergy destruction and working efficiency under different conditions are calculated. • The efficiency increases up to 31% while the primary temperature rises to 313.7 K. • With the back pressure of 4.0 MPa, the exergy destruction ratio is as high as 72.9%. [ABSTRACT FROM AUTHOR]

With a rising number of buildings being equipped with private distributed energy resources (DERs) such as rooftop PV panels and energy storage devices, an effective energy management method for a building cluster becomes increasingly imperative. This paper proposes a novel decentralized transactive energy management (TEM) method for a hybrid cluster of residential and commercial buildings, which enables peer-to-peer (P2P) energy trading among the DER owners and consumers. The strategic interactions among the DER owners and consumers are modeled as a multi‑leader-multi-follower (MLMF) Stackelberg game and formulated as a bi-level model. The DER owners, the commercial and residential consumers are all autonomous entities, optimizing their individual welfare functions and sharing necessary trading-related information, which are expressed as the upper-level leaders' models and the lower-level followers' models, respectively. To preserve the privacy and autonomy of each entity within the building cluster, a distributed algorithm incorporated with an efficient P2P pricing mechanism is designed for the formulated MLMF Stackelberg game model. Simulation results demonstrate the effectiveness of the proposed method on mitigating the reliance of the building cluster on the power grid, motivating the DERs to actively participate in P2P trading, and reducing the consumers' energy consumption costs • A decentralized transactive energy management framework is designed for a real-world hybrid building cluster. • The strategic interactions among the owners of distributed energy resources (DERs) and the consumers within the building cluster are captured by a multi‑leader-multi-follower Stackelberg game. • A novel distributed algorithm is developed to protect the privacy and autonomy of participants in a large-scale building cluster. [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]

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]

In both practice and literature, there is a lack of a concept for a supra-regional district heating network that efficiently transports heat from renewable and industrial waste heat sources to multiple heat sinks and regional district heating networks, like the high-voltage electricity transmission network in the electricity sector. This paper addresses this gap by presenting a novel method for the basic design of a supra-regional district heating network and its evaluation, along with key performance indicators for assessment. The method highlights essential data requirements and the derivation process necessary to enable the integration of such a heating network. Additionally, it describes how a basic design of such a network can be made feasible and evaluated. This method is then applied to a case study to demonstrate its implementation for a real-world application. Furthermore, this case study aims to either demonstrate or provisionally disprove the general feasibility of a supra-regional district heating network. The results indicate that the implementation of such a network has a positive impact on the CO 2 balance and primary energy demand. The case study further demonstrates the technical feasibility of such a network, showing that a high linear heat density can be achieved through integration and that temperature levels within the network can be maintained adequately. This study confirmed that the developed method can effectively assess whether further investigations into implementing a supra-regional district heating network in a specific region are warranted. Additionally, the method offers a guideline on how to initially design such a network. • Industrial Waste Heat • Load Flow Calculation • District Heating • Energy Efficiency [ABSTRACT FROM AUTHOR]

An understanding of the spatial evolution of global household energy and its determinants is needed to achieve global access to clean cooking energy before 2030. Based on a novel global household energy database, we adopt the log t regression test to identify the global trend and find the potential convergent clubs. Besides, we utilize Random Forest method and Ordered Probit model to investigate the drivers affecting the club convergence. As a result: (1) The global sample exhibits divergence from 2000 to 2020; (2) The 191 countries and regions can be classified into 3 convergent clubs, characterized by significant disparities in both the mean and growth rate of their access to clean energy; (3) Per capita GDP, urbanization level, infrastructure development, and educational attainment are identified as the most important factors influencing the club convergence; (4) Countries with a higher level of socio-economic development generally have a higher proportion of residents using clean fuels, with gas energy playing a pivotal role in driving this transformation. This paper adds more evidence to understand the disparity of global household cooking energy use, we propose that economic and infrastructure development, education attainment, and international cooperation are favorable. • Convergence analysis of country-level clean cooking is conducted. • 191 countries are classified into 3 convergent clubs by access to clean cooking. • Drivers that affect the formation of club convergence are investigated. • Policy suggestions to reduce the gap of SDG 7 are proposed. [ABSTRACT FROM AUTHOR]

The intrusion of saline tide into estuaries is one of the major environmental issues impedding the regional development of the Pearl River Estuary, which has been exacerbated by increasing water use associated with energy consumption in the estuary areas. However, the relationship among water use, energy consumption and saltwater intrusion remains ambiguous. Firstly, this paper established a k-nearest neighbor model that identified the correlation between inputs of precipitation and sectoral energy consumption and output of runoff in the Humen Channel as part of the Pearl River Estuary during 2019–2020. Then, the Finite–Volume Community Ocean Model (FVCOM) was employed to explore the process-based mechanism between runoff and salinity via three scenarios simulated for the periods 2030–2031 and 2060–2061 with different water use and precipitation levels. Finally, the impacts of sectoral energy consumption on saltwater intrusion under the different scenarios were analyzed. The results showed that the increase of the energy consumption was positively correlated to runoff, especially in the wet season of Humen Channel. The monthly average salinity exhibited remarkable seasonal variations with similar trends under three scenarios. Totally 654 days with salinity exceeding the standard were projected to take place in the downstream of Humen Channel during the simulation period. • The relationship between sectorial energy consumption, precipitation and runoff is established. • The individual and coupling effects of sectorial energy consumption and precipitation on salinity are quantified. • The evaluation of days of salinity exceeding the standard are made to assess the influence of different scenarios. [ABSTRACT FROM AUTHOR]

Under the trend of energy saving and carbon reduction, reducing household energy consumption, which is an important part of social energy consumption, has become a hot topic. In order to solve the problem of mismatch between supply and demand of household environment control systems as well as to better meet the individual needs of users, we innovatively designed a solar-driven domestic regional environment mobile robot as auxiliary heating and cooling method. In this paper, a simplified thermal network analysis of the thermal conditions of a dwelling is utilized to calculate the energy saving and carbon reduction potential on summer and winter reference days in Beijing and Guangzhou, China. The simulation results show that the system has good energy saving potential and carbon reduction effect. In particular, the single-day energy saving potential of the system on winter days in Beijing reaches to 583.6 kWh compared to the traditional method. Under the power supply mode, the single-day carbon reduction can reach 462 kg CO 2 on winter days in Guangzhou, and it can reach 333.2 kg CO 2 on summer days in Beijing. We also discuss the effect of regional space size and clothing thermal resistance. As the local space size and the clothing thermal resistance increases, the energy saving potential and carbon reduction benefits of the system decrease, but the overall impact is small. The proposed mobile robot following in the vicinity of home inhabitants to help maintain their thermal comfort, can improve the efficiency of dwellings heating and cooling systems and utilize the limited solar energy resources in family area. • We have proposed the regional household auxiliary heating and cooling method, which is more energy efficient; • Solar-driven domestic environment mobile robot (DREMR) is low-carbon and environmentally friendly; • TEC/THP components combined with human-machine interface can effectively meet the user's individual needs; • The energy demand and carbon emission analyses models can also be used in other household environment control systems. [ABSTRACT FROM AUTHOR]

Flexible microgrids with dynamic boundaries have recently been introduced in the literature. With the ability to reconfigure the topology of the microgrids dynamically through remotely controlled switches, flexible microgrids with dynamic boundaries can further improve the resiliency and energy efficiency of microgrids with distributed energy resources (DERs). This paper focuses on the optimal operation considering one of the predominant characteristics of microgrids and distribution systems – unbalanced networks and loads. In existing literature, balanced modeling of microgrids is more common due to its attractive simplicity. The three-phase power unbalance has not been considered as a constraint on the generation units in a microgrid. Negative sequence constraints have also been neglected. In this article, we propose a set of constraints that is specifically related to the capabilities of inverter interfaced resources to supply unbalanced current/power when the microgrid is islanded from the main distribution grid. We incorporate the new set of constraints into two optimization formulations leveraging two convex relaxations of the three-phase power flow equations: mixed-integer linear programming (MILP) and mixed-integer semidefinite programming (MISDP) that optimize the dispatch of controllable switches and DERs in the microgrid. The algorithms are then extended to networked microgrids with grid-forming sources. We test the algorithms on a realistic community microgrid model in Puerto Rico as well as standardized IEEE distribution test feeders. The testing results demonstrate the performance of the proposed algorithms. The MILP is fast and scalable, and the MISDP enforces the negative sequence voltage constraints. • Optimal operation of dynamic boundary MGs considering network and load unbalance. • Integration of unbalanced power and voltage constraints to ensure stable operation. • Fast and scalable mixed-integer linear programming model. • Mixed-integer semidefinite programming captures unbalanced voltage. • Better renewable utilization and resiliency in dynamic boundary microgrids. [ABSTRACT FROM AUTHOR]

Existing data-driven models of building energy consumption have incorporated solar irradiance as a covariate without accounting for key physical constraints, leaving room for improvement in model performance and interpretability. In this paper, we propose the Time-Of-Week, Solar and Temperature (TOWST) model for quantifying building energy consumption. This new model incorporates solar gain into the well-known Time-Of-Week and Temperature (TOWT) model by accounting for important physics constraints, including, building orientation and the opposite impact of solar gain on heating and cooling. An iterative algorithm is developed to cluster the energy demand into heating and cooling categories, identify building orientation, and estimate the unknown model parameters. The model is applied to datasets generated from a building simulation according to international standard ISO 52016-1:2017, for a range of buildings types and locations. Results show a 30%–72% reduction in mean squared error relative to the TOWT model, across a range of different climatic conditions, demonstrating the significance of accounting for solar gain in a physically meaningful way. • Data-driven building energy model incorporating the physics of solar gain. • Identifies building orientation to capture the irradiance received by a building. • Infers heating and cooling hidden states to model the physical dependence. • Significant improvement in model fit across range of climatic conditions. [ABSTRACT FROM AUTHOR]

Reasonable road design can not only improve traffic efficiency, ensure driving safety, reduce construction costs, but also reduce the energy consumption of road transportation. However, existing road design research mainly focuses on safety, efficiency, and economic factors, with few studies considering the actual construction difficulty as well as energy saving and emission reduction of transportation. To fill the research gap, this paper proposes a longitudinal slope design method for highway sections that considers both vehicle fuel consumption minimization and construction feasibility. First, the passenger car trajectory data and fuel consumption data from the Beihuan Highway in Guangzhou, Guangdong Province, China were collected, and the optimal combination features of the random forest algorithm were extracted using parameters such as vehicle operating conditions and road grades, thus establishing a fuel consumption estimation model for passenger cars on highways. Second, by combining the fuel consumption estimation model with dynamic programming algorithm, an ecological speed planning model for passenger cars on highways was developed. Finally, for the scenario of ecological driving of intelligent vehicles, a fuel-efficient slope design (FSD) method was proposed based on the Nested Monte Carlo Tree Search algorithm, and the best fuel-efficient slope design was selected considering the balance of cut and fill factors. The results show that FSD can reduce the fuel consumption of passenger cars on the section by about 4.50%–6.52%; the fuel-efficient slope design considering the balance of cut and fill not only can reduce the fuel consumption of passenger cars on the section by about 5.21%, but also has the advantage of lower engineering volume. The research results conduce to establish a theory and standards for fuel-efficient slope design of highways suitable for ecological driving scenarios of intelligent vehicles, which can reduce the trip fuel consumption of highway traffic flow in the long run while considering traffic efficiency, and have great significance for reducing carbon emissions, saving travel costs, and protecting non-renewable resources. • The study proposes a fuel-efficient longitudinal slope design method for highway. • The method can reduce the fuel consumption of a passenger car by about 4.50%–6.52%. • The concave curve is effective in reducing fuel consumption for passenger cars. • Considering the balance of cut and fill makes the slope scheme more fuel-efficient. [ABSTRACT FROM AUTHOR]

Air-conditioners (ACs) can fully utilize the inherent characteristics of storing heat/cold in demand response (DR), achieving peak load shifting and renewable energy consumption. However, traditional control strategies based on compressor ON/OFF state require retrofitting of the internal components of ACs, making it challenging to implement directly through ACs' infrared control protocol. Given the widespread availability of infrared temperature-set-point (TSP) adjustment functions in existing ACs, this paper proposes a day-ahead optimal scheduling strategy of aggregate ACs under TSP control. Based on the second-order equivalent thermodynamic parameter (ETP) model, the TSP adjustment characteristics are considered to establish the equivalent energy storage (EES) model of ACs. On this basis, taking the overall optimization objectives of power system into account, the optimal scheduling model of aggregate ACs under TSP control is established to solve collaborative optimization problems for power generation and consumption. Case study demonstrates that, compared to optimal scheduling models based on traditional EES model, the proposed model exhibits superior peak load shifting effects and allows for smooth control of ACs through the issuance of control commands. • An optimal scheduling model of aggregate air-conditioners based on equivalent energy storage model is established. • Discrete temperature-set-point control realized through air-conditioners' infrared control protocol. • The aggregate air-conditioners' energy storage capacity and their potential in demand response are enhanced. [ABSTRACT FROM AUTHOR]

Deep Reinforcement Learning (DRL) has emerged as a promising approach to address the trade-off between energy efficiency and indoor comfort in buildings, potentially outperforming conventional Rule-Based Controllers (RBC). This paper explores the real-world application of a Soft-Actor Critic (SAC) DRL controller in a building's Thermally Activated Building System (TABS), focusing on optimising energy consumption and maintaining comfortable indoor temperatures. Our approach involves pre-training the DRL agent using a simplified Resistance-Capacitance (RC) model calibrated with real building data. The study first benchmarks the DRL controller against three RBCs, two Proportional-Integral (PI) controllers and a Model Predictive Controller (MPC) in a simulated environment. In the simulation study, DRL reduces energy consumption by 15% to 50% and decreases temperature violations by 25% compared to RBCs, reducing also energy consumption and temperature violations compared to PI controllers by respectively 23% and 5%. Moreover, DRL achieves comparable performance in terms of temperature control but consuming 29% more energy than an ideal MPC. When implemented in a real building during a two-month cooling season, the DRL controller performances were compared with those of the best-performing RBC, enhancing indoor temperature control by 68% without increasing energy consumption. This research demonstrates an effective strategy for training and deploying DRL controllers in real building energy systems, highlighting the potential of DRL in practical energy management applications. [Display omitted] • Implementation of SAC as a continuous action thermostatic controller. • SAC pre-trained in simulation before the deployment in a real-world office. • Development of a benchmarking strategy to compare DRL and RBC in real deployment. • Reduced temperature violations by 68% maintaining the same energy consumption. • Discussing practical DRL controller deployment challenges. [ABSTRACT FROM AUTHOR]

Indirect evaporative cooling has garnered considerable attention and research owing to its notable advantages, especially high cooling efficiency and low energy consumption. However, the complexity and diversity of parameters affecting indirect evaporative cooler (IEC) performance necessitate the development of a fast and efficient performance prediction model, which remains a critical research objective. To address the particular challenge, this paper introduces a novel approach that utilizes the principles of neural network theory to construct a predictive model based on backpropagation artificial neural networks (BP-ANN). The developed model is employed to investigate the impact of six main operating parameters of the water spray system on the IEC performance. Wet-bulb efficiency and pressure drop of secondary air channel are utilized as the performance evaluation indexes. The model is rigorously validated by relevant experimental data, enabling a comprehensive analysis of the influence of each parameter on the operational performance of the whole system. The significance of the influential parameters is evaluated through grey relational analysis. Furthermore, a multi-objective optimization approach employing a genetic algorithm (GA) is introduced to effectively determine the optimal operating parameters of the IEC system. The results demonstrate that the whole system can efficiently operate with low energy consumption when the wet-bulb efficiency and pressure drop weights are balanced. The optimal operating parameters include a nozzle pressure of 0.15 MPa, spacing of 85 mm, aperture of 6 mm, count of 11, spray density of 0.85 kg/(s ∙ m2) and air velocity of 2 m/s, at which time the wet-bulb efficiency was 0.85, the pressure drop was 210 Pa, and the coefficient of performance (COP) was 12.4, which demonstrated an efficiency increase of 23.2% in comparison with the base system. The findings of this research significantly contribute to a comprehensive understanding of the influence of water-spray operating conditions on IEC performance, and provide valuable insights for the design and operation of high-efficient IEC systems in practical applications. • The datasets were generated by a laboratory-scale prototype of the IEC system. • The ANN model for IEC considering spray operation was trained and validated. • Effects of nozzle and operation characteristics parameters were investigated. • The significance of the influential parameters was evaluated. • The optimal parameters for IEC were determined based on genetic algorithms. [ABSTRACT FROM AUTHOR]

The paper delves into the examination of energy transfer efficiency from a floating mass transducer, a crucial element of a middle ear implant, to the middle ear structure. The study compares two types of electromagnetic transducers: one employing cumulatively coupled coils and the other utilizing differentially coupled coils. Furthermore, it introduces a novel nonlinear model of electromechanical coupling, establishing a connection between a biomechanical representation of the middle ear and an electromechanical model of the transducer to elucidate the relative positioning of the coils to the magnet. Calculations are conducted using the Runge–Kutta method in Matlab to assess the induced vibrations of the stapes and the energetic efficiency of the implanted middle ear (middle ear with transducer). The simulation results highlight a significant dependence on the magnet and coils configuration, underscoring the necessity for optimization to ensure appropriate comfort for implant users. Lastly, the energetic efficiency of the transducer is compared across the frequency range of 0.2–8 kHz, leading to practical insights and conclusions. • A new nonlinear model of electromechanical coupling for floating mass transducer is developed. • Two coils coupled cumulatively and differentially are tested for the sake of energy efficiency. • The maximum value of energy efficiency occurs in the 8th octave. • A coil-to-magnet shift is required to increase the energy efficiency. [ABSTRACT FROM AUTHOR]

The development and utilization of solar energy, meteorological forecasting, and agricultural production all require accurate predictions of radiance. However, due to the influence of meteorological conditions and the failure of observation instruments and design defects, the data collection of all-sky radiance distribution is often missing or inaccurate. In this paper, a new modified method of all-sky radiance distribution (CASR) is proposed based on the Spatial distribution of daylight model recommended by Commission Internationale de l'Eclairage (CIE model). Firstly, a functional relationship between sky luminance and radiance was established based on measured data. Secondly, the abnormal values were divided into four categories according to the number of anomalous regions of radiance and the frequency of abnormal values in each sky region is determined, and the reasons were analyzed according to different altitude angles. Thirdly, the radiance and sky luminance anormal values were screened to determine their sky type by comparing them to a dataset of 15 standard sky types. The abnormal values were then corrected using the CIE luminance model with the relationship between radiance and sky luminance. Finally, the CASR model was validated against existing models. The results show that the CASR model accuracy was better than the existing model, which can accurately correct the sky luminance and radiance abnormal values and accurately characterize the distribution of radiance of the whole sky. It provides a theoretical basis for radiance prediction, natural lighting analysis and building energy consumption analysis. • Linear and polynomial functional relationships between radiance and sky luminance were established. • Classified and summarized the anomaly data according to their causes and the number of radiance anomaly regions. • A new modified method of all-sky radiance distribution based on the principle of photothermal integration was proposed. [ABSTRACT FROM AUTHOR]

Range-extended electric vehicles combine the benefits of electric propulsion with the convenience and range flexibility of conventional internal combustion engine vehicles. In the energy management system, an equivalent consumption minimization strategy (ECMS) ensures balanced utilization of various energy sources to meet driving demand in real time, thereby enhancing energy efficiency. This paper proposes a driving pattern personalized reconstruction (DPPR) method to maximizes the accuracy of velocity prediction and to serve a predictive ECMS (P-ECMS) with high adaptability to uncertain scenarios. In this context, open-source driving cycles are segmented, and features of each driving pattern are extracted for cluster analysis. Utilizing the results from clustering, new driving cycles are reconstructed for the purpose of training adaptive neuro-fuzzy inference system (ANFIS) models. Personalized velocity prediction is achieved through bias fusion in the multi-ANFIS model, adapting to various driving conditions. The performance of the proposed P-ECMS is validated across various driving conditions and compared with conventional ECMS (C-ECMS), adaptive ECMS (A-ECMS), dynamic programming (DP), and fuzzy logic (FL) strategy. The results indicate that using the proposed P-ECMS is strongly robust in terms of battery pack SoC stabilization under various driving conditions, whose SoC difference achieves 97.02% of using the DP strategy. Compared to using other strategies, using P-ECMS achieves better SoC's stability and lower fuel consumption than using the FL strategy (11.1%), using A-ECMS (1.7%), and using C-ECMS (26.6%). • A method of driving pattern personalized reconstruction is proposed. • Adaptability of the equivalent factor for uncertain scenarios is improved. • A multi-ANFIS model is designed to capture the diversity of driving conditions. • A co-simulation platform is established for algorithm validation. • Better charge-sustaining performance and fuel economy are achieved. [ABSTRACT FROM AUTHOR]

Air-cooled chillers are extensively employed to meet cooling demand in commercial and tertiary sectors. To enhance their performance during part-load operation, variable-speed drives have been integrated into compressors and condenser fans. In existing systems, condenser fans are either operated at a fixed speed or modulated to maintain a predetermined condensing pressure or temperature difference between the refrigerant and outdoor air. In the search for increased efficiency, it is worth investigating innovative control strategies aimed at minimizing energy consumption. A preliminary mapping of chiller performance at different fan speeds, loads, and operating conditions is required to achieve this goal. In this respect, this paper investigates the operation of air-cooled chillers equipped with variable-speed condenser fans, both in the case of constant- and variable-speed compressors. An ad hoc matrix test is adopted to cover appropriate ranges conditions, in terms of load and outdoor air temperatures. Performance maps are developed for a 50-kW c chiller using a 1-D simulator. As the main findings, it may be stated that (i) in the case of the variable-speed chiller, the energy efficiency ratio increases almost linearly with fan speed, resulting in an 8.8% increase when increasing the speed from the nominal to the maximum; (ii) in the case of constant-speed chillers, the system exhibits a different behavior with the cooling capacity and the energy efficiency ratio increasing with the fan speed between 380 and 980 rpm and then decreasing (slightly or sharply, at full and part load respectively) with fan speed between 980 and 1280 rpm, and a percentage increase of EER in the range 7.8–45% is observed. Also, the sensitivity of such results to the system design is investigated, analyzing a chiller equipped with a larger condenser that resulted in achieving minimum energy consumption at an optimal 980 rpm fan speed. Finally, for a constant-speed chiller serving an office building in the Mediterranean, the proposed fan control strategy could yield an electricity saving of up to 12.1% compared to the base case, confirming the potential for energy savings through optimized and system-tailored management of condenser fan speed. • The effect of variable-speed fans on the performance of air-cooled chillers is investigated. • Maps are developed for chillers with both variable-speed and constant-speed compressors. • Fan speed significantly influences the cooling capacity of constant-speed chillers. • In variable-speed chillers, it is advisable to always run the condenser fan at high speed. • The enhanced fan strategy enables 12.1% energy savings for a chiller in an office building. [ABSTRACT FROM AUTHOR]

There is growing interest in demand-side interventions aimed at reducing residential peak-hour electricity consumption in response to energy transition. However, limited knowledge exists regarding residents' willingness to accept compensation (WTC) for power rationing during peak hours. This study empirically evaluates the compensation standards based on residents' willingness to accept power rationing in peak hours based on the Choice Experiment (CE). The results indicate that residents are concerned most with the attributes of power rationing seasons and power rationing periods, requiring higher compensation for power rationing in summer and at night, whether on weekdays or non-weekdays. Further analysis reveals significant regional and group heterogeneity. Specifically, residents in northern regions are not significantly concerned about the power rationing durations compared to those in southern regions, while they are more on power rationing degrees. Analysis based on the Latent Class Analysis (LCA) model shows that two classes of residents are willing to participate in the power rationing agreement for free on both weekdays and non-weekdays, as well as two categories of residents unwilling to participate in the compensated power rationing on non-weekdays. Based on this, we estimate that the adjusted participation rate of residents in compensated power rationing is 96.59% on weekdays, with compensation ranges from 0.67 ￥/kWh to 13.38 ￥/kWh, and a participation rate of 69.69% on non-weekdays, with compensation levels ranging from 3.99 ￥/kWh to 15.34 ￥/kWh. This paper also assesses the costs of power rationing compensation to provide evidence for policymakers when determining the compensation level of the power rationing program. • Power rationing season and periods are the most concerned by residents compared with other attributes. • Heterogeneity in regional and group preferences for compensated power rationing is estimated. • Residents' participation rate in power rationing is 96.59% on weekdays and 69.69% on non-weekdays. • The compensation level is 0.67–13.38 ￥/kWh on weekdays and 3.99–15.34 ￥/kWh on non-weekdays. [ABSTRACT FROM AUTHOR]

In this paper, an equilibrium-heuristic online prediction optimization approach is proposed for multi-market peer-to-peer (P2P) electricity–hydrogen trading of integrated energy systems (IESs) with uncertainties. First, the IES, consisting of a hydrogen energy storage subsystem and a combined cooling, heating, and power subsystem, is constructed in the distribution network to improve energy utilization and market efficiency. Then, a bi-level optimization model for IESs, participating in the P2P electricity–hydrogen energy trading, the real-time electricity, and the ancillary service markets, is proposed, in which the top-level model can be formulated as a P2P electricity–hydrogen trading pricing model through the social welfare maximization problem, and the lower-level model is used to maximize the IES operating profit. To effectively solve the bi-level model, the game equilibrium-based ADMM distributed algorithm is used to obtain the P2P trading volume and prices of the top-level model, and a new hybrid heuristic algorithm, called hybrid sand cat swarm optimization and improved honey badger algorithm (SCIHB), is proposed to solve the lower level model. SCIHB utilizes a hybrid mechanism and an adaptive learning rate parameter to balance local exploitation and global exploration. Furthermore, a new online learning probabilistic prediction method based on the hidden Markov model and wavelet transform is introduced to describe the uncertainties of wind power, electricity prices, and frequency regulation prices. Finally, case studies are conducted on an IEEE 33-bus test system, and the numerical results verify the effectiveness of the proposed P2P trading model and solution approach. [Display omitted] • Designed a new cooling-heating-power-hydrogen integrated energy systems (IESs). • Developed a novel multi-market P2P trading model for IESs considering uncertainties. • Game equilibrium-based ADMM is employed to obtain the P2P trading volume and prices. • The operating profit of P2P trading is improved by the proposed heuristic method. • A new online learning model for efficient prediction of uncertainties in P2P trading. [ABSTRACT FROM AUTHOR]

• Decoupling of GDP from all energy sources under multiple principles are examined. • World economy achieved weak energy decoupling in most years during 2000–2011. • World economy coupled with coal use and tended to couple with cleaner energy use. • None of studied economies achieved long-term GDP-embodied energy decoupling. • Decoupling delusions occurred when only considering direct and total energy use. Decoupling indicators are widely used to understand links between economic growth and energy use. However, traditional decoupling analyses mostly focus on domestic energy consumption (i.e., the production-based principle) and neglect off-site energy use across global supply chains to satisfy an economy's final consumption (i.e., the consumption-based principle). Moreover, analyses for total primary energy conceal an economy's preference for different energy sources. Therefore, this paper evaluates decoupling states of GDP from all types of primary energy use under consumption-based principle, for world economy and eight typical economies during 2000–2011. Regarding total primary energy, world economy witnessed weak decoupling in most years, and most economies studied (e.g., USA, Japan and China) achieved decoupling initially but performed negative decoupling finally. For EU, USA, Japan, Russia and India, production-based decoupling performances were generally better than consumption-based ones. Decoupling phenomena detected under production-based principle even became coupling or negative decoupling under consumption-based principle in some cases. As for each energy source, world economy decoupled from oil use, but still coupled with coal use, and gradually showed a trend to couple with natural gas and renewables use. Different energy sources showed distinct decoupling degrees from GDP, affected by individual embodied energy requirement structure. This paper uncovers potential energy decoupling delusions to deepen the understanding of relationships between energy use and economic growth. [ABSTRACT FROM AUTHOR]

• Scenario settings are adopted to predict the heating load of campus buildings. • An improved multi-objective algorithm is proposed with three optimisation targets. • Operating costs, system efficiency, and thermal comfort are taken into account. • Optimisation results provide the optimal solution for operation strategies. The time-varying nature of the heating loads of public buildings creates scope for exploring strategies to improve the energy system efficiency and to reduce the energy consumption and system operating costs. A well-researched and refined energy system operation strategy based on time-varying heating load demands is proposed in this paper. The proposed strategy is more effective and efficient than the existing experience-based operation strategies used to run energy systems. With full consideration of the factors affecting building heating loads under various scenarios, a multi-objective particle swarm optimisation algorithm combined with a scenario analysis is presented in this paper. The system efficiency and operation cost are set as two basic objectives to generate a Pareto frontier, and the occupant thermal comfort level is the dominant consideration while selecting an optimal state point for the final operation strategy. Using this simplified decision-making process, this approach can simultaneously calculate both the starting sequence and parameter settings for an optimised operation of the heat supply units. An energy plant on a university campus in Tianjin was selected to implement and evaluate this optimisation strategy. The case study results show that, without compromising the requirements of the thermal comfort of the building occupants, the energy system operating cost can be reduced by 38.9%, with an increase by a factor of 2.24 in the system coefficient of performance when compared with the current experience-based operation strategies. [ABSTRACT FROM AUTHOR]

Highlights • Carbon price between $10 and $20 is the best option for China's national ETS. • Output of energy industries is more sensitive to ETS price than other industries. • Low carbon price levels are meaningless for emissions reductions. • Appropriate subsidy for clean energy generation under ETS the market is necessary. Abstract The problems of excessive CO 2 emissions and global warming caused by human activities are becoming more and more severe. Emission Trading Scheme (ETS) may be an effective mean of combating global warming. However, little research focuses on the influence of ETS price on energy consumption, CO 2 emissions, and the economy. This paper analyzes the impact of different ETS price level by applying a dynamic recursive Computable General Equilibrium model. The results show that GDP will reduce more with increasing ETS price level. The output of energy industries is more sensitive to ETS price than other industries. Higher ETS price, lower marginal reduction of fossil energy consumption of ETS price. Moreover, low ETS prices will undermine the capacity of the carbon market to reduce emissions. Higher ETS price will lead to a higher reduction in CO 2 emission, but the economic costs cannot be ignored. Therefore, this paper argues that ETS prices in China's ETS pilot cities are too low, and would provide little emission reduction. Maintaining ETS prices at $10 and gradually increasing carbon price to $20 is suggested in this paper. Also, we should focus on the appropriate subsidies for new energy generation. [ABSTRACT FROM AUTHOR]

Highlights • A methodology to obtain urban-energy diagnoses of different city areas is developed. • The methodology identifies critical urban-energy aspects in different city areas. • Energy improvement measures can be addressed specifically to each city area. • The current methodology allows building long-term scenarios in future works. Abstract The promotion of energy efficiency (EES) and renewable energy (RES) programs has a long history in developed countries; however, in the rest of the world such initiatives are incipient. Meanwhile, cities are the main recipients of this type of programs; therefore, the development of methodologies for the elaboration of prospective urban-energy studies is really important to efficiently use the available resources. Nevertheless, cities have substantial differences among themselves in terms of structure, conformation and growth, along different regions in the world. Hence, it is necessary that methodologies can respond to different realities that each case presents. In Argentina, as in many developing countries, cities are heterogeneous within their own urban structure, manifesting large socio-economic, morphological and infrastructure inequalities. This situation forces us to board the territory in a particular way and, therefore, to develop a proper methodology for the construction of prospective studies for our context. Thus, this paper proposes an innovative methodology that disaggregates the city in urban areas with similar internal behaviors [homogeneous areas (HA)]; which allows us to assess specific EES/RES measures for each of them, such as the application of regulations, programs or incentives for: the improvement of new and existing building's envelopes; the insertion of solar energy systems for space heating, sanitary hot water or PV; the replacement of energy-intensive appliances; among other options. This analysis considers socio-demographic, urban-morphology, building and energy consumption variables/indicators, which enable us to identify critical aspects in each homogenous area. This approach is unusual in most prospective urban-energy studies, where the study areas are addressed as a single element or, alternatively, a small urban sector is used as an example. In this context, this paper presents the methodology for the urban-energy diagnosis stage, obtaining as results the construction of the "base year" model into the LEAP software and the energy guidelines for each homogenous area. The continuation of this research, with the assessment of "alternative scenarios", will be presented in future publications. [ABSTRACT FROM AUTHOR]

Highlights • A nonlinear relationship between industrial agglomeration and energy efficiency. • A threshold effect exists when industrial agglomeration affects energy efficiency. • When agglomeration degree is less 1.3865, it will promote energy efficiency. • Future policy for energy efficiency in China's textile industry is suggested. Abstract Based on provincial panel data on the textile industry in China, this paper calculates the total-factor energy efficiency of this industry as the dependent variable. Additionally, based on linear panel analysis of the relationship between industrial agglomeration and energy efficiency, in-depth analysis of the industry is performed at different industrial agglomeration levels. The paper identifies different impacts of industrial agglomeration on energy efficiency, uses the threshold regression model to extend the research to a nonlinear framework, and constructs a double threshold regression model in which the threshold of the textile industry agglomeration level serves as the threshold variable. The results show, first, a threshold effect occurs when industrial agglomeration affects total-factor energy efficiency. Second, a significant positive correlation exists among the degree of economic development, energy prices, research and development investment (R&D), enterprise scale, and total factor energy efficiency of the textile industry. Third, a non-linear relationship exists between industrial agglomeration and energy efficiency in the industry. When industrial agglomeration is low, promoting it improves energy efficiency. However, when industrial agglomeration reaches a certain level, agglomeration and energy efficiency show a negative relationship. Finally, based on the empirical results, ways of improving energy efficiency in the industry are suggested. [ABSTRACT FROM AUTHOR]

Highlights • Review of the application of reinforcement learning (RL) for demand response (DR) • DR is relevant for integrating renewable energy source into the smart grid. • Considering RL/DR from energy generation and storage, to supply and user satisfaction. • Typical algorithms and open research directions are discussed. • Most articles focus on single-agent systems in stationary environments. Abstract Buildings account for about 40% of the global energy consumption. Renewable energy resources are one possibility to mitigate the dependence of residential buildings on the electrical grid. However, their integration into the existing grid infrastructure must be done carefully to avoid instability, and guarantee availability and security of supply. Demand response, or demand-side management, improves grid stability by increasing demand flexibility, and shifts peak demand towards periods of peak renewable energy generation by providing consumers with economic incentives. This paper reviews the use of reinforcement learning, a machine learning algorithm, for demand response applications in the smart grid. Reinforcement learning has been utilized to control diverse energy systems such as electric vehicles, heating ventilation and air conditioning (HVAC) systems, smart appliances, or batteries. The future of demand response greatly depends on its ability to prevent consumer discomfort and integrate human feedback into the control loop. Reinforcement learning is a potentially model-free algorithm that can adapt to its environment, as well as to human preferences by directly integrating user feedback into its control logic. Our review shows that, although many papers consider human comfort and satisfaction, most of them focus on single-agent systems with demand-independent electricity prices and a stationary environment. However, when electricity prices are modelled as demand-dependent variables, there is a risk of shifting the peak demand rather than shaving it. We identify a need to further explore reinforcement learning to coordinate multi-agent systems that can participate in demand response programs under demand-dependent electricity prices. Finally, we discuss directions for future research, e.g., quantifying how RL could adapt to changing urban conditions such as building refurbishment and urban or population growth. [ABSTRACT FROM AUTHOR]

Highlights • EnergyPlus is capable of accurately estimating the heat transfer in parked vehicles. • Spectrally selective glazing can cause an average temperature difference inside the cabin as high as 12.5 °C. • Solar-reflective opaque coating in combination with ChLC glazing can reduce the cabin air temperature up to 23.8 °C. Abstract Parked vehicles have the tendency to overheat quickly. This can lead to a negative impact on the thermal comfort of the driver and its passengers, as well as intensive use of air conditioning systems, and thus fuel consumption of the vehicle or, in the case of electric mobility, a reduced cruising range. In the search for effective measures to mitigate this effect, important guidance can be provided by the field of sustainable building design. On the one hand, inspiration can come from design strategies in terms of shapes and advanced cover materials, but this paper advocates that this can also pertain to the simulation-based design support tools that are used by building engineers. This paper first presents the results of a thermal soak test, and then uses this data to demonstrate the suitability of the building performance simulation tool EnergyPlus for predicting the thermal behavior of parked vehicles. This fit-for-purpose validated model is used to evaluate the performance of three overheating mitigation measures for two car models in two climates. The results show that spectrally selective glazing can reduce the cabin air temperature by 12.5 °C and when combined with solar reflective opaque surfaces, the reduction of cabin air temperature can reach 23.8 °C. Increased use of building performance simulation in the automotive domain can help to further optimize the overheating reduction potential of cars. [ABSTRACT FROM AUTHOR]

Highlights • Alternating current(AC)- and direct current (DC)-based marine vessels are presented. • Improvements in electrical performance achieved by DC-based systems is derived. • A genetic algorithm-based technique to improve fuel efficiency is proposed. • Quantitative benefits of moving to DC-based systems for marine vessels are studied. Abstract The initial widespread electrification of marine vessels primarily used alternating current (AC)-based systems as they were prevalent in the electrical distribution infrastructure for land-based systems. At present, there are marine vessels that operate based on a hybrid diesel-electric system, in which the on-board diesel engines generate AC power to a common AC bus, which in turn supplies power to the electrical propulsion drives and other service loads. Recently, there has been an active interest in a transition to direct current(DC)-based electrical distribution system with diesel-electric hybrid propulsion systems for marine vessels due to potential improvements in electrical performance and fuel consumption. This paper evaluates the improvement in the move to DC-based distribution system for marine vessels in terms of electrical performance and fuel efficiency. An overview of a typical AC electrical system currently in use is provided, and modifications of the system to a DC-based system is presented. The power factor, total harmonic distortion, and voltage regulation are discussed. Both symmetrical and asymmetrical generator scheduling are examined and the potential fuel savings for an example diving support vessel is presented. A discussion on the recommendations to shift towards DC-based systems is then provided based on the findings of this paper. [ABSTRACT FROM AUTHOR]

Highlights • This paper broadens current conceptualizations of energy justice for smart grids. • The study explores values in the public debates on smart grids in two countries. • Value conflicts show the importance of distributive and procedural justice. • It is debated if the systems lead to more equity or reinforce injustices. • Energy justice needs to be broadened to include data privacy and security issues. Abstract Smart grid systems are considered as key enablers in the transition to more sustainable energy systems. However, debates reflect concerns that they affect social and moral values such as privacy and justice. The energy justice framework has been proposed as a lens to evaluate social and moral aspects of changes in energy systems. This paper seeks to investigate this proposition for smart grid systems by exploring the public debates in the Netherlands and the United Kingdom. Findings show that smart grids have the potential to effectively address justice issues, for example by facilitating small-scale electricity generation and transparent and reliable billing. It is a matter of debate, however, whether current smart grid designs contribute to cost and energy savings, advance a more equitable and democratic energy system, or reinforce distributive and procedural injustices. The increased use of information and communication technology raises value conflicts on privacy and cyber security, which are related to energy justice. This research contributes by conceptualizing energy justice in the context of smart grids for the first time. The energy justice framework is broadened by including values and value conflicts that pertain directly to the increased use of information and communication technology. For policy makers and designers of smart grids, the paper provides guidance for considering interconnected social and moral values in the design of policies and smart grid technologies. [ABSTRACT FROM AUTHOR]

Highlights • One-stop archive for developments in solid desiccant coated heat exchangers (DCHEs). • Comprehensive study on the selection of desiccants and binders. • Thorough analysis on the effect of operating parameters on dynamic performance. • Detailed review of advances in mathematical modeling of DCHEs. • Discussion on the potential system-level applications of DCHEs. Abstract Rapid urbanization and higher thermal comfort requirements have sharply increased buildings' energy demands. In tropical countries, for achieving energy efficient air-conditioning, desiccant dehumidifiers, which decouple sensible and latent load, are essential. While fixed-bed and rotary wheel desiccant dehumidifiers suffer from reduced adsorption capacity and lowered heat transfer efficiencies, desiccant coated heat exchanger (DCHE) system is a promising technology that can improve the energy efficiency of the standard vapor-compression air conditioners. Moreover, DCHEs also have prospective benefits in heat pump, adsorption chiller, and atmospheric water harvesting applications. In this paper, the advantages of DCHEs over other dehumidifiers are established, and a comprehensive review of various desiccant materials and binders used in DCHEs is carried out. The possibilities of using advanced materials to achieve superior energy efficiencies are also underlined. Different methods of coating the desiccant on heat exchangers are described, and quantitative parameters required to analyze the performance of the system are presented. Operating parameters affecting the dehumidification and thermal performance are identified, and the effect of their variation on the dynamic behavior of DCHEs is elaborately studied. The significance of using mathematical models as design tools is recognized, and the underlying assumptions, applicability, and limitations of the models for simulating the complex heat and mass transfer phenomenon are highlighted. The possibility of integrating an additional heat transfer equipment such as sensible heat exchangers, heat recovery devices, solar evacuated tube collectors, cooling towers, and evaporative coolers with DCHE is also presented. Finally, the potential system-level applications of DCHEs as heat pumps, adsorption chillers, water harvesters, and standalone air-conditioners are assessed. This review paper provides an update on recent developments in desiccant dehumidification and is intended to be a "one-stop" archive of known practical solutions that DCHEs can offer. [ABSTRACT FROM AUTHOR]

This paper investigates the cyber resiliency of future power systems with high penetration of distributed energy resources using advanced distributed and (or) hierarchical control architectures. Specifically, we simulate cyberattacks on three prototypical use cases, and we identify attack scenarios that are the most damaging to the overall system performance. We show that these attacks can have a significant impact on grid operation. Results provide additional insights into the robustness of the system to the most common cyberattacks. • This paper delves into the realm of cyber-resiliency, examining its implications across three distinct use cases encompassing distributed, distributed-hierarchical, and hierarchical control architectures. • Through exhaustive numerical simulations, we discern and highlight the precise signals within the system that exhibit heightened susceptibility to cybersecurity attacks. • The results obtained from our analysis offer valuable insights into the robustness of these systems, shedding light on their ability to withstand potential cybersecurity threats. [ABSTRACT FROM AUTHOR]

• Metal hydride tanks were investigated experimentally. • Levelized cost of hydrogen was determined. • Alkaline and PEM- based generators were compared. • Avoided energy consumption comparing to compression was shown. • Universal methods for economic analysis were developed. This study presents methodology for the evaluation of appropriateness of a hydrogen generator for gas production in multiple distributed plants based on renewable energy sources. The general idea is to form hydrogen clusters integrated with storage and transportation. The paper focuses on the financial viability of the plants, presenting the results of economic evaluation together with sensitivity analysis for various economic factors. The analyzed case study proves that over a wide range of parameters alkaline electrolyzers show favorable economic characteristics, however, a PEM-based plant is more resilient to changes in the price of electricity, which is the main cost component in hydrogen generation. The study is enriched with an experimental investigation of low-pressure storage methods, based on porous metal hydride tanks. The effectiveness of the tanks (β) compared to pressurized hydrogen tanks, in the same volume and pressure is equal to β = 10.2. A solution is proposed whereby these can be used in a distributed hydrogen generation concept due to their safe and simple operation, without additional costly equipment, e.g., compressors. A method for evaluation of the avoided energy consumption as a function of the effectiveness of the tanks is developed. Avoided energy consumption resulting from implementing MH tanks equals 1.33 – 1.37 kWh per kilogram of hydrogen depending on the number of stages of a compressor. The methods proposed in this paper are universal and can be used for various green hydrogen facilities. [ABSTRACT FROM AUTHOR]

• A commuting-pattern-oriented representation of random driving conditions is developed. • Integrated stochastic optimization of powertrain design and control is engineered. • Machine-learning solvers for stochastic optimization are developed. • The stochastic optimization improves the expected energy economy by up to 12%. • Topology modification to EV powertrains improves energy economy by over 17%. The performance of an electric vehicle (EV) powertrain is greatly influenced by the design scheme, control strategy, and driving conditions. Owing to uncertainties in driving conditions and the mutual correlation between design scheme and control strategy, identifying the optimal topology and component parameters of EV powertrains for improving the energy efficiency of EVs remains a challenging task. Oriented to the commuting patterns of individuals, this paper proposes a stochastic optimization method for identifying the superior powertrain design that optimizes the expected energy economy of the EV powertrain and reduces the energy economy variability in random driving conditions. This paper also introduces a machine-learning solution to the complex mutual restrictions among design variables, control strategies, and driving conditions, which determines the feasible design space and powertrain performance. Suggested by massive validations, the stochastic optimization method reduces the expectation and deviation of the energy consumption of EV powertrains up to 12.0% and 5.6%, compared with deterministic optimization methods. Additionally, by comparative evaluation among stochastic optimal design results of different topologies, contributions of topology modifications to the energy efficiency of EV powertrains are clarified, and the superior powertrain topology is identified, which improves 17.1% expected energy economy compared with the current popular topology. [ABSTRACT FROM AUTHOR]

• Electricity, the most direct and effective use of RES, is in center of energy transition. • An interdisciplinary procedure suitable for the planning of RES is presented. • Key points and procedures for the assessment of large-scale RES installation is presented. • Simulation on Eygiption case shows the technical and economical viability. Due to the environmental impacts brought by current energy schemes, the energy transition, a new paradigm shift from fossil fuels to renewable energy, has been widely accepted and is being realized through collective international, regional, and local efforts. Electricity, as the most direct and effective use of renewable energy sources (RES), plays a key role in the energy transition. In this paper, we first discuss a viable pathway to energy transition through the electricity triangle, highlighting the role of RES in electricity generation. Further, we propose methodologies for the planning of wind and solar PV, as well as how to address their uncertainty in generation expansion problems. Finally, by using a web-based tool, "RES-PLAT" 1 1 https://res-plat.est.polito.it. , we demonstrate the scheme in a case study in Egypt, which evaluates the impacts and benefits of a large-scale RES expansion. 2 2 The short version of the paper was presented at Applied Energy Symposium: MIT A+ B, August 13–14, Boston. This paper is a substantial extension of the short version of the conference paper. [ABSTRACT FROM AUTHOR]