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In this work, the Design and Operation of Integrated Technologies (DO-IT) framework is developed, a comprehensive tool to support short- and long-term technology investment and operation decisions for integrated energy generation, conversion and storage technologies in buildings. The novelty of this framework lies in two key aspects: firstly, it integrates essential open-source modelling tools covering energy end uses in buildings, technology performance and cost, and energy system design optimisation into a unified and easily-reproducible framework. Secondly, it introduces a novel optimisation tool with a concise and generic mathematical formulation capable of modelling multi-energy vector systems, capturing interdependencies between different energy vectors and technologies. The model formulation, which captures both short- and long-term energy storage, facilitates the identification of smart design and operation strategies with low computational cost. Different building energy demand and price scenarios are investigated and the economic and energy benefits of using a holistic multi-energy-vector approach are quantified. Technology combinations under consideration include: (i) a photovoltaic-electric heat pump-battery system, (ii) a photovoltaic-electric heat pump-battery-hot water cylinder system, (iii) a photovoltaic-electrolyser‑hydrogen storage-fuel cell system, and (iv) a system with all above technology options. Using a university building as a case study, it is shown that the smart integration of electricity, heating, cooling and hydrogen generation and storage technologies results in a total system cost which is >25% lower than the scenario of only importing grid electricity and using a fuel oil boiler. The battery mitigates intra-day fluctuations in electricity demand, and the hot-water cylinder allows for efficiently managing heat demand with a small heat pump. In order to avoid PV curtailment, excess PV-generated electricity can also be stored in the form of green hydrogen, providing a long-term energy storage solution spanning days, weeks, or even seasons. Results are useful for end-users, investment decision makers and energy policy makers when selecting building-integrated low-carbon technologies and relevant policies. • Building demand, technology and energy system optimisation models are integrated. • The framework uses open-source tools to model complex multi-energy-vector systems. • The novel formulation is generic while capturing both short- and long-term energy storage. • Smart integration of electrical, thermal and hydrogen systems leads to minimum cost. • Demand patterns and resource prices greatly impact PV, heat pump and hydrogen synergies. [ABSTRACT FROM AUTHOR]

The global shift towards renewable energy highlights the significance of building photovoltaic (PV) systems as a sustainable solution. Assessing a building's solar PV potential is essential for advancing green energy initiatives. This study establishes an integrated system for analyzing both roofs and facades, incorporating physical, geographical, and technical dimensions. Utilizing the Perez model for solar irradiance, Hillshade analysis for shading effects, and Ladybug tools for facade obstruction simulation, we assess the PV potential and its spatial-temporal variations across 180,349 buildings in Hong Kong. The results show that Hong Kong's roofs and facades have a physical potential of 4.00 × 1013 Wh and 2.48 × 1014 Wh, respectively. The feasible installation areas are identified as 14.44 km2 for roofs and 71.31 km2 for façades, yielding geographical potentials of 1.48 × 1013 Wh and 3.44 × 1013 Wh. Based on this, the estimated installed capacities are 1.27 GW for roofs and 12.75 GW for facades, which could generate between 2.07 × 1012–2.66 × 1012 Wh and 3.61 × 1012–4.65 × 1012 Wh, respectively. These projections account for 12.68%–16.32% of Hong Kong's total electricity consumption in 2022. This study underlines the substantial role of building-integrated solar PV systems in Hong Kong's transition towards a low-carbon future, offering valuable insights for policymaking and implementation strategies. • Assessed PV capacity for Hong Kong's roofs and facades using a bottom-up approach. • Analyzed PV potential and variations across 180,349 buildings in Hong Kong. • Installed PV capacities: 1.27 GW for roofs, 12.75 GW for facades in Hong Kong. • Discussed technology and policy recommendations for enhancing urban PV integration. [ABSTRACT FROM AUTHOR]

We formulate a new, computationally inexpensive framework based on Mean–Variance Portfolio Theory to determine the optimal allocation of wind and solar capacity, which can leverage the complementarity between intermittent solar and wind generation to meet electricity demand at the lowest cost, with gas acting as reserve generation in times of insufficient wind and solar output, and the variance of the mismatch between demand and the output of wind and solar is taken as the risk indicator. We then apply this framework to the context of the National Electricity Market in Australia. Our result reveals that, assuming sufficient network capacity, the estimated lowest generation cost achievable is $45.26/MWh, where the expected percentage of unserved energy (USE) is 0.0044%. For a risk-averse planner, a level of USE less than 0.0001% may be achieved at the expense of almost doubling the cost to $88.23/MWh. Under the current network capacity constraints, we estimate these costs to increase to $53.09/MWh and $89.90/MWh respectively, while the USE could rise to 0.017%. Moreover, network expansions in Queensland are found to be the most beneficial. On the other hand, a 4-hour battery storage system with power capacity equivalent to 20% of the peak load could help reduce the cost by up to 6.8% alongside further decrease in the USE. Our results are also shown to differ drastically from the conventional, output-based approach. Since the two approaches address two different questions depending on whether demand is flexible, demand flexibility is also an important factor. • New demand and cost based portfolio selection methodology for wind and solar. • Minimise cost of meeting demand, with gas generation as backup energy source. • Variance of difference between demand and wind and solar output is risk indicator. • Results differ significantly from conventional output based approach. • Quantify marginal impact of transmission expansion on portfolio cost and risk. [ABSTRACT FROM AUTHOR]

This study evaluates the effectiveness of the long and short-term (LSTM) implementation with a particular emphasis on assessing the impact of transfer learning techniques in improving prediction accuracy for building energy demand and on-site power outputs using empirical data from real-world building environments. The initial study utilized simulated data from a single-family prototype building model, employing cluster analysis to segment the training and testing datasets based on distinct cooling and heating periods. Subsequently, real-world data from an existing residential building was incorporated by utilizing LSTM-based transfer learning to improve the prediction accuracy of building energy demand and on-site power generation within a target domain. The training and testing phases involved pre-processed datasets with distinct time-series datasets for environmental, electricity demand, and on-site power generation data. The input variables in the architecture of the machine learning model included environmental, time-related data, and past-day energy datasets. This study also implemented interquartile range (IQR) analysis during the data pre-processing phase to effectively bridge the gap between the source and target domain feature and label spaces to minimize discrepancies and improve the accuracy of prediction performance. The results showed the LSTM model initially developed for a source domain effectively predicted energy demand and on-site power generation across summer and winter. Within target tasks, while initial transfer learning enhancements in prediction accuracy were modest due to each domain's low relevancies in their features and labels, significant improvements were achieved following strategic data pre-processing. The results underscored the importance of detailed pre-processing analysis in LSTM-based transfer learning models for accurate energy demand forecasting in real-world settings. The integration of transfer learning with IQR analysis refined the prediction capabilities of the models under practical conditions. • LSTM-based model enhances energy prediction accuracy. • Transfer learning enhances accuracy in on-site power predictions. • Cluster analysis segments training/testing in simulated data. • Real-world data integration improves LSTM predictions. • IQR analysis bridges gaps, enhancing prediction accuracy. [ABSTRACT FROM AUTHOR]

Electricity demand is a crucial factor in energy system planning. Understanding future electricity demand is vital for developing effective energy and climate policies, as well as establishing a resilient and sustainable energy system. In light of these considerations, the escalating challenges posed by climate change are anticipated to have a substantial impact on electricity demand. This study, therefore, provides a comprehensive analysis delving into the dynamic nature of Temperature Response Functions (TRFs) of electricity demand across Europe. By examining various factors influencing electricity demand in residential buildings, such as thermal insulation, heating electrification, space cooling, and passive cooling, we aim to understand their collective impact on shaping future Temperature Response Functions. To project electricity demand, our study incorporates these factors into our scenario assumptions. Through a comprehensive investigation of these scenarios, our findings reveal distinctive regional influences of these factors. In regions where heating demand prevails, an initial increase in electricity demand is anticipated due to increased electrification rates. However, improved building thermal insulation is expected to substantially reduce winter electricity demand in the long run. Conversely, in regions with pronounced cooling demand, a notable escalation in electricity demand is foreseen due to increased space cooling penetration rate. Nevertheless, the application of effective passive cooling measurements is expected to mitigate and markedly diminish this increase. By highlighting the differential influences of these factors on electricity demand across Europe, our findings can offer valuable insights and guidelines first for energy system modelers for considering the change in Temperature Response Functions and second for policymakers to develop effective climate change adaptation and mitigation strategies. • Examination of the dynamic characteristics of Temperature Response Functions (TRFs). • Space and passive cooling, electric heating, insulation effects on electricity demand. • Projection of TRFs and electricity demand for 36 European countries until 2100. • Thermal insulation and passive cooling are essential for reducing electricity demand. [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]

The ever-growing penetration rate of distributed energy resources in the building sector, coupled with the ongoing improvements in electrification policies within the transportation industry, has posed challenges to cost-effectiveness and reliable operation of the utility grid. This study conducts a comparative study on electricity transactions between multi-microgrids comprising heterogeneous building communities considering electric vehicles (EVs), utilizing a hybrid game theory-based Peer-to-peer (P2P) electricity trading paradigm and a traditional Time-of-use (TOU) strategy under the Peer-to-grid (P2G) paradigm. Firstly, a hybrid game-based P2P framework, incorporating a non-cooperative game for determining internal electricity transactions and prices between multi-microgrid representing different building communities, and proposed a cooperative game for determining transactions and revenue distribution within the same microgrid's buildings. Then, the study investigates the effectiveness of this approach in enabling efficient and sustainable electricity exchange within microgrids. Through a comprehensive analysis, considering the dynamic electricity demand and techno-economic factors, the results demonstrates that the hybrid game theory-based P2P paradigm offers significant advantages over the traditional TOU strategy under the P2G paradigm. The proposed approach enables improved energy supply-demand balancing, increased energy autonomy, equitable revenue distribution, and reduced reliance on the utility grid. Meanwhile, utilizing EVs and flexible loads as energy flexibility assets further enhances the system's flexibility and responsiveness to demand fluctuations. The findings provide references for promoting energy efficiency and supporting the transition towards a more decentralized and resilient power system by fostering energy sharing and enhancing grid stability. • A hybrid game-based P2P paradigm for multi-microgrid electricity transactions is proposed. • The interactive impact between EVs charging load and building energy consumption in heterogeneous building communities is explored. • The techno-economic performance of the multi-microgrid under the proposed P2P paradigm and the traditional P2G paradigm are compared. • Integration of EVs and flexible loads enhances system flexibility and responsiveness to demand fluctuations. [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]

Integrating renewable energy technologies into a decentralised smart grid presents the 'Duck Curve' challenge — the disparity between peak demand and solar photovoltaic (PV) yield. Smart grid operators still lack an effective solution to this problem, resulting in the need to maintain standby fossil fuel-fired plants. The COVID-19 pandemic-induced lockdowns necessitated a shift to remote work (work-from-home) and home-based education. The primary objective of this study was to explore mitigating strategies for the duck curve challenge by investigating this notable shift in behaviour by examining the effect of remote work and education on grid and decentralised solar PV electricity use in 100 households with battery energy storage in the southwest of the UK. This study examined 1-min granular grid electricity and decentralised solar energy consumption data for April–August 2019 and 2020. The findings revealed statistically significant disparities in energy demand. Notably, there was a 1.4—10% decrease in average electricity consumption from April to August 2020 (during and following the lockdown) compared to the corresponding months of 2019. Furthermore, household grid electricity consumption was reduced by 24—25%, while self-consumption from solar PV systems increased by 7—8% during the lockdown in April and May 2020 compared to 2019. This increase in self-consumption was particularly prominent in the morning and afternoon, possibly attributed to the growing prevalence of work-from-home and home-based education. The dynamic shifts in energy consumption patterns emphasised the role of decentralised solar PV energy in meeting the evolving needs of households during unprecedented societal changes. Additionally, remote work might unlock decentralised solar PV's potential in resolving the 'Duck Curve', urging further investigation into the implications for energy infrastructure and policy development. • Analysed 1-minutely grid and decentralised solar PV energy demand data from 100 houses in a southwestern UK city. • Average electricity consumption decreased by 1.4—10% in April—August 2020 compared to 2019. • Grid electricity consumption was reduced by 24—25%, and from solar PV self-consumption increased by 7—8%. • Increased solar PV self-consumption was prominent in the morning and afternoon. • Might unlock solar PV's potential of resolving the 'Duck Curve.' [ABSTRACT FROM AUTHOR]

Trading on the day-ahead electricity markets requires accurate information about the realization of electricity prices and the uncertainty attached to the predictions. Deriving accurate forecasting models presents a difficult task due to the day-ahead price's non-stationarity resulting from changing market conditions, e.g., due to changes resulting from the energy crisis in 2021. We present a probabilistic forecasting approach for day-ahead electricity prices using the fully data-driven deep generative model called normalizing flow. Our modeling approach generates full-day scenarios of day-ahead electricity prices based on conditional features such as residual load forecasts. Furthermore, we propose extended feature sets of prior realizations and a periodic retraining scheme that allows the normalizing flow to adapt to the changing conditions of modern electricity markets. Our results highlight that the normalizing flow generates high-quality scenarios that reproduce the true price distribution and yield accurate forecasts. Additionally, our analysis highlights how our improvements towards adaptations in changing regimes allow the normalizing flow to adapt to changing market conditions and enable continued sampling of high-quality day-ahead price scenarios. • Conditional normalizing flows for scenarios of day-ahead electricity prices. • Multivariate forecasting tailored to day-ahead market structure. • Full 24-h forecasts horizon in contrast to conventional point forecasting. • Periodic retraining adjusts forecast to changing market conditions. • Measure for prediction uncertainty via continuous probability distribution. [ABSTRACT FROM AUTHOR]

Understanding how and when residential electricity is used throughout the day is integral to the successful implementation of potential residential demand management strategies. Our analysis characterizes the daily hourly load profiles of approximately 160,000 residential electricity customers across the Southern California Edison (SCE) service area during the period spanning 2015 to 2016 and 2018 to 2019 across weekends, weekdays, seasons, and climate zones. We find that total daily electricity usage was highest in the hottest months of the year compared to milder months, particularly for households located in the hottest climate zones. The most energy-consumptive hours occurred during the mid-afternoon during the hottest months, in contrast to early evening high consumption in cooler months. We find that customers with average daily consumption at or above the 80th percentile cumulatively consume over 40% of electricity during the hottest months of the year residential load, while the bottom half of customers cumulatively consume <25% of the total residential load. The disparities in electricity usage across SCE households are higher in the mid-day, especially in milder months across all regions, and in mild climate zones compared to hotter climate zones since loads are not as dependent on high HVAC loads. • Hourly smart-meter data of 160,000 homes were used to construct diurnal load profiles. • Disparities in hourly loads between households were measured using the Gini index. • The top 20% of households consumed about 40% of the daily electricity load. • The bottom half of consumers used <25% of the daily electricity load. • Customers in mild climates show less seasonal variability than those in hot climates. [ABSTRACT FROM AUTHOR]

As the current mainstream method for electricity theft detection, the data-driven detection model's performance noticeably deteriorates in new regions due to variances in electricity consumption characteristics. Moreover, owing to the lack of theft labels in new regions without on-site inspections, training a high-accuracy theft detection model becomes challenging. To address these issues, this study proposes a novel two-stage generalizable approach for electricity theft detection in new regions. In this approach, Stage 1 presents an adaptation method based on Deep Subdomain Adaptation Network (DSAN). This stage precisely aligns electricity consumption features between different regions. It transfers the characteristics of electricity consumption curves from the established region to the unlabeled new region, successfully adapting the model to the latter. Next, in Stage 2, an enhancement method based on Core-Set Active Learning (CSAL) is proposed to select the representative electricity consumption samples in the new region for fine-tuning. At the early stage of on-site inspections, Stage 2 rapidly and stably enhances the model's performance within limited costs, providing reliable guidance for regular on-site inspections. Stage 1 innovatively transfers a lot of learned features for the theft detection model, and Stage 2 further learns new regions' representative samples combined with inspections for the first time. They complement each other in terms of rich information on electricity consumption behaviors and new regions' electricity consumption characteristics. Finally, experiments on real-world datasets in China demonstrate the superiority of our proposed approach. The results show its high detection performance and rapid enhancement in unlabeled new regions, providing strong support for anti-electricity theft work. • A novel two-stage generalizable approach for electricity theft detection in new regions is proposed. • A precise adaptation method for electricity theft detection in unlabeled new regions is presented. • A rapid enhancement method during the early stage of on-site inspections in new regions is provided. • The effectiveness and superiority are verified on two real-world electricity datasets. [ABSTRACT FROM AUTHOR]

The transportation sector is the third-largest global energy consumer and emitter, making it a focal point in the transition toward the net-zero future. To accelerate the decarbonization of passenger cars, this work is the first to propose a bottom-up charging demand model to estimate the operational electricity use and associated carbon emissions of best-selling battery electric vehicles (BEVs) in various climate zones in China during the 2020s. The findings reveal that (1) the operational energy demand of the top-20 selling BEV models in China, such as Tesla, Wuling Hongguang, and BYD, increased from 601 to 3054 giga-watt hours (GWh) during 2020–2022, with BEVs in South China contributing more than half of the total electricity demand; (2) from 2020 to 2022, the energy and carbon intensities of the best-selling models decreased from 1364 to 1095 kilowatt-hour per vehicle and from 797 to 621 kilograms of carbon dioxide (CO 2) per vehicle, respectively, with North China experiencing the highest intensity decline compared to that in other regions; and (3) the operational energy demand of BEV stocks in China increased from 4774 to 12,048 GWh during 2020–2022, while the carbon emissions of BEV stocks rose to 6.8 mega-tons of CO 2 in 2022, reflecting an annual growth rate of ~50%. In summary, this work delves into the examination and contrast of benchmark data on a nation-regional scale, as well as performance metrics related to BEV chargings. The primary aim is to support nationwide efforts in decarbonization, aiming for carbon mitigation and facilitating the swift evolution of passenger cars toward a carbon-neutral future. Energy demand and carbon emissions from BEV operations in China, 2020–2022: (a) contribution of each model to the top-20 selling models' electricity consumption, (b-c) trends in electricity consumption and associated carbon emissions for the top-20 selling models, and (d-e) trends in electricity consumption and associated carbon emissions for the top-20 selling models, BEV sales, and BEV stocks. [Display omitted] • A bottom-up charging demand model was established to estimate the emissions of the best-selling BEVs. • The energy demand for the top-20 selling BEV model operations in China reached 3054 GWh in 2022. • Samples in South China contributed ~50% of total electricity demand for the best-selling BEVs in 2022. • The national CO 2 intensity of best-selling BEVs decreased from 797 to 621 kg per vehicle in 2020–2022. • Samples in North China faced the highest carbon intensity decrease compared to that in other regions. [ABSTRACT FROM AUTHOR]

The industry of temperature-controlled transportation has shown significant growth in recent years, and this growth is expected to continue in the future. As the sector expands, it's crucial to focus on reducing energy consumption and greenhouse gas emissions related to transport refrigeration systems to meet the planned decarbonization goals. In this study, the energy and environmental benefits of implementing an electric Kinetic Energy Recovery System (KERS) on a refrigerated light-duty commercial van, equipped with a vapor compression refrigeration (VCR) system, are assessed by means of dynamic simulation. The KERS considered involves a LiFePO 4 battery as electricity storage system, a brushless motor-generator unit and a hybrid inverter able to both charge the battery and power the refrigeration system. For each component of the system, i.e. the engine, the alternator, the transmission system and the KERS, the real efficiencies have been considered. The dynamic behaviour of the KERS is simulated by using data obtained by performing a real urban single-delivery 40 km mission, during which the vehicle operating conditions, as well as the electricity demand of the refrigeration system, have been measured. The estimation of the potential benefits of the proposed solution has been performed by comparing the electricity produced by the KERS (and available for use) and the measured energy demand of the refrigeration system. The results have shown that the electricity available for use could cover more than 47% of the total electricity demand. This means that nearly half of the primary energy/fuel consumption can be saved by employing a KERS in refrigerated-light duty vehicles. In particular, emissions savings ranging between 9 and 13 g CO2,e and cost savings between 0.4 and 0.7 c€ per kilometer travelled can be achieved, resulting in an average payback period of 8 years. In addition, when considering the entire useful life of a refrigerated van equal to 10 years, CO2,e savings of 4515–6710 kg CO2,e are obtained. The low complexity of the proposed system and the availability of the components on the market, together with the results obtained by simulation, make using KERS in refrigerated transport a promising solution throughout the decarbonization of the refrigerated transport sector. • A KERS for powering a commercial transport refrigeration system is proposed. • The dynamic performances of a KERS are evaluated through numerical modelling. • Performance evaluation is based on a real single-delivery mission. • The real conversion efficiencies are considered. • The electrical energy demand coverage ratio and subsequent benefits are assessed. [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]

Utilizing surplus renewable energy to produce and store green hydrogen can help meet energy demands during periods of low renewable energy production, and thus contributing greatly to decarbonize emission-intensive sectors, such as the residential and building sector. In this context, this manuscript reports the design, construction and operation of a hybrid PV‑hydrogen demonstrative pilot (PVHyP) for the 100% electrical self-sufficiency of a social housing in the town Novales (Spain). The work addresses i) the collection of real consumption data from the dwelling, ii) the design considering real characteristics and market availability of the devices, iii) final balance of plant implementation, and iv) the energy management strategy followed. The implemented control system allows the facility to be tracked and operated remotely. Besides, this flagship implementation in the SUDOE region (Spain, Portugal and southwest of France) has achieved the electrical independence from the grid under diverse climate conditions, eliminating 2260 kg of CO 2 emissions associated to electricity consumption, saving almost 15,200 kWh of primary energy during two years of monitoring. Thus, around 1170 € where avoided in electricity bills (100% savings), resulting on multiple benefits for social dwellers in risk of energy poverty. • Hybrid PV-hydrogen system for electricity decarbonization in social housing. • First real design and implementation of a PVHyP in the SUDOE region. • Remote operation and continuous monitoring of the PVHyP for 22 months. • 100% reduction of CO 2 emissions and bills in the social home throughout the year. • Sustainable power system for vulnerable tenants in risk of energy poverty. [ABSTRACT FROM AUTHOR]

The Japanese government is advocating for the introduction of decentralized renewable energy devices such as photovoltaics (PV) and high thermal performance houses in urban areas. Due to variations in housing stock among different urban areas, the implementation of these houses and devices would have diverse effects on the supply and demand sides, potentially leading to diverse overproduction of PV electricity, referred to as surplus electricity. Existing research focuses on increasing the utilization of surplus electricity and quantifying the surplus electricity from individual buildings at a regional scale. Considering the lack of research that quantifies surplus electricity through shared PVs under the urban-scale contextual changes (widespread introduction of high-performance houses). Therefore, this study aims to address this gap by utilizing an archetype house simulation model to simulate and compare two scenarios (with or without houses retrofitting) regarding electricity demand for each street-level area, maximum power and duration time of surplus electricity in areas where surplus electricity would occur in the target city Fukuoka in 2025. The results indicate that, under the current residential PV stock and existing introduction rates, the peak surplus power occurs around noon in May, approximately 396 out of 1051 areas in Fukuoka would generate surplus electricity during 2025. Houses retrofitting would increase the surplus electricity (include maximum power and duration time) in these areas. Additionally, this study identifies the residential characteristics of areas that generate surplus electricity, including construction year, household composition, and the proportion of detached houses, whether with retrofitting or not. The results suggest attention to surplus electricity increasing in areas with a high proportion of detached houses when considering houses retrofitting. • Estimation of house number and the probabilities of appliance usage by GA. • Aggregated time-series electricity load and shard PVs generation of houses. • Future change in surplus electricity from houses due to housing retrofitting. • Quantitative assessment of urban-level time-series PV surplus electricity. • Relationship between surplus electricity and characteristics of houses. [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]

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]

To evaluate the effects of different energy retrofit scenarios on the residential building sector, in this study, an urban building energy model (UBEM) was developed from open data, calibrated using energy performance certificates (EPCs), and validated against hourly electricity use measurement data. The calibrated and validated UBEM was used for implementing energy retrofit scenarios and improving the energy performance of the case study city of Varberg, Sweden. Additionally, possible consequences of the scenarios on the electricity grid were also evaluated in this study. The results showed that for a calibrated UBEM, the MAPE of the simulated versus delivered energy to the buildings was 26 %. Although the model was calibrated based on annual values from some of the buildings with EPCs, the validation ensured that it could produce reliable results for different spatial and temporal levels than calibrated for. Furthermore, the validation proved that the spatial aggregation over the city and temporal aggregation over the year could considerably improve the results. The implementation of the energy retrofit scenarios using the calibrated and validated UBEM resulted in a 43 % reduction of the energy use in residential buildings renovated based on the Passive House standard. If this was combined with the generation of on-site solar energy, except for the densely populated areas of the city, it was possible to reach near zero (and in some cases positive) energy districts. The results of grid simulation and power flow analysis for a chosen low-voltage distribution network indicated that energy retrofitting of buildings could lead to an increase in voltage by a maximum of 7 %. This particularly suggests that there is a possibility of occasional overvoltages when the generation and use of electricity are not in perfect balance. • A new approach was developed by coupling UBEM and electricity grid model. • It enables the exploration of the impact of large-scale energy retrofits on the building stock and power grid. • The approach was validated and applied in large-scale combined building and power grid simulations for a Swedish city. • Findings show net-zero energy districts are feasible but risk occasional overvoltages in the grid. • The proposed UBEM-grid approach is viable, and its accuracy is expected to increase with higher EPC coverage. [ABSTRACT FROM AUTHOR]

Demand electrification, system flexibility and energy demand reduction (EDR) are three central tenets of most energy system decarbonisation pathways in the UK and other high-income countries. However, their combined impacts on local energy systems remain understudied. Here, we investigate the impact of different UK energy demand future scenarios on the loading of local electricity networks, and the ability of electrified demand to act flexibly in (i) mitigating the need for network reinforcement and (ii) shifting demand around according to variable tariffs reflecting wider system needs. These scenarios are used to drive spatially- and temporally-explicit technology uptake and energy demand modelling for heating and transport in a localised context, for application to a local electricity network. A particular case study energy network in Scotland, representative of many networks in the UK and Northern Europe, is selected to demonstrate the method. On the basis of the presented case study, which considered a typical winter demand day, energy futures based on EDR policies were found on average to reduce evening transformer loading by up to 16%. Further reductions of up to 43% were achieved with flexible smart charging and up to 69% with the use of vehicle-to-grid. Therefore, we find that policies focused on EDR can mitigate the need for reinforcement of electricity networks against the backdrop of demand electrification. However, flexibility in electricity demand contributes a larger difference to a network's ability to host electrified heat and transport than relying solely on EDR. When used in tandem, policies that simultaneously pursue EDR and electricity system flexibility are shown to have the greatest benefits. Despite these benefits, peak electricity demand is very likely to increase significantly relative to the current baseline. Therefore, widespread reinforcement is required to local electricity networks in the net-zero transition and, accordingly, urgent investment is required to support the realisation of the UK's legally-binding climate goals. • A method to translate energy demand futures to impacts on local electricity systems. • Energy demand reduction policies are shown to mitigate the need for reinforcement. • Flexibility combined with demand reduction policies has significant network benefits. • Widespread reinforcement of the power system will still be necessary in the future. [ABSTRACT FROM AUTHOR]

Accurate carbon accounting is foundational for power enterprises' participation in the carbon market. Current research on estimation of carbon dioxide emissions through electricity‑carbon index analysis primarily relies on an enterprise's total electricity consumption, which often leads to uncertainty and poor interpretability. In reality, carbon dioxide emissions within an enterprise are predominantly generated in specific key processes, indicating a strong correlation between the electricity consumption of key equipment and carbon dioxide emissions. In this context, to enhance both estimation accuracy and interpretability, a two-stage deep learning structure is proposed. This structure leverages non-intrusive load monitoring and employs deep learning algorithms to first disaggregate an enterprise's total electricity consumption into the consumption of key equipment and then use these data to estimate carbon dioxide emissions. Utilizing real-time data from a power plant in China, the proposed two-stage deep learning structure as well as three representative deep learning networks (Recurrent Neural Network, Long Short-Term Memory network, and Gated Recurrent Unit network) are applied to construct electricity‑carbon models for carbon dioxide emission estimation. The experimental results highlight that when employing the two-stage structure, models across three deep learning networks demonstrate a marked enhancement in estimation accuracy compared to traditional models. The two-stage structure reduces the mean squared error (MSE) for models across these three networks by 55.1%, 47.9%, and 46.9%, respectively, compared to their baseline values. This research aims to enhance the precision of carbon dioxide emission estimation and serves as a valuable reference for electricity‑carbon research in various sectors. • First to use the electricity-carbon nexus to estimate carbon dioxide emissions from power generation. • Utilize non-intrusive load monitoring method and deep learning algorithms to increase the accuracy of estimation. • Introduce a two-stage deep learning structure to enhance model's interpretability. [ABSTRACT FROM AUTHOR]

This work explores the implementation of an energy supply chain considering the analysis of photovoltaic, biogas, biomass and conventional grid energy systems to satisfy electricity demand minimizing costs, greenhouse gas emissions, water consumption and maximizing energy security. To address this problem, a multi-criteria decision-making environment was implemented that takes into consideration priorities over economic, environmental and energy security objectives obtaining a compromise solution that minimizes stakeholder dissatisfaction and conflicts and, in addition, possible alliances between stakeholders are analyzed using fuzzy C-means clustering. The compromise solution of multi-objective optimization shows the optimal configuration of the electric power and biomass distribution network, the sizing, behavior, installation and location of the technologies. A case study of the Patzcuaro lake region in Mexico is used to evaluate the performance of the proposed methodology. The results indicate that the implementation of this type of methodology leads to attractive solutions for stakeholders, who are the main influencers in the design and implementation of energy supply chains. The compromise solution found reduces emissions by 73.5% and water consumption by 71.8% compared to grid power and achieves economic returns in most locations of up to $517,000 per year. In addition, the compromise solution satisfies on average 70% of the stakeholders either individually or in alliance. [Display omitted] • Water-energy‑carbon nexus and energy security are considered. • Conflict resolution strategy is presented using a multi-objective approach. • Compromise solutions are proposed for reaching trade-offs among multiple criteria. • Alliances among stakeholders are analyzed through fuzzy C-means clustering. [ABSTRACT FROM AUTHOR]

Future power systems, in which generation will come almost entirely from variable Renewable Energy Sources (vRES), will be characterized by weather-driven supply and flexible demand. In a simulation of the future Dutch power system, we analyze whether there are sufficient incentives for market-driven investors to provide a sufficient level of security of supply, considering the profit-seeking and myopic behavior of investors. We co-simulate two agent-based models (ABM), one for generation expansion and one for the operational time scale. The results suggest that in a system with a high share of vRES and flexibility, prices will be set predominantly by the demand's willingness to pay, particularly by the opportunity cost of flexible hydrogen electrolyzers. The demand for electric heating could double the price of electricity in winter, compared to summer, and in years with low vRES could cause shortages. Simulations with stochastic weather profiles increase the year-to-year variability of cost recovery by more than threefold and the year-to-year price variability by more than tenfold compared to a scenario with no weather uncertainty. Dispatchable technologies have the most volatile annual returns due to high scarcity rents during years of low vRES production and diminished returns during years with high vRES production. We conclude that in a highly renewable EOM, investors would not have sufficient incentives to ensure the reliability of the system. If they invested in such a way to ensure that demand could be met in a year with the lowest vRES yield, they would not recover their fixed costs in the majority of years. • Prices in a highly flexible future market will be determined primarily by the demand. • Energy-only-markets do not provide investments to ensure the security of supply. • Weather uncertainty becomes the main driver of price and cost recovery variability. • Installed dispatchable capacity ultimately determines the reliability of the system. [ABSTRACT FROM AUTHOR]

This study investigates typical domestic energy demand profiles and their variation over time. It draws on a sample of 13,000 homes from Great Britain, applying k-means cluster analysis to smart meter data on their electricity and gas demand over a three-year period from September 2019 to August 2022. Eight typical demand archetypes are identified from the data, varying in terms of the shape of their demand profile over the course of the day. These include an 'All daytime' archetype, where demand rises in the morning and remains high until the evening. Several other archetypes vary in terms of the presence and timing of morning and/or evening peaks. In the case of electricity demand, a 'Midday trough' archetype is notable for its negative midday demand and high overnight demand, likely a combination of the effects of rooftop solar panels exporting to the grid during the day and overnight charging of electric vehicles or electric storage heating. The prevalence of each archetype across the sample varies substantially in relation to different temporally-varying factors. Fluctuations in their prevalence on weekends can be identified, as can Christmas Day. Among homes with gas central heating, the prevalence of gas archetypes strongly relates to external temperature, with around half of homes fitting the 'All daytime' archetype at temperatures below 0 °C, and few fitting it above 14 °C. COVID-19 pandemic restrictions on work and schooling are associated with households' patterns of daily demand becoming more similar on weekdays and weekends, particularly for households with children and/or workers. The latter group had still not returned to pre-pandemic patterns by March 2022. The results indicate that patterns of daily energy demand vary with factors ranging from societal weekly rhythms and festivals to seasonal temperature changes and system shocks like pandemics, with implications for demand forecasting and policymaking. [Display omitted] • Eight typical domestic energy demand profiles identified using cluster analysis. • Gas and electricity demand archetypes characterised for 13,000 homes over 3 years. • Archetypes include 'All daytime' and 'Early morning, and evening' usage patterns. • Seasonal variations in archetype energy demand, particularly for gas. • Variations in prevalence on weekends, by temperature and during COVID-19 pandemic. [ABSTRACT FROM AUTHOR]

Assessing the solar photovoltaic (PV) potential on buildings is essential for environmental protection and sustainable development. However, currently, the high costs of data acquisition and labor required to obtain 3D building models limit the scalability of such estimations extending to a large scale. To overcome the limitations, this study proposes a method of using freely available multi-source Remote Sensing (RS) data to estimate the solar PV potential on buildings at the city scale without any labeling. Firstly, Unsupervised Domain Adaptation (UDA) is introduced to transfer the building extraction knowledge learned by Deep Semantic Segmentation Networks (DSSN) from public datasets to available satellite images in a label-free manner. In addition, the coarse-grained land cover product is utilized to provide prior knowledge for reducing negative transfer. Secondly, the building heights are derived from the global open Digital Surface Model (DSM) using morphological operations. The building information obtained from the above two aspects supports the subsequent estimation. In the case study of Wuhan, China, the solar PV potential on all buildings throughout the city is estimated without any data acquisition cost or human labeling cost through the proposed method. In 2021, the estimated solar irradiation received by buildings in Wuhan is 289737.58 GWh. Taking into account the current technical conditions, the corresponding solar PV potential is 43460.64 GWh, which can meet the electricity demands of residents. The code and test data for building information extraction are available at https://github.com/WHU-USI3DV/3DBIE-SolarPV. • Open multi-source data is utilized without data acquisition costs. • Building footprints are obtained by unsupervised domain adaptation without labeling. • Building heights are derived from the global open DSM using morphological operations. • In-depth analysis of the spatial and temporal, rooftop and facade distributions. • Supply and demand balance provides a basis for planning and decision-making. [ABSTRACT FROM AUTHOR]

Monthly energy benchmarking supports identifying trends, improving energy efficiency, and conducting cost management for building owners, managers, and policymakers better than annual or hourly benchmarking. Annual data cannot fully reflect operation utility status, and hourly data poses the issue of high-cost data mining and incomparability due to its minor scale. However, the primary challenges of monthly energy benchmarking are data limitation, "black-box" barrier, and building classification uncertainty. This study proposes a novel AI-powered Monthly Energy Benchmarking Approach (MEBA) to better assess building energy use patterns, benchmark end-use loads, and track utility bills. MEBA addresses two scenarios: (1) predict complete year-round monthly energy using partial monthly energy data; (2) estimate monthly energy loads from annual total energy data. The study collects monthly electricity and natural gas energy use from two U.S. cities. For the first scenario, the entire dataset is clustered into two primary groups by Gaussian Mixture Model (GMM). Then, the two groups are divided by Self-Organizing Map (SOM) models into five subclusters via energy use patterns. For the second scenario, an additional step is needed to locate the subcluster labels with advanced Light Gradient Boosting Machine (LGBM) classifications. All five subclusters have high prediction performance with an average accuracy of >95%. Both scenarios require the last stage to predict monthly electricity and natural gas by LGBM regressions. MEBA's prediction performance achieves R2s ranging from 0.50 to 0.73, with RMSEs between 0.15 and 2.35, outperforming the state-of-the-art XGBoost model. Each subcluster exhibits distinct energy use patterns, with EUIs, electricity loads, and year built as the most significant attributes. [Display omitted] • Classify buildings via intelligent clustering models based on energy use patterns. • Predict monthly energy use by partial monthly or total annual energy data scenarios. • Building types do not significantly correlate with energy use patterns. • EUI, electricity use, and year built are the critical attributes for energy predictions. • Outperform the state-of-the-art model in classification, explainability, and prediction. [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]

Timely implementation of electricity power consumption (EPC) analysis and evaluation helps assess energy-saving potential and develop energy management plans. Yet, timely EPC data of subnational regions are usually public unavailable, especially for developing countries. Lacking timely and local EPC data makes it difficult to satisfy the requirements for local governments to quickly monitor energy consumption and establish early warning mechanisms. As a solution, this study establishes a quick accounting method for local residential EPC utilizing nighttime light data, based on which we investigate the variations in residential EPC before and after the COVID-19 pandemic. Our method has demonstrated robust performance in estimation and proven its reliability. Compared to previous studies, the proposed methodology has four advantages. First, it monitors local-level residential EPC data at a monthly scale, which allows for observing EPC statistics at a higher frequency. Second, it separates residential EPC from urban and rural sectors, which is useful for analyzing urban-rural dichotomy energy consumption patterns. Third, it provides a less costly and time-consuming approach to estimating energy consumption over space and time. Fourth, it covers almost the whole China (except Tibet, Hongkong, Macao, and Taiwan due to data unavailability), providing useful information for achieving national energy-saving goals. Based on the methodology, we see great potential for developing "global-country-region" energy monitoring through satellite image data service globally. • Timely monitoring of residential EPC is crucial for assessing current consumption and future energy-saving potential. • Established a simple and accurate method to monitor monthly residential EPC at a local level across nearly all of China. • The method separates urban and rural residential EPC, aiding analysis of urban-rural dichotomy energy consumption patterns. • Observed variations in residential EPC pre and post-COVID-19, with heterogeneous effects across different geographical areas. • The method enables "global-country-region" energy monitoring, fostering global energy sustainability. [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]

Higher penetration of renewable energy and increased electricity consumption makes delivering a reliable power supply a complex task. Power system operators are nevertheless required to provide a reliable power supply with a power quality within specified limits. Power quality events can cause malfunctioning of sensitive equipment for consumers in the distribution network. Communities in the periphery of radial distribution networks, such as rural and island communities, often experience more challenges with power quality than other areas. This study focuses on a small island community in the Norwegian Arctic that exemplifies the challenges of power reliability. It explores an alternative approach to utilise the existing electrical infrastructure. By analysing one year of measurements from an electrical substation near a large industry complex, combined with weather forecast data, machine learning models are employed to recognise conditions leading to low power quality. Although these models show satisfactory performance, some data is missing to fully understand the causes of such events. To address this, feature selection techniques are applied to identify the most significant data features and find new important features. Through this process, a refined dataset is obtained, enabling more accurate detection of power quality events in the Arctic community. This research not only enhances the detection capability but also provides valuable insights into the specific data features that contribute to identifying power quality events, offering opportunities for targeted mitigation strategies and generalisation to other locations. • It is valuable for power system operators to know why low power quality (PQ) occurs. • Machine learning models are able to detect power quality events using electrical measurements and weather forecast data. • Feature selection analysis reveals the significance of various data features. • A general pipeline for creating a dataset for PQ event detection is developed based on the feature selection analysis. [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]

Due to the substantial portion of total electricity use attributed to the residential sector and projected rises in demand, anticipating future energy needs in the context of a warming climate will be essential to maintain grid reliability and plan for future infrastructure investments. Machine learning has become a popular tool for forecasting residential electricity demand, but previous studies have been limited by lack of access to high spatiotemporal resolution at a regional scale, which reduces a model's ability to capture the relationship between electricity and its driving factors. In this study, we develop and execute a machine learning framework to predict residential electricity demand at varying temporal and spatial resolutions using hourly smart meter electricity records from roughly 58,000 homes provided by Southern California Edison as well as local weather data, building characteristics, and socioeconomic indicators. The best performing model at the household level, multilayer perceptron (MLP), was able to predict electricity demand most accurately at a monthly resolution, achieving an r2 of 0.45, while the most accurate annual and daily models (also MLP) had r2 values of 0.34 and 0.38, respectively. The results also show that models trained with data aggregated to the census tract level were more accurate (e.g., r2 = 0.82 for the monthly MLP model) than at the household level across all three temporal resolutions analyzed. Total square footage and various climate indicators had the highest feature importance values. Square footage was ranked first in feature importance for the annual and daily models, while the month of the year, which is strongly tied to temperature, was most important to the monthly model. Through this analysis we gain insight into factors that drive electricity demand and the usefulness of machine learning for predicting residential electricity use. • A generalized ML framework is developed. • Smart meter data from ∼58,000 homes used to predict residential electricity demand. • Models also trained with weather, building, and socioeconomic datasets. • Annual, monthly and daily usage estimated at household and census tract resolutions. • Feature selection and importance used to improve models and their interpretability. [ABSTRACT FROM AUTHOR]

Faced with increasing uncertainties in the integrated electricity and gas system (IEGS), the multi-energy operating reserve(MOR), i.e., extra electric and gas reserve capacity, is becoming vitally important to guarantee the reliable operation of the system. Smart energy hubs(SEHs) located at the demand side of the IEGS can adjust their electricity and gas consumption through energy substitution, which has great potential to provide the MOR. Current studies mainly concentrate on the energy trading mechanism among different SEHs in the energy-only market, which may not effectively incentivize SEHs to provide MOR for system operation. Besides, due to the limited energy capacity, SEHs cannot directly participate in the wholesale reserve market along with other large-capacity resources, e.g., power plants and gas wells. To address these, a two-stage market framework is proposed for exploiting the multi-energy operating reserve of SEHs. In the first stage, the wholesale market is cleared considering the participation of large-capacity resources to determine the clearing results of MOR. Based on the results, an energy-loss-cost calculation model is developed to measure the value of the specific MOR level. By altering the reserve requirements, the values of different MOR levels can be accordingly calculated to formulate the multi-energy operating reserve demand surface(MOR-DS), which can quantify varying MOR values with different levels of the reserve. With the MOR-DS describing the price elasticity of the MOR, a distributed MOR clearing mechanism is developed in the second stage to determine the quantity and price of the reserve from SEHs. The clearing mechanism is constructed as a distributed optimization model considering optimal collaboration among different SEHs. Case studies verify that the proposed market framework can effectively stimulate SEHs to provide MOP through economic incentives, which can realize the reliability improvement of IEGS and the revenue increase of SEHs. • A market framework to exploit the multi-energy operating reserve is proposed. • The reserve demand surface quantifies the value of multi-energy operating reserve. • A distributed optimization method clears the multi-energy operating reserve. • The multi-energy operating reserve improves reliability level of the system. [ABSTRACT FROM AUTHOR]

Local flexibility markets are becoming increasingly popular smart grid solutions. They connect customers who require flexible electricity supply and demand with local flexibility providers. However, the growing number of diverse solutions has led to a proliferation of concepts, projects, and companies in this market, with this diversity making understanding and comparison difficult. To tackle this challenge, we propose a multi-layered taxonomy of local flexibility market solutions. This focuses on congestion management on the distribution side of this activity; a crucial service for distribution system operators. Our taxonomy utilizes the Smart Grid Architecture Model to describe these markets comprehensively. We employ an iterative taxonomy-building method, refining and evaluating it through insights from ongoing implementations and twenty-eight expert interviews. Moreover, we present a complete instantiation of our taxonomy and offer a discussion with practical recommendations for practitioners in the local flexibility market landscape. • We examine the evolution of European local flexibility markets (LFMs). • We propose a broader definition for LMFs without design characteristics. • We introduce a LFM multi-layered taxonomy focused on congestion management services. • The multi-layered taxonomy adheres to the Smart Grid Architecture Model framework. • We discuss design aspects crucial for developing, selecting, and regulating LFMs. [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 this article, we investigate the mismatch of renewable electricity production to demand and how flexibility options enabling spatial and temporal smoothing can reduce risks of variability. As a case study we pick a simplified (partial) 2-region representation of the Norwegian electricity system and focus on wind power. We represent regional electricity production and demand through two stochastic processes: the wind capacity factors are modelled as a two-dimensional Ornstein–Uhlenbeck process and electricity demand consists of realistic base load and temperature-induced load coming from a deseasonalised autoregressive process. We validate these processes, that we have trained on historical data, through Monte Carlo simulations allowing us to generate many statistically representative weather years. For the investigated realisations (weather years) we study deviations of production from demand under different wind capacities, and introduce different scenarios where flexibility options like storage and transmission are available. Our analysis shows that simulated loss values are reduced significantly by cooperation between regions and either mode of flexibility. Combining storage and transmission leads to even more synergies and helps to stabilise production levels and thus reduces likelihoods of inadequacy of renewable power systems. [Display omitted] • A case study minimising risk of mismatch of power production and demand in Norway. • We investigate distributions of wind power generation and demand as stochastic processes. • The dynamics of storage and transmission lead to different benefits for ex-/importer. • Cooperation and mixing temporal and spatial smoothing is beneficial to all. [ABSTRACT FROM AUTHOR]

Power generation and consumption predictions are fundamental for smart grid operations, addressing challenges posed by renewable energy variability and irregular consumer demand. This study introduces the Dual Sequence Predictive Model (DSPM) based on Spatiotemporal CNN (STCNN) architecture, offering a holistic solution for forecasting Electricity Generation (EG) and Electricity Consumption (EC) collectively. Leveraging STCNN, the DSPM efficiently extracts spatial and temporal information, improving prediction accuracy and processing speed. A 1D spatial attention module is added to capture crucial spatial dependencies in historical data. Incorporating shared historical weather information streamlines learning process and reduces model complexity. Extensive benchmark evaluations demonstrate the DSPM superior performance, achieving the lowest error rates compared to baseline models. The DSPM also employs the Kullback–Leibler Divergence (KLD) algorithm for power generation and consumption matching, ensuring efficient power distribution within smart and microgrids. Furthermore, the DSPM is evaluated alongside Single Sequence Prediction (SSP) models, providing a comprehensive analysis of its capabilities and comparisons with state-of-the-art models. The SSP model achieved an average RMSE reduction of 17.3% for solar EG data and 15.86% for IHEPC residential EC data, while DSPM reduced RMSE by 7.57% over PowerGrid dataset compared to baseline models. • Developed a DSPM for collective prediction of power generation and consumption. • Unified architecture for spatiotemporal feature learning. • Model evaluation for load and demand prediction individually. • Integration of the KLD algorithm for efficient power matching. • DSPM outperforms baseline models and reducing operational complexity by up to 20%. [ABSTRACT FROM AUTHOR]

One strategy to significantly reduce greenhouse gas emissions in end-use sectors such as building heat is to switch from fossil fuels to electricity. To date, electricity consumption follows a regular and predictable pattern; however, electrifying building heat, which is driven by ambient temperature, causes this pattern to change. Previous work has not fully addressed the implications of building heat electrification on the timing and magnitude of peak demands and on flexibility requirements of the electricity grid. In this work, a high-resolution heating demand model, featuring building stock turnover forecasts, is developed, and applied to British Columbia. The study determines energy, capacity, and flexibility requirements by computing regional energy load profiles for space and water heat. Six scenarios were developed to examine the decarbonization potential in the residential and commercial building sector, considering increased heat pump adoption, building envelope energy efficiency improvements, and the implementation of building energy codes. With an emphasis on building stock improvements, 90% heat pump penetration results in only 6% increase in electrical energy even with an annual population growth rate of 1.1%; however, this scenario leads to a 37% increase in peak electricity demand. Over the time-period considered, building energy codes and retrofit rates contribute relatively little to achieving net-zero emissions in the buildings sector, while building heat electrification has significant impacts on future flexibility requirements of the electricity grid. • A high-resolution spatiotemporal heat demand simulation model with detailed building stock considerations is introduced. • Changes of electric load are analyzed for variations in building stock evolution and envelope efficiency improvements. • Mixtures of heating technologies can be applied to examine the shape and magnitude of peak electricity demands. • With an emphasis on efficiency improvements, 90% heat pump penetration results in only 6% increase in electrical energy. • Heating electrification can increase hourly load changes by up to 144%. [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]

Agrivoltaic systems represent an intelligent solution combining electricity production from solar photovoltaic technology with agricultural production to avoid land use conflicts. Geographic Information System technologies can support the implementation and spread of agrivoltaic systems by identifying the most suitable areas using useful spatially explicit information concerning techno-agro-socio-economic criteria. In this study, we have developed a procedure to identify and classify suitable areas for agrivoltaic systems in Sweden. An Ordinal Priority Approach based multi-criteria decision-making algorithm is established to calculate the weights of the selected evaluation criteria through expert interviews. The land use data refers to the Corine Land Cover 2018 product. The results show that about 8.6% of the Swedish territory, approximately 38,485 km2, is suitable for installing agrivoltaic systems. Among this area, about 0.2% is classified as "excellent", about 15% as "very good", about 72% as "good", about 13% as "moderate", and about 0.1% as "poor". Most "excellent"-classified areas are in Kalmar, Skåne, and Gotland. In contrast, most "very good" sites are in Skåne, Kalmar, and Östergötland. By deploying vertically mounted agrivoltaic systems with bifacial photovoltaic modules, the total potential installed capacity for "excellent" areas is about 2.5 GW p , while for areas classified "excellent" and "very good" is about 221 GW p. The total "excellent" areas can potentially supply about 2.4 TWh of electricity against the electricity consumption in 2021 of about 143 TWh. On the other hand, the land classified as "excellent" and "very good" could potentially provide about 207 TWh. The County of Västra Götaland shows the greatest potentials in terms of total potential electricity supply from agrivoltaic systems with about 227 TWh, followed by Skåne with a total potential of 206 TWh. • A GIS-MCDM approach to identify and classify suitable areas for agrivoltaic systems in Sweden is developed. • About 8.6% of the Swedish territory is suitable for installing agrivoltaic systems. • Among the suitable areas, about 0.2% is classified as "excellent" while about 15% as "very good". • The total potential installed capacity for "excellent" areas is about 2.5 GW p. • The areas classified as "excellent" have the potential to supply about 2.4 TWh. [ABSTRACT FROM AUTHOR]

Urban lighting system is an important part of urban space service functions. With the continuous advancement of sustainable development goals, governments of various countries are gradually paying attention to the energy, social, economic and environmental benefits of urban lighting systems, and promote the use of LED lighting to replace HPS lamp lighting to save urban energy consumption. In this study, we used the glimmer imager data of SDGSAT-1 launched in 2021, combined with the streetlight dataset of the District of Columbia, to establish a classification model for distinguishing LED lights from HPS lamps, with an overall recognition accuracy of 80.3% and the kappa coefficient is 0.603. Based on this model, we identify light types in the central cities of China's three urban agglomerations: Beijing, Shanghai, Guangzhou, and Shenzhen, and describe their lighting composition and spatial distribution patterns. Additionally, we make a preliminary estimate of the money and electricity consumption saved by lighting renovations, and analyzed the impact of urban lighting changes on public perception. This study combines SDGSAT-1 data to propose a training model in urban lighting classification and applies it to a wide range of urban, the results of which help to provide data to support the implementation of SDG7. • A model for identifying LED lights and HPS lamps. • Obtained and analyzed the light type composition of four central cities in China. • The economic and social effects are analyzed. [ABSTRACT FROM AUTHOR]

The domestic sector is a significant energy consumer – accounting for around 40% of global electricity demand – due to household demand diversity and complexity. An accurate and robust estimation of domestic electrical loads, environmental impacts, and energy-efficiency potential is crucial for optimal planning and management of energy systems and applications. However, uncertainties resulting from simplistic socio-technical attributes, microclimatic variations, and oversimplification of the effects of interdependent variables make domestic energy modelling challenging. In this research, a hybrid bottom-up community energy forecasting framework is developed to estimate sub-hourly domestic electricity demand using a combination of statistical and engineering modelling approaches by considering key factors influencing household consumption, including demographic characteristics, occupancy patterns, and the features, ownership, and utilisation patterns of electric appliances. The framework is tested on a community in Wales, UK and validated on an annual, daily, and sub-hourly basis with monitored electricity usage averages derived from the UK Energy Follow-Up Survey and the sub-national electricity consumption datasets. Results closely reflect annual and daily household demand at individual dwellings and aggregated levels, with an estimation accuracy of up to 90%. Moreover, the framework facilitates more reliable sub-hourly demand profiles compared to conventional simulation practices that overestimate daily electricity demand and sub-hourly peaks by up to 15% and 50%, respectively. • A bottom-up stochastic forecasting model for domestic electricity demand. • Integration of occupancy behaviour and electric load modelling for improved accuracy. • Integrating physics-based modelling to consider internal thermal behaviour. • Annual and daily household electricity demand with estimation accuracy up to 90%. • Conventional simulation practices overestimate electricity demand peaks by up to 50%. [ABSTRACT FROM AUTHOR]

Decarbonizing the residential building sector by replacing gas boilers with electric heat pumps will dramatically increase electricity demand. Existing models of future heat pump demand either use daily heating demand profiles that do not capture heat pump use or do not represent sub-national heating demand variation. This work presents a novel method to generate high spatiotemporal resolution residential heat pump demand profiles based on heat pump field trial data. These spatially varied demand profiles are integrated into a generation, storage, and transmission expansion planning model to assess the impact of spatiotemporal variations in heat pump demand. This method is demonstrated and validated using the British power system in the United Kingdom (UK), and the results are compared with those obtained using spatially uniform demand profiles. The results show that while spatially uniform heating demand can be used to estimate peak and total annual heating demand and grid-wide systems cost, high spatiotemporal resolution heating demand data is crucial for spatial power system planning. Using spatially uniform heating demand profiles leads to 15.1 GW of misplaced generation and storage capacity for a 90% carbon emission reduction from 2019. For a 99% reduction in carbon emissions, the misallocated capacity increases to 16.9-23.9 GW. Meeting spatially varied heating load with the system planned for uniform national heating demand leads to 5% higher operational costs for a 90% carbon emission reduction. These results suggest that high spatiotemporal resolution heating demand data is especially important for planning bulk power systems with high shares of renewable generation. • Future heat pump demand is likely to vary in space and time within electricity systems. • Regional heat demand is modeled using heat pump trial data and reanalysis temperature. • Regional heat pump demand leads to spatial differences in capacity expansion. • High spatiotemporal resolution heat demand profiles are key for power systems planning. [ABSTRACT FROM AUTHOR]

This article presents a novel hybrid approach using classic statistics and machine learning to forecast the national demand of electricity. As investment and operation of future energy systems require long-term electricity demand forecasts with hourly resolution, our mathematical model fills a gap in energy forecasting. The proposed methodology was constructed using hourly data from Ukraine's electricity consumption ranging from 2013 to 2020. To this end, we analysed the underlying structure of the hourly, daily and yearly time series of electricity consumption. The long-term yearly trend is evaluated using macroeconomic regression analysis. The mid-term model integrates temperature and calendar regressors to describe the underlying structure, and combines ARIMA and LSTM "black-box" pattern-based approaches to describe the error term. The short-term model captures the hourly seasonality through calendar regressors and multiple ARMA models for the residual. Results show that the best forecasting model is composed by combining multiple regression models and a LSTM hybrid model for residual prediction. Our hybrid model is very effective at forecasting long-term electricity consumption on an hourly resolution. In two years of out-of-sample forecasts with 17520 timesteps, it is shown to be within 96.83% accuracy. • Long-term forecast in hourly resolution with MAPE below 3.2% over 2 years. • High accuracy AI national demand predictions for future energy system. • Complementary ensemble of classical statistics and machine learning algorithms. • Fully automated forecasting. [ABSTRACT FROM AUTHOR]

Energy systems for flexibility in buildings are hybrid, primarily including rooftop photovoltaics (PV), cooling storage, and battery. Considering their techno-economic patterns, this research establishes an optimization model to determine the optimal technology portfolio and financial advantages of PV-battery-cooling storage systems for commercial buildings in China. The analysis of all cases indicates that cooling storage outperforms batteries in economic benefits, suggesting the prioritization of cooling storage installation. Once the optimal cooling storage rate is exceeded, it is advisable to proceed with batteries. Meanwhile, PV integration significantly enhances the system efficiency and promotes battery utilization. For example, a 40% PV penetration combined with a 0.006 $/(a·kWh e) energy storage investment results in an impressive 27.3% cost reduction in a Beijing mall, while the optimal cooling storage rate decreases from 55% to 40%. Furthermore, the study emphasizes the impact of tariff patterns and electricity demand on the economic feasibility of hybrid energy systems. The museum's substantial annual cooling requirements and nighttime loads make cooling storage favorable, with PV less suitable than the mall and office. Notably, cities like Beijing, Shanghai, Chongqing, and Guangzhou exhibit considerable peak-to-valley tariff differences, yielding higher economic benefits ranging from 23% to 27%. Finally, as battery costs decline and electricity price becomes more volatile, the battery would gradually replace cooling storage, especially when battery cost drops from 150 $/kWh to 70 $/kWh. [Display omitted] • Technology portfolio and cost savings of hybrid energy systems are optimized. • Application potential of PV-battery-cooling storage systems is discussed in China. • Cooling storage is prioritized due to economic performance compared to batteries. • PV integration enhances energy storage efficiency and promotes battery utilization. • Tariff patterns and electricity demand impact economic feasibility of hybrid systems. [ABSTRACT FROM AUTHOR]

With the rapid advancements in smart meters, energy internet, and high-performance computing technologies, deep learning methods are increasingly used for detecting anomalies in building electricity consumption. However, these models grapple with challenges like outliers, missing data, and imbalanced categories. Moreover, these models are often considered as black boxes due to their lack of interpretability. This limitation impedes the practical deployment of deep learning methods. Focusing on these issues, this study presents an anomaly detection method aimed at improving anomaly detection accuracy and interpretability in low-quality and class imbalanced data. Initially, our method established data preprocessing models to handle with outliers and missing data. Subsequently, an unbalance elimination model, named GRU-MACGANS, was designed to augment the minority category data. The SA-CNN-BiGRU-FCN model, incorporating a knowledge transfer strategy, was implemented to identify anomalous behavior. Finally, the results of SA-CNN-BiGRU-FCN model were interpreted based on the contrastive explanations method. Our case study of a low-voltage distribution system demonstrates the effectiveness of our approach. Data preprocessing models and GRU-MACGANS enhance data quality and alleviate category imbalance issues in anomaly detection. The interpretable analysis provides insights into the ability of detection models to identify representative features of different electricity consumption behaviors for effective anomaly detection. • We propose an interpretable and efficient anomaly detection method. • An efficient solution for the low-quality data problem is introduced. • We introduce GRU-MACGANs to address the data imbalance problem. • Interpretable analysis is employed to infer the motivations behind the detection results. • A cloud-edge collaborative deployment strategy is induced to save hardware resources. [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]

Exploring cost-effective wind-solar-storage combinations to replace conventional fossil-fuelled power generation without compromising grid reliability becomes increasingly important in a steadily decarbonizing electricity system. For a renewable energy-rich state in Southern India (Karnataka), we systematically assess various wind-solar-storage energy mixes for alternate future scenarios, using Pareto frontiers. The simulated scenarios consider assumed growth in electricity demand, and different levels of base generation and supply-side flexibility from fossil fuels and hydropower. Our approach uses hourly load data, simulates generation based on hourly weather reanalysis data, and models the effects of battery charging and discharging on battery lifetimes. In the context of declining base generation and limited flexibility in the state electricity grid, the reliability of meeting demand is limited by the generation curtailment that is permitted. We show that adding battery storage capacity without concomitant expansion of renewable generation capacity is inefficient. Keeping the wind-solar installations within the officially assessed renewable potential, a fully decarbonized grid with limited flexibility can have approximately 63% reliability even after using adequate battery storage. Achieving 99% grid reliability would be expensive, requiring large wind-solar installations that exceed officially assessed potential, constrained by land allocation. The findings highlight the importance of a fresh examination of curtailment thresholds, available renewable potential, and possibilities of demand-side management based on consumers' willingness to modify hourly demand patterns. • We discuss trade-offs between annualized wind-solar-storage cost and reliability. • Our algorithm analyses hourly demand – generation data using Pareto frontier. • Adding storage without concomitant expansion of renewable capacity is inefficient. • Grid reliability is limited by allowed generation curtailment and grid flexibility. • Achieving 99% reliability needs wind-solar capacity beyond assessed potential. [ABSTRACT FROM AUTHOR]