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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]

[Display omitted] • We considered two strategies of electricity purchasing for industries: TB and NTB. • We applied an evolutionary game theory approach to examine industries' behavior. • We studied three real-world cases of Iran: cement, steel, and paper industries. • We mainly focused on finding the population ratio convergence to TB/NTB strategies. Electricity shortages severely affect the production chain and cause damage to manufacturing productivity. This study examines how the industrial sector can transform technological innovations toward renewable electricity procurement. We consider two strategies for manufacturers: purchasing electricity from a technology-based supplier (TB strategy) and purchasing it from a non-technology-based supplier (NTB strategy). Manufacturers need technological transformation, such as upgrading their electrical system toward digitalization to manage power outages. We examine manufacturers' pricing decisions and their long-term behavior to adopt the TB and NTB strategies using a one-population evolutionary game-theoretic approach. Also, we investigate the majority of manufacturers' population that eventually choose the best strategy. Moreover, we apply the present model to real-world cases of Iran's cement, steel, and paper industries. The results show that the evolutionary behavior of sectors with high annual electricity consumption, such as steel, converges to the TB strategy faster than cement and paper industries. In contrast, the cement and paper industries are more sensitive to subsidy allocation than the steel industry with respect to electricity procurement strategies. [ABSTRACT FROM AUTHOR]

The carbon dioxide (CO 2) emissions have reached their highest levels since measurements began in recent years. This is widely regarded as one of the primary factors contributing to the increasing frequency of extreme climate events. Consequently, accurate and reliable CO 2 emission data becomes the benchmark in shaping policies and setting targets aimed at reducing emissions, which are essential for carbon emission-related research. Among all industries, the electric power sector takes the highest proportion of CO 2 emissions, making the carbon emission accounting for this sector a crucial component of overall carbon emissions accounting. Therefore, the objective of this paper is to provide a comprehensive review of the applied technologies and research progress in electricity carbon emissions accounting. The paper begins by introducing the fundamental concept of electricity carbon emission accounting. Electricity carbon emissions are categorized into two types: direct carbon emissions from electricity production and indirect carbon emissions from electricity consumption. Then, the current applied approaches for accounting for these two types of emissions are reviewed separately. After analyzing the shortages of existing applied carbon emission accounting approaches, the paper proceeds to review the research on the improved approaches of direct and indirect carbon emission accounting. Finally, the paper outlines the future development prospects of carbon emission accounting technology. • Application status of carbon emission accounting approaches in the electric power industry is analyzed. • Direct carbon emission accounting approaches based on big data technologies are introduced. • Research exploration in electricity indirect carbon emission accounting is reviewed and compared. [ABSTRACT FROM AUTHOR]

Under the policy of renewable portfolio standard (RPS), many microgrid (MG) users with high electricity consumption are installed with renewable energy (RE) generation devices to complete their RE consumption responsibility and reduce carbon emissions. In view of the conflict between the goals of microgrid operators (MGO) and MG users, it is a challenge to achieve economic optimization for both MGO and MG users at the same time when they participate in system dispatch and complete RE consumption responsibility. Therefore, this paper proposes a bi-level noncooperative dispatch strategy that takes into account user satisfaction and RE consumption willingness. In this bi-level model, the upper level is led by an MGO who decides electricity retail prices, while the lower level is followed by MG users who participate in demand response (DR) and change electricity purchase strategies. First, a novel comprehensive evaluation model is formulated to evaluate users' willingness to consume green electricity. Based on this, users' consumption behavior of green and thermal electricity is modeled independently not only in electricity trading but also in power dispatching. Second, optimization objectives are established with the goal of maximizing MG users' welfare, including user satisfaction improvement and energy purchase cost reduction, as well as maximizing the revenue of the MGO. Then, a Stackelberg game model is constructed, and a differential evolution algorithm nested CPLEX is used to solve the Stackelberg game problem of balancing the interests of both parties. Finally, our simulation analysis shows that under the dispatch strategy in this paper, load peak-valley difference is reduced. In addition, when the green electricity generated within the system is sufficient, based on a high level of users' willingness to consume green electricity, the total consumption of green electricity is increased by 29.5% and the overall welfare of users is increased by 0.75%. • Comprehensive evaluation model on users' green electricity consumption willingness. • Logical separation in the dispatch of green and thermal electricity. • Leader-follower game to balance the interests of the microgrid operator and users. [ABSTRACT FROM AUTHOR]

In the effort to achieve carbon neutrality through a decentralized electricity market, accurate short-term load forecasting at low aggregation levels has become increasingly crucial for various market participants' strategies. Accurate probabilistic forecasts at low aggregation levels can improve peer-to-peer energy sharing, demand response, and the operation of reliable distribution networks. However, these applications require not only probabilistic demand forecasts, which involve quantification of the forecast uncertainty, but also determining which consumers to include in the aggregation to meet electricity supply at the forecast lead time. While research papers have been proposed on the supply side, no similar research has been conducted on the demand side. This paper presents a method for creating a portfolio that optimally aggregates demand for a given energy demand, minimizing forecast inaccuracy of overall low-level aggregation. Using probabilistic load forecasts produced by either ARMA-GARCH models or kernel density estimation (KDE), we propose three approaches to creating a portfolio of residential households' demand: Forecast Validated, Seasonal Residual, and Seasonal Similarity. An evaluation of probabilistic load forecasts demonstrates that all three approaches enhance the accuracy of forecasts produced by random portfolios, with the Seasonal Residual approach for Korea and Ireland outperforming the others in terms of accuracy and computational efficiency. • We proposed new low-voltage aggregation techniques based on portfolio theory to improve probabilistic demand forecasting. • The increased volatility at the low voltage level requires probabilistic forecasting to measure uncertainties. • The proposed methods were found to outperform random aggregation with the real-world data from Korea and Ireland. • A simple forecasting approach minimizing the standard deviation of deseasonalized demands was accurate and efficient. • The new method can contribute to achieving carbon neutrality, enable P2P energy sharing, and benefit prosumers. [ABSTRACT FROM AUTHOR]

• A method integrating the artificial neural network with genetic algorithm is applied. • The MATLAB-Simulink model of electric assisted bicycle is employed for data generation and validation. • The required power and electric consumption are optimized by combining artificial neural network and genetic algorithm (ANN-GA). • The effective performance area of electric assisted bicycle under various a structure and operating parameters is identified. • Deep-learning results reflect major physical mechanism of electric assisted bicycle operation. The purpose of this research is to study how the operating and structure parameter affect the dynamic, require power and electric consumption of electric assisted bicycle (EAB). The paper applied a combined an artificial neural network (ANN) with genetic algorithm (GA) method to predict the required power, electric consumption of EAB and find an effective performance area. The MATLAB-Simulink simulation model is established to create 1000 data point, that is applied in an ANN for training, testing and verifying. The ANN-GA method is applied to identify the optimized required power and electric consumption under four typical slope grades in the area of (0–0.65%), four typical wheel radius in the area of (0.3–0.0.39 m), four typical frontal areas in the range of (0.423–1.323 m2), four typical speed levels from speed level 1 to speed level 4. After the ANN is trained, it is applied into the genetic algorithm to identify the effective performance. The prediction results reflect the major physical mechanism that governs ES performance and are validated against the MATLAB-Simulink simulation model. Additionally, the electric assisted bicycle can reach an effective performance area at 27.16 km/h with the optimized required power of 545.5 W at slope grade of 0%, wheel radius of 0.39 m, speed level 4, frontal area of 0.423 m2. The results show that the ANN-GA method is suitable to identifying structure and operating parameters including various slope grades, frontal areas, wheel radius, speed level for optimizing effective performance of EAB, which contributes a helpful method for EAB design and control. Beside that, the experimental test was conducted on real road test at Taehwa river to verify the simulated results. The experiment results and simulation results have the same trend at the same conditions. (The short version of the paper was presented at ICAE2022, Bochum, Germany, Aug 8–11, 2022. This paper is a substantial extension of the short version of the conference paper.) [ABSTRACT FROM AUTHOR]

The rapid ramp-up of heat pump installations in modern power systems constitutes an outstanding challenge for energy community and distribution grid operators. Accurate load forecasts can help community and grid operators to reduce electricity demand peaks by managing flexible devices. This paper shows that installed heat pumps change the load patterns, autocorrelation and peak loads of energy communities, as well as the most suitable forecasting methods. Based on a case study with real-world household and heat pump loads from Hamelin, Germany, we show significant improvements in forecasting quality by employing Transformer models. We publish our underlying data set, feature engineered data, forecasting results, best-performing methods, and benchmarking pipeline open-source, to contribute to the advancement of load forecasting in energy communities with heat pumps. • The impact of heat pumps on energy community load forecasting is investigated. • Transformers outperform other load forecasting models after heat pump installation. • Separate heat pump load measurements do not improve energy community load forecasts. • We show discrepancies between forecasting metrics and actual application performance. • We publish our benchmarking pipeline, results and best-performing models open-source. [ABSTRACT FROM AUTHOR]

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

Power pooling has emerged as a regional strategy for accelerating generation capacity expansion in West Africa with the aim of leveraging vast domestic energy resources and promoting investment in regional power infrastructure. As part of their climate action pledges, most West African countries have committed to increasing the shares of variable renewable energy (VRE), particularly solar photovoltaic and wind power, in their generation mix. It, however, appears that approaches to utility-scale capacity expansion planning tend to overlook the inherent temporal intermittency and spatial variability of VRE-based generation. Moreover, despite influencing the techno-economic rationale for grid expansion and off-grid electrification, as well as the trade-offs between high renewable supply areas and grid expansion, the spatial distribution of demand has been overlooked in planning approaches, leading to conservative and weak prospects for the contribution of utility-scale VRE. Such inconsistencies with the region-wide potentials and policy ambitions highlight that it is paramount for West Africa to design its power pooling such that spatial and temporal fluctuations of VRE supply are duly considered in capacity expansion planning while taking advantage of complementarities between VRE supply and national electricity demand profiles. To address this, the present paper applies a long-term generation capacity expansion model, OptGen, soft-linked to a least-cost operation module, the SDDP tool, which is enhanced by geospatial electrification analysis using the OpeN Source Spatial Electrification Tool (OnSSET), to a subset of four member countries of the West African Power Pool – Burkina Faso, Côte d'Ivoire, Ghana, and Mali – for the 2023–2040 time horizon. The results highlight that current frameworks lead to missed opportunities for bridging the supply-demand gap in all countries, not only in terms of VRE generation capacity, but also of cross-border power trade. • Long-term capacity expansion planning and dispatch models are soft-linked. • The spatial and hourly distributions of demand and renewables supply are considered. • The 10% limit on renewables share used in the regional master plan is not optimal. • The current system operation is not conducive to optimal cross-border trade. • The relaxed transmission scenario yields higher dispatch factors for renewables. [ABSTRACT FROM AUTHOR]

A large part of French electricity consumption variation is due to temperature fluctuations. While HVAC (heating, ventilation and air-conditioning) systems consumption are directly affected by the temperature, other systems (refrigerator, freezer, water heater) can also be driven by weather changes making thermal contribution to overall consumption difficult to extract. This paper presents a "by-design" unsupervised data-driven method to separate the consumptions due to the weather in the overall electricity consumption. The proposed deep-learning model is based on the separation of meteorological parameters from calendar ones within the model architecture. The performances of this model, in particular its ability to split consumption mechanisms, is tested on a synthetic dataset and on the french consumption dataset. Being relatively simple and interpretable, this approach can be generalized to other countries whereasenergy sobriety represents an important challenge we are facing. • The hourly power load of a country due to temperature is unsupervisingly identified. • Deep learning model provides Large Scale Non Intrusive Load Separation. • Engineering-driven neural network design separates weather and calendar consumption. • Model's performance is assessed on country's actual electricity consumption. [ABSTRACT FROM AUTHOR]

Across recent years, there has been a growing prevalence of extreme weather events throughout the United States, posing significant challenges to the reliable and resilient operation of power systems. Specifically, summer droughts threaten to severely reduce available generation capacity to meet regional electricity demand, potentially leading to power outages. This underscores the importance of accurate resource adequacy (RA) assessment to ensure the reliable operation of the nation's energy infrastructure. Accurately evaluating the usable capacity of regional generation fleets is a challenging undertaking due to the intricate interactions between power systems and hydro-climatic systems. This paper proposes a systematic and analytical framework to evaluate the impacts of extreme summer drought events on the available capacity of various generating technologies, incorporating both meteorological and hydrologic factors. The framework provides detailed plant-level capacity derating models for hydroelectric, thermoelectric, and renewable power plants, facilitating evaluations with high temporal and spatial resolution. The application of the proposed impact assessment framework to the 2025 generation fleet of the real-world power system within the PJM and SERC regions of the United States yields insightful results. By analyzing the daily usable capacity of 6,055 at-risk generators across the study region, it shows that the summer capacity deration is most significant for hydroelectric and once-through thermal power plants, followed by recirculating thermal power plants and combustion turbines. In the event of the recurrence of the 2007 southeastern summer drought event in the near future, the generation fleet could experience a substantial reduction in available capacity, estimated at approximately 8.5 GW, compared to typical summer conditions. The sensitivity analysis reveals that the usable capacity of the generation fleet would suffer an even more significant decrease under conditions of increasingly severe summer droughts. The proposed approach and the findings of this study provide valuable methodologies and insights, empowering stakeholders to bolster the resilience of power systems against the potentially devastating effects of future extreme drought events. [Display omitted] • A systematic summer droughts generating capacity assessment framework is developed. • Capacity derating models for various generation technologies are presented. • The impacts of summer droughts on generating fleet in PJM and SERC are studied. • Impact of extreme summer drought on usable capacity of generators is significant. [ABSTRACT FROM AUTHOR]

• Demand response is used to address the uncertainty of solar generation. • Forecast errors are modeled based on solar generation forecasts at different times. • Model Predictive Control is formulated, incorporating solar generation forecasts. • Demand response reduces the aggregator's individual imbalances. • Aggregator is not financially incentivized to reduce individual imbalances. The high level of uncertainty of renewable energy sources generation creates differences between electricity supply and demand, endangering the reliable operation of the power system. Demand response has gained significant attention as a means to cope with uncertainty of renewable energy sources. Demand response of residential and service sector consumers, when accumulated and managed by aggregators, can play a role in existing electricity markets. This paper addresses the question to what extent aggregator-mediated demand response can be used to deal with the impacts of the uncertainty of solar generation. Uncertain solar generation leads to imbalances of an aggregator. These imbalances can be reduced by shifting flexible loads, which is called demand response for internal balancing. The aim of this paper is to assess the impact of demand response from loads in residential and service sectors for internal balancing to reduce the imbalances of an aggregator, caused by uncertain solar generation. For this purpose, a Model Predictive Control model which minimizes the imbalances of the aggregator through load shifting is presented. The model is applied to a realistic case study in the Netherlands. The results show that demand response for internal balancing succeeds in reducing imbalances. Even though this is favorable from the power system's perspective, economic analysis shows that the aggregator is not financially incentivized to implement demand response for internal balancing. [ABSTRACT FROM AUTHOR]

Highlights • Analysis of electricity demand in neighbourhoods, districts and municipalities. • Data-driven classification of electricity demand profiles of nearly 15000 urban areas. • Three statistically different urban area types found: residential, business and mixed. • Urban area types differ in terms of demand profiles and energy user composition. • Often-used residential-type demand represents only a minority of analysed areas. Abstract Cities and communities worldwide are seeking to become more sustainable by transitioning to renewable energy resources, and by introducing electric transportation and heating. The impact and suitability of such technologies for a given area heavily depend on local conditions, such as characteristics of local demand. In particular, the shape of a local demand profile is an important determinant for how much renewable energy can be used directly, and how charging of electric vehicles and use of electric heating affect a local grid. Unfortunately, a systematic understanding of local demand characteristics on different urban scales (neighbourhoods, districts and municipalities) is currently lacking in literature. Most energy transition studies simplify local demand to household demand only. This paper addresses this knowledge gap by providing a novel data-driven classification and analysis of demand profiles and energy user compositions in nearly 15000 neighbourhoods, districts and municipalities, based on data from the Netherlands. The results show that on all urban scales, three types of areas can be distinguished. In this paper, these area types are termed "residential", "business" and "mixed", based on the most prevalent energy users in each. Statistic analysis of the results shows that area types are pairwise significantly different, both in terms of their profiles and in terms of their energy user composition. Moreover, residential-type demand profiles are found only in a small number of areas. These results emphasise the importance of using local detailed spatio-temporal demand profiles to support the transition of urban areas to sustainable energy generation, transportation and heating. To facilitate the implementation of the obtained insights in other models, a spreadsheet modelling tool is provided in an addendum to this paper. [ABSTRACT FROM AUTHOR]

• Shadow pricing is a popular method for abatement cost estimation. • Most previous studies ignore the materials balance condition. • Materials balance consistent Convex Nonparametric Least Squares estimator developed. • Shadow prices for 160 US power producers from stochastic and deterministic models. • Abatement costs severely understated when the materials balance is ignored. Marginal abatement cost is an essential input to optimal environmental policies. Shadow pricing has become a popular method for estimating abatement costs subject to parsimonious data requirement. This paper provides a novel contribution to the literature on shadow pricing by considering the implication of the materials balance principle for shadow prices. To that end, the paper establishes a Convex Nonparametric Least Squares estimator for the weak G-disposable production model, which for the first time enables modeling a composite error term and joint estimation of the production frontier and contextual variables within this production model framework. Applying the Directional Distance Function, environmental efficiencies and shadow prices for carbon dioxide emissions are estimated for a sample of power producers using both stochastic and deterministic frontier models. Average shadow price estimates for carbon dioxide range between 14,000 and 40,000 $/ton CO 2 for the weak G-disposable model and between 70 and 77 $/ton CO 2 for the conventional production model that ignores the materials balance. These findings cast doubt on previous shadow price estimates since a majority of comparable studies ignore the strict technical relationship among pollution-generating inputs and bad outputs under the materials balance condition. [ABSTRACT FROM AUTHOR]

This paper presents a model for the realistic planning of multi-microgrids in the context of Active Distribution Networks with the assistance of Geographic Information Systems. The model considers the distribution system grid as well as the geographic features of the Region of Interest. It also includes long-term purchase decisions and short-term operational constraints, and considers uncertainties associated with electricity demand and Renewable Energy Resources using an existing Two-Stage Stochastic Programming approach. Geographic Information Systems along with Deep Learning are used to estimate the areas of rooftops within the Region of Interest and model the Low Voltage grid. The planning model is used to study the feasibility of implementing a multi-microgrid system consisting of 4 individual microgrids at an Active Distribution Network in a municipality in the state of São Paulo, Brazil. The results of the model presented in this paper are compared with the results obtained using Monte Carlo Simulations and an existing, less detailed, Two Stage Stochastic model. It is demonstrated that the stochastic solutions are close to those obtained with Monte Carlo at a lower computational cost, and that the use of Geographic Information allows to determine both the capacity and location of the PV panels, batteries, and distribution transformers on the microgrids grid, thus providing more precise and useful planning results. • Geographic Information (GI) -based model for the planning of Multi-microgrids (MMGs). • An existing Two-Stage Stochastic Programming (TSSP) model is used. • Uncertainties in demand and solar generation are considered for study and analysis. • GI allows the modeling of the Low Voltage (LV) distribution network. • Results show where rooftop solar panels and batteries can be physically deployed. [ABSTRACT FROM AUTHOR]

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

Battery electric vehicles (BEVs) are gaining market shares due to their ability to employ clean energy, their smooth operation and reduced noise, pollutant emissions and maintenance. Batteries are one of the key technologies in BEV since they strongly affect the vehicle cost and driving range, two of the major concerns of BEV costumers. While the energy storing capabilities of BEVs usually exceed commuting requirements, batteries can be also utilized for home energy management system using bi-directional charging technology. This paper introduces an efficient energy management system for a smart home with BEVs and a bidirectional charger by addressing the corresponding optimal control problem of deciding the battery charging and discharging strategy to minimize energy expenditure and cost. To this aim, the electricity price and expenditure of upcoming weeks is forecasted using data from the present week, and a model of the complete system is used to find the optimal solutions by means of backward induction in a receding horizon approach. The proposed strategy relies on currently available information about the home and vehicle energy expenditure and energy prices in the recent past. The results of the study show a reduction of the electricity cost above 20% in the considered use-case. • Combined use of BEV and smart home for global energy consumption optimization. • Optimization of energy consumption considering hourly cost and power demand. • Backward induction technique embedded in MPC controller for energy optimization. • Reduction in electricity consumption costs by over 20% in a real application. [ABSTRACT FROM AUTHOR]

Modeling and simulation of electricity markets have increasingly involved the use of a system dynamics (SD) approach. Accordingly, the resulting dynamic hypothesis and the stock-flow structures are represented and simulated using softwares such as Stella, Powersim, iThink, or Vensim. However, SD models can be exploited even more, of which the investigation of signals in the time domain or the sensitivity analysis is just a small part of the study. Since SD models are mathematical objects, they deserve an analytical or numerical study using tools provided by the dynamic systems (DS) methodology. Therefore, this paper not only studies the dynamic hypothesis or the stock-flow structure of an electricity market model in the classical form, but also uses its inner mathematical object to provide a deeper insight into the system. Using MATLAB/Simulink®, the system is evaluated from a different approach not yet reported in the literature. The combination of the SD and DS methodologies can open the door to a new and alternative method of analysis for electricity market models and even for any SD model. In fact, this paper demonstrates that with this methodologies combination, more detailed analysis strategies and novel insights of the SD models can be developed, which can be easily exploited by policy makers to suggest improvements in regulations or market structures. Moreover, considering that the energy market is evolving, and a series of macro and microstructural changes are impacting demand, we consider as an example a simplified version of the Colombian electricity market to report a detailed description of its dynamics under a broad range of growth rate of demand ( GRD ) scenarios. Our study, inspired by the bifurcation and control theory of DS, primarily shows that Colombia is in dire need of a new capacity before 2020 to avoid rationing events expected to occur in the upcoming years. [ABSTRACT FROM AUTHOR]

Energy systems at district/urban level are getting more complex and diversified from time to time. Different energy carriers are coupled each other to meet various types of energy demands. The conventional way of analyzing energy networks independently does not reflect the true nature of the coupled networks. One of such a promising coupled multi-carrier energy system (MCES) is the combination of district heating and electricity networks. The coupling between these two networks is increasing due to the integration of co– and poly-generation technologies at the distribution networks. Recent literatures tried to address a load flow analysis for lightly coupled networks by formulating case-specific load flow models. This paper presents a more general and flexible tool developed using Matlab® which can be used to conduct the load flow analysis of highly coupled electricity and heating networks. An energy hub concept is extended further to formulate a general model in which local generations and detailed network parameters of MCES can be taken into account. Coupled heating and electricity networks are modeled in detail for illustration. The flexibility and generality of the model are then tested by considering case studies with different network topologies (tree and meshed). A comparison is also made with a model used in recent literature. The results show that the proposed model is more accurate. The main contribution of this paper can be summarized by the following five points: (1) Coupling matrices are used to relate network power flow equations of different energy carriers; (2) Hybrid hydraulic head and pipe flow equations are used to develop the hydraulic model which can be applied for both types of tree and meshed heating networks with the possibility of pumping units; (3) A general thermal model that relates steady state temperature drops and mass flow rates, even during change of flow direction, is developed for the heating network; (4) The electricity network is modeled with the possibility of tap changing transformers; (5) The overall system of equations are solved as a single problem using Newton-Raphson iterative method. [ABSTRACT FROM AUTHOR]

This paper presents a recurrent neural network model to make medium-to-long term predictions, i.e. time horizon of ⩾ 1 week, of electricity consumption profiles in commercial and residential buildings at one-hour resolution. Residential and commercial buildings are responsible for a significant fraction of the overall energy consumption in the U.S. With advances in sensors and smart technologies, there is a need for medium to long-term prediction of electricity consumption in residential and commercial buildings at hourly intervals to support decision making pertaining to operations, demand response strategies, and installation of distributed generation systems. The modeler may have limited access to information about building’s schedules and equipment, making data-driven machine learning models attractive. The energy consumption data that is available may also contain blocks of missing data, making time-series predictions difficult. Thus, the main objectives of this paper are: (a) Develop and optimize novel deep recurrent neural network (RNN) models aimed at medium to long term electric load prediction at one-hour resolution; (b) Analyze the relative performance of the model for different types of electricity consumption patterns; and (c) Use the deep NN to perform imputation on an electricity consumption dataset containing segments of missing values. The proposed models were used to predict hourly electricity consumption for the Public Safety Building in Salt Lake City, Utah, and for aggregated hourly electricity consumption in residential buildings in Austin, Texas. For predicting the commercial building’s load profiles, the proposed RNN sequence-to-sequence models generally correspond to lower relative error when compared with the conventional multi-layered perceptron neural network. For predicting aggregate electricity consumption in residential buildings, the proposed model generally does not provide gains in accuracy compared to the multi-layered perceptron model. [ABSTRACT FROM AUTHOR]

Some scholars have addressed the relationship between the Human Development Index (HDI) and energy consumption in terms of the semi-logarithmic or hyperbolic function. One of the results from this relationship is to divide the world countries into two categories, developed and developing countries. This classification considers pre-developing countries and developing countries into one category (developing) while the proportion of people with fundamental energy needs in the former is far higher than in the latter. The objective of this paper is to allocate a separate class for pre-developing which their fundamental energy needs have not completely satisfied. The methodology of the study is divided into two parts. Initially, based on the longitudinal data (112 country’s data during the period of 2005–2013), a linear Quality of Life (QoL) indicator is proposed in terms of six variables. Then an S-shape (sigmoid) curve is fitted to the QoL indicator data against energy consumption (total primary energy supply as a proxy) per capita (ECpc) and electricity consumption per capita (Elcpc) data. Three types of countries, developed, developing, and pre-developing are identified based on the sigmoid function. The results of the proposed model demonstrate that the “pre-developing” category has different QoL and ECpc as compared to developing and developed classes. Another result of the paper shows the entry of new technologies has influenced the QoL and Elcpc to a greater extent in developing countries than in developed countries. One-way analysis of variance is a method to shed light on the latter consequence. In the case of pre-developing countries, the pertinent analysis shows an insignificant impact. This paper concludes the new classification of countries appropriately addresses variation of QoL against ECpc in each class. [ABSTRACT FROM AUTHOR]

Faced with international climate obligations and domestic pressure, Singapore has recently raised its climate goals significantly. The government had affirmed its intention for carbon emissions to peak before 2030 and for Singapore to attain net zero greenhouse gas emissions by 2050. However, as Singapore transitions to green energy, there remains much uncertainty if this transformation, coupled with rapidly evolving geopolitics, would present vulnerabilities to energy security. Hence, this paper is an attempt to investigate Singapore's possible paths to achieve these goals while enhancing energy security in view of present and future energy plans. Key parameters of energy security in Singapore's context are identified to determine how different policy decisions would influence them. The effects of various current and potential policies can thus be evaluated using a systems thinking and system dynamics approach to propose new energy solutions to improve energy security. System dynamics modelling reveals that new energy supply solutions would have to be considered in Singapore's Energy 2050 Plan while furthering efforts in reducing energy demand. Findings reveal that Singapore should have a target of >80% renewable energy by 2050, pursue a central strategy towards sustainable diversification of its energy sources into hydrogen, electricity imports and solar energy and reduce energy consumption. Accordingly, Singapore's energy security can then be improved in terms of the economic, energy supply chain and environmental dimensions, and towards attaining net-zero emissions. [Display omitted] • Systems approach to Singapore Green Plan 2030 and simulation of policies towards energy security and net zero. • Systems thinking and system dynamics modeling to evaluate key assumptions and energy security parameters. • Model validation, both structural and behavioral, through base model simulations as well as behavioral tests. • Effect of government policies on hydrogen energy, low carbon electricity imports and energy saving on energy security. • Simulations suggest policies for higher renewable energy targets to attain net zero carbon emissions and energy security. [ABSTRACT FROM AUTHOR]

The virtual power plant (VPP) that aggregates demand-side resources, is a new type of entity to participate in the electricity market and demand response (DR) program. Accurate customer baseline load (CBL) estimation is critical for DR implementation, especially the financial settlement in incentive-based DR. However, this is a challenging task as CBLs cannot be measured and are not equal to actual loads when DR events occur. Moreover, VPPs with different aggregation scales form heterogeneous electricity customers, which increases the difficulty of CBL estimation. In order to address this challenge, this paper proposes a novel deep learning-based CBL estimation method for varied types of electricity customers with different load levels. Specifically, we first transform the CBL estimation problem into a time-series missing data imputation issue, by regarding actual load sequences as CBL sequences with missing data, during DR periods. Then, we propose an attention mechanism-based neural network model to learn load patterns and characteristics of various CBLs, and also create the DR mask to avoid the disturbance of actual loads of DR periods on CBL's normal pattern. Further, we develop the generative adversarial networks (GAN)-based data imputation framework to produce the corresponding complete CBL sequence according to the actual load sequence, and then recover the missing values accordingly. Finally, comprehensive case studies are conducted based on public datasets, and our proposed method outperforms all benchmarks, where the mean and standard deviation of its estimation percentage error are 5.85% and 1.74%, respectively. This validates the effectiveness and superiority of the proposed method. • The customer baseline load estimation for virtual power plants in demand response. • Proposed baseline load estimation method is generalized for heterogeneous customers. • An attention mechanism-based model is proposed to learn customer load characteristic. • A generative adversarial networks framework is proposed to improve the robustness. [ABSTRACT FROM AUTHOR]

Globally, a considerable amount of energy is consumed by the building sector. The building envelope can highly influence the energy consumption in buildings. In this regard, innovative technologies such as thermal energy storage (TES) can help to boost the energy efficiency and to reduce the CO 2 emissions in this sector. The use of phase change materials (PCM), due to its high heat capacity, has been the centre of attention of many researchers. A considerable number of papers have been published on the application of PCM as passive system in building envelopes. Researches have shown that choosing the PCM melting temperature in different climate conditions is a key factor to improve the energy performance in buildings. In the present paper, a simulation-based optimization methodology will be presented by coupling EnergyPlus and GenOpt with an innovative enthalpy-temperature (h-T) function to define the optimum PCM peak melting temperature to enhance the cooling, heating, and the annual total heating and cooling energy performance of a residential building in various climate conditions based on Köppen-Geiger classification. Results show that in a cooling dominant climate the best PCM melting temperature to reduce the annual energy consumption is close to the maximum of 26 °C (melting range of 24–28 °C), whereas in heating dominant climates PCM with lower melting temperature of 20 °C (melting range of 18–22 °C) yields higher annual energy benefits. Moreover, it was found that the proper selection of PCM melting temperature in each climate zone can lead to notable energy savings for cooling energy consumption, heating energy consumption, and total annual energy consumption. [ABSTRACT FROM AUTHOR]

• Regularization is a novel way to address the collinearity in variables driving electricity access. • Increasing electricity access is a multidimensional challenge confounded by collinearities. • Population growth has an impact on electricity access rates in rural areas of the Global South. • Electricity consumption has an impact on rural electricity access in Latin American and Caribbean. Electricity plays a fundamental role in sustainable development by facilitating the provision of basic services and improving the quality of life for poor urban and rural populations. The dominant approach to rural electrification research in the Global South has been to focus on economic and social indicators, resulting in a limited understanding of rural electrification. This research diversifies the current literature by including technical and environmental criteria in the analysis of electricity access in rural areas. This paper aims to understand the criteria that have a significant impact on access to electricity in rural areas across the Global South, specifically in Latin America and the Caribbean. This paper examines the influence of multidimensional variables (e.g. social, economic, environmental and technical) on access to electricity in rural areas. This study encompasses two sets of data: the first from a group of countries of the Global South and the second from the Latin American and Caribbean region, using mostly countries with low and middle human development index. To understand the behavior of these multivariable relationships, this paper uses a linear regression methodology and penalizes regression (i.e., LASSO and Ridge). For the countries of the Global South, the model shows a negative correlation between access to electricity in rural areas and population growth rates. For the Latin American and Caribbean countries the model indicates a negative correlation between access to electricity in rural areas and electricity consumption per capita. These results possibly indicate that countries of the Global South that have high population growth rates have challenging paths towards universalizing electricity access, and raise the question if Latin American and Caribbean countries have prioritized their access to electricity agendas in non-rural areas. This paper confirms that population growth rates and electricity consumption per capita are criteria that have a strong impact in access to electricity rates in rural areas of the Global South, and Latin America and the Caribbean, respectively. [ABSTRACT FROM AUTHOR]

Demand-side management (DSM) has the potential to reduce electricity costs and the carbon emissions associated with electricity use for industrial consumers. It also has an important role to play in integrating variable forms of generation, such as wind and solar, into the grid. This will be a key part of any grid decarbonisation strategy. This paper describes a method that can be used to develop a new production schedule for a wide range of manufacturing facilities. The new schedule minimises either electricity costs or electricity-derived CO 2 emissions. It does so by rescheduling production to low cost or low carbon periods, without loss of overall production, within the constraints of available inventory storage. A case study of a single cement plant in the UK was performed in order to determine the potential benefits of increased load-shifting DSM using this method. The alternative production scheduled showed the potential to decrease electricity costs by 4.2%. Scaled to values from a typical plant this would lead to a cost saving of £350,000, a substantial saving. A schedule optimised to minimise carbon emissions would save an estimated 2000 tonnes per year of CO 2 , a 4% decrease in electricity-derived emissions. It was also observed that the actual electricity consumption of the plant was considerably higher than the minimum consumption predicted by the model. This could indicate potential for significant savings in both cost and CO 2 due to improvements in energy efficiency. The potential savings from DSM doubled when the prices passed to the plant were replaced with a price that varied in proportion to the wholesale cost of electricity. This indicates that a potential mutual benefit exists for both industrial consumers and electricity generators by passing on more of the variation in price. A larger share of generation from wind and solar will also lead to increased variation in prices and grid carbon intensity in future. The value of applying the method described in this paper is therefore likely to increase further in future. [ABSTRACT FROM AUTHOR]

System inertia reduction, driven by the integration of renewables, imposes significant challenges on the primary frequency control. Electrification of road transport not only reduces carbon emission by shifting from fossil fuel consumption to cleaner electricity consumption, but also potentially provide flexibility to facilitate the integration of renewables, such as supporting primary frequency control. In this context, this paper develops a techno-economic evaluation framework to quantify the challenges on primary frequency control and assess the benefits of EVs in providing primary frequency response. A simplified GB power system dynamic model is used to analyze the impact of declining system inertia on the primary frequency control and the technical potential of primary frequency response provision from EVs. Furthermore, an advanced stochastic system scheduling tool with explicitly modeling of inertia reduction effect is applied to assess the cost and emission driven by primary frequency control as well as the benefits of EVs in providing primary frequency response under two representative GB 2030 system scenarios. This paper also identifies the synergy between PFR provision from EVs and “smart charging” strategy as well as the impact of synthetic inertia from wind turbines. [ABSTRACT FROM AUTHOR]

Energy storage is extensively recognized as a significant potential resource for balancing generation and load in future power systems. Although small residential and commercial consumers of electrical energy can now purchase energy storage systems, many factors, such as cost, policy and control efficiency, limit the spread of distributed energy storage (DES). This paper proposes a new type of DES—cloud energy storage (CES)—that is capable of providing energy storage services at a substantially lower cost. This grid-based storage service enables ubiquitous and on-demand access to a shared pool of grid-scale energy storage resources. It provides users the ability to store and withdraw electrical energy to and from centralized batteries. This paper describes the concept of CES and the control and communication technologies that are required for its implementation and its operating mechanism, as well as its business model. Simulation results that are based on actual power system operating data demonstrate the feasibility and economic benefit of CES. [ABSTRACT FROM AUTHOR]

The energy balancing problem is the main challenge for the effective application of micro combined heat and power (m-CHP) in a residential context. Due to its high energy density and relative robustness, the lead-acid battery is widely used for power demand management to compensate the mismatch between the m-CHP electrical output and domestic demand. However, batteries are not suited to respond effectively to high frequency power fluctuations, but when coupled to the m-CHP, they experience frequent short-term charge/discharge cycles and abrupt power changes, which significantly decreases their lifetime. This paper addresses this problem by hybridising the lead-acid battery storage with superconducting magnetic energy storage (SMES) to form a hybrid energy storage system (HESS) that is coordinated by a novel sizing based droop control method. The control method for the first time considers both the capacity sizing of the HESS technologies and the droop control method of the battery and the SMES. A hardware in the loop test circuit is developed coupling with the real time digital simulator (RTDS) to verify the performance of the HESS with the new control algorithm. The experimental results show that control method is able to exploit the different characteristics of the SMES and the battery to meet the mismatch of m-CHP power generation and domestic demand. In addition, the lifetime analysis is implemented in this paper to quantify the battery lifetime extension in the HESS, which further proves the validity of the proposed control strategy. [ABSTRACT FROM AUTHOR]

Residential buildings are responsible for a substantial and steadily growing share of the global electricity consumption, approximately 30%. The ability to control the timing and magnitude of the aggregate electricity consumption of buildings is acquiring critical relevance. Buildings play a pivotal role in defining the shape and composition of the final electricity demand, and have an impact on the existing and projected electrical system infrastructure. This paper proposes distributed electrical storage using electrical batteries at the residential level, as an economical and technically feasible way to introduce a degree of responsiveness with the demand of residential buildings without compromising the comfort of users. The objective of the paper is to devise the operative principles governing the relation between the grid operator and a community of low exergy (lowEx) buildings, under the assumption that the grid operator is interested in controlling the form of the aggregate electricity consumption in that community. This paper presents a pricing policy aimed to stimulate a power-aware consumption, and consequently peak-shave the total electricity profile. The introduced pricing scheme separately addresses energy and power, and provides an incentive for buildings to invest in decentralized generation and storage technologies. The paper presents a methodology to determine the optimal amount of decentralized electrical storage and PV necessary to meet the objectives of the grid operator and allowing users to obtain a profit from the dynamic electricity tariff. A model is used to determine the optimal battery and PV investment from the perspective of the user. The analyses show that the introduced electricity tariff triggers an equilibrium, in which users invest between 20% and 25% of the total incurred cost in battery and PV. The PV and the battery input power were found to be mainly related to the objective of reducing the energy cost. On the other hand, the battery capacity and output power were found to be associated with the peak reduction objective. Users invest less than 10% of the total battery investment share in the battery input power. This fact indicates that contrary to a battery usage driven by the price volatility, which is proposed in many models, the battery is mainly used as power-to-energy buffer. Energy is slowly stored in the battery and rapidly released at critical peak hours. [ABSTRACT FROM AUTHOR]

Solar Thermal Electric (STE) plants can integrate Thermal Energy Storage (TES) in order to generate electricity when the energy source (Sun radiation) has vanished. TES technology has become a very important asset for this type of renewable energy source, but it has induced a rise in electricity cost in many cases. One of the reasons is the need of larger solar fields as the TES capacity increases because the solar field has to provide thermal power both to generate electricity and to charge the storage. The economic effects of improving the plant performance seem to have some internal complexities that must be investigated covering the internal relations among the main parts of a STE plant: the solar field, the power block and the energy storage. This paper presents an analytical study of these relations aimed at deriving a better understanding of the cost/performance behavior of STE plants. As the power block is a mature and commercial technology with well-established efficiencies and specific costs (in $/W, for instance), it has been taken as the reference element in modelling the plant. The other parts of the plant, i.e., the solar field and the energy storage, have been characterized in cost and energy management by a set of high-level parameters. Of course, a coarse definition cannot give very accurate results for a specific design, but it can be the guideline for the selection and sizing of a plant. It is worth noting that each type of solar thermal power plant has a different parametric scenario, corresponding to its essential design window. In this paper, comparisons among plants with different parametric scenarios are restricted to one-axis concentration solar fields, where the coarse model is easily characterized. The results show that the optimum plant configuration, in terms of TES capacity and solar field size, depends on the solar field and TES costs relative to power block cost. Moreover, it is shown that some parametric scenarios always lead to an increase in the cost of electricity when the energy storage capacity is enlarged. On the contrary, parametric scenarios associated to cheaper solar fields yield a much better economic result when TES is embodied in the plant. Additionally, TES efficiency is also identified as a parameter with high impact in the performance of the whole system. This result seems obvious, but the model gives numerical values that can help to optimize the selection process in a project. For instance, it is assessed that the lower the TES efficiency, the greater the relevance of reducing solar field costs is in order to obtain low electricity generation costs. As a general conclusion, the model points out that Fresnel-type solar fields are much better suited than parabolic trough collectors for integrating thermal energy storage. This implies that Fresnel plants present a higher potential to cover the peaks of electricity demand, which results into bigger profits. [ABSTRACT FROM AUTHOR]

Multi-energy microgrids (mMGs) are gaining rapid popularity due to the incorporation of multiple types of energy sources. Given the importance of mMGs in future energy networks, resilient, accurate economic, and environmental assessments of mMGs, as well as their interconnection, have become immense challenges. To deal with this problem, this paper presents a resilient optimization method for optimal sizing and operation of renewable-based mMGs to meet electricity and heating demand. The primary goals of this research are to reduce the system's overall energy cost, ensure continuous power supply during power outages, and reduce environmental emission rates in mMGs enriched by the combined heat and power (CHP) unit, photovoltaic (PV), boiler unit, battery, thermal energy storage (TES), and geothermal heat pump (GHP) technologies. Game theory concepts, such as nucleolus and Shapley value , are leveraged to allocate costs between interconnected mMGs running under a coalitional paradigm, resulting in a lower optimized cost. Further, a techno-economic analysis is performed to investigate the performance of the proposed system over the business as usual (BaU) case. The results affirm the lucrativeness of the proposed model and the substantial reduction in life cycle cost, utility cost, and emission while remaining outage resilient. • All the proposed microgrid's power supply units are extensively modeled. • The optimal sizing and dispatch strategy of each source of multi-energy microgrid is performed with the purpose of minimizing cost. • The environmental impact of purchasing electricity from the grid and using on-site CHP fuel is greatly modeled. • Participating microgrids employ a cost-based interconnection strategy which further reduces their overall operational cost. • Developed and tested a microgrid resilience index for prolonged grid outages under various loads. [ABSTRACT FROM AUTHOR]

Agrivoltaics systems have emerged as an approach to alleviate competition for land use between food and energy production. Conducting a thorough analysis of the impact that shading from PV modules can have on crops is crucial for the correct design of the system, as excessive shading can lead to important crop yield reductions. This paper focuses on integrating agrivoltaics systems within super-intensive olive groves in the Mediterranean region. A dual model is used to calculate the suitable transparency of PV modules, representing the area not occupied by PV cells. This model customizes the results based on the site's meteorological parameters and the photosynthetic light-response curve of the olive cultivar. The results indicate that transparency levels vary between 0.57 and 0.71, with the lowest values observed in locations with higher solar radiation, such as Egypt and Tunisia. Using these transparency values and typical 20%-efficiency monocrystalline silicon modules, the annual average energy generation per m2 in the selected locations is 65.9 kWh. Another finding reveals that optimizing the model by considering only the months corresponding to the olive growth cycle can reduce the required transparency, thereby increasing the installed PV capacity by up to 3.5%. Furthermore, the potential deployment of these systems is evaluated in terms of installed PV capacity, energy generation, CO 2 emissions and job creation. The calculations show that installing agrivoltaics systems into 1% of the total olive surface area in the Mediterranean region would: (i) result in a 2.5% increase in the global PV capacity, (ii) generate 1.8% of the current electricity demand in the selected Mediterranean countries, (iii) avoid the emissions of 4 Mt. (CO 2) per year, and (iv) create around 560,000 jobs. • The potential integration of photovoltaics into olive groves is assessed. • Representative locations in top olive producers Mediterranean countries are selected. • Suitable PV transparency levels between 57% and 71% are found. • The average generation with the proposed APV configuration is approximately 65 kWh/year per m2 of ground. • These agrivoltaics systems could lead to the creation of >560,000 jobs. [ABSTRACT FROM AUTHOR]

The increasingly popular electric vehicles (EVs) are changing the control paradigm of the power grid due to their uncoordinated charging behaviors. However, if well coordinated, smart homes, workplaces, and other locations that support EV charging could provide the grid with the urgently required flexibility via virtual power plants (VPP). In this paper, we develop the EV charging schedule model by capturing the unwillingness of EV drivers to alter their initial charging behaviors, referred to as the discomfort function. Predictability and the value of charging time, which represent the electricity consumption stability and the time value of EV drivers, characterize the discomfort function. Rather than existing works capturing discomfort by a direct simple parameter, such a computable data-driven quantification of discomfort enables us to customize an efficient VPP dispatch mechanism for EVs. In addition, to deal with the unknown charging efficiencies of EVs, we apply chance constraints only with the knowledge about mean and standard deviation of charging efficiencies, rather than their specific distribution. Using the concept of conditional value-at-risk (CVaR), we build an effective algorithm to solve the practical non-convex VPP dispatch model considering charging efficiencies. The effectiveness of our proposed models and associated algorithms are validated by numerical studies. • Data-driven computable model to quantify discomfort of EV drivers' charging schedule adjustment. • Practical generalization of EVs embedded VPP dispatch by considering uncertain charging efficiency. • Efficient algorithm design for non-convex chance-constrained dispatch model based on CVaR. [ABSTRACT FROM AUTHOR]

International Energy Agency predicts that the global number of installed heat pumps (HP) will increase from 180 million in 2020 to approximately 600 million by 2030, covering 20% of buildings heating needs. Electric power consumption is one of the main key performance indicators for the heat pump systems from techno-economic perspective. However a common issue prevalent in many existing heat pumps is the lack of electric power measurement. The modern installations might be equipped with electric power measurement sensors but this comes at a higher system cost for the manufacturers and end-users. The primary objective of this work is to propose a virtual measurement for estimating power consumption, thereby eliminating the need for field measurement of power for heat pumps. To achieve the objective, a data-driven approach is proposed. Firstly, the in-situ data is preprocessed through data merging, cleaning, and normalization. Then, input features are pre-selected using Spearman correlation coefficients, and further refined by addressing multicollinearity problem. Following this, Extreme Gradient Boosting (XGBoost) models and polynomial models are developed by considering different features as inputs. All models are finally validated against the in-situ data from multi-units of ground source heat pump (GSHP) and air source heat pump (ASHP) installations. The results showed that the electric power consumption of GSHP can be estimated with high accuracy (99% for R 2 , 10 W for MAE, and 1% for MAPE) through generic data-driven models using only four easy-to-measure input features. Taking three input features as inputs for ASHP generic model, the accuracy can be reached to 83% for R 2 , 125 W for MAE, and 9% for MAPE. The method presented in this paper can be applied to estimate power consumption of millions of heat pumps and consequently add a significant value as well as provide different types of services, such as cost-saving benefits for manufacturers and end-users, flexibility services for aggregators and electricity grids. • Collect and analyze in-situ data from multiple residential GSHP and ASHP units. • Propose a two-step feature selection method to indicate the most essential inputs. • Evaluate the most predominant features by data-driven models with different inputs. • Develop high accuracy generic data-driven models with limited inputs features. [ABSTRACT FROM AUTHOR]

Reliability evaluation of active distribution networks (ADNs) considering customer satisfaction is studied in this paper. Operation optimization model of ADNs is established, which aims to maximize the operation benefit of ADNs using demand response. However, according to optimization decisions, customers may have to change their electricity consumption habit, which affects customer satisfaction and the reliability of customers and ADNs. Two customer satisfaction indices are defined therefore as constraints in the operation optimization to quantify these effects. By a Sequential Monte Carlo (SMC) simulation, the optimization processes is innovatively integrated into the reliability evaluation, and thus the impacts of customer satisfaction constraints are incorporated in reliability evaluation. Further, four new reliability indices are defined in this paper to visibly reflect their impacts. The presented models and methods are validated by extensive studies conducted on a standard test system. Evaluation results accurately quantify the impacts of customer satisfaction constraints on load profiles, reliability and economic performance of ADNs. Conclusions drawn from evaluation results can provide helpful insights for distribution system operators (DSOs) to effectively improve the reliability and operation economy of ADNs using demand resources. [ABSTRACT FROM AUTHOR]

After almost three years of preparation, the tiered pricing for household electricity (TPHE) was implemented across China in July 2012. The feedbacks of household electricity consumption to the new pricing policy are investigated in this paper based on the micro household-level data from China’s Residential Energy Consumption Survey (CRECS). We document that electricity consumption of 82% surveyed households did not affected by the TPHE, which is in conformity with the principle that the TPHE policy based on. More importantly, factors that influence elasticity of household electricity consumption are examined by empirical model in this paper. Results indicate that factors such as the energy price, household income, and demographic attributes have significant impacts on residential electricity consumption. Further discussion outcomes show that the publicity of the TPHE, energy price and some other factors are strongly correlated with the probability that households change the inefficient and lavish pattern of electricity consumption. Future directions for the design and improvement of residential electricity pricing reform are thus suggested. [ABSTRACT FROM AUTHOR]

Active Demand Response (ADR) can contribute to a more cost-efficient operation of, and investment in, the electric power system as it may provide the needed flexibility to cope with the intermittent character of some forms of renewables, such as wind. One possibly promising group of demand side technologies in terms of ADR are electric heating systems. These systems could allow to modify their electrical load pattern without affecting the final, thermal energy service they deliver, thanks to the thermal inertia in the system. One of the major remaining obstacles for a large scale roll-out of ADR schemes is the lack of a thorough understanding of interactions between the demand and supply side of the electric power system and the related possible benefits for consumers and producers. Therefore, in this paper, an integrated system model of the electric power system, including electric heating systems (heat pumps and auxiliary resistance heaters) subjected to an ADR scheme, is developed, taking into account the dynamics and constraints on both the supply and demand side of the electric power system. This paper shows that only these integrated system models are able to simultaneously consider all technical and comfort constraints present in the overall system. This allows to accurately assess the benefits for, and interactions of, demand and supply under ADR schemes. Furthermore, we illustrate the effects not captured by traditional, simplified approaches used to represent the demand side (e.g., price elasticity models and virtual generator models) and the supply side (e.g., electricity price profiles and merit order models). Based on these results, we formulate some conclusions which may help modelers in selecting the approach most suited for the problem they would like to study, weighing the complexity and detail of the model. [ABSTRACT FROM AUTHOR]

Concentrated Solar Power (CSP) is a promising alternative for generating renewable energy. One of the most prominent CSP technologies is the central receiver tower with heliostat field, which has received attention in the last decade due to its high achievable temperatures and potential cost reduction. In order to make this technology economically viable, research has been focused on the solar field, solar receiver, energy storage and power block. The power block is one of the important components since improving system efficiency leads to reductions in the storage, solar field and receiver sizes and costs. Recently, supercritical CO 2 Brayton cycles have emerged as an alternative for power block with central receiver tower systems due to higher thermal efficiencies and compactness compared to traditional steam Rankine cycles. In this paper, detailed energy and exergy analysis of four different supercritical CO 2 Brayton cycle configurations (Simple Brayton cycle, Recompression Brayton cycle, Partial cooling with recompression and Recompression with main compression intercooling) were performed with and without reheat. Prior the compressor inlet, dry air cooler is used for all supercritical Brayton cycles studied in this paper. A solar receiver, replacing the heater and reheater for conventional Brayton cycles, is also used to provide heat input to the cycles. The simulations were carried out for Alice Springs (Australia) solar conditions and optimum operating conditions of the supercritical cycle were obtained by optimising the first law thermodynamic efficiency. The effect of turbine inlet temperature and the cycle configuration on the thermal performance and exergy destruction was analysed. The results showed that the thermal efficiency of the supercritical CO 2 Brayton cycle increases monotonically with the temperature of the cycle. The recompression cycle with main compression intercooling achieved the best thermal performance ( η I = 55.2 % at 850 °C). The detailed exergy analysis also showed that the solar receiver has the highest exergy destruction (>68%) while turbines and compressors have minimal contribution (less than 3%). Furthermore, it is noted that the exergy efficiency has a bell shaped curve, reaching at maximum value between 700–750 °C depending on the cycle configuration. [ABSTRACT FROM AUTHOR]

Selection of appropriate climatic variables for prediction of electricity demand is critical as it affects the accuracy of the prediction. Different climatic variables may have different impacts on the electricity demand due to the varying geographical conditions. This paper uses multicollinearity and backward elimination processes to select the most appropriate variables and develop a multiple regression model for monthly forecasting of electricity demand. The former process is employed to reduce the collinearity between the explanatory variables by excluding the predictor which has highly linear relationship with the other independent variables in the dataset. In the next step, involving backward elimination regression analysis, the variables with coefficients that have a low level of significance are removed. A case study has been reported in this paper by acquiring the data from the state of New South Wales, Australia. The data analyses have revealed that the climatic variables such as temperature, humidity, and rainy days predominantly affect the electricity demand of the state of New South Wales. A regression model for monthly forecasting of the electricity demand is developed using the climatic variables that are dominant. The model has been trained and validated using the time series data. The monthly forecasted demands obtained using the proposed model are found to be closely matched with the actual electricity demands highlighting the fact that the prediction errors are well within the acceptable limits. [ABSTRACT FROM AUTHOR]

The high penetration of both Distributed Energy Resources (DER) and Demand Response (DR) in modern power systems requires a sequence of advanced strategies and technologies for maintaining system reliability and flexibility. Real-time electricity markets (RTM) are the non-discriminatory transaction platforms for providing necessary balancing services, where the market clearing (nodal or zonal prices depending on markets) is very close to real time operations of power systems. One of the primary functions of RTMs in modern power systems is establishing an efficient and effective mechanism for small DER and DR to participate in balancing market transactions, while handling their meteorological or intermittent characteristics, facilitating asset utilization, and stimulating their active responses. Consequently, RTMs are dedicated to maintaining the flexibility and reliability of power systems. This paper reviews advanced typical RTMs respectively in the North America, Australia and Europe, focusing on their market architectures and incentive policies for integrating DER and DR in electricity markets. In this paper, RTMs are classified into three groups: Group I applies nodal prices implemented by optimal power flow, which clears energy prices every 5 min. Group II applies zonal prices, with the time resolution of 5-min. Group III is a general balancing market, which clears zonal prices intro-hourly. The various successful advanced RTM experiences have been summarized and discussed, which provides a technical overview of the present RTMs integrating DER and DR. [ABSTRACT FROM AUTHOR]

Fuel cells offer many benefits for residential micro-cogeneration because of their high electrical efficiency, low emissions, and low heat-to-power ratio. However, the current design of fuel cell micro combined heat and power (CHP) systems for dwellings can be improved by better matching of the heat generated by the plant with the dwelling’s load profile and heat emitters. This paper explores the design options in building services using fuel cell micro-CHP and aims to bring the two research fields together in order to design superior integrated systems. Therefore a review of current literature on the field is performed and current available options for heat recovery from fuel cells, including the grades of heat available from different fuel cell process units are discussed. This is discussed in relation to the heat demand in a dwelling and the required temperature of the most suitable heat emitters for building services design. The paper motivates a methodology that considers all possible interconnections of plant in the design of fuel cell micro-CHP in dwellings and provides an example of how multi-objective optimisation can be used to identify promising system designs. [ABSTRACT FROM AUTHOR]

The paper presents the operational results of a real life residential microgrid which includes six apartments, a 20 kWp photovoltaic plant, a solar based thermal energy plant, a geothermal heat pump, a thermal energy storage, in the form of a 1300 l water tank and two 5.8 kW h batteries supplying, each, a couple of apartments. Thanks to the thermal energy storage, the solar based thermal energy plant is able to satisfy the 100% of the hot water summer demand. Therefore the thermal energy storage represents a fundamental element in the management of the residential demand of thermal energy. It collects renewable thermal energy during day-time to release it during night-time, effectively shaving the peak of the thermal energy demand. The two electric storages, on the other hand, provide the hosted electrical subsystems with the ability to effectively increase the self-consumption of the local energy production, thus lowering the amount of energy surplus to be sold back to the grid, and increasing the self-sufficiency of the microgrid. For instance, the storage has supported self-consumption up to the 58.1% of local energy production with regard to the first battery, and up to the 63.5% with regard to the second one. Also, 3165 and 3365 yearly hours of fully autonomous activity have been recorded thanks to the first, and the second battery respectively. On the other hand, the yearly average efficiency amounts to 63.7%, and 65.3% respectively, for the first and second battery. In the second part of the paper we propose a computational framework to evaluate the overall performance of the microgrid system, while accounting different operating conditions and energy management policies. From this perspective, the framework acts as a useful modeling and design tool, to assess the opportunity of employing alternative energy management system topologies and strategies. Eight different configurations, with growing complexity, have been derived from the original system on purpose. The simulations, carried out based on real data related to one-year time period, have provided results showing that, the higher the integration level of electrical and thermal storage is, the higher degree of self-sufficiency can be achieved by the microgrid, and, in turn, the more consistent the yearly energy saving become. Nevertheless, despite the energy cost reduction achievable with the availability of storage systems in the Leaf House, their high investment cost made them not really profitable at the current price conditions for devices and energy purchase. [ABSTRACT FROM AUTHOR]

This paper presents a high-resolution bottom-up model of electricity use in an average household based on fit to probability distributions of a comprehensive high-resolution household electricity use data set for detached houses in Sweden. The distributions used in this paper are the Weibull distribution and the Log-Normal distribution. These fitted distributions are analyzed in terms of relative variation estimates of electricity use and standard deviation. It is concluded that the distributions have a reasonable overall goodness of fit both in terms of electricity use and standard deviation. A Kolmogorov–Smirnov test of goodness of fit is also provided. In addition to this, the model is extended to multiple households via convolution of individual electricity use profiles. With the use of the central limit theorem this is analytically extended to the general case of a large number of households. Finally a brief comparison with other models of probability distributions is made along with a discussion regarding the model and its applicability. [ABSTRACT FROM AUTHOR]

The increased integration of variable renewable generation into the power systems, along with the phase-out of fossil-based power stations, necessitate procuring more flexibility from the demand sectors. The electrification of the residential heat sector is an option to decarbonise the heat sector in the United Kingdom. The inherent flexibility that is available in the residential heat sector, in the form of the thermal inertia of buildings, is expected to play an important role in supporting the critical task of short-term balancing of electricity supply and demand. This paper proposes a method for characterising the locally aggregated flexibility envelope from the electrified residential heat sector, considering the most influential factors including outdoor and indoor temperature, thermal mass and heat loss of dwellings. Applying the method to England and Wales as a case study, demonstrated a significant potential for a temporary reduction of electricity demand for heating even during cold days. For a scenario envisaged a fully electrified residential heat sector in England and Wales, total electricity demand reductions of approximately 25 GW and 85 GW were shown to be achievable for the outdoor temperature of 10 °C and -5 °C, respectively. Improving the energy performance of the housing stock in England and Wales was shown to reduce the magnitude of available flexibility to approximately 18 GW and 60 GW for the outdoor temperature of 10 °C and -5 °C, respectively. This is due to the use of smaller size heat pumps in the more efficient housing stock. However, the impact of the buildings' retrofit on their thermal mass and consequently on the duration of the flexibility provision is uncertain. • A methodology is proposed to characterise the thermal parameters of dwelling stock. • Flexibility from thermal mass of buildings in England and Wales is quantified. • Electricity demand reduction of 87 GW is available at outdoor temperature of -5 °C. • Impacts of buildings retrofits on the amount of flexibility are assessed. [ABSTRACT FROM AUTHOR]

Accurate energy load forecasting can not only provide favorable conditions for ensuring energy security but also reduce carbon emissions and thereby slow down the process of global warming. With the continuous construction of smart grids, energy consumption information can be easily obtained, which provides a good foundation for forecasting work. In the household energy consumption forecasting field, the current mainstream methods combine signal processing with artificial intelligence methods to improve forecasting accuracy. However, due to differences in residents' daily habits and sensor errors, the energy consumption data of individual households exhibit high volatility and contain noise. Existing methods' accuracy, robustness, generalization ability, and efficiency are challenged when considering multi-time scales and multi-samples. Based on this, this paper proposes a deep learning framework using multi-feature fusion recurrent neural networks for univariate energy consumption data. The proposed framework employs improved Singular Spectrum Analysis (SSA) and multivariate input recurrent neural network (RNN). To verify the effectiveness of the proposed framework, a series of comparative experiments were conducted on the Moroccan buildings' electricity consumption dataset (MORED). Experiments reveal that the proposed framework achieves adaptive decomposition of raw time series data and multi-feature fusion input. Moreover, under the evaluation of multiple metrics, the proposed framework outperforms the state-of-the-art methods in terms of forecasting accuracy, robustness, generalization ability, and efficiency. • A deep learning framework is proposed for forecasting energy consumption. • The framework achieves adaptive decomposition and multi-feature fusion. • The proposed framework's performance has been validated using MORED. [ABSTRACT FROM AUTHOR]

Although water and energy systems are interdependent, they are still designed individually due to their complexity. This paper proposes a multiple input multiple output (MIMO) model predictive control (MPC) for demand management of an integrated water distribution system (WDS) supplied by an islanded microgrid (MG) system, offering an additional opportunity for energy-saving at the WDS. This strategy benefits the island and remote communities, which have limited access to the centralized electric grid. The existing literature either used an open-loop approach for energy management of water-energy microgrids or ignored the interconnected nature and MIMO nature of these systems. Compared to that, the proposed closed-loop energy management system is developed to address the detailed dynamics of assets, the inherent interconnections between water and energy networks, and to provide resilience as well as energy saving and demand management capabilities. The integrated MPC algorithm is designed in discrete time, utilizing a state-space model and sequential quadratic programming (SQP) as the solution method. Considering the intensive use of electricity by the pumps in WDS, their energy consumption is accommodated by an islanded microgrid system that incorporates a diesel generator, renewable energy sources (wind and solar), and a battery bank that serves as an energy source and a storage device. The proposed approach is investigated using base water demand from a real-world community, Telford, in Pennsylvania. Several case studies, such as variations in renewable energy contributions and increments of the control actions, are carried out to demonstrate the feasibility and reliability of the integrated MPC to provide efficient water and energy demand management concurrently. • Model predictive control algorithm of the interconnected water-energy is developed. • The full dynamics of energy-consuming assets in the water system are considered, including quadratic pump relations. • Effect of the renewable energy variations on the 24-hr energy dispatch is studied. • Multiple optimal configurations result in up to $360k/year savings, reduced carbon footprint, and smoother control. • The developed framework is applied in a Telford, PA case study, showcasing its real-world implementation. [ABSTRACT FROM AUTHOR]

[Display omitted] • The diurnal, weekly, and seasonal mismatch coefficients are defined. • Buildings have similar mismatch but distinct requirements for energy storage. • Medium- and long-duration electrical storage technologies should be promoted. • Tiny relaxation of standard for zero emissions saves more-than-half investments. • Buildings complexes largely saves storage capacity than isolated buildings. The cooperation of renewable energy and electrical energy storage can effectively achieve zero-carbon electricity consumption in buildings. This paper proposes a method to evaluate the mismatch between electricity consumption and renewable generation at different timescales and calculate energy storage requirements to achieve zero carbon. All five types of buildings analyzed in this paper have 30–40% of their electricity demand that cannot be fulfilled by real-time renewable generation. Moreover, the impact of energy storage on the mismatch is examined in terms of three factors: storage capacity, power rating, and storage duration. Despite that diurnal mismatch accounts for more than half of the annual mismatch, it is relatively manageable through energy storage. In contrast, the seasonal mismatch is a much thornier issue, even if it is the least component. Although buildings have comparable proportions of unsatisfied demand, the storage capacity requirement varies widely (350–800 times average hourly electricity consumption). This paper provides various implications. First, it is critical to persistently develop medium- and long-duration electrical storage technologies (appropriate storage duration is 37–185 h), such as large-scale compressed air or pumped hydroelectricity. Second, the adoption of nearly zero-carbon electricity can save most investment in electrical energy storage because of the diminishing marginal benefit. Third, compared with isolated buildings, cooperation of buildings significantly saves storage capacity due to their complementary electricity consumption. [ABSTRACT FROM AUTHOR]

• Feasibility of the city level off-grid hybrid PV-hydro system is verified. • The PV installation plan is optimized based on GIS. • Influence of the consumption structure on the optimal PV size was studied. • Optimal operation plan in typical weather is analysed. Increasing attentions have been paid on the design and operation optimization of hydro-photovoltaic complementary systems due to the inherent uncertainty associated withthe solar energy. However, studies mainly focused on the household or community integrated power system while the city-level off-grid systems remained less explored. That was because both the large scale of the studied area and the complex factors associated with the city attached difficulties to the problems. This paper proposed an off-grid PV-hydro hybrid optimization model for the cities which were located nearby sufficient water resources based on Geographic Information System. The proposed model can guarantee the electricity demand with less economic cost and less storage facilities by taking full advantage of water resources. Firstly, the optimization-based economic analysis considering PV capital cost and hydroelectricity price was proposed to determine whether to install PV panels and the optimal size if install. Then, we set six scenarios to identify the relationship between the electricity consumption structure and the optimal PV scale due to the structure transformation according to the current energy policies. Finally, the detailed optimal power generation plan in typically weather conditions during operation period was analyzed. Results indicated that: (1) The city level off-grid PV-hydro complementary system was feasible according to the studied case. (2) The critical PV panel capital cost was 920 $/kW when the hydroelectricity price was 5¢/kW·h, and there was a linear relationship between these two parameters. (3) Optimal PV size was influenced by electricity consumption structure and peak month type, and the current structure transformation policy was beneficial for the PV-hydro systems. (4) The power generation plan varied a lot in different weather conditions and the detailed operation plan could be obtained in this paper. [ABSTRACT FROM AUTHOR]

• A water and electricity combined supply system in a smart community is proposed. • Shapley value method is firstly introduced to distribute the cooperation revenues. • Forecast errors are addressed by a two-stage electric and hydraulic combined method. • The iterative procedure can solve the hydraulic and electric dispatching problem efficiently. As a new application form of sustainable energy generation, building-integrated photovoltaics provide great flexibility for energy conservation and emission reduction. However, due to the randomness and volatility from photovoltaic generation, the full exploitation of the advantages of building-integrated photovoltaics has become an important question for the utilities. To address this issue, this paper proposes a novel and comprehensive (combined) water-energy supply system applied to the smart community with building-integrated photovoltaics and two-stage electric-hydraulic dispatching method. It was designed to minimize the electricity consumption cost and power fluctuation while meeting the water demand loads, followed by the iterative algorithm as well as linearized techniques to solve the optimisation model. The Shapley value method was introduced to ensure the fair revenue between the property company and owners. Simulation studies on a building-integrated-photovoltaics community and a 15-node water distribution system indicates that the annual operation cost of the water-electricity combined supply system was reduced by 29.6% while the external grid obtained a better prediction regarding the real-time exchange power. The results confirm the effectiveness of the proposed model on peak shaving and valley filling and fluctuation smoothing. [ABSTRACT FROM AUTHOR]