Search

China's carbon emissions from households have increased with the growth of the consumption scale. To accomplish the ambitious goals of reducing emissions, carbon emission reduction on the household side should not be neglected. Based on the 2012–2018 CFPS database, This paper examines the relationship between household carbon emissions(HCEs) and the digital economy by analyzing data on household consumption expenditure and carbon emission coefficients. Utilize instrumental variables and the "Broadband China" natural experiment to conduct robustness tests, and further clarify how the digital economy influences direct and indirect household carbon emissions. The study finds that (1) Direct HCEs witness an increase while indirect HCEs experience a decrease due to the digital economy. (2) The digital economy increases direct HCEs by expanding the consumption scale, but surprisingly, the digital economy has not made a significant impact on enhancing residents' environmental awareness. (3) When analyzing indirect HCEs, we introduced a macro perspective, and it is found that the digital economy results in a decline in indirect HCEs primarily due to a reduction in the energy intensity of the whole industry. This paper provides new evidence on the digital economy and its impact on carbon emissions at the household level, which is instructive for understanding the interaction between the production and consumption sides in achieving carbon neutrality and how to promote emission reduction on the consumption side. • Household carbon emissions are measured from direct and indirect perspectives. • The impact of digital economy on household carbon emissions is heterogeneous. • Digital economy increases household carbon emissions through consumption scale. • Digital economy has no significant impact on environmental awareness. • Digital economy reduces indirect household carbon emissions through energy intensity. [ABSTRACT FROM AUTHOR]

Electricity theft has emerged as a notable concern for smart grids, as fraudulent users illicitly access electricity from utilities without a contractual agreement or manipulate meter readings to evade bill payments. Consequently, the importance of electricity theft detection (ETD) in preserving the cost-effectiveness of smart grids has increased. In recent years, the utilization of deep learning models in electricity theft detection has surged in popularity, owing to their capacity to capture the periodic patterns within time-series data on electricity consumption. Nonetheless, deep learning techniques for electricity theft detection encounter several challenges, such as data imbalance, higher false positive rates, and the determination of periodic cycle timings. In this paper, we present an innovative approach to tackle these challenges: the Multi-Objective Evolutionary Hybrid Deep Learning (MOE-HDL) architecture, specifically designed to identify electricity theft. In MOE-HDL techniques, first, a novel multi-objective evolutionary algorithm based on ranking scheme (RMOE) is proposed to optimize architectural model parameters and the detection window size, with precision and recall as conflicting objectives. Next, an optimized dynamic detection window is generated to effectively capture the periodic patterns in consumption data and convert the one-dimensional data into a grid structure. Finally, a hybrid deep learning model is designed with the help of optimized model parameters to identify abnormal patterns in electricity consumption data. We conducted a comprehensive set of experiments, comparing our approach with eight baseline methods for electricity theft detection. The experimental results demonstrate that the proposed MOE-HDL approach outperforms these alternatives in terms of performance. • In this paper, a novel multi-objective evolutionary hybrid deep learning approach was developed for theft detection in smart grids. • Precision and recall are two conflicting yet effective objective functions designed to optimize the model parameters and detection window. • A dynamic detection window is obtained through optimization to transform electricity consumption data. • The hybrid deep learning model is designed according to the model parameters obtained through evolutionary multi-objective optimization. [ABSTRACT FROM AUTHOR]

In the last decade, with the introduction of smart meters to smart grids, demand response programs (DRPs) have been widely adopted to establish a generation and consumption balance. DRPs provide many benefits for efficient grid management. However, these programs are conducive to higher levels of dissatisfaction by changing grid customers' consumption patterns. This paper aims to investigate the effects of DRPs on social welfare (SW). To this end, the paper presents a mathematical model for SW during the implementation of DRPs. In the proposed model, the level of customer satisfaction is assumed the main factor contributing to SW. This mathematical model considers different types of DRPs in terms of their impacts on SW. The paper also seeks to obtain linear and nonlinear models of DRPs and the coefficient of participation (CoP). CoP as an indicator shows the percentage of customers who actively participate in each DRP and plays a significant role in the assessment of the SW level. Moreover, owing to the sparsity and variety of distribution network customers, load patterns are classified into different clusters to take the load types into account. As a matter of fact, this process aims to identify similar patterns and thus, the same level of satisfaction for each separate cluster. The classification process is performed by using a machine learning-based clustering method known as the Affinity Propagation (AP) algorithm. Then, the model calculates the level of SW for the clusters based on the usage of electrical equipment and the time of day when they are turned on. The obtained levels of SW help operators select the best programs for every cluster in terms of customer satisfaction, and achieve the highest performance of DRPs. Lastly, the model is evaluated using real data of a distribution network to ensure the effectiveness and accuracy of the model. [Display omitted] • Development of a mathematical model for assessing Social Welfare during DRPs execution, considering customer satisfaction. • Integration of Coefficient of Participation (CoP) as an important factor in evaluating SW during DRP implementation. • Development of Affinity Propagation algorithm to cluster load patterns, enabling tailored DRPs to maximize effectiveness. [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]

Currently, building energy consumption prediction models are usually based on a large amount of historical operational data in high demands of building operating hours and monitoring systems. However, many buildings may lack operational data due to relatively limited monitoring systems, causing the failure to use data-driven methods to characterize the energy profile. In this context, transfer learning is a promising method to establish the knowledge transfer between many high-quality building operation datasets and a small amount of target building data, and to help predict energy consumption in the target building. This paper studies the possibility of employing transfer learning to achieve both short and long-term building energy consumption prediction. Firstly, a similarity analysis method, based on variable modal decomposition and dynamic time warping, is proposed for identifying the source buildings with the most similar energy features to the target building. Then, transfer learning for long-term prediction air-conditioning energy consumption is developed with weather parameters generated by the Morphing method as inputs. For the short-term single-step prediction, the proposed model CV-RMSE improves 81.3% (AEC) and 77.4% (EEC), respectively, compared to the prediction model that does not implement the transfer learning strategy and directly uses the target BEC data. As for the short-term multi-step prediction, the proposed model CV-RMSE improves 62.0% (AEC) and 65.5% (EEC), respectively. For the long-term prediction, the average CV-RMSE for the whole year is 6.62% and 11.15% for the proposed and directly target domain-based model, respectively. The proposed method explores the practicality of transfer learning in building energy forecasting, contributing to the use of existing building operation data for energy management at different timespan. • A similarity analysis method for building energy consumption data based on VMD-DTW is proposed. • The Seq2Seq-Transformer model is applied to short-term multi-step prediction of building energy consumption. • Transfer learning idea is first introduced to the long-term building energy consumption prediction. [ABSTRACT FROM AUTHOR]

The ever-intensifying threat of climate change renders the electric power system undergoing a profound transition toward net-zero emissions. Energy efficiency measures, such as demand response, facilitate the transformation to jointly relieve consumers' financial burden and improve the operability of the electric power grid, in a carbon-free way. In this paper, we design a cluster-based appliance-level-of-use demand response program, based on the massive volume of appliance consumption data, to expand the role demand response can play in the power grid's low-carbon transition. We systematically model the appliance-level utility function to distinguish consumers' distinct consumption patterns. We then develop a bi-level optimization model to capture the interactions between individual consumers and a distribution system operator (DSO) and enable appliance-level-of-use demand response functions. To further improve the efficiency and scalability of the proposed mechanism, we propose a cluster-based approach to capture the heterogeneity of users based on their energy consumption behaviors. Simulation results show that by capturing the detailed appliance-level response patterns, the proposed approach can systematically improve overall social welfare compared with conventional demand response mechanisms. • Data Empowered Appliance-level Utility Modeling. • Demand Response Design to Offer Appliance-level Incentive. • Heterogeneity Characterization to Improve Scalability and Efficiency. [ABSTRACT FROM AUTHOR]

Load shapes derived from smart meter data are frequently employed to analyze daily energy consumption patterns, particularly in the context of applications like Demand Response (DR). Nevertheless, one of the most important challenges to this endeavor lies in identifying the most suitable consumer clusters with similar consumption behaviors. In this paper, we present a novel machine learning based framework in order to achieve optimal load profiling through a real case study, utilizing data from almost 5000 households in London. Four widely used clustering algorithms are applied specifically K-means, K-medoids, Hierarchical Agglomerative Clustering and Density-based Spatial Clustering. An empirical analysis as well as multiple evaluation metrics are leveraged to assess those algorithms. Following that, we redefine the problem as a probabilistic classification one, with the classifier emulating the behavior of a clustering algorithm, leveraging Explainable AI (xAI) to enhance the interpretability of our solution. According to the clustering algorithm analysis the optimal number of clusters for this case is seven. Despite that, our methodology shows that two of the clusters, almost 10% of the dataset, exhibit significant internal dissimilarity. As a result, these clusters have been excluded from consideration for DR programs. The scalability and versatility of our solution makes it an ideal choice for power utility companies aiming to segment their users for creating more targeted DR programs. • Assessment of four ML clustering algorithms of different nature. • Re-definition of the problem using probabilistic classification and xAI. • Optimization of demand-side management clusters combining ML and empirical knowledge. • Experimental application on historical data from 4438 households in London. [ABSTRACT FROM AUTHOR]

Multi-energy systems with carbon capture system (CCS) and power to gas (P2G) can reduce carbon emissions while increasing the consumption of renewable energy. This paper proposed a long-term equilibrium including electricity, gas and carbon markets to study the price effect of the system and its impact on the carbon-gas- electricity sector. The simulation results indicate that the main impact of the system on the electricity sector is to reduce energy curtailment and provide flexible resource for the electric system. P2G used an excess of 22.21 TWh of renewable energy, while CCS used 2.65 TWh of electricity to operate the capture system. The multi-energy system will reduce the market clearing price in the gas market (the annual average gas price decreased from 276.90 to 276.30 yuan/MWh). However, the partial transfer of flexibility to the electricity sector may increase cost in the gas industry, which in turn will be transferred back to the electricity sector again. In addition, through the operation of multi-energy system, carbon elements will circulate in the three sectors. CCS will become a carbon price-maker when electricity scarcity and carbon quota tightening, and increase carbon prices, which will increase the utilization hours of CCS and P2G and their profitability. • A carbon-gas-electricity market coupling model is proposed. • The multi-energy system will reduce the market clearing price in the gas market. • The partial transfer of flexibility may increase cost in the gas industry • CCS will become a carbon price-maker when electricity scarcity. [ABSTRACT FROM AUTHOR]

The challenges of power quality are an emerging topic for the past couple of years due to massive changes occurring in low voltage distribution networks, being even more emphasised in the years marked by the novel COVID-19 disease affecting people's behaviour and energy crisis increasing the awareness and need of end-users energy independence. Both of these phenomena additionally stress the need for changes in the planning and operation of distribution networks as the traditional consumption patterns of the end-users are significantly different. To overcome these challenges it is necessary to develop tools and methods that will help Distribution System Operators (DSOs). In this paper, we present a geographic information system (GIS)-based tool that, by using open source technologies, identifies and removes errors both in the GIS data, representing a distribution network, and in the consumption data collected from the smart meters. After processing the initial data, a mathematical model of the network is created, and the impact of COVID-19-related scenarios on power quality (PQ) indicators voltage magnitude, voltage unbalance factor (VUF), and total voltage harmonic distortion (THD u) are calculated using the developed harmonic analysis extension of the pandapower simulation tool. The analyses are run on a real-world low voltage network and real consumption data for different periods reflecting different COVID-19-related periods. The results of simulations are visualised using a GIS tool, and based on the results, time periods that are most affected by the change of end-users characteristial behaviour are detected. The potential of the end-users in the PQ improvement is investigated and an algorithm that shifts consumption to more adequate time periods is implemented. After modifying the consumption curve, power quality analysis is made for newly created scenarios. The results show that the pandemic negatively affect all analysed PQ indicators since the change in the average value of PQ disturbances increased both during the hard and post-lockdown period. The time-shifting of consumption shows significant potential in how the end-users can not only reduce their own energy costs but create power quality benefits by reducing all relevant indicators. • Integrated tool for modelling, analysing, and visualising a network is presented. • Simulating the impact of end-user pattern consumption change on power quality (PQ). • The results show negative impact of passive end-user demand changes on PQ indicators. • Demand time-shifting is shown to be an efficient method for reducing PQ disturbances. [ABSTRACT FROM AUTHOR]

Traffic energy consumption estimation is significant for the sustainable transportation. However, it is difficult to directly employ macro traffic flow data to accurately estimate the traffic energy consumption due to many traffic energy consumption models need second-by-second vehicle trajectory. To solve this problem, this paper proposes a traffic energy consumption model based on the macro-micro data, which the macro data derived from the fixed-location sensors and sparse micro data derived from the Connected and Automated Vehicles (CAVs). The completed vehicle trajectories are constructed by the nonparametric kernel smoothing algorithm and variational theory. To test the performance of the proposed method, the Next Generation Simulation micro (NGSIM) dataset and Caltrans Performance Measurement System macro dataset obtained from the same road and time are used. The results indicate that the proposed method not only can reflect the characteristics of traffic kinematic waves caused by traffic congestion, but also minimize the errors generated by the macro-micro transformation. In addition, it can significantly improve the accuracy of energy consumption estimation. • Estimation of fuel consumption and emissions is closer to the real values than other methods. • Reconstructing second-by-second vehicle trajectories based on macroscopic traffic data. • Introduces CAVs trajectory data as a reference in the process of estimating the road spatio-temporal speed evolution. • Proposes a spatio-temporal consistency validation framework by using macro and micro data. • Experimental tests the effect of cell size and probe vehicle penetration on reconstruction accuracy. [ABSTRACT FROM AUTHOR]

In the 21st century, urbanization has caused rapid growth in the global construction area, resulting in high building resource consumption and long-term environmental impacts. Analyzing the resource consumption of public buildings and identifying the key factors that affect the consumption of building resources is important for developing of targeted policies. In this paper, we analyzed each branch of the People's Bank of China based on energy, water, and land resource consumption as well as the structure of energy consumption. Based on resource-consumption indicators, we divided 31 provincial-level administrative regions into four resource consumption levels; 65% were in the low-to-medium resource consumption levels. Medium-sized buildings had the highest energy consumption level and a more balanced energy consumption structure. The energy consumption per capita, water consumption, and utilization rate of small-sized buildings were low. To reduce resource consumption by public buildings, the construction of large-sized buildings should be limited. • The comprehensive evaluation method is used to calculate the resource consumption of the People's Bank of China. • Regression equations were established for energy consumption and resource consumption. • Medium-sized institutions had the highest energy consumption level and a more balanced energy consumption structure. [ABSTRACT FROM AUTHOR]

The market for solar energy in the US has grown exponentially due to increased consumer demand resulting from price reduction via economies of scale, technological progress, and a variety of incentives from federal and state governments as well as utility companies. The installation of solar power in Tennessee has more than doubled each year from 2009 to 2011. In this paper, we focus on the behavior of the Tennessee Solar Value Chain (TNSVC) to study the factors that influence growth of solar industry in the state. The impact of existing incentives on the TNSVC is analyzed. The TNSVC is simulated based on inputs from on-site survey to estimate economic impact in terms of the number of jobs added and the tax revenue generated in the state. In addition, a sensitivity analysis for the impact on the TNSVC under different policies those may be adopted by the state of Tennessee in the future is conducted. This paper employs a holistic model which can predict PV installation demand, understand solar value chain capacity, and estimate the revenue generation. It should be noted that the method employed in this study is not unique to the solar energy industry in Tennessee. The data utilized in this study is a combination of public domain information and surveys of suppliers, manufacturers, distributors, and installers. This makes the model in this paper flexible enough to be applied to assess the solar value chain in other state or country. [ABSTRACT FROM AUTHOR]

• Examines the effect of banking performance on renewable energy consumption (REC). • Improved banking performance enhances renewable energy consumption globally. • Heterogenous effect of the relationship between banking performance and REC across income groups. • Market capitalization, asset quality and managerial inefficiency explain REC in HI countries. • Return on asset, market capitalization, asset quality and z-score explain REC in MI and LC countries. • Heterogenous effect of the relationship between banking performance and REC types is observed. To secure future universal access to modern energy, large investments in renewable energy technology are required. This paper estimates the impact of five banking sector performance indicators (return on asset, market capitalisation, asset quality, managerial efficiency and financial stability) on renewable energy consumption for a global panel consisting of 124 countries. The study used two-step system-GMM panel model to handle potential endogeneity and serial correlation. The paper considers three homogenous subpanels which are constructed based on the income group classification (high-, middle-, and low-income countries). Generally, our results show that improved banking sector performance enhances renewable energy consumption, with heterogenous effect across income group classification. For high -income (HI) countries, an increase in bank size together with improved asset quality and managerial efficiency have positive effects on renewable energy consumption. For middle-income (MI) and low-income (LI) countries, a high return on asset, an increase in bank size and financial stability are positive determinants of renewable energy consumption. We also find heterogenous effect of banking performance indicators across various renewable energy consumption types. The results highlight the importance of a well-functioning bank sector to achieve the investment in renewable energy needed to meet future energy demand and simultaneous decrease CO 2 emissions. [ABSTRACT FROM AUTHOR]

Multiple geographically dispersed data center buildings have been increasingly positioned in renewable-rich areas due to their high energy consumption. This paper proposes an optimal multi-energy portfolio for zero carbon data center buildings in the presence of proactive demand response programs. In the data center buildings, the complementarities of hydro-solar-wind hybrid renewable energy sources (RESs) are fully exploited via multi-energy conversion and storage devices, which are modeled as 100% renewable energy hubs for zero carbon multi-energy supplies. In order to exploit the hub-internal operational dispatchability and flexibility, a comprehensive energy consumption model of data center buildings is proposed to facilitate the demand response in terms of available serves, while the variable speed pumped storage (VSPS) is modeled to capture its dynamic operational characteristics. The energy‑carbon production, conversion, storage, and consumption are formulated as an energy‑carbon matrix, which are solved via a two-stage method to obtain the optimal portfolio. Case studies on southwest China data center buildings are implemented to demonstrate the effectiveness and superiority of the proposed methodology on cost-effective accommodation of RESs. Simulations results show that the system portfolio cost can be reduced by at most 17.8% with a higher operational flexibility. • A cost-effective data center building is proposed to accommodate zero carbon targets. • Hydro-solar-wind 100% renewables are formulated for multi-energy supplies. • VSPS and data center energy consumption are modeled to exploit inherent flexibility. • The energy-carbon production, conversion, storage, and consumption are optimized. [ABSTRACT FROM AUTHOR]

The paper presents an innovative coordination model between flexible final customers and an aggregator in providing flexibility service to the Distribution System Operator called Coordination Via Aggregator (CVA). The problem is formulated as a stochastic two-stage bilevel model in which the leader acts as both the aggregator and the supplier, and maximizes its profit by determining the price signal for flexible final customers composed of energy-based component and the incentive for service provision. Final users, modeled as followers in the lower level of the model, minimize their costs following the price signal from the upper level. The aggregator in this model is a non-zero-profit market entity, i.e., the price determined by the aggregator covers the balancing costs, the energy supply costs and at the same time incentivizes flexible customers to adequately change their consumption patterns. The CVA results are compared with the Direct Service Activation (DSA) approach in which final customers have a predefined two-tariff pricing option and receive a fixed amount of annual network charges discount for participating in the direct load control program. Based on the small-scaled and medium-scaled MV network results, customers with Renewable Energy Sources (RES) face lower electricity costs when providing flexibility service under the CVA approach compared to DSA (15% lower costs in small-scaled network). However, the results show that prosumers with high PV capacity (inject more energy in the grid than withdraw on an annual level) if not involved in providing flexibility, are more prone to signing up for a contract with the aggregator achieving lower electricity cost compared to two-tariff pricing option, however if they are providing flexibility services, direct load control results in 14% lower costs. In contrast, providing flexibility service under DSA with reduced network charges results in 9% lower cost in small-scaled and 16% in medium-scaled networks for final users without RES. • A two-stage collaboration model between the DSO, aggregator, and flexible prosumers. • Stochastic bi-level optimization algorithm for a unique price signal calculation for energy supply and service provision. • Prosumers profit more if flexibility is remunerated in the coordination with the aggregator. • Customers without RES face lower cost with distribution network charges reduction when providing flexibility. [ABSTRACT FROM AUTHOR]

The increasing effects of global warming and energy depletion have raised concerns about the pollution caused by traditional oil and fossil energy usage. Distributed energy resources (DERs) have emerged as promising techniques to address these issues. However, the growing proportion of power injection from DERs presents technical challenges such as power quality decline and power flow reversal, which may destabilize the grid. Non-intrusive load monitoring (NILM) is a promising and cost-effective approach that can provide residential power information to improve grid scheduling, dispatching, and optimize residential power consumption behavior. This paper proposes a novel NILM method that can decompose the power of both household appliances and DERs integrated with residential houses. The proposed approach employs a data segment method that utilizes adaptive window lengths to identify the behavior of DERs based on the operating periods and characteristics of different equipment. A non-intrusive load monitoring method is proposed based on the ResNet-seq2seq framework to address the problem of model degradation and gradient vanishing. Post-processing techniques are applied to the output results using a feature list of the target equipment. Experiments are conducted on a dataset organized based on the REDD and Pecan Street datasets to demonstrate the accuracy and effectiveness of the proposed method. The results show that compared to the start-of-the-art CNN-seq2seq model, the proposed method achieved 54.95%, 69.18%, 56.95%, and 84.98% reduction in MAE for PV, EV, refrigerators, and microwaves, respectively. • A novel ResNet-seq2seq-based NILM approach is proposed for load disaggregation of residential houses integrating DERs. • Wavelet denoising and adaptive window length data segments are used for data pre-processing and accurate disaggregation. • Residual connections are employed to solve model degradation and vanishing gradient issues as network depth increases. • A post-processing technique is applied to modify the output of the network and improve disaggregation accuracy. [ABSTRACT FROM AUTHOR]

Accurate building energy consumption forecasting is crucial for developing efficient building energy management systems, improving energy efficiency, and local building energy supervision and management. However, short-term building energy consumption forecasting is challenging due to highly non-smooth and volatile trends. In this paper, we present a novel methodology that combines interpretable decomposition methods with an interpretable forecasting model. We first illustrate a daily energy consumption pattern recognition (DECPR) method, which decomposes daily energy consumption patterns into interpretable energy consumption subsequences. To achieve satisfactory forecasting performance, we design the vector representation of each subsequence as a static input to the temporal fusion transformers (TFT) model. This vector representation integrates the DECPR method into the TFT model. The TFT model produces interpretable outputs, such as the attention analysis of different step lengths and the visualization of the importance ranking of exogenous variables, including meteorological data, calendar information, and the vector representation. Empirical studies demonstrate that our proposed DECPR-TFT system outperforms comparable models with a mean absolute percentage error (MAPE) of 6.11%, which is significantly lower than other models. These interpretable outputs provide valuable insights for researchers seeking to develop energy-saving operation strategies in buildings. Overall, our methodology offers a promising solution for short-term building energy consumption forecasting that can contribute to more efficient building energy management and energy-saving operation strategies. • This study proposes DECPR, an interpretable decomposition method, explaining decomposed subsequences. • TFT, a deep learning-based approach with interpretability, predicts hourly energy consumption for the next day. • The interpretability of TFT model are used to analyze the effects of exogenous variables on energy consumption • The proposed DECPR-TFT model outperforms others, with 6.11% MAPE, significantly lower than competitors. [ABSTRACT FROM AUTHOR]

Highlights • New formulations enhance the modeling of certain characteristics or processes. • Integration into the electricity market and solar self-consumption management. • Real-world case study for the test-bed plant representative of an industrial cluster. Abstract The energy hub concept and modeling methodology are widely employed tools for solving resource conversion and storage scheduling problems. For instance, industrial clusters might benefit from determining the suitable time to operate their facilities and to sell electricity to the public power grid, according to legal, economic or environmental factors. In this paper, novel elements are introduced in order to more accurately represent real plants and to reduce the amount of decision variables. The major innovation is to consider devices consuming a resource which is not related to the quantity of output produced, by attaching binary decision variables to certain energy hub outputs. Secondly, a path vector is defined to take into account the flows of resources within the system instead of employing a variable for each branch between the components. The third innovation consists of an additional vector to express the amount of output resources sold from the energy hub, including constraints for those resources which are exported and imported through the same medium. An extended energy hub model is first proposed and then applied to a real plant example, including multiple and heterogeneous resources and performing a comparison between days with different demands, weather conditions and electricity prices. The results obtained in the selected scenarios demonstrate a logical operation scheduling, and therefore validate the proposed approach. [ABSTRACT FROM AUTHOR]

• Taking real-time price as a feature affecting power load. • Selecting features by minimal redundancy maximal relevance. • Distinguishing holiday and non-holiday when selecting the historical load sequence. • Using an improved particle swarm optimization for parameter optimization. Accurate power load forecasting contributes to guaranteeing safe dispatch and stable operation of a power system. As a great forecasting tool, support vector machine is widely used in power load forecasting. However, due to the rapid development of information technology, the prediction result of simple support vector machine is no longer accurate enough to forecast in the smart grid. To enhance the prediction accuracy, this paper makes some improvements on support vector machine, and proposes a hybrid model integrated with intelligent methods for feature selection and parameter optimization. Firstly, real-time price becomes an important influencing factor of power load as people increasingly rely on demand and real-time price to adjust their electricity consumption patterns. Thus, real-time price, together with other factors that affect power load, is taken as a candidate feature, and minimal redundancy maximal relevance is applied to derive informative features from candidate features. Secondly, as for another feature, the historical load sequence, to make its selection more general, this paper employs the weighted gray relation projection algorithm for holidays to be predicted. Finally, second-order oscillation and repulsion particle swarm optimization is used for optimizing parameters of support vector machine. Moreover, the proposed model is tested via simulations on datasets of Singapore. By comparing prediction results of the proposed model, the support vector machine before improvement and other three forecasting models, this paper confirms that the improvements on support vector machine are effective, and the proposed model outperforms the other forecasting models in aspect of accuracy, stability and effectiveness. [ABSTRACT FROM AUTHOR]

The repercussions from excessive penetration of uncoordinated Home Energy Management Systems (HEMSs) have proven to be pernicious in the distribution system regarding contingencies, instabilities, and rebound peaks. This paper aims to design a distributed coordination technique with the required topology to coordinate transactive HEMSs with demand response enabled electric baseboard heater thermostats to avoid the detrimental effects of uncoordinated HEMSs in a residential group. Specifically, the proposed technique establishes a consensus to fulfill individual as well as shared objectives by modifying consumers' consumption patterns. The shared objective is to flatten the aggregated profile and decrease the total cost in the grid. In addition, an incentive policy has been designed to pay a total reward to the team for encouraging consumers to participate in the coordination. The presented coordination technique comprises a Shapley game-based reward-sharing mechanism and an incentive-compatible mechanism, where the team's gain is distributed among the players based on their contribution. Besides, the coordination leads to agents' complementary decision-making and mitigates the grid challenges. The functionality and effectiveness of the proposed coordinated HEMSs algorithm are tested for a set of different case studies based on user preferences and coordination levels. The simulation results indicate that the proposed coordination improves aggregated profile's load factor up to 0.85 and reduces the electricity bill by 21.4%. [Display omitted] • A HEMSs coordination is proposed to handle grid challenges and individual objectives. • An incentive is designed to encourage consumers to participate in coordination. • Reward-sharing and incentive-compatible mechanisms are proposed to distribute gain. • The coordination improves aggregated profile's load factor from 0.45 up to 0.85. • The aggregated electricity bill of the residential group decreased by up to 21.4%. [ABSTRACT FROM AUTHOR]

• The ADJST realizes users' anomalous electricity consumption behaviors detection. • The MS-GCN achieves extracting spatial features and preventing feature coupling. • The MS-TCN module reduces the loss of time sequence information in long sequences. • Enhance the model's stability and generalization by joint spatio-temporal learning. The use of electric energy is an integral part of people's daily life. Anomaly detection of electricity consumption data, as a classification problem, has always been a hot research topic of scholars. Anomaly detection can not only reduce energy waste, but also prevent small problems from becoming overwhelming problems. At present, most anomaly detection algorithms mainly focus on the time series information of electricity consumption data, while ignoring the spatial feature of electricity consumption data. To fill this research gap, the paper proposes an Anomaly Detection based on Joint Spatio-Temporal learning (ADJST) method for building electricity consumption. First, a Multi-Scale Graph Convolutional Network (MS-GCN) is proposed to learn the spatial features of building electricity consumption data. Specifically, two types of graphs are constructed to extract short-term correlation features and long-term regularity features of building electricity consumption data. Second, a Multi-Scale Temporal Convolutional Network (MS-TCN) is proposed to learn the temporal features of building electricity consumption data. Adopt a multi-scale vanilla convolution structure to extract multi-scale time series information from building electricity consumption data. Third, the combination of temporal features and spatial features detects anomalous electricity consumption of marked users. Final, taken the user electricity consumption data collected by the State Grid Corporation's smart meter as examples, compared with a variety of classical anomaly detection algorithms, the results of F1-score and AUC of the proposed method are 0.935 and 0.977 respectively, which proves the superiority of the method. The model shows good stability in dealing with extreme imbalance of data, and is proved to be generalized by experiments and can be transferred to other datasets. [ABSTRACT FROM AUTHOR]

In this paper, we propose a realistic multiple dynamic pricing approach to demand response in the electricity retail market. First, an adaptive clustering-based customer segmentation framework is proposed to categorize customers into different groups to enable the effective identification of usage patterns. Second, customized demand models with important market constraints which capture the price–demand relationship explicitly, are developed for each group of customers to improve the model accuracy and enable meaningful pricing. Third, the multiple pricing based demand response is formulated as a profit maximization problem subject to realistic market constraints. The overall aim of the proposed scalable and practical method aims to achieve 'right' prices for 'right' customers so as to benefit various stakeholders in the system. The proposed multiple pricing framework is evaluated via simulations based on real-world datasets. We find that: (1) the adaptive clustering based approach can capture the dynamically changing consumption patterns of customers, and enable the dynamic group based demand modelling; and (2) the multiple pricing strategy could achieve better profit gain for the retailer compared with the uniform pricing due to its reduced electricity purchasing cost in the wholesale market. • A novel multiple pricing approach is proposed for demand response in smart grids. • Create an integrated machine learning and optimization decision making framework. • An adaptive clustering based customer segmentation is developed. • The adaptive clustering can capture customers' behaviour changes. • Multiple pricing can achieve better profits for retailers than uniform pricing. [ABSTRACT FROM AUTHOR]

The recent strong increase in the penetration of renewable energy sources (RESs) in medium-voltage distribution grids (MVDNs) has raised the need for congestion management in such grids, as they were not designed for this new condition. This paper examines to what extent producing green hydrogen through electrolyzers can profitably contribute to congestion alleviation in MVDNs in the presence of high amounts of RES, as well as flexible consumers of electricity and a local heat system. To address this issue, an incentive-based method for improving flexibility in MVDNs is used which is based on a single-leader–multiple-followers game formulated by bi-level mathematical programming. At the upper level, the distribution system operator, who is the leader of this game, determines dynamic prices as incentives at each node based on the levels of generation and load. Next, at the lower level, providers of flexibility, including producers using electrolyzers, price-responsive power consumers, heat consumers, as well as heat producers, respond to these incentives by reshaping their output and consumption patterns. The model is applied to a region in the North of The Netherlands. The obtained results demonstrate that converting power to hydrogen can be an economically efficient way to reduce congestion in MVDNs when there is a high amount of RES. However, the economic value of electrolyzers as providers of flexibility to MVDNs decreases when more other options for flexibility provision exist. • High levels of renewable generation may cause congestion in distributed grids. • Power-to-gas (P2H) is a potential provider of flexibility to solve this congestion. • The value of P2H depends on the presence of other flexible sources, such as heat systems. • Grid operators can encourage market parties to invest in P2H by applying dynamic prices. [ABSTRACT FROM AUTHOR]

Better understanding the behavior of various participants in smart grids, such as electricity consumers and generators, is important and beneficial for flexibility exploration and renewable energy accommodation. Clustering, as an effective data-driven approach to behavior analysis, has been widely applied for extracting the typical electricity consumption behavior of consumers and the bidding behavior of generators in smart grids. Traditionally, the clustering algorithms are implemented centrally with the assumption that data from all consumers or generators can be accessed. However, it may not be the case in the real world because the consumers and generators may not be able to or willing to share their own data due to privacy concerns or commercial competition. To address this issue, in this paper, we propose a federated fuzzy k-means method for privacy-preserving behavior analysis in smart grids. Specifically, two learning strategies, i.e., model averaging and gradient averaging, are designed for the implementation of the federated fuzzy k-means clustering. Both methods are investigated and comprehensively compared on both the electricity consumption behavior dataset and the generator bidding behavior dataset. Experimental results show that our proposed methods achieve similar performance to the traditional centralized fuzzy clustering method on independent and identically distributed (i.i.d.) data, as well as protecting the privacy of different participants in smart grids. As for non-i.i.d., the performance of the model averaging-based method worsen; in contrast, the gradient averaging-based method is more robust to this situation. • Propose a novel privacy-preserving clustering method for behavior analysis. • Develop two learning strategies for federated fuzzy k-means clustering. • Conduct case studies on smart meter data and generator bidding data. • The gradient-sharing method is more robust to non-i.i.d. datasets. [ABSTRACT FROM AUTHOR]

• An incremental incentive mechanism considering consumer differences is proposed. • Excessive consumer surplus is avoided through changes of incremental incentive. • Highly flexible consumers can obtain higher revenue through the redistribution of incentive. • A model-free approach is proposed to solve the asynchronous optimization problem. Demand response has been proven to be an effective way to improve energy utilization efficiency. It is notable that the diversified characteristics of residential consumers, which many greatly affect its performance in demand response, have not been fully considered in existing incentive mechanisms. In this paper, an incremental incentive mechanism for incentive-based demand response (IBDR) is proposed, in which consumers obtain different incentives according to the increment of response, so that the incentive can follow the change of consumers' marginal cost. We theoretically illustrate that the proposed incremental incentive mechanism can effectively improve the profit of load service entity (LSE), as well as the benefit of highly flexible consumers, compared with other existing incentive mechanism. In practice, LSE's bidding strategy in the day ahead market is affected by the intraday IBDR strategy that cannot be known in advance. In order to solve the bidding problem with incomplete information in the day ahead market, we propose an asynchronous double-interaction deep reinforcement learning (DRL) algorithm to maximize LSE's cumulative profit of multiple time slots throughout the day. Numerical simulation results show that the proposed mechanism can improve the consumers' response depth while reducing the unit incentive cost, and the proposed DRL algorithm has relatively stable and satisfactory performance even in highly uncertain environment. [ABSTRACT FROM AUTHOR]

The evolution towards Smart Grids (SGs) represents an important opportunity for modernization of the energy industry. It is characterized by a bidirectional flow of information and energy between consumers and suppliers. However, the rapid increase of energy demands in residential areas is becoming a challenging problem. In order to address this issue, Demand-Side Management (DSM) has proven to be an effective solution. In this paper, we propose LOSISH, a price-based Demand Response (DR) system for load scheduling in residential Smart Homes (SHs) that achieves a trade-off between electricity payments and consumer's discomfort. Our proposed system considers Renewable Energy Sources (RESs), Battery Energy Storage System (BESS) and Plug-in Electric Vehicle (PEV). We formulate our scheduling as a constrained optimization problem and we propose a new hybrid algorithm to solve it. The latter combines two well known heuristic algorithms: Particle Swarm Optimization (PSO) and Binary Particle Swarm Optimization (BPSO). Moreover, we propose a new clustering algorithm based on Machine Learning (ML) to extract consumer's preferences from a real dataset that contains the historical consumption patterns of his smart appliances. We test our approach on real data traces obtained from a SH and we set up an experiment to evaluate our algorithm on a Raspberry Pi and measure its energy consumption. To prove the effectiveness of our approach, we compare our results with another approach from the literature in terms of electricity bill, Peak-to-Average Ratio (PAR), energy consumption, and execution time. Numerical results show that LOSISH outperforms the other approach in terms of electricity bill (up to 52.92% cheaper), PAR (up to 44% decrease in peak demands), energy consumption (up to 69.44% less consumption), and execution time (up to 63.15% faster). • The load scheduling problem in residential smart homes is studied. • A constrained problem is formulated to make a trade-off between costs and comfort. • A hybrid heuristic algorithm and a Machine Learning clustering model are proposed. • Real data collected from a smart home in the UK was used to validate our approach. • Simulation and experimental results demonstrate the effectiveness of our approach. [ABSTRACT FROM AUTHOR]

• A data set of 4.3 million bicycle records are used as dynamic input for the method. • Propose a dynamic electric fence planning algorithm base on real demand. • Propose an agent-based model to simulate massive bicycle-sharing trips. • Evaluation of potential energy consumption for electric fence implementation. As a newly-emerging option of shared transportation, Internet-enabled dockless bicycle sharing is well accepted by the public. The implementation of electric fences has great potential to tackle the problem of random parking in bicycle sharing services. However, the deployment of electric fences would have a negative impact on the convenience of bicycle sharing services, which might lead to an increase in energy consumption among customers who switch their methods of transportation. This paper proposes a dynamic electric fence planning method with an assessment of resource-saving and potential energy consumption increasement. An agent-based model is proposed to simulate the trips and evaluated the performance of static and dynamic electric fences. The results show that dynamic electric fences require significantly shorter walking distances than static electric fences. The implementation of electric fences in the city center can significantly avoid random parking and improve the parking tidiness of bicycles. The implementation of dynamic and static electric fences can averagely save 25.31% and 27.76% bicycle resources. By estimating travel mode shifting, dynamic electric fence can reduce energy consumption by 5.79% per day compared to the static electric fence situation. [ABSTRACT FROM AUTHOR]

• This research proposes a time-varying price elasticity of demand estimation algorithm for understanding and measuring the relationship between electric energy consumption and price. • This research proposes a non-interruptible appliance load reconstruction technique to restore the estimated electric energy consumption of non-interruptible appliances to a realistic level. • This research proposes a demand-side smart dynamic pricing model to foster demand response. The rapid development of the smart energy system promotes bidirectional communications between the supply-side and demand-side. End users can handily receive real-time prices and adjust their electric energy consumption behaviors. Acquiring the time-varying price elasticity of demand (PED) of electricity can help utility companies to understand the time-varying electricity consumption behavior affected by price, thereby facilitating demand-side management. However, estimating time-varying PED is rarely considered in existing studies. This paper bridges the gap, proposing a time-varying PED estimation algorithm. To analyze PEDs more precisely, the proposed algorithm is applied to each appliance based on the advanced non-intrusive load monitoring (NILM) technology. Moreover, a demand-side smart dynamic pricing mechanism is also proposed to provide decision support of individually optimal dynamic pricing for utility companies to encourage end users to participate in the demand response (DR) program. Comprehensive experiments have been conducted to validate the practical feasibility of the proposed mechanism. Numerical simulations show that the proposed mechanism can facilitate the DR program by reducing the peak-to-average ratio (PAR) in electricity consumption without suffering from the price risk. [ABSTRACT FROM AUTHOR]

• Detailed energy consumption models for different appliance categories are developed. • Modular models can be used as building blocks enhancing their flexibility. • MILP models of appliance operation can be incorporated in energy management systems. • Models allow for the integrated optimization of all energy resources. • Models enable the participation in demand response programs. Demand response programs are essential to enable accommodating larger shares of variable power generation based on renewable sources, the deployment of which is imperative for decarbonizing the economy and mitigating global warming. Consumers/prosumers are increasingly exposed to and may benefit from time-differentiated energy prices aimed to induce changes in regular consumption patterns. These changes are also beneficial for retailers and grid operators in face of the variability of wholesale market prices, renewable energy availability and grid conditions. The optimization models to be implemented in autonomous home energy management systems require a rigorous modeling of appliance operation to generate effective load scheduling solutions, respecting their physical operation principles and use patterns in everyday life. A balance should be sought between the detail level of optimization models and the computational requirements to generate usable solutions having in mind their implementation in low-cost processors. This paper presents a comprehensive and modular set of mixed-integer linear programming models aimed at enabling their seamless incorporation in home energy management systems, allowing for the integrated optimization of all energy resources (exchanges with the grid, load management, electric vehicle and stationary battery, local microgeneration). Detailed energy consumption optimization models for shiftable, interruptible and thermostatic loads are presented, also including the power cost component and ways of dealing with user's discomfort. The modular models are presented in a building block manner enhancing the flexibility of their utilization in overall models with different objective functions encompassing the economic and comfort dimensions. Computational results are presented for a case study using actual data, which considers a time-of-use tariff with six periods. In addition to comparing with a plain tariff scheme, different consumer profiles are simulated to assess the impact of comfort requirements on cost. These results show that whenever consumers have the flexibility to change their consumption patterns, they are able to lower the net electricity bill by having an energy management system endowed with the models herein proposed to make optimized decisions on their behalf. [ABSTRACT FROM AUTHOR]

• Hypothetical experiment with broad geographic scope and nationally representative sample of US households. • Monetary costs can have substantial deterring effect on residential smart meter adoption. • Subsidy covering smart meter installation costs would be justified from a benefit-cost perspective. • Households react differently to installation costs depending on their electricity consumption and perception of technology. This paper utilizes an experiment embedded within a hypothetical survey to a nationally representative sample of US households to quantify the extent to which monetary installation costs (in the form of a monthly electric bill surcharge) can present a barrier to the residential adoption of smart meters. The results suggest that a $2.50/month surcharge would decrease the average probability of smart meter adoption by 18 percentage points. This estimate is robust to a variety of alternative empirical specifications and sample restrictions. There is considerable heterogeneity in the estimated effect based on household electricity consumption, familiarity with smart meter technology, and perceived accuracy of traditional meters. These findings have important implications for electricity providers and policymakers, as they suggest that eliminating installation charges would be justified from a benefit-cost perspective and point to potential gains from redesigning smart meter policy to account for household heterogeneity. [ABSTRACT FROM AUTHOR]

A recent worldwide uptake of electric vehicles (EVs) has led to an increasing interest for the EV charging situation. A proper understanding of the former is required to understand charging needs and to dimension the corresponding infrastructure. In the paper, we develop models that allow us to approximate the steady-state distribution of State-of-Charge (SoC) levels for EVs at the beginning of the day and infer its dependence regarding the daily relative range, r defined as the ratio of mean daily-driven distance to the maximum range. The framework combines: (i) a generic parametric model for decision to charge in terms of the SoC and r ; and (ii) a simulation model in which we utilize longitudinal log-data for a fleet of cars to track charging events and SoC over time. The model brings about several interesting use cases, of which two stand out. Firstly, it provides a simple parametric way of circumventing transient behaviour in SoC distributions when applied to simulation frameworks. Secondly, it offers a clear method to infer crucial information regarding EV fleets and the total energy storage potential. Such information is useful for vehicle-to-grid (V2G) applications in that it provides expected lower and upper bounds for the energy that can be stored and charged. The model is applied in a Danish context, and it is suggested that while a full electrification of the vehicle fleet could lead to significant stress on the power grid, it will at the same time hold a large potential for deploying V2G as an important mechanism to dampen the effect of temporal high-demand. The potential for V2G stems from a low battery utilization between charging events of approximately 40%, which in turn provides a large storage buffer that could be harnessed with little to no impact on EV utilization. Results also indicate that tapping into just half of the available buffer could serve the EV demand and also meet close to 50% of the household consumption. • Estimation of steady-state distribution for decision to charge and level of SoC for EVs. • Models have simple parametric form and can be readily applied to different scenarios. • Assessment of general battery utilization and the interval between charging events. • Probabilistic assessment of power demand for different scenarios. • Estimation of storage capacity available for vehicle-to-grid systems. [ABSTRACT FROM AUTHOR]

• A multi-regional input–output model is applied for empirical analysis. • Relationships between rebound effects and consumption patterns are investigated. • The rebound risks are measured from three perspectives. • Repressing the direct rebound effect can counteract the total rebound effect. Improving energy efficiency is considered to be essential to carbon emission reduction. However, it may not be as effective as expected due to the rebound effect. This paper attempts to investigate the carbon rebound effects and rebound risks in households. For this purpose, a comprehensive analysis framework incorporating elastic estimates, environmentally extended multi-regional input–output model and re-spending model, were built. Three rebound risk indicators were then constructed, following which a simulation of direct and indirect carbon rebound effects was conducted for urban households in China. By comparing the estimation results of the four re-spending scenarios, we found these households' carbon rebound effect to be significant. The empirical results further indicate significant differences in the rebound risk among different provinces. Overall, Xinjiang, Qinghai and Ningxia are the provinces most vulnerable to the rebound effect. These findings point to the necessity of the government adopting other measures besides energy efficiency policies, with regional differences taken into account, in order to curb the carbon rebound effect. [ABSTRACT FROM AUTHOR]

This paper considers the time of use (TOU) pricing scheme to propose a consumer aware pricing policy (CAPP), where each customer receives a separate electricity pricing signal. These pricing signals are obtained based on individualized load demand patterns to optimally manage the flexible load demand. The main objective of CAPP is to reduce the peaks in overall system demand in such a way that the pricing signals remain non-discriminatory. To achieve this goal, firstly the mathematical model of CAPP comprising TOU electricity price, and its variation based on consumption patterns is formulated. Secondly, the proposed CAPP model is further extended by integrating renewable energy and storage sources to overcome the possible creation of rebound peaks due to scheduling. This objective is achieved by implementing a control variable modeling the upper bound of the low tariff area. Thirdly, the cost minimization optimization problem is solved by using a Genetic Algorithm (GA) with the objective of the fair price distribution. Numerical and simulation results are obtained to validate the proposed model in terms of convergence, optimality, and cost reduction objective function. Results reveal that each customer receives a separate electricity price signal based on his demand pattern without affecting the utility/retailer revenue. Furthermore, the total cost results are also compared with and without TOU & CAPP schemes to further validate the nondiscrimination in electricity price. • A novel electricity pricing mechanism based on individualized load consumption and electricity price. • Mathematical models of load consumption, renewable energy, and electricity cost are provided. • Individualized load consumption, RTP, and renewable energy are input parameters. • A constraint optimization problem is formulated and solved using GA. • User comfort is modeled in terms of a fair price distribution among all the customers. [ABSTRACT FROM AUTHOR]

With the urbanization and the decarbonization of the heating sector, thermostatically controlled loads (TCLs) with a rising energy consumption proportion, have become important demand response (DR) resources, which can provide considerable regulation flexibility for transition to renewable energy. Depending on the measurement or the forecast, TCLs dispatch methods established from the static perspective, failed to sufficiently consider the impact of DR chain reactions and the varying characteristics of TCLs response flexibility, which lead to the deterioration of the reliability and the availability of its dispatch potential. To address these issues, this paper proposes an interactive dispatch strategy based on the source–load collaborative evolution. Firstly, in light of system dynamics, a response evolution dynamic model is established by dividing of TCLs response process into multiple subsystems, which constitutes a comprehensive picture of TCLs response dynamics. Secondly, TCLs response flexibility is evaluated from the current and the evolution perspectives, which can enhance the reliability and stability of TCLs response by considering the potential response flexibility variations caused by the chain reaction of responses. Thirdly, an interactive dispatch strategy is devised by synthesizing of the source–load collaborative evolution dynamics from the dimensions of the elaborate state and the equivalent energy, which not only improves the dispatch performance, but also reduces the computational complexity. Based on the established strategy, TCLs response flexibility can be guided explicitly and quantitatively, like the energy storage in the dispatch, which improves the controllability and feasibility of TCLs response in DR service. Finally, the validity of the proposed strategy is verified via the comparison of it with the methods based on the static response mode. • The evolution law of TCLs flexibility is revealed by the multi-state correlation. • The dynamic evaluation is derived by combining TCLs' current status & evolution trend. • The collaborative dispatch is devised from both the state and the energy dimensions. • TCLs flexibility variations can be explicitly & quantitatively guided by the interaction. [ABSTRACT FROM AUTHOR]

Abstract: In this paper cogeneration benefits applied to a user with a high steam demand are analyzed. The methodology for the feasibility study and the economical analysis of the investment is presented under the Italian legislative framework. The methodology is applied to an actual case and a detailed description and discussion of all data input is provided. Especially this last key point will be faced using starting data usually available in these kind of studies (i.e., not very detailed for thermal consumption). Finally a comparison of different CHP technologies and a sensitivity analysis is done. [Copyright &y& Elsevier]

• Forecasted hourly single household electricity consumption (42 target users for DR form 71 original users). • Proposed an analysis method of single household multi-time scale electricity consumption behavior. • Constructed a combined load forecasting model to realize stable and high-accuracy load forecasting. • Compared the accuracy of combined model against 3 benchmark models. • Novel combined model reduces the forecasting error by 5%-12%. With the growth of residential load and the popularity of intelligent devices, resident users have become important target customers for demand response (DR). However, due to the strong volatility of individual household load and the large difference in user's behavior, the accuracy of residential load forecasting is generally low and the forecasting effect is unstable, which is not conductive to the implementation of DR. To improve the accuracy of residential load forecasting, this paper proposes a combined deep learning load forecasting model considering multi-time scale electricity consumption behavior of single household resident user to achieve high-accuracy and stable load forecasting. Aiming at the electricity consumption behavior, the multi-time scale similarity analysis is carried out. For the time scale of one year, Normalized Dynamic Time Warping (N-DTW) and Density-Based Spatial Clustering of Applications with Noise (DBSCAN) are used to analyze the significance of single user's long-term electricity consumption behavior. For the time scale of 7 days, behavior similarity is used to analyze the consistency of single user's short-term electricity consumption behavior. Then, Mutual Information (MI) and Principal Component Analysis (PCA) are used to select features and reduce dimensions of multi-dimensional weather influencing factors, so as to avoid the interference of irrelevant factors and improve the calculation speed. On this basis, combined with Back Propagation (BP) neural network, Extreme Gradient Boosting (XGBoost) and Long Short-Term Memory (LSTM) neural network, a combined deep learning network load forecasting model (Co-LSTM) is constructed by using multi-model and multi-variable method to achieve stable and high-accuracy load forecasting. Finally, based on the actual load data from the American Pecan Street Energy Project, the forecasting accuracy of the proposed model of resident user is evaluated. From the performance of load forecasting for 42 target users, the minimum, maximum and average Mean Arctangent Absolute Percentage Error (MAAPE) of Co-LSTM is 18.70%, 45.95% and 31.20% (the average MAAPE is 4.97% less than the traditional LSTM model) respectively. [ABSTRACT FROM AUTHOR]

• An online two-level HEMS to reduce operation cost and power consumption peaks. • Identification of load flexibility in MG using Non-Intrusive Load Monitoring (NILM). • Automated extraction of the occupants' power consumption patterns and preferences. • Analyzing the performance of the proposed NILM-assisted HEMS in an AC/DC Microgrid. • Validating the coordination of optimization and forecast systems with NILM modules. Traditional electric energy systems are experiencing a major revolution and the main drivers of this revolution are green transition and digitalization. In this paper, an advanced system-level EMS is proposed for residential AC/DC microgrids (MGs) by taking advantage of the innovations offered by digitalization. The proposed EMS supports green transition as it is designed for an MG that includes renewable energy sources (RESs), batteries, and electric vehicles. In addition, the electricity consumption behaviors of residential users have been automatically extracted to create a more flexible MG. Deep learning-supported Non-intrusive load monitoring (NILM) algorithm is deployed to analyze and disaggregate the aggregated consumption signal of each household in the MG. A two-level EMS is designed that coordinates both households and MG components using optimization, forecasting, and NILM modules. The proposed system-level EMS has been tested in a laboratory environment in real-time. Experiments are performed considering different optimization periods and the effectiveness of the proposed EMS has been shown for different optimization horizons. Compared to a peak shaving strategy as a benchmark, the proposed EMS for 24-hour horizon provides a 12.36% reduction in the residential MG daily operation cost. [ABSTRACT FROM AUTHOR]

• A multiagent framework embedded with metaheuristics is presented. • A dynamic intra-community trading pricing scheme is proposed. • Agents with different goals and preferences profit from community energy sharing. • Vulnerable consumers contribute positively to energy communities' sufficiency. Local energy trading has the potential to foster the deployment of novel business models especially valuable in citizen-centered self-sufficiency-driven collective energy systems. In this setting, special attention should be paid to vulnerable consumers, who may be neglected in the energy transition due to their limited financial capacity to purchase energy products and services as well as the low energy literacy impairing sound consumption decisions. This paper presents a community-based electricity exchange model designed to prioritize the inclusion of vulnerable consumers. A multiagent framework embedded with metaheuristics is proposed to model a community of prosumagers and vulnerable consumers. Heterogeneity between agents is considered by exploiting different consumption behaviors regarding load utilization, thermal comfort requirements and sensitivity to cost. A supervisor entity is also included to manage and facilitate electricity procurement between the local energy community participants while interfacing with retailers. Several scenarios are exploited to examine the performance of the proposed model for different shares of sellers and buyers and the results are evaluated through performance indicators. Results reveal that local electricity trading, in addition to economic benefits for all participants, also contribute to enhance the community's self-sufficiency. Additionally, participants benefit differently from local electricity trading: while buying-oriented ones benefit from the larger presence of profit-driven members, selling-oriented participants achieve better results in scenarios with higher shares of buyers. These results unveil relevant clues for the design of frameworks enhancing the inclusion of vulnerable consumers in the energy transition. [ABSTRACT FROM AUTHOR]

• 60% of the food-related carbon footprint is released during the production stage. • Consumption of fruit and vegetable obtains a higher carbon footprint than red meat. • Meat consumption is a key factor behind income- and region-based disparities in emissions. • Dining out is responsible for the highest level of carbon footprint. Given its substantial contribution to greenhouse gas emissions, household consumption has been identified as a major contributor to climate change. Among various household activities, eating is a basic activity undertaken by everyone, and thus the environmental consequences of food consumption are attracting increasing attention. Although household food consumption has been widely discussed, there is still no comprehensive carbon footprint analysis of the entire food supply chain, including the differences among household segments. To address this knowledge gap, in this paper we use a modified environmental input–output model to quantify food-related carbon footprints throughout the entire supply chain. Taking Japan as a case study, results indicate that more than 60% of the food-related carbon footprint occurs at the production stage, while carbon emissions from the wholesale and retail stages account for up to about 38%. Furthermore, the consumption of the richest households produces the highest per capita carbon emissions of all income groups, and this holds true for their consumption across food types. Other findings include that in metropolitan areas, such as the Kanto area, dining out accounts for the biggest share, contributing 16% of total emissions, and that both the regional- or income-based disparity in emissions are significantly induced by red meat consumption. Optimizing food supply chain management and encouraging local consumption of fresh fruit, vegetables and meat are therefore essential for facilitating reduced household food carbon emissions. [ABSTRACT FROM AUTHOR]

Abstract: Many factors influence the energy consumption of buildings, particularly those related to their design and use. Other factors, such as orientation and the nature of the surroundings, also have an influence, but these are often ignored. In this paper, we consider the influence of trees planted as a shelterbelt, using computer simulations to model the varying energy consumption of a building with or without the shelterbelt trees. It is concluded that a reduction of up to 18% in heating energy consumption can be made by planting an optimum shelterbelt of trees in appropriate situations. [Copyright &y& Elsevier]

Abstract: This paper describes the effects of the installation of various countermeasures against urban heat-island (UHI) and energy-saving measures on UHI and global warming. A UHI and energy-consumption simulation model was developed by combining the one-dimensional meteorological canopy and building energy use models; further, the proposed model was expanded to evaluate the year-round air temperature and annual energy consumption. The simulation results showed that the humidification and albedo increase at building-wall surfaces reduced the total number of hours for which the air temperature was more than 30°C during the daytime by more than 60 (h) per year. The UHI countermeasures reduced the annual energy-consumption despite causing a small increase during the winter period. However, they may result in certain unfavorable conditions for pedestrians. Energy-saving measures, on the other hand, reduce the total number of hours for which the air temperature is more than 30°C by only a few hours per year. Thus, we demonstrate the effectiveness of the UHI countermeasures and measures against global warming by extending the calculation period from summer to an entire year. [Copyright &y& Elsevier]

• We estimate the energy efficiency and direct rebound effect of residential electricity consumption by using the stochastic energy demand frontier model. • The average value of efficiency is 0.491, and the average rebound effect is 48%. • We find that electricity price and household size have a negative impact on the rebound effect, while the impact of income is inverted U-shaped. • The rebound effect varies across before and after the implementation of the increasing block pricing policy, considering rural and urban residents and different regions. The rebound effect is a potential threat to energy efficiency related policies to save energy. Based on data from China family panel studies over the period 2010–2018, this paper measures the energy efficiency and direct rebound effect of residential electricity consumption by using the stochastic energy demand frontier model. The result shows that the average value of efficiency is 0.491, and the average rebound effect is 48%. It means that there is still room for improvement in China's household electricity consumption efficiency. But the expected electricity- saving after improving efficiency can only achieve 52%. Besides, we find that electricity price and household size have a negative impact on the rebound effect, while the impact of income is inverted U-shaped. Finally, the rebound effect varies between before and after the implementation of the increasing block pricing policy, considering rural and urban residents and different regions. These findings provide some policy implications for the residential electricity pricing system under the background of energy conservation and climate change. [ABSTRACT FROM AUTHOR]