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Abstract: Paper production is an energy-intensive process and accounted for about 9% of industrial energy demand in Germany in 2008. There have only been slow improvements in energy efficiency in the paper industry over the past twenty years. Policies can accelerate the progress made, but knowledge about the remaining efficiency potentials and their costs is a prerequisite for their success. We assess 17 process technologies to improve energy efficiency in the German pulp and paper industry up to 2035 using a techno-economic approach. These result in a saving potential of 34 TJ/a for fuels and 12 TJ/a for electricity, which equal 21% and 16% of fuel and electricity demand, respectively. The energy savings can be translated into mitigated CO2 emissions of 3 Mt. The larger part of this potential is found to be cost-effective from a firm''s perspective. The most influential technologies are heat recovery in paper mills and the use of innovative paper drying technologies. In conclusion, significant saving potentials are still available, but are limited if we assume that current paper production processes will not change radically. Further savings would be available if the system boundaries of this study were extended to e.g. include cross-cutting technologies. [Copyright &y& Elsevier]

A new process for the production of bioethanol from paper mill sludge (PMS) is described in this work. PMS biomass feedstock was processed via the simultaneous saccharification and fermentation (SSF) with and without accelerants. The enzymatic hydrolysis and fermentation were first evaluated, and the energy demand was 2.2 MJ/L of produced ethanol. When the enzymatic hydrolysis and fermentation were combined, the energy demand was reduced to 1.0 MJ/L ethanol, the sugars production increased, and the overall capital cost of the process decreased. The sugar yield was improved by adding accelerant and selecting the optimal fiber recovery method. The accelerant improved the enzymatic hydrolysis via a pathing/bridging mechanism. The SSF with the chemical fiber recovery method coupled with accelerant addition would be the best process configuration. Upon this combination, the glucose profile was enhanced from 9.8 g/L to 17.0 g/L. The sludge fiber conversion by SSF was improved by selecting an efficient fiber recovery method combined with the accelerant addition. SSF in chemical fiber recovery with accelerant addition was the best process by a 10% improvement of ethanol yield. The proposed process configuration offers a lower cost and sustainable process and contributes to the circular economy of zero waste discharges. • A combined SSF process using PMS feedstock to produce bioethanol was developed. • The energy saving in an integrated process was 1.2 MJ/L ethanol. • The optimal fiber recovery from paper mill sludge was achieved via acid treatment. • The optimal fiber recovery increased fractional glucose by 27%. • The cationic accelerant improved the enzymatic hydrolysis by 10% due to the patching mechanism. [ABSTRACT FROM AUTHOR]

There has been a growing demand for energy consumption statistics in the manufacturing industry to establish national energy and greenhouse gas policies. Despite its importance, the Korean government faces significant challenges in collecting energy data at a facility level in a precise and timely manner. To address the lack of timely data, this paper employs machine-learning models to predict the annual total energy consumptions of each manufacturing facility. We first designed four prediction models that take into account the characteristics and energy consumption behaviors of industry sub-sectors. As input variables, these prediction models mainly included electricity consumption, employee size, energy types, gas consumption and other accessible data. Finally, we conducted numerical experiments on approximately 100,000 facilities and evaluated the prediction performance of various machine-learning algorithms such as linear regression, decision tree regression, random forest regression, gradient boost regression, and extreme gradient boosting regression. The numerical experiments provided insights into which model and algorithm offer the best prediction performance for each industry sub-sector. In addition, we identified the important variables for predicting total energy consumption, revealing that not only electricity but also various other energy sources and variables representing industry-specific characteristics play a crucial role in improving prediction performance. • This paper predicts the annual total energy consumption of individual manufacturing facilities in Korea. • Several prediction models capable of considering industry-specific features are presented. • This paper employs machine-learning models. • The numerical experiments provided insights into which model and algorithm offer the best prediction performance. • Not only electricity but also variables representing industry-specific characteristics play a crucial role. [ABSTRACT FROM AUTHOR]

This paper investigates the relationship between financial development and access to electricity for rural and urban India by incorporating economic growth, trade, and foreign direct investment in energy production function. In doing so, we have applied autoregressive distributed lag bounds testing to examine long run cointegration between the variables by accommodating structural break(s) stemming in the series. Our empirical results indicate the presence of cointegration association between access to electricity and its determinants. We note that financial development enhances access to electricity in rural India but declines it in urban India. Economic growth has positive effect on access to electricity. Trade openness and foreign direct investment lower access to electricity in rural India. In urban India, trade openness and foreign direct investment increase access to electricity. The association between financial development and access to electricity is inverted-U shaped for rural and urban India. This paper would present new policy direction for Indian government to enhance access to electricity by using financial development as an economic tool. • Financial development and access to electricity have a strong linkage. • In India financial development enhances access to electricity in rural area but has adverse effect in urban area. • Rise in economic growth of India has increased the access to electricity. • Growth of Trade Openness and Foreign Direct investment increase access to electricity. [ABSTRACT FROM AUTHOR]

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

The proper integration of wind power-driven grids relies heavily on a reliable balance between electricity production and demand. Therefore, accurate prediction is essential for planning and efficient operation of wind power systems to ensure their continuous supply. However, increasingly severe power quality disturbance (PQD) constantly disturbs this equilibrium, which affects the accuracy of wind power prediction to a large extent. For this purpose, this paper developed a novel optimization methodology to improve wind power prediction accuracy considering micro PQD dimension reduction and elimination for wind-storage integrated systems. A novel idea has been presented in this optimization methodology to eliminate the barrier of PQD and wind power prediction. In the micro aspect, a PQD dimension reduction and elimination strategy based on dynamic mode decomposition (DMD) and Wiener Filter (WF) is proposed to eliminate the autonomy of PQD. This elimination of autonomy allows the WF to get a higher signal-to-noise ratio (SNR). In the macro aspect, this paper takes PQD of different complexity into full consideration, and compares their effects on improving wind power prediction accuracy based on deep learning-based approaches. Through the experimental verification, it is confirmed that the proposed DMD-WF optimization method has demonstrated an effective dimension reduction and elimination of PQD. Moreover, it is found that the proposed PQD optimization method contributes to improve the deep learning-based prediction accuracy when PQD is more complex. The proposed methodology creates a novel perspective to improve the short-term wind power prediction accuracy, which provides a theoretical and methodological guidance for future development of large-scale integrated wind-storage systems. • Development of a novel methodology of PQD optimization and wind power prediction. • A Wiener Filter and dimension reduction-based PQD optimization method is proposed. • WOA-Attention-LSTM is used to analyze forecast accuracy based on PQD optimization. [ABSTRACT FROM AUTHOR]

Electricity cross-subsidy policies have long been criticized for they involve subsidizing households by charging above-cost prices in industrial sectors. This paper examines the environmental and economic impacts of phasing out cross-subsidy in electricity prices in the context of China's Carbon Peaking and Neutrality goals by exploiting a large panel data from 2012 to 2020. We first adopt a price-gap approach to calculate the scale of negative cross-subsidy in China and different sectors. Next, we estimate the electricity demand price elasticity of high-, medium-, and low-emission industries to evaluate how the cross-subsidy affects electricity consumption, carbon emissions, and the output value. Finally, with the 2020 scenario as our baseline, we simulate three distinct policy change Schemes and evaluate the environmental and economic effects of phasing out electricity cross-subsidy in China. We find that if policy makers do not consider the emission levels of different industries when implementing cross-subsidy removal, great environmental harm will be caused. We also find that the potential burden on households of electricity cross-subsidy removal is likely to be relatively small at all implementation stages. This study provides timely policy implications for the ongoing electricity system reform in China. • This paper examines the environmental and economic impacts of phasing out cross-subsidy in electricity prices in the context of China's Carbon Peaking and Neutrality goals. • We find that if policymakers do not consider the emission levels of different industries in cross-subsidy removal, it will lead to large carbon emissions but small output in average. • If the cross-subsidy is fully borne by high-emission industries, the optimal Scheme is to reduce the cross-subsidy by approximately 50%–70% to obtain more economic output and less carbon emissions. • If high-emission bear twice as much cross-subsidy as the medium-emission industries, the carbon reduction and economic output will be maximised at 10%–30% of cross-subsidy removal. • We also find that the potential burden on households is relatively small at all implementation stages. [ABSTRACT FROM AUTHOR]

Demand side management is a promising alternative to offer flexibility to power systems with high shares of variable renewable energy sources. Numerous industries possess large demand side management potentials but accounting for them in energy system analysis and modelling is restricted by the availability of their demand data, which are usually confidential. In this study, a methodology to synthetize anonymized hourly electricity consumption profiles for industries and to calculate their flexibility potential is proposed. This combines different partitioning and hierarchical clustering analysis techniques with regression analysis. The methodology is applied to three case studies in Chile: two pulp and paper industry plants and one food industry plant. A significant hourly, daily and annual flexibility potential is found for the three cases (15%–75%). Moreover, the resulting demand profiles share the same statistical characteristics as the measured profiles but can be used in modelling exercises without confidentiality issues. • The electricity demand of paper and food companies in Chile is analysed. • The analysis is based on two years of data from three companies. • 22 clustering methods are tested to generate synthetic electricity load profiles. • The demand response potential for a typical hour is approximately ± 15%. • On a daily basis up to 70% of the demand can be mobilized. [ABSTRACT FROM AUTHOR]

In the context of the increasing popularity of distributed energy, this paper proposes a home energy management system method considering the working state of flexible load to deal with the problem of mismatch between home energy supply and demand. Specifically, in this paper, the flexible load in the system is studied, and the model of flexible load and its working state are established. On this basis, combined with the real-time electricity price of the grid, the model of home energy management system is constructed to minimize the electricity purchase cost and improve the utilization rate of renewable energy, the genetic algorithm is used to solve it, and the influence of time step, flexible load ratio and whether to use energy storage equipment on the optimization degree of the model are analyzed. The research results show that, while meeting the requirements of human comfort and electricity demand, the daily electricity purchase cost of the system with flexible load scheduling is 22.5% lower than that of the system without flexible load scheduling, and the utilization rate of renewable energy is increased by 16.5%. In terms of model optimization, shortening the prediction time step, increasing the proportion of flexible load and using energy storage equipment can further reduce the electricity purchase cost. The case study confirms the effectiveness of the proposed home energy management system model, which can provide an efficient optimal scheduling scheme for household energy saving. • The optimal scheduling model for home energy management system of rural dwellings with flexible loads is proposed. • Influence of the proportion of flexible load and different time step sizes on the scheduling model are studied. • The operation cost by 26.62% of battery integration strategy based on load scheduling is further reduced. [ABSTRACT FROM AUTHOR]

Rapid development of the aviation industry has brought great convenience to people's travel and goods transportation. The inevitable issues are high level of energy consumption and the huge environment pollution by kerosene combustion. On the other hand, there is a greater requirement for the electricity capacity as well as the power ratings of aircraft generators to meet the continuous intensification of the electrification degree in modern aircraft. The question that is tried to identify in this paper is: can the waste heat in the aircraft be collected to generate power for meeting the electricity demand? In this paper, an electricity supply system driven by the heat collected from the commercial aeroengine wall is first proposed. It is an integrated system by coupling the topping gas turbine with bottomed closed Brayton cycle via the liquid sodium. A comprehensive mathematical model is developed for the system in the aspects of thermodynamics and economics. The analysis results demonstrate that the proposed system is capable of satisfying the megawatt-scale power requirements of commercial aeroengine. The fluid He–Xe (40 g/mol) is a better choice to cycle the given power system than He and air considering its larger net power output, lower compression ratio and smaller component size. The system with He–Xe can supply 1299 kW electricity power when the compression ratio is just about 7.1. In that case, the oil cost savings is about 3.66 percentages lower than that of the original oil consumption. • Novel electricity system is given by using waste heat of commercial aeroengine. • Effects of different working fluids and operation parameters are examined. • He–Xe (40 g/mol) is recommended as the system working fluid than air and He. • The compression ratio is suggested at 7.1with an operation pressure of 3.55 MPa. • Oil cost savings is about 3.66% lower than that of original oil consumption. [ABSTRACT FROM AUTHOR]

The imbalance between power supply and demand is increasing around the world. As an important basis for the coordination between supply side and demand side to solve the problem of supply and demand balance, the rational formulation of demand response strategy is very important. Aiming at the shortcomings of current demand response strategies in optimizing power flexible allocation, this paper establishes a flexible allocation strategy model under three different objectives of flexible optimization, economic benefit, and energy conservation to realize energy scheduling and flexible optimization under different objectives. The accuracy and rationality of the flexible allocation strategy are verified through experimental comparison. The maximum fluctuation value of the electricity load is 115 W in all electricity parity periods of the experimental days, accounting for 4.9% of the building electricity load, avoiding the occurrence of secondary peak electricity consumption. The power flexibility value provided by the strategy can reach 143% of the building electricity load before the demand response, making the building can be more flexible to face the power supply and demand balance problem of the grid. • A flexible allocation strategy model under three different objectives is established in this paper. • The impact of the energy scheduling at peak power consumption period on other times is considered. • The coupling of various forms of flexible sources makes the strategy have good performance. • Different objectives have significant benefits in flexibility, economy and energy conservation. [ABSTRACT FROM AUTHOR]

This paper proposes a joint optimization framework for the demand response-capacity configuration design of the integrated energy system (IES), in which both the economic cost and closed-loop dynamic regulation performance under source-load fluctuations are considered. The joint optimization consists of two layers. The upper layer optimizes the time of use electricity price to adjust the demand response of electric load; while the lower layer optimizes the capacity configuration, scheduling instructions and incentive-based thermal load demand response under typical scenarios. To fairly evaluate the impact of equipment capacity and demand response on the dynamic control performance of the IES, multi-parameter programming-based predictive control approach is applied to develop an offline design and scheduling-perceptive control system. A closed-loop dynamic scheduler is then developed based on the control system, which can optimize the operating instruction of each equipment considering their dynamic operating practice. Simulation studies on a typical off-grid combined heat and power IES show that the proposed approach can effectively match the demand response to the capacity configuration of the IES, helping the system achieve a dynamically flexible and economic energy supply. • Joint capacity configuration and demand response optimization is proposed for IES. • Dynamic control performance is considered in the optimization. • Design and scheduling-perceptive controller is developed to bridge gap between design and operation. • Dynamic scheduling is proposed to consider IES operating practice in the optimization. • Both economic and dynamic control performance of IES is improved by the proposed method. [ABSTRACT FROM AUTHOR]

The average electricity consumption per capita in China still has significant room for growth. Under the current rigid residential electricity pricing, it is very important to study how factors other than price can promote electricity conservation among residents. This paper uses micro-survey data of 2018 and 2020 to analyze energy efficiency and the rebound effect through a stochastic energy demand frontier model, as well as to examine the role of news propaganda in this context. The research conclusions are as follows: (1) News propaganda promoting electricity saving can weaken the rebound effect of electricity use, which can lead to better electricity savings. (2) The energy efficiency of residential electricity has improved significantly compared to previous years, and the rebound effect has decreased. (3) Electricity prices have a negative impact on electricity consumption, while income, household size, home area, and extreme temperatures have a positive impact. Based on these conclusions, we propose policy recommendations to address the current inflexibility in residential electricity pricing and to improve the efficiency of electricity use among residents. • News propaganda promoting electricity saving can weaken the rebound effect. • The energy efficiency of residential electricity has improved. • The rebound effect of residential electricity has decreased. • News propaganda can be a complement to the inflexibility of pricing policy. [ABSTRACT FROM AUTHOR]

Hydrogen energy and the pioneering power-to-hydrogen (P-2-H) systems have garnered significant interest due to their potential to revolutionize power systems, offering exceptional long-term energy storage capabilities and augmenting the resilience and flexibility of the grid. This paper presents a novel approach for optimizing the day-ahead proactive scheduling of power distribution networks with high solar penetration. The approach utilizes power-to-hydrogen (P-2-H) technology to enhance resiliency and mitigate risks. Uncertainties in solar irradiation, wind speed, electricity demands, and day-ahead market prices are modeled using a scenario-based probabilistic procedure. The optimization problem is formulated as a mixed-integer linear program that minimizes the network's expected normal and resiliency costs while incorporating downside risk constraints. The approach is applied to an IEEE 33-bus power distribution network, demonstrating the effectiveness of P-2-H facilities and risk assessment in improving resiliency during normal and power outage scenarios. Implementing P-2-H facilities in circumstances with and without risk results in a reduction of total operation costs by 40.89% and 40.32%, respectively, according to the simulation findings. Furthermore, the numerical analysis results show that the network's overall operation cost increase for achieving zero risk is only 3.93%, which reduces to 2.32% with the use of P-2-H facilities. • Optimization of power distribution network with hydrogen systems. • Analysis impacts of hydrogen system to mitigate natural disasters' impacts. • Optimal day-ahead scheduling under uncertainty in photovoltaics and disasters. • Risk evaluation using effective risk tool to reach trade-off between risk and cost. [ABSTRACT FROM AUTHOR]

Highly dense and integrated data centers face key challenges of realizing efficient cooling and improved energy efficiency. To overcome these challenges, this study experimentally investigated the flow and heat transfer characteristics of single-phase immersion liquid cooling (SPILC) systems. The influence of two opposite coolant flow directions on the SPILC performance was examined. Furthermore, a correlation mechanism between coolant thermal properties and SPILC performance was established, along with a control chart for regulating the working conditions of SPILC systems. The results indicate that compared to a pro-gravity flow, an anti-gravity flow scheme reduces the chip case temperature and thermal resistance by 33.8% and 55.6%, respectively, while decreasing the power usage effectiveness (PUE) by 1.4%. Using coolant with the lowest viscosity reduces the chip case temperature and thermal resistance by 9.3% and 10.5%, respectively, while decreasing the PUE by 0.4%. Moreover, the cooling water temperature has a greater impact on the performance of SPILC systems than the volume flow rate of coolants. Additionally, this paper provides control charts for the cooling water temperature and coolant flow rate to improve the PUE while ensuring the safe operation of SPILC systems, with the highest chip temperature and total electricity consumption as indicators. • Immersion cooling performance of system-level data center servers is experimentally studied. • PUE of the anti-gravity flow decreases by 1.4% compared to the pro-gravity flow. • Correlation between coolant properties and immersion cooling performance is established. • Energy-efficient control charts for cooling water and immersion coolant are provided. [ABSTRACT FROM AUTHOR]

The integration of smart grids with Advanced Metering Infrastructure (AMI) has bridged the realms of the Internet of Vehicles (IoV) and Electric Vehicles (EVs), yet challenges persist in managing EV Battery Range and charging infrastructure effectively. This paper presents an innovative IoV-based framework tailored for EVs, with a specific focus on forecasting electricity consumption in a Vehicle-to-Grid (V2G) scenario. By exploring the hurdles surrounding electric vehicle usage, the research lays the foundation for Electric Vehicles (EVs). The proposed IoV model optimizes IoT sensor utilization through a fog layer and employs a Back Propagation (BP) neural network for battery State of Charge (SoC) estimation, integrating Principal Component Analysis (PCA) for data dimensionality reduction. Leveraging substantial computing capabilities, the cloud layer predicts Electricity Consumption Data (ECD) associated with EVs in V2G scenarios. Performance evaluation metrics like Akaike Information Criteria (AIC), Mean Error (ME), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and Root Mean Squared Error (RMSE) are assessed across state-of-the-art forecasting algorithms. Incorporating EV potential into the optimal model reveals a significant 10% reduction in electricity demand. This research advances IoV-based frameworks, offering insights to enhance EV efficiency within the broader energy infrastructure. • An innovative Internet of Vehicles (IoV) framework for depicting electricity demand on the demand side in V2G scenarios. • In-depth experimentation and evaluation of the state-of-the-art approaches of clustering and forecasting. • The devised model successfully projected a 10% decrease in demand side for consumers. [ABSTRACT FROM AUTHOR]

This paper compares the macroeconomic impacts of different decarbonization storylines until 2050 using two Computable General Equilibrium models. The modeling shocks are harmonized across models to simulate four decarbonization scenarios through three main drivers: technical development, societal attitude, and policies. The study explores the contribution of each of these drivers to the European decarbonization. The results show that the decarbonization scenarios have moderate effects on GDP; decarbonization scenarios rather result in sectoral shifts. The impact of temperature on labor productivity minimally alters expected growth levels, since larger differences in temperature between the scenarios are only expected to occur in 2100, not yet in 2050. Electricity demand is increasing in all scenarios, particularly with stronger political guidance leading to additional tax and subsidy measures to encourage the use of electricity and discourage fossil-based fuels. When society is a driving factor, it is found that circular business models result in an increase in the service industry but might have a slightly negative impact on overall growth due to stronger spillover effects linked to the manufacturing industry. Technology appears to be the only driver to facilitate decarbonization while maintaining steady economic growth. • We address the macroeconomic effects of five climate scenarios using two CGE models. • We focus on the effects of changes in society, technology, and policy. • We incorporate climate change effects through labor productivity. • Effect of decarbonization on GDP is moderate under given technological improvement. • Decarbonization scenarios result in relatively large sectoral shifts. [ABSTRACT FROM AUTHOR]

In response to the high uncertainty associated with residential net load due to the coupling of distributed photovoltaic generation and user demand, this paper proposed a novel cluster-based stacking ensemble learning model for net load interval prediction. Firstly, the k-means algorithm is employed to discover the similarity in user electricity consumption patterns. Then, a RIME optimization algorithm with local enhancement (LRIME) is developed to optimize the parameters and weights of the base learners in stacking ensemble learning. Subsequently, base learners with strong predictive capabilities and significant diversity are chosen as the first-layer predictive models, extreme learning machine (ELM) is utilized as the second-layer predictive model, ultimately resulting in the proposed stacking ensemble learning prediction model. And utilizing the bootstrap method to fit the volatility of point predictions, different prediction intervals are obtained at varying confidence levels, aiming to quantify the integrated uncertainty in photovoltaic generation and load. Testing on the open Ausgrid electricity load data in Australia provided robust validation of the proposed method's effectiveness. In comparison with other outstanding prediction models, the proposed ensemble model can effectively capture the uncertainty in integrating photovoltaic generation and user load. • A RIME optimization algorithm with local enhancement (LRIME) is developed. • A stacking ensemble model is proposed for residential net load interval prediction. • The proposed model demonstrates higher forecasting accuracy and reliability. [ABSTRACT FROM AUTHOR]

This paper presents a heuristic model predictive control (MPC) methodology to activate energy flexibility in fully electric school buildings in cold climates to reduce electricity demand during peak demand periods of the electric grid. To streamline the implementation of MPC, the proposed approach employs grey-box archetypes, a clustering of weather conditions to identify typical scenarios and a limited number of possible setpoint profiles. A data-driven grey-box approach is used to create archetype models for different thermal zones in a typical school building; this approach enables rapid development and requires much less calibration data than black-box models. A third-order resistance-capacitance thermal network for zones with convective heating and a fourth-order model for zones with radiant floor heating are developed and calibrated using measured data from an all-electric school building in Québec, Canada. The weather data are clustered into several categories, representing different weather conditions (6 clusters representing two ambient temperature ranges and three solar radiation ranges). The heuristic MPC strategy uses predefined optimal setpoint profiles for each cluster and weather prediction one day ahead to shift the building load from on-peak to off-peak hours. For each heuristic MPC scenario, the model runs a simulation using forecast weather data to quantify and activate energy flexibility in response to grid requirements. The developed MPC framework was implemented in the school used as the case study. Ten classrooms are investigated, with six using the MPC and four as a reference case with the reactive control system and default zone setpoint profiles. Results indicate that the school building can provide between 47% and 95% energy flexibility (load shifting relative to reference) during on-peak hours and up to 44% electricity cost reduction while satisfying acceptable temperature constraints. By implementing the proposed MPC, energy flexibility of 32 W/m2 of floor area for the zones with a convective heating system and 65 W/m2 of floor area for the zones with radiant heating can be achieved during a demand response event. The proposed strategy can be generalized and replicated in other school buildings. • A model predictive control methodology for school buildings was presented. • The methodology was applied to a fully electric school building near Montréal, Canada. • Data-driven grey-box models were developed and calibrated using measured data. • The model predictive control provided between 47% and 95% energy flexibility during on-peak hours. • The model predictive control reduced the cost of electricity by 44%. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to model electricity consumption using Artificial Neural Networks (ANN). Total electricity consumption and consumption generated by households (HH) were modeled. The input variables of the ANN were based on nighttime light images from VIIRS DNB. Studies conducted thus far have covered mainly linear models. Most of case studies focused on single countries or groups of countries with only few focusing on the sub-national scale. This paper is pioneering in covering an area of Poland (Central Europe) at NUTS-2 level. The use of ANN enabled the modeling of the non-linear relations associated with the complex structure of electricity demand. Satellite data were collected for the period 2013–2016, and included images with improved quality (inter alia higher resolution), compared to the DMSP/OLS program. As images are available from April 2012 onwards, it is only recently that their number has become sufficient for ANN learning. The images were used to create models of multilayer perceptrons. The results achieved by ANN were compared with the results obtained using linear regressions. Studies have confirmed that electricity consumption can be determined with higher precision by the ANN method. • Innovative modeling of electricity consumption by artificial neural networks based on nighttime light images from VIIRS DNB. • A new way of preparing the input data of the model. • Total electricity consumption and electricity consumed by households were modeled. • The first electricity consumption analysis covered regions of Poland (Central Europe) at NUTS-2 level. [ABSTRACT FROM AUTHOR]

In today's China, the reverse distribution of electricity resources and electricity demand makes large-scale and long-distance transmission of electricity inevitable, resulting in the problem of electricity allocation optimization. To address this problem in the region covered by the China Southern Grid (CSG), the paper establishes an optimized model for electricity generation and interprovincial trading strategies in southern China. Based on data from 2015, this paper simultaneously optimizes electricity generation and interprovincial trading strategies in each of the five provinces. The result shows that interprovincial trading in the CSG-served area can yield significant energy, economic, environmental and interconnection benefits. It can reduce the power supply cost and environmental pressure in Guangdong province, help make Yunnan province a hydropower base in China and a thermal power base in southern China, facilitate the completion of the "West-to-East Power Transmission" project, and partly resolve the issues raised in each province's 13th Five-Year Plan as well as the problem of optimizing electricity allocation in the region covered by the CSG. • We propose an annual total optimized model for electricity generation and interprovincial trading in Southern China. • The model optimizes the electricity trading and transmission routes across regions. • The model considers the generation cost and penalty cost of emitting CO 2 , SO 2 and NO x. • Corresponding policies and proposals are put forward for power grid and five regions in South China. [ABSTRACT FROM AUTHOR]

Achieving further reductions in building electricity usage requires a detailed characterization of electricity consumption in homes. Understanding drivers of consumption can inform strategies for promoting conservation and efficiency. While there exist numerous approaches for modeling building energy demand, the use of regularization methods in statistical models can address challenges inherent to building energy modeling while also enabling more accurate predictions and better identification of variables that influence consumption. This paper applies five regularization techniques to regression models of original survey and electricity consumption data for more than one thousand households in California. It finds that of these, elastic net and two extensions of the lasso—group lasso and adaptive lasso—outperform other approaches in terms of prediction accuracy and model interpretability. These findings contribute to methodological approaches for modeling energy consumption in buildings as well as to our understanding of key drivers of consumption. The paper shows that while structural factors predominate in explaining annual electricity consumption patterns, habitual actions taken to save energy in the home are important for reducing consumption while pro-environmental attitudes and energy literacy are not. Implications for improving building energy modeling and for informing demand reduction strategies are discussed in the context of the low-carbon transition. • Reviews literature on modeling energy consumption and determinants of residential electricity consumption. • Reviews regularization methods in regression analysis to improve prediction and model interpretability. • Assembles dataset of 58 predictive factors and annual electricity usage for one thousand households California. • Applies five regularization techniques to these data to compare model prediction and variable selection. • Highlights influence of occupant socio-demographics, physical dwelling characteristics, and occupant behaviors. [ABSTRACT FROM AUTHOR]

Abstract The Battery Swapping Station (BSS) is emerging as a viable means for fast energy refill of Electric Vehicles (EVs), in addition to many potential benefits in providing energy and ancillary services to the distribution grid. This paper focuses on developing a mathematical model for uncertainty-constrained BSS optimal operation that not only covers the random customer demands of fully charged batteries, but also leverages the available batteries to reduce its operation cost through demand shifting and energy sellback. The battery degradation is further modeled and formulated to ensure a practical solution. Numerical simulations on a test BSS demonstrate the effectiveness of the proposed model and show its viability in achieving the predefined objectives as discussed in this paper. Highlights • Developed a mathematical model for uncertainty-constrained BSS optimal operation. • Proposed random customer demands, demand shifting and energy sellback. • Developed the costs of power exchange with the utility grid and battery degradation. • Proposed robust optimization for capturing uncertainties in demand and electricity price. • Proposed mixed integer linear programming for problem modeling and formulation. [ABSTRACT FROM AUTHOR]

Abstract The implementation of Increasing Block Electricity Prices (IBEPs) aims to guide residents towards electricity savings and rational energy use, but there are questions over the effectiveness of the IBEPs to achieve these goals. To this end, this paper uses residential IBEPs policy implemented in Sichuan Province of China in 2006 as a natural experiment to answer the question of whether IBEPs effectively regulate residents' electricity demand. Synthetic control method (SCM) was used to evaluate the treatment effect of the policy. The paper finds that the IBEPs policy significantly reduces urban and rural residential electricity consumption. The per capita electricity consumption of urban residents fell by 26.87–100.76 kWh/year with an average of 51.40 kWh/year, equivalent to a decrease of 5.93%–17.50% and average of 11.17%. On the other hand, the per capita electricity consumption of rural residents decreased by 20.86–48.28 kWh/year with an average of 26.28 kWh/year, which is equivalent of a decreased of 7.8%–16.79% and average of 12.75%. Electricity demand in urban areas decreased more than in rural areas, but rural residents are more sensitive to IBEPs than urban residents. In order to achieve "equity" and "efficiency", China needs to further improve the design mechanism of residential IBEPs. Highlights • Synthetic control method is used to evaluate the treatment effect. • The impact of the implementation of IBEPs on residential electricity demand is studied. • IBEPs policy significantly reduces urban and rural residential electricity consumption. • Rural residents are more sensitive to IBEPs than urban residents. • China needs to further improve the design mechanism of residential IBEPs. [ABSTRACT FROM AUTHOR]

Abstract One of the main obstacles to Iraq's economic and social development is the lack of reliable electricity supply. In recent years there has been a significant increase in grid-based electricity capacity, but it is still far from being sufficient to meet demand growth. Therefore, it is necessary to build a suitable and flexible forecasting model for this energy system. In this paper, the results of two models are compared to other previous studies of Iraq's energy system to provide the yearly unsuppressed load forecast in the long term. The relationship between the actual load supply and four sets of historical data: population, gross national product, consumer price index and temperature, is examined. The result shows that a reduction in the prediction of electricity demand including suppressed demand occurs when increasing the growth of the consumer price index and removing the war affect. The suppressed consumer demand is estimated by developing a heuristic algorithm and the impact of the reserve margin and load diversity factor is considered to obtain the final forecast. The main contribution of this paper integrates various factors, after rebuilding the lost information, and includes the influence of relevant independent variables, each one for a given weight. Highlights • The data issues are processed to provide an accurate output. • A heuristic approach is used to estimate the suppressed load demand. • Two models are developed to enhance the results of previous studies. • Different variables are checked to obtain a suitable forecast model. • Results comparison proved the flexibility of model to any update the data. [ABSTRACT FROM AUTHOR]

The performance of regional distributed energy systems is closely related to operation strategies and is also affected by the renewable energy penetration rate of the power system. According to the load characteristics of a public building in Changsha, a regional distributed energy system of a combined heating and power unit coupled with a photovoltaic, ground source heat pump, air-source heat pump, and energy storage is proposed to explore the impact of the power system's renewable energy penetration rate on its optimization and operation. Based on the following electric load, following thermal load, and following hybrid electric-heating load strategies, this paper conducts a multi-objective optimization design for the proposed system according to two scenarios, where Scenario 1 considers the change of renewable energy penetration in the power system, and Scenario 2 does not. According to the optimization results, the impact of the change in the power system's renewable energy penetration rate on the system performance is comparatively analyzed. The results show that the electricity consumption share of the system from Scenario 1 is greater than that of the system from Scenario 2, regardless of the design and operation conditions. With the increase of renewable energy penetration, the primary energy saving rate of the system from Scenario 1 is higher than that of the system from Scenario 2, with increasing rates of 37.60%, 7.92%, and 36.87% under three strategies, respectively. Furthermore, for Scenario 1, the system operation performance under the following thermal load strategy is better than that under the other two strategies. The results of this paper provide some guidance for the optimal design of grid-connected distributed energy systems. • A RDES is proposed based on building load characteristics. • A multi-objective optimization model considering the power system's renewable energy penetration rate is developed. • The impact of the power system's renewable energy penetration on the design and operation of the RDES is explored. [ABSTRACT FROM AUTHOR]

Under "double carbon" policies, the shift towards bus electrification is unavoidable but is hindered by operational challenges due to electric buses' limited range. These challenges are intensified by heterogenous bus fleets, inadequate charging facilities, and depot limitations. This paper introduces a pragmatic approach to address the multi-depot electric bus scheduling problem, comprehensively addressing all aforementioned complexities. A tailored adaptive large neighborhood search (ALNS) is designed to solve the problem, wherein several ingenious accelerating techniques are integrated to generate a high-quality solution within an acceptance time frame, including: 1) relaxation of hard constraints to expedite iterations; 2) specialized operators designed to enhance the diversity of neighborhood spaces; 3) the meticulous setting of operator scores to prioritize operators with higher efficiency. A small-scale case study is conducted to compare the tailored ALNS with the commercial solver. The results substantiate the superior performance of the proposed algorithm and the significant role of these accelerating techniques. Furthermore, the algorithm presents its practical applicability in large-scale scenarios. The findings reveal that the proposed approach reduces the fleet size by 35 vehicles and concurrently achieves a significant 13.61 % reduction in electricity consumption, which provides a holistic solution to real-world operational challenges and offers recommendations for operators. • A holistic solution designed for the multi-depot electric bus scheduling problem. • Considering limitations of charging facilities and depot capacity constraints. • An adaptive large neighborhood search algorithm embedded with ingenious techniques. • Real cases verify the method enhances vehicle utilization and energy efficiency. [ABSTRACT FROM AUTHOR]

This paper proposes an innovative algorithm for predicting short-term electrical maximum demand by using historical demand data. The ability to recognize in peak demand pattern for commercial or industrial customers would propose numerous direct and indirect benefits to the customers and utility providers in terms of demand reduction, cost control, and system stability. Prior works in electrical maximum demand forecasting have been mainly focused on seasonal effects, which is not a feasible approach for industrial manufacturing facilities in short-term load forecasting. The proposed algorithm, denoted as the Adaptive Rate of Change (ARC), determines the logarithmic rate-of-change in load profile prior to a peak by postulating the demand curve as a stochastic, mean-reverting process. The rationale behind this analysis, is that the energy efficient program requires not only demand estimation but also to warn the user of imminent maximum peak occurrence. This paper analyzes demand trend data and incorporates stochastic model and mean reverting half-life to develop an electrical maximum demand forecasting algorithm, which is statistically evaluated by cross-table and F-score for three different manufacturing facilities. The aggregate results show an overall accuracy of 0.91 and a F-score of 0.43, which indicates that the algorithm is effective predicting peak demand in predicting peak demand. [ABSTRACT FROM AUTHOR]

Load following is the possibility for a power plant to adjust its power output according to the demand and electricity price fluctuation throughout the day. In nuclear power plants, the adjustment is usually done by inserting control rods into the reactor pressure vessel. This operation is inherently inefficient as nuclear power cost structure is composed almost entirely of sunk or fixed costs; therefore, lowering the power output, does not significantly reduce operating expenses and the plant is thermo-mechanical stressed. A more attractive option is to maintain the primary circuit at full power and use the excess power for cogeneration. This paper aims to present the techno-economic feasibility of nuclear power plants load following by cogenerating hydrogen. The paper assesses Small Modular nuclear Reactors (SMRs) coupled with: alkaline water electrolysis, high-temperature steam electrolysis, sulphur-iodine cycle. The analysis shows that in the medium term hydrogen from alkaline water electrolysis can be produced at competitive prices. High-temperature steam electrolysis and even more the sulphur-iodine cycle proved to be attractive because of their capability to produce hydrogen with higher efficiency. However, the coupling of SMRs and hydrogen facilities working at high temperature (about 800 °C) still requires substantial R&D to reach commercialisation. [ABSTRACT FROM AUTHOR]

To reflect the trend of electricity consumption (EC) in China's Pearl River Delta (PRD) under a balanced environment and economy, this paper proposes an EC forecasting framework from the perspective of optimal economic growth. The historical GDP statistics of the PRD are calibrated with data from the new "NPP-VIIRS-like" nighttime light dataset from 2000 to 2020, and two simulation scenarios of optimal economic growth are constructed according to the carbon emission reduction rate. Finally, based on the electricity data from 2000 to 2017, an error correction model is constructed to predict and compare the trend of EC in the PRD under different carbon emission scenarios. The results demonstrate the following. (1) The economic growth path under low-carbon constraints is more closely aligned with the actual economic development of the PRD. (2) The electricity demand required to sustain optimal economic growth in the PRD under low-carbon constraints is projected to reach saturation around 2037, approximately a decade earlier than the scenario without carbon constraints. The results of the projections are expected to guide future work on power system planning and economic development assessment in the context of carbon reduction. • The balance between environmental resources and economic development is considered. • The optimal economic growth path under the low-carbon goal is measured. • Calibration of regional statistics with the nighttime light dataset "NPP-VIIRS-like". • The error correction model is built as a short-term regulation mechanism for power prediction. • Predicted electricity demand for optimal economic growth in the PRD under different scenarios. [ABSTRACT FROM AUTHOR]

Taxiing aircraft using electric vehicles is seen as an effective solution to meet aviation targets of climate neutrality. However, making the transition to electric taxiing operations is expected to significantly increase the electricity demand at airports. In this paper we propose a mixed-integer linear program to schedule electric vehicles for aircraft towing and battery charging, while considering a limit for the supply of energy. The objective of the schedule is to maximize emissions savings. For computational tractability, we develop an Adaptive Large Neighbourhood Search which makes use of multiple local search heuristics to identify scheduling solutions. For daily scheduling with a small fleet size, the developed heuristic achieves solutions with an average 4% gap to the best linear programming solution. The results show that charging the vehicles during daytime is essential to maximize saved emissions: removing charging opportunities for a few hours during the day reduces the performance by an average of 6.4%. In addition, it is found that fast charging leads to low vehicle downtime, unless the battery size exceeds 750kWh, when charging rates over 150kW become unnecessary. Overall, our model provides support for infrastructure planning of airports during the transition to aircraft electric taxiing. [ABSTRACT FROM AUTHOR]

In recent decades, the ever-increasing demand for electricity in large cities is leading the electricity distribution network (EDN) to an operational state of fatigue, especially at peak load times. In this context, load congestion, technical losses, and voltage deviations across the EDN can contribute to the high waste of electricity as well as to the worsening of the supply service. To face this problem, this paper proposes a mixed integer linear programming (MILP) model that aims to reshape the demand profile (i.e., load reduction in peak periods without load rebound in the remaining periods of the day) of total consumers while the voltage deviations and technical losses are minimized. In the proposed model, the demand profile reshaping is carried out through direct (DC) and indirect (IC) bulk control of residential, commercial, and industrial loads, while the load factor (LF), i.e., indicator related to the efficient use of electricity, is improved. Due to the scheduling of these loads at certain times of the day, the occurrence of voltage deviations and technical losses can be mitigated by the efficient control of capacitor banks (CBs). To corroborate the applicability of the proposed MILP model, the 33-node IEEE test system was used. The results show the technical and economic gains for the distribution company (DISCO) and the total number of consumers. • A MILP model is proposed to reduce electricity waste in EDN. • DC and IC of bulk loads to reduce peak demand without occurrence of load rebound. • Control of CBs to mitigate EDN under and over voltages due to load variation. • Improvement of LF related to the EDN demand profile to alleviate operational stress. [ABSTRACT FROM AUTHOR]

In the past 20 years, China has undergone a rapid process of electrification, which has contributed to the modernization of people's lives. This paper associated household electricity consumption, appliance service function, total cost of electricity consumption to life well-being for the first time, and explained the structural characteristics of these changes. The welfare index from appliance services is defined as the result of the interaction between electricity consumption and appliance operation, and is used as an indicator to assess the improvement of household well-being. We sorted out 9 types of service functions of appliances, estimated the structure of household electricity consumption, total electricity cost and welfares received, and analyzed the factors that enhance welfare and their contributions. The results show that the electricity consumed by 12 main household appliances per household per year increased from 358 kWh in 2000 to 1419.5 kWh in 2020, and the total household electricity cost increases from 1315.74 yuan (RMB) to 6772.68 yuan. The quantity and structure of both have changed greatly. The service functions provided by appliances are increasingly diversified, have expanded from the basic services such as lighting, watching TV, refrigerated food to nine aspects including communication and transportation. The quality of life of residents continues to be improved, and the welfare index has increased from 2.48 to 7.82. There is a good linear relationship between the index and the three independent variables. The unit contributions of household disposable income, total electricity cost and household population size to the welfare index were 0.0836, 0.1195 and −1.6161, respectively. To accelerate electrification and enhance people's well-being, China needs to further improve the power and communication infrastructure, promote the application of intelligent technology, and implement precise encouragement policies. • Electricity consumption and appliance services cloud be linked to residential welfare. • The changes of household electricity structure and cost are quantitatively measured. • The welfare index from home appliance services are estimated for the first time. • The well-being is influenced by ability to pay, willingness to pay and family size. • The domestic electrification has greatly improved people's quality of life. [ABSTRACT FROM AUTHOR]

Energy consumed by HVAC (heating, ventilating and air conditioning) systems represents a considerable part of the energy consumed in buildings. This paper focuses on achieving energy efficiency, through automatic control strategies, of HVAC systems in the biopharmaceutical industry, a sector little covered by previous studies, mainly focused on residential and commercial buildings. The system under study is an air handling unit (AHU). The main contributions of this research are the obtaining of a dynamic, multivariable, and non-linear model of the AHU, proposing a relatively simple structure and the procedure to estimate its parameters; a non-linear static model of the power consumption of its bank of electrical resistors, also simple, but useful to guide the PID tuning toward energy efficiency; and the approximation to the model of a PID controller whose control low is unknown. The methods proposed to obtain the models and to perform the simulations are also provided. Results for a close-to-reality simulation scenario that suggests the possibility of reducing the power consumed by the resistor bank by 29 % are presented. The use of an industrial PI control algorithm, instead of the classical textbook algorithm, also distinguishes this work from others. • Energy-efficient control of AHUs with cascade control loops requires energy efficiency and effectiveness of internal loops. • Mathematical models and simulations allow to increase the efficiency of control systems by re-tunning its controllers. • To use Hammertein-Wiener models with a proper experiment design is a simple way to capture the dynamics of complex systems. • It is necessary to validate the control algorithms of industrial controllers with experimental data. • Proper tuning of PID loops in AHUs with electric heating, temperature and humidity control, allows energy saving over 20%. [ABSTRACT FROM AUTHOR]

This paper presents a strategy based on the hierarchical rolling horizon control, also called model predictive control (MPC), for efficiently managing a hydrogen-energy storage system (HESS) within an islanded wind–solar microgrid. An electrolyzer uses electricity generated from renewable sources to produce clean hydrogen, which is then re-electrified by a fuel cell as needed to meet the microgrid's loads. The main contribution lies in the incorporation of multiple hydrogen storage tanks in the HESS, distinguishing it from existing literature, which typically focuses on a single tank. The incorporation of multiple tanks in the HESS enables the storage of large volumes of hydrogen for long-term use, allowing the microgrid to operate autonomously without interaction with the utility grid. In order to ensure optimal performance, the selection of the most suitable device for operation at each time-step is crucial. The proposed control strategy takes into account the economic and operational costs, degradation aspects, and physical constraints of the HESS, while simultaneously ensuring the tracking of reference demands and with the highest priority smoothing out the variations of renewable energy sources. Numerical simulations and a lab-scale microgrid setup demonstrate that the controller effectively manages the HESS thus satisfying economic constraints and optimizing device costs, even when deviations occur between the predicted and real-time scenarios. Furthermore, the inclusion of multiple hydrogen tanks allows the microgrid to both mitigate fluctuations in renewable power sources and effectively meet load demand. • Development of a hierarchical MPC strategy for efficient microgrid management. • Incorporation of multiple hydrogen storage tanks to enhance long-term autonomy. • Consideration of costs, degradation, and constraints for the device selection. • Efficacy shown through numerical simulations and a lab-scale microgrid setup. [ABSTRACT FROM AUTHOR]

The uncertainty of wind power (WP) poses a significant challenge to power systems with a high percentage of WP. Accurate WP forecasting is an important approach to mitigate this issue. With the increasing demand for electricity, some wind farms (WFs) have been built or expanded. Such WFs lack abundant historical data for establishing forecasting models, i.e., data scarcity. This paper proposes a novel method for very short-term probabilistic forecasting based on transfer learning (TL) and conditional screening for WP. Utilizing numerical weather prediction as predictive features to address deterministic forecast data scarcity. Considering the data orientation, combining bidirectional long short-term memory with attention mechanisms to enhance prediction accuracy. Based on TL and conditional screening, it achieves data completion and singular value filtering to enhance the comprehensive performance of probabilistic forecasting errors. Using real world data, the proposed WP forecasting model reduces MAPE by at least 0.87% and RMSE by at least 0.6483 compared to the benchmark at different forecast steps. The comprehensive performance of probabilistic forecasting improves by at least 1.9134 at different forecast steps and confidence levels. The results indicate that the proposed forecasting model for data scarcity WFs is feasible, providing new insights for data scarcity WF forecasting. • A model tackles data scarcity via transfer learning-based probabilistic forecasting. • Proposed conditional strategy, easing width-coverage conflicts in forecasting. • Incorporates BiLSTM with AM for accurate, very short-term forecasting. • Validated model with real-world wind farm data, displaying robust performance. [ABSTRACT FROM AUTHOR]

The smart grid system has addressed the problems of traditional power grid by not only meeting energy demand in real-time but also limiting its wastage. The two key objectives of a smart grid system are to ensure a higher Quality of Experience (QoE) for consumers and to reduce consumer costs using dynamically varying pricing concepts. However, these two objective parameters oppose each other as maximizing the consumer QoE requires the availability of sufficient electric power at any given time, which in turn increases power purchase cost. In this paper, we introduce an efficient power management system architecture of a smart grid and develop an Optimal Energy Allocation and Prediction system based on Deep Q-Leaning, namely OEAP-DQL, that brings a trade-off between the two. The developed OEAP-DQL system is a multi-objective linear programming (MOLP) problem that predicts consumer electricity demand by exploiting four different weighted and regressive moving average forecasting methods in the action space to accurately capture dynamically varying customer demand behaviors. Furthermore, iterative exploitation of multiple learning methods decreases forecasting error and intelligent stored power management helps the OEAP-DQL smart grid operator (SGO) to enhance its profit. The results of our simulation experiments show that the OEAP-DQL system outperforms the state-of-the-art works in terms of QoE and cost. [ABSTRACT FROM AUTHOR]

Lack of capital hindered the development of agricultural production and the residential energy consumption transition in rural areas, but, with the continuous advancement of digital finance technologies, rural digital credit eased the liquidity constraint of agricultural production, which influenced residential energy consumption. However, few studies explored the relationship between rural digital credit and residential energy consumption. Hence, the instrumental variable (IV) method, the mediation effect model and the survey data that covered 295 effective samples were used to analyze the impact of rural digital credit on residential energy consumption from the agricultural production perspective in this study. The results indicated that rural digital credit resulted in a significant decline of the consumption of traditional energy products, but impacts of rural digital credit on electricity consumption and the new-style energy products consumption were not significant. Further, rural digital credit reduced traditional energy products consumption via boosting the level of agricultural mechanization and the adoption of precision fertilizer applying technique. This paper was conducive to not only developing the green credit markets in rural areas, but also enhancing the residential energy consumption transition and sustainable development. • Digital credit resulted in a significant decline of the consumption of traditional energy products. • Impacts of digital credit on electricity consumption and the new-style energy products consumption were not significant. • Digital credit reduced traditional energy products consumption via boosting agricultural mechanization. • Digital credit reduced traditional energy products consumption by adopting precision fertilizer applying technique. [ABSTRACT FROM AUTHOR]

Home energy management systems (HEMS) have transformed the traditional structure of electricity consumption on the customer side and facilitate real-time interaction between the customers and the grid. However, the HEMS scheduling is currently challenged by a variety of uncertainties, including photovoltaic (PV) output, electric vehicle (EV) charging/discharging behavior, and real-time pricing (RTP), which can seriously impact home equipment scheduling and critical objectives like total cost and users' comfort. Therefore, a hybrid robust-stochastic (HRS) optimization approach for multi-objective home energy management with the consideration of uncertainties and users' comfort has been proposed in this paper. Firstly, a detailed classification and modeling of the loads in the household are carried out to facilitate the exchange between the user-side resources and the grid in a benchmark based on RTP. Then the stochastic charging/discharging behavior of EV is modeled by stochastic optimization (SO) methods, and uncertainties in PV production and RTP are modeled by robust optimization (RO) methods, making full use of the flexibility of various uncertainty parameters. Meanwhile, the users' requirements for comfort are considered, and a multi-objective function for the economy and comfort of the HEMS is established. Finally, the effectiveness of the proposed method has been verified by case studies. The results show that the proposed HRS is effective to deal with different levels of the uncertainty parameters and ensures the users' comfort requirements in home energy system. Moreover, the users can make trade-offs between various levels of robust decisions according to their needs of economic and comfort level, thus obtaining a variety of electricity consumption decisions. • A new hybrid robust-stochastic method is proposed to address the uncertainties for home energy management system. • A MILP optimized scheduling strategy of home energy management systems is proposed. • Multi-objectives for both electrical cost and customer comfort are considered. • The optimal scheduling solutions are selected subject to users' demand. [ABSTRACT FROM AUTHOR]

In this paper, economic and environmental impacts of energy efficiency programs associated with new and existing buildings are evaluated for the Kingdom of Saudi Arabia (KSA). In particular, the analysis considers optimized designs for new buildings as well as different energy retrofit programs for existing buildings are considered in the bottom-up analysis using archetypical building energy models located in five sites representing a wide range of KSA climates. In particular, the analysis presented in this paper provides specific estimations to the extent of the energy and demand savings that could be achieved in the building sector even under highly subsidized energy prices. It is found that even a basic energy retrofit program using low-cost energy efficiency measures implemented to the existing building stock can provide significant economic and environmental benefits. Indeed, a level 1 energy efficiency retrofit program targeting only the existing residential building stock could reduce electricity consumption by 10,054 GWh/year and peak demand by 2290 MW and carbon emission by 7.611 million tons/year. [ABSTRACT FROM AUTHOR]

The major hindrance in popularization of box type solar cookers (SBCs) is cooking incapability of these appliances in low availability of sun light and in night. This paper introduces a new parameter “required electric back-up power ( P rb )” for SBCs to remove this limitation. An analytical model is presented here to derive P rb for SBCs under different weather and cooking time conditions. To validate proposed model, a Solar cum Electric Cooker (SEC) has been fabricated as per P rb value estimated via analytical model. SEC has been tested under different conditions. Experimental results are in support to the analytical approach. SEC is capable for cooking of 1.2 kg food load under indoor and outdoor. For outdoor, cooking time on sunny day without back-up is recorded between 1.5 and 2.5 h. The cooking time on scattered cloudy day with back-up is found to be 100 min (back-up is 0.12 kWh that is 82% less than the conventional electric heater). For indoor, cooking time is found to be 85 min (with 0.23 kWh electric back-up). Paper also reveals that electric back-up in SBC also reduces its payback period and increases its net present value (NPV) in respect to different cooking fuels. [ABSTRACT FROM AUTHOR]

Demand side management (DSM) and net metering (NM) are two important strategies that can be used in smart energy management systems to help utilities reduce peak loads and increase the penetration of renewable energy, while also enabling customers to save energy and reduce energy bills. In order to effectively implement DSM and NM programs, it is important to identify the customers who are most likely to benefit from these programs and to quantify the economic impact and benefit for both utilities and end-users. This paper proposes a decision framework that utilizes smart meter data and clustering analysis to identify representative load patterns, and a five-stage method for segmenting load patterns to create a consumption intensity matrix. A fuzzy ensemble based multi-criteria decision making framework is then applied to identify the most potential customer groups and quantify the economic benefit and impact. This paper presents a novel approach for identifying potential customers for DSM and NM programs and developing personalized services for energy demand management. The proposed framework is evaluated through a case study, which demonstrates its robustness in decision making and identifies 13 typical load patterns, seven for weekdays and six for weekends, representing diverse consumption behaviors. The weekday load patterns perform better than the weekend load patterns in terms of the five-stage method. • Fuzzy ensemble Multi-Criteria Decision Making framework. • A framework for identifying potential DSM and NM customers. • Personalized energy demand management services developed. • Analysis of residential energy consumption behavior using FCM clustering. [ABSTRACT FROM AUTHOR]

Industry poses one of the biggest challenges in the renewable energy transition. In this paper, fossil fuels in the European industrial sector are replaced by renewable energy using a novel tool, IndustryPLAN, a planning tool for the assessment of national industrial sectors. In a bottom-up approach, each industry sub-sector is addressed with energy efficiency and fossil fuel replacement measures based on best available and innovative technologies, and in a top-down approach, the fuel and electricity consumption per country is analysed and decarbonised. The results indicate that: 1. Known technologies can decarbonise most of the industrial sector; 2. Costs and efficiencies are improved by energy savings and electrification; 3. Limiting bioenergy consumption is a critical challenge, emphasising the key role of energy savings and electrification, and the alternative of using hydrogen or hydrogen-based electrofuels will make the transition more expensive and induce energy losses. A full transition to renewable energy and a decarbonised industry sector may be possible before 2050, however, this requires that all investments are sustainable from 2030 onwards and that grid electricity is fully decarbonised. This paper presents several pathways toward 100% renewable energy supply in the European industrial sector and discusses the implications of the outlined scenarios. • Four tangible 100% renewable energy scenarios for industry in EU27 + UK are established. • 100% renewable energy in industry is feasible before 2050. • Energy efficiency improvements are essential to limit biomass consumption. • Industry energy transition needs to be prioritised in short- and long-term planning. [ABSTRACT FROM AUTHOR]

The number of energy consumption in the residential sector experienced a rapid growth trend over the past 40 years, of which residential energy consumption in rural areas was an important reason for the quick growth in energy consumption. With the continuous development of farmland rental market, rural factor allocation efficiency and household income were improved in the actual world, which affected residential energy consumption. Thus, the main goal of this paper was to study the impact of farmland rental market participation on residential energy consumption by using a data set that covered 5018 effective samples. And the instrumental variable (IV) approach and the mediation effect model were used to test empirically the impact of farmland rental market participation on residential energy consumption. Compared with the existing studies, the main contributions of this paper were that: (i) a systematic theoretical model for the relationship between farmland rental market participation and residential energy consumption was established, which expanded the research perspective; (ii) the mediation effects of labor migration to urban areas and household income for the relationship between farmland rental market participation and residential energy consumption were analyzed, which enriched the research conclusions. The results indicated that: (i) compared with farm households without renting in land, residential energy consumption and electricity consumption for farm households with renting in land were significantly increased by 12.52% and 9.53%, respectively; (ii) expanding farm size decreased significantly the number of labor migration to urban areas by 16.31%, which boosted residential energy consumption; (iii) expanding farm size increased significantly household income by 7.68%, which raised residential energy consumption. This study was conducive to not only enhancing the sustainable economic development, but also expanding and improving the research conclusions of the existing literature. • expanding farm size enhanced significantly residential energy consumption. • renting-out land affected insignificantly residential energy consumption. • expanding farm size boosted residential energy consumption by reducing labor migration to urban areas. • expanding farm size enhanced residential energy consumption by raising household income. [ABSTRACT FROM AUTHOR]

The development of the new economic normal is influencing the total economy, the industrial structure and the layout in China to a great extent. The pillar industry faces a transformation from factor-driven to innovation-driven. The traditional electricity demand prediction model is no longer applicable due to these new factors. Thus, this paper makes a quantitative analysis of the new influencing factors, and establishes the quantitative relationship based on econometrics. According to industry division, this paper proposes a long term electricity demand forecasting model that is suitable for the new economic normal by using system dynamics method. Finally, the paper combines the actual situation of Tianjin and the forecasting model to predict the electricity demand of Tianjin. Simultaneously, the paper makes some suggestions about the development of the grid company. [ABSTRACT FROM AUTHOR]

This paper proposes an alternative method for applying the Moran's I test in exploratory analyses for spatial autocorrelation. An application of this new method found evidence that regional electricity consumption in Brazil is spatially dependent, presenting a spatial pattern of dissimilarity among regions. Such dependence suggests that the space dimension must be included in the specification of the forecast model to be used so as to ensure consistent, unbiased and efficient estimates. To achieve a higher energy security in the Brazilian Electricity Sector, it is essential to have accurate forecasts of the electricity consumption, and so a forecasting method that considers the spatiotemporal dynamics was proposed. The Spatial ARIMA model (ARIMASp) presented in this paper shows a better predictive performance - measured by a reduction of the Mean Absolute Percentage Error of forecasts - as compared to the ARIMA model. These results confirm that spatiotemporal models can improve forecasts of electricity demand in Brazil and demonstrate that considering spatial correlations is paramount to achieving the Brazilian Electricity Sector goals of security of electricity supply, affordability of tariffs and universalization of access to the Brazilian population. [ABSTRACT FROM AUTHOR]

Technical and financial uncertainties may put distribution system planning at risk. In this paper, a new risk-based planning method is proposed which pays more attention to low-probability and high consequences events in energy supplying systems. The proposed approach is adopted for determining the optimal structure of a Medium Voltage network where risk-based determination of the radial network structures is implemented through an uncertainty model of the system's variables based on discrete states, called scenarios. The cost of distribution system planning consists of investment cost, maintenance cost, power losses cost, reliability cost, and technical risk cost. In this paper, appropriate models are proposed to consider the monetary effects of technical risks. The proposed approach is applied to a test system consisting of 52 electric load points and two substations. It is observed that the proposed risk-based method for planning the optimal network structure can properly reduce the cost of extreme events, therefore reducing the concerns of distribution system operators about these possible situations. [ABSTRACT FROM AUTHOR]

Long term peak electricity demand forecasting is a crucial step in the process of planning for power transmission and new generation capacity. This paper discusses an application of the Generalized Pareto Distribution to the modelling of daily peak electricity demand using South African data for the period 2000 to 2010. The main contribution of this paper is in the use of a cubic smoothing spline with a constant shift factor as a time varying threshold. An intervals estimator method is then used to decluster the observations above the threshold. We explore the influence of temperature by including it as a covariate in the Generalized Pareto Distribution parameters. A comparative analysis is done using the block maxima approach. The GPD model showed a better fit to the data compared to the GEVD model. Key findings from this study are that the Weibull class of distributions best fits the data which is bounded from above for both stationary and non-stationary models. Another key finding is that for different values of the temperature covariate the shape parameter is invariant and the scale parameter changes for different values of heating degree days. [ABSTRACT FROM AUTHOR]

Demand response (DR) can serve as an effective tool to better balance the electricity demand and supply in the smart grid. It is defined as "the changes in electricity usage by end-use customers from their normal consumption patterns" in response to pricing and incentive payments. This paper focuses on new opportunities for DR with electric vehicles (EVs). EVs are potential distributed energy resources that support both the grid-to-vehicle and vehicle-to-grid modes. Their participation in the time-based (e.g., time-of-use) and incentive-based (e.g., regulation services) DR programs helps improve the stability and reduce the potential risks to the grid. Smart scheduling of EV charging and discharging activities also supports high penetration of renewables with volatile energy generation. This paper proposes a novel stochastic model from the Independent System Operator's perspective for risk management and participation planning of EVs in the smart grid for DR. The risk factors considered in this paper involve those caused by uncertainties in renewables (wind and solar), load patterns, parking patterns, and transmission lines' reliability. The effectiveness of the model in response to various settings such as the area type (residential, commercial, and industrial), the EV penetration level, and the risk level has been investigated. [ABSTRACT FROM AUTHOR]

The new power sector scenario has focused on integrating renewable energy sources into the grid and achieving trustworthy consumer-utility-stakeholder relationships using smart meters. Granular consumption data obtained from the data management systems of Advanced Metering Infrastructure helps power utilities build an individual consumer's electricity consumption profile that facilitates better load forecasting, efficient management of demand and supply, and more reliable service. However, different challenges concerned with accessibility of data through different communication layers, such as breaches of privacy of consumer data and security of grid have emerged. Due to this, many customers lose faith in the smart grid. To utilize the full potential of the smart grid system these issues need to be addressed to attain a secure, stable, and reliable grid that brings profit to the utility. This paper provides a comprehensive literature survey to achieve the above-mentioned purposes using data analytics. It also addresses different challenges of using data analytics methods and machine learning applications in mitigating these issues. This study would enable the electric utilities to structure a reliable grid to prevent revenue loss and aid the researchers to recognize the potential and challenges of machine learning techniques in the field of the power sector. • Challenges of acquiring smart meter data, privacy concerns identified. • To ensure Reliable and secure grid, threats of Smart Grid recognized and alleviated. • Detection of attacks and power theft using smart meter data-leads to a robust grid. • Research challenges of application of smart meter data analytics elaborated. • The need and means of constructing a secured, reliable, robust grid debated. [ABSTRACT FROM AUTHOR]