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Although there are opportunities to reduce electrical energy demand in unit processes of mechanical pulp-based paper and paperboard production, this may not be financially beneficial. This is generally because energy optimization opportunities connected to reduced refiner electricity demand in mechanical pulping systems also results in less steam available for the drying of the paper. As modern high consistency refiner systems produce approximately one ton of steam for each MWh of electricity when producing one ton of pulp, a reduction in electric energy demand leads to increased fuel demand in steam boilers to compensate for the steam shortage. In this study, we investigated what the financial and environmental situation would look like if we were to expand the system border from a paper mill to a larger system consisting of a mechanical pulp-based paper or paperboard mill, a district heating system with an incineration boiler and a chemical pulp mill. Mechanical pulp production has a wood to product yield of >90%, a high electric energy demand to separate woodchips to pulp and is a net producer of heat and steam while chemical pulp-based production has a wood to product yield of 50%, a low electric energy demand and is a net heat and electricity producer due to the combustion of dissolved wood polymers. The aim of this research is to create useful and robust models of how to use excess heat from certain industry sites to cover the steam shortage in other industry sites by means of utilizing and optimizing the district heating systems connecting these sites. For this purpose, we used a simulation tool which dynamically allows us to evaluate different scenarios. Our results shows that there is great potential to reduce both carbon dioxide emissions and production costs for industry sites and society by means of these tools. [ABSTRACT FROM AUTHOR]

In this paper, a systematic literature review is presented, through a survey of the main digital databases, regarding modelling methods for Short-Term Load Forecasting (STLF) for hourly electricity demand for residential electricity and to realize the performance evolution and impact of Artificial Intelligence (AI) in STLF. With these specific objectives, a conceptual framework on the subject was developed, along with a systematic review of the literature based on scientific publications with high impact and a bibliometric study directed towards the scientific production of AI and STLF. The review of research articles over a 10-year period, which took place between 2012 and 2022, used the Preferred Reporting Items for Systematic and Meta-Analyses (PRISMA) method. This research resulted in more than 300 articles, available in four databases: Web of Science, IEEE Xplore, Scopus, and Science Direct. The research was organized around three central themes, which were defined through the following keywords: STLF, Electricity, and Residential, along with their corresponding synonyms. In total, 334 research articles were analyzed, and the year of publication, journal, author, geography by continent and country, and the area of application were identified. Of the 335 documents found in the initial research and after applying the inclusion/exclusion criteria, which allowed delimiting the subject addressed in the topics of interest for analysis, 38 (thirty-eight) documents were in English (26 journal articles and 12 conference papers). The results point to a diversity of modelling techniques and associated algorithms. The corresponding performance was measured with different metrics and, therefore, cannot be compared directly. Hence, it is desirable to have a unified dataset, together with a set of benchmarks with well-defined metrics for a clear comparison of all the modelling techniques and the corresponding algorithms. [ABSTRACT FROM AUTHOR]

The European Union, in pursuit of the goal of reducing emissions by at least 55% by 2030 and achieving climate neutrality by 2050, is deploying different actions, with industry decarbonization as a key strategy. However, increasing electricity demand requires an intensification of energy generation from clean technologies, and the energy system's expansion is hindered by renewable generation's climatic dependencies and the imperative for substantial electrical infrastructure investments. Although the transmission grid is expected to grow, flexibility mechanisms and innovative technologies need to be applied to avoid an overwhelming growth. In this context, this paper presents a thorough assessment, conducted within the FLEXINDUSTRIES project, of the flexibility potential across seven energy-intensive industries (automotive industry, biofuel production, polymer manufacturing, steel manufacturing, paper mills, pharmaceutical industry, and cement production). The methodology followed during the analysis entails reviewing the state-of-the-art existing flexibility mechanisms, industries' energy markets engagement, and technical/operational readiness. The results highlight the feasibility of the proposed actions for enabling energy market flexibility through demand-response programs, quantifying energy opportunities, and pinpointing regulatory and technical barriers. [ABSTRACT FROM AUTHOR]

Smart meters for measuring electricity consumption are fast becoming prevalent in households. The meters measure consumption on a very fine scale, usually on a 15 min basis, and the data give unprecedented granularity of consumption patterns at household level. A multitude of papers have emerged utilizing smart meter data for deepening our knowledge of consumption patterns. This paper applies a modification of Okoli’s method for conducting structured literature reviews to generate an overview of research in electricity customer classification using smart meter data. The process assessed 2099 papers before identifying 34 significant papers, and highlights three key points: prominent methods, datasets and application. Three important findings are outlined. First, only a few papers contemplate future applications of the classification, rendering papers relevant only in a classification setting. Second; the encountered classification methods do not consider correlation or time series analysis when classifying. The identified papers fail to thoroughly analyze the statistical properties of the data, investigations that could potentially improve classification performance. Third, the description of the data utilized is of varying quality, with only 50% acknowledging missing values impact on the final sample size. A data description score for assessing the quality in data description has been developed and applied to all papers reviewed. [ABSTRACT FROM AUTHOR]

The three-phase imbalance in low-voltage distribution networks (LVDNs) seriously threatens the security and stability of the power system. At present, a standard solution is automatic phase commutation, but this method has limitations because it does not address the branch imbalance and premature convergence or instability of the commutation algorithm. Therefore, this paper proposes a novel refined regulation commutation system, combined with a modified optimized commutation algorithm, and designs a model and simulation for feasibility verification. The refined regulatory model incorporates branch control units into the traditional commutation system. This effectively disperses the main controller's functions to each branch and collaborates with intelligent fusion terminals for precise adjustment. The commutation algorithm designed in this paper, combined with the above model, adopts strategies such as symbol encoding, cubic chaotic mapping, and adaptive adjustment based on traditional genetic algorithms. In addition, in order to verify the effectiveness of the proposed method, this paper establishes a mathematical model with the minimum three-phase imbalance and commutation frequency as objectives and establishes a simulation model. The results of the simulation demonstrate that this method can successfully lower the three-phase imbalance of the low-voltage distribution network. It leads to a decrease of the main circuit's three-phase load imbalance rate from 27% to 6% and reduces each branch line's three-phase imbalance ratio to below 10%. After applying the method proposed in this paper, the main and branches circuit three-phase imbalance are both lower than the limit ratio of the LVDNs, which can improve the quality and safety of electricity consumption. Additionally, the results also prove that the commutation algorithm under this method has faster convergence speed, better application effect, and better stability, which has promotion and application value. [ABSTRACT FROM AUTHOR]

A reasonable allocation of production schedules and savings in overall electricity costs are crucial for large manufacturing conglomerates. In this study, we develop an optimization model of off-site industrial production scheduling to address the problems of high electricity costs due to the irrational allocation of production schedules on the demand side of China's power supply, and the difficulty in promoting industrial and commercial distributed photovoltaic (PV) projects in China. The model makes full use of the conditions of different PV resources and variations in electricity prices in different places to optimize the scheduling of industrial production in various locations. The model is embedded with two sub-models, i.e., an electricity price prediction model and a distributed photovoltaic power cost model to complete the model parameters, in which the electricity price prediction model utilizes a Long Short-Term Memory (LSTM) neural network. Then, the particle swarm optimization algorithm is used to solve the optimization model. Finally, the production data of two off-site pharmaceutical factories belonging to the same large group of enterprises are substituted into the model for example analysis, and it is concluded that the optimization model can significantly reduce the electricity consumption costs of the enterprises by about 7.9%. This verifies the effectiveness of the optimization model established in this paper in reducing the cost of electricity consumption on the demand side. [ABSTRACT FROM AUTHOR]

The digitization of distribution power systems has revolutionized the way data are collected and analyzed. In this paper, the critical task of harnessing this information to identify irregularities and anomalies in electricity consumption is tackled. The focus is on detecting non-technical losses (NTLs) and energy theft within distribution networks. A comprehensive overview of the methodologies employed to uncover NTLs and energy theft is presented, leveraging measurements of electricity consumption. The most common scenarios and prevalent cases of anomalies and theft among consumers are identified. Additionally, statistical indicators tailored to specific anomalies are proposed. In this research paper, the practical implementation of numerous artificial intelligence (AI) algorithms, including the artificial neural network (ANN), ANFIS, autoencoder neural network, and K-mean clustering, is highlighted. These algorithms play a central role in our research, and our primary objective is to showcase their effectiveness in identifying NTLs. Real-world data sourced directly from distribution networks are utilized. Additionally, we carefully assess how well statistical methods work and compare them to AI techniques by testing them with real data. The artificial neural network (ANN) accurately identifies various consumer types, exhibiting a frequency error of 7.62%. In contrast, the K-means algorithm shows a slightly higher frequency error of 9.26%, while the adaptive neuro-fuzzy inference system (ANFIS) fails to detect the initial anomaly type, resulting in a frequency error of 11.11%. Our research suggests that AI can make finding irregularities in electricity consumption even more effective. This approach, especially when using data from smart meters, can help us discover problems and safeguard distribution networks. [ABSTRACT FROM AUTHOR]

The ongoing electrification of the transport sector is expected to cause an increase in electricity demand and, therefore, trigger significant network investments to accommodate it. This paper focuses on investment decision-making for electricity distribution grids and specifically on the strategic and incremental investment network planning approaches. In particular, the former involves network planning with the consideration of a long-term multi-stage study horizon, as opposed to a shorter–term view of the future that applies to the latter case. An investment analysis that is carried out underlines the economic savings generated from adopting a strategic investment perspective over an incremental one. These economic savings are achieved from the fact that the associated fixed investment costs are incurred only once in the horizon under strategic planning. On the other hand, incremental planning involves a series of network reinforcement decisions, thereby incurring the fixed cost multiple times. In addition, sensitivity analyses that are carried out capture the effect of key parameters, such as investment cost, discount rate and investment delay, on the generated economic savings. [ABSTRACT FROM AUTHOR]

The non-technical losses caused by abnormal power consumption behavior of power users seriously affect the revenue of power companies and the quality of power supply. To assist electric power companies in improving the efficiency of power consumption audit and regulating the power consumption behavior of users, this paper proposes a power consumption anomaly detection method named High-LowDAAE (Autoencoder model for dual adversarial training of high low-level temporal features). High-LowDAAE adds an extra "discriminator" named AE3 to USAD (UnSupervised Anomaly Detection on Multivariate Time Series), which performs the same function as AE2 in USAD. AE3 performs the same function as AE2 in USAD, i.e., it is trained against AE1 to enhance its ability to reconstruct average data. However, AE3 differs from AE2 because the two "discriminators" correspond to the high-level and low-level time series features output from the shared encoder network. By utilizing different levels of temporal features to reconstruct the data and conducting adversarial training, AE1 can reconstruct the time-series data more efficiently, thus improving the accuracy of detecting abnormal electricity usage. In addition, to enhance the model's feature extraction ability for time-series data, the self-encoder is constructed with a long short-term memory (LSTM) network, and the fully connected layer in the USAD model is no longer used. This modification improves the extraction of temporal features and provides richer hidden features for the adversarial training of the dual "discriminators". Finally, the ablation and comparison experiments are conducted using accurate electricity consumption data from users, and the results show that the proposed method has higher accuracy. [ABSTRACT FROM AUTHOR]

Electricity consumption in buildings is one of the major causes of energy usage and knowledge of this can help building owners and users increase energy efficiency and conservation efforts. For Pacific Island countries, building electricity demand data is not readily accessible or available for constructing models to predict electricity demand. This paper starts to fill this gap by studying the case of schools in Fiji. The aim of the paper is to assess the factors affecting electricity demand for grid-connected Fijian schools and use this assessment to build mathematical models (multiple linear regression (MLR) and artificial neural network (ANN)) to predict electricity consumption. The average grid-connected electricity demand in kWh/year was 1411 for early childhood education schools, 5403 for primary schools, and 23,895 for secondary schools. For predicting electricity demand (ED) for all grid-connected schools, the stepwise MLR model shows that taking logarithm transformations on both the dependent variable and independent variables (number of students, lights, and air conditioning systems) yields statistically significant independent variables with an R2 value of 73.3% and RMSE of 0.2248. To improve the predicting performance, ANN models were constructed on both the natural form of variables and transformed variables. The optimum ANN model had an R2 value of 95.3% and an RMSE of 59.4 kWh/year. The findings of this study can assist schools in putting measures in place to reduce their electricity demand, associated costs, and carbon footprint, as well as help government ministries make better-informed policies. [ABSTRACT FROM AUTHOR]

To support increasing renewable capacity for a net-zero future, energy storage will play a key role in maintaining grid stability. In this paper, all current and near-future energy storage technologies are compared for three different scenarios: (1) fixed electricity buy-in price, (2) market-based electricity buy-in price, and (3) energy storage integrated into a fully renewable electricity system. In the first part of this study, an algorithm is devised to simulate strategic buy-in of electricity for energy storage. This analysis yields a qualitative decision-making tool for a given energy storage duration and size. Building upon the first part's findings, an integration study gives insight into expected power prices and expected storage size in a typical northwestern European fully renewable energy system. The integration study shows significant need for electricity storage with durations spanning from one to several days, typically around 40 h. Pumped Hydro Storage and Pumped Thermal storage surface as the best options. The overall levelized costs of storage are expected to be in the USD 200–500/MWh range. Integration of storage with renewables can yield a system-levelized cost of electricity of about USD 150/MWh. Allowing flexibility in demand may lower the overall system-levelized cost of electricity to USD 100/MWh. [ABSTRACT FROM AUTHOR]

This paper presents a methodology to forecast electrical demand for the Chilean Electrical Power System considering a national, regional, district and bus spatial disaggregation. The methodology developed was based on different kinds of econometric models and end-use models to represent the massification of low carbon emission technologies such as electromobility, electric heating, electric water heating, and distributed generation. In addition, the methodology developed allows for the projection of the electric demand considering different kinds of clients as regulated and non-regulated clients, and different economic sectors. The model was applied to forecast the long-term electricity demand in Chile for the period 2022–2042 for 207 districts and 474 buses. The results include projections under the base case and low carbon scenarios, highlighting the significant influence of new technologies on future demand. [ABSTRACT FROM AUTHOR]

With the development of the economy and society, the importance of a secure and stable electricity supply continues to increase. However, the power grid is facing the test of excess installed capacity, the waste of renewable energy, and a low comprehensive utilization rate. This problem stems from the inconsistent peak–valley differences between power production and consumption, and the lack of clear electricity price signals, which disrupts the safe and stable operation of the power market. This paper combines the interactive transactions among clean energy power generation companies, users, and energy storage, explores how the system optimization model can be reflected in the power market through regulatory measures, and formulates the optimal output scheme of the system under the constraints of clean energy power generation forecast data, user base load forecast data, demand-side resource regulation ability, and energy storage system regulation ability to achieve the goals of comprehensive clean energy power consumption and minimum cost for users. A comprehensive analysis of the proposed model was conducted using actual data from a certain province in China, the results show that the consumption of clean energy will increase by 3% to full consumption and the total cost of users will be 32% lower than that of time-of-use (TOU) power prices, which proves the potential of the proposed joint optimization model in absorbing clean energy and the effectiveness of the market mechanism. [ABSTRACT FROM AUTHOR]

In the context of power system decarbonization, the demand-side strategy for increasing the share of renewable energy is studied for two constrained energy systems. This strategy, which is currently widely suggested in policies on the energy transition, would impact consumer behavior. Despite the importance of studying the latter, the focus here is on decisions regarding the type, location, and timeframe of implementing the related measures. As such, solutions must be assessed in terms of cost and feasibility, technological learning, and by considering geographical and environmental constraints. Based on techno-economic optimization, in this paper we analyze the evolution of the power system and elaborate plausible long-term trajectories in the energy systems of two European islands. The case studies, Procida in Italy and Hinnøya in Norway, are both electrically connected to the mainland by submarine cables and present issues in their power systems, which are here understood as relatively isolated power systems. Renewable energy integration is encouraged by legislative measures in Italy. Although not modeled here, they serve as a backbone for the assumptions of increasing these investments. For Procida, rooftop photovoltaics (PV) coupled with energy storage are integrated in the residential, public, and tertiary sectors. A price-based strategy is also applied reflecting the Italian electricity tariff structure. At a certain price difference between peak and off-peak, the electricity supply mix changes, favoring storage technologies and hence decreasing imports by up to 10% during peak times in the year 2050. In Norway, renewable energy resources are abundant. The analysis for Hinnøya showcases possible cross-sectoral flexibilities through electrification, leading to decarbonization. By fine-tuning electric vehicle charging tactics and leveraging Norway's electricity pricing model, excess electricity demand peaks can be averted. The conclusions of this double-prospective study provide a comparative analysis that presents the lessons learnt and makes replicability recommendations for other territories. [ABSTRACT FROM AUTHOR]

Australia, like many other countries around the world, is undergoing a transition toward net zero emissions. It requires changes and development in many sectors, which not only bring benefits but also challenges. The rapid growth in renewable energy sources (RESs) is necessary to decarbonise electricity generation but negatively affects grid stability. Residential buildings also contribute to this issue through specific load profiles and the high penetration of rooftop photovoltaic (PV) installations. Maintaining grid balance will be crucial for further emissions reductions. One of the potential solutions can be the replacement of conventional heating and cooling systems in houses with solutions capable of storing energy and shifting the electrical load. As presented in this paper, heat pumps and hydronic systems can significantly improve the electrical load of a typical South Australian household when they are controlled by algorithms reacting to the current grid conditions and household-generated electricity compared to conventional solutions. TRNSYS 18 simulations of air source and ground source heat pump systems with smart control based on measured electricity consumption and domestic hot water usage data showed the possibility of total energy consumption reduction, shifting the load from peak periods towards periods of excessive RES generation and increasing self-consumption of rooftop PV electricity. These improvements reduce the amount of emissions generated by such a household and allow for further development of other sectors. [ABSTRACT FROM AUTHOR]

This paper examines how Australian residential load profiles may evolve in the short to medium term future. These profiles can be used to support simulation studies of the future Australian network within an environment that is transitioning to renewable energy and broader use of electricity as a tool for decarbonisation. The daily profiles rely heavily on the Australian Energy Market Operator (AEMO) forecasts for future annual energy usage. The period from 2024 to 2050 will be transformational. In the residential networks, two secular trends are particularly important in expanding residential generation and electrification. New daily load profiles have been constructed using historical Australian profiles and adding additional components for solar generation, battery operation and electrification activities. The entire aggregated residential network is expected to have reverse midday power flow on any average day from 2024 onwards due to the rapid increase in electric vehicle (EV) usage. The domestic energy demand forecasting methodology presented in this work related to Australia can easily be adopted to carry out similar forecasting for any country of the world. [ABSTRACT FROM AUTHOR]

Sierra Leone is an electricity-poor country with one of the lowest electricity consumption per capita rates across sub-Saharan Africa. Yet, with ambitious targets to transform and stimulate its economy in the coming decades, energy demand forecasting becomes an integral component of successful energy planning. Through applying the MAED-D (version 2.0.0) demand software, this research study aims to generate Sierra Leone's electricity demand forecasts from 2023 to 2050. Three novel scenarios (baseline-, high-, and low-demand) are developed based on socio-economic and technical parameters. The baseline scenario considers the current electricity sector as business-as-usual; the high-demand scenario examines an ambitious development future with increased economic diversification and mechanisation, and the low-demand scenario examines more reserved future development. The modelled scenario results project an increase in electricity demand ranging from 7.32 PJ and 12.23 PJ to 5.53 PJ for the baseline-, high-, and low-demand scenarios, respectively, by 2050. This paper provides a base set of best-available data needed to produce an electricity demand model for Sierra Leone which can be used as a capacity-building tool for in-country energy planning alongside further integration into data modelling pipelines. [ABSTRACT FROM AUTHOR]

With the energy structure transition and the development of the green power market, the role of electricity retailers in multi-market green power trading has become more and more important. Particularly in China, where aggressive green energy policies and rapid market transformations provide a distinct context for such studies, the challenges are pronounced. Under demand-side response, electricity retailers face the uncertainty of users' electricity consumption and incentives, which complicates decision-making processes. The purpose of this paper is to explore the optimization decision-making problem of multi-market green power trading for electricity retailers under demand-side response, with a special focus on the Chinese market due to its leadership in implementing substantial green energy initiatives and its potential to set precedents for global practices. We first construct a two-party benefit optimization model, which comprehensively considers the profit objectives for electricity retailers and utility maximization for users. Then, the model is solved by the Lagrange multiplier method and distributed subgradient algorithm to obtain the optimal solution. Finally, the effectiveness of the incentive optimization strategy under the multi-market to promote green power consumption and improve the profit of electricity retailers is verified by arithmetic simulation. The results of this study show that the incentive optimization strategy under multi-market, particularly within the Chinese context, is expected to provide a reference for electricity retailers to develop more flexible and effective trading strategies in the green power market and to contribute to the process of promoting green power consumption globally. [ABSTRACT FROM AUTHOR]

Behind the meter (BTM) distributed energy resources (DERs), such as photovoltaic (PV) systems, battery energy storage systems (BESSs), and electric vehicle (EV) charging infrastructures, have experienced significant growth in residential locations. Accurate load forecasting is crucial for the efficient operation and management of these resources. This paper presents a comprehensive survey of the state-of-the-art technologies and models employed in the load forecasting process of BTM DERs in recent years. The review covers a wide range of models, from traditional approaches to machine learning (ML) algorithms, discussing their applicability. A rigorous validation process is essential to ensure the model's precision and reliability. Cross-validation techniques can be utilized to reduce overfitting risks, while using multiple evaluation metrics offers a comprehensive assessment of the model's predictive capabilities. Comparing the model's predictions with real-world data helps identify areas for improvement and further refinement. Additionally, the U.S. Energy Information Administration (EIA) has recently announced its plan to collect electricity consumption data from identified U.S.-based crypto mining companies, which can exhibit abnormal energy consumption patterns due to rapid fluctuations. Hence, some real-world case studies have been presented that focus on irregular energy consumption patterns in residential buildings equipped with BTM DERs. These abnormal activities underscore the importance of implementing robust anomaly detection techniques to identify and address such deviations from typical energy usage profiles. Thus, our proposed framework, presented in residential buildings equipped with BTM DERs, considering smart meters (SMs). Finally, a thorough exploration of potential challenges and emerging models based on artificial intelligence (AI) and large language models (LLMs) is suggested as a promising approach. [ABSTRACT FROM AUTHOR]

The growing demand for electricity in long-duration space missions has become a pressing concern. The space nuclear closed-Brayton-cycle (CBC) power generation system offers advantages in power output, operational lifespan, and range. However, a significant speed disparity exists between its compressor and alternator. To address this challenge, this paper proposes a double-rotor CBC configuration. A corresponding dynamic model that couples the nuclear reactor and radiator is formulated, and dynamic analysis is conducted to facilitate system control. The study delves into the dynamic start-up process of the double-rotor CBC system and examines how various component parameters impact its power generation performance. The findings indicate that through the introduction of suitable reactivity to regulate reactor power and the incorporation of a PID controller to manage flow distribution between two turbines, the system can achieve start-up within 5200 s. Moreover, the innovative double-rotor structure suggested in this paper enables the separation of compressor and alternator speeds. Consequently, the compressor and alternator can operate within their optimal speed ranges independently, which is a feature that holds potential benefits for the system's practical implementation. In addition, the steady-state operation of the system showcases the recuperator's heat transfer power at around 1127.60 kW, a parameter of significant importance. Following steady-state operation, the double-rotor CBC system demonstrated an electrical power output of 175.99 kW and a thermal efficiency of 32.38%. [ABSTRACT FROM AUTHOR]

Electricity load forecasting is of great significance for the overall operation of the power system and the orderly use of electricity at a later stage. However, traditional load forecasting does not consider the change in load quantity at each time point, while the information on the time difference of the load data can reflect the dynamic evolution information of the load data, which is a very important factor for load forecasting. In addition, the research topics in recent years mainly focus on the learning of the complex relationships of load sequences in time latitude by graph neural networks. The relationships between different variables of load sequences are not explicitly captured. In this paper, we propose a model that combines a differential learning network and a multidimensional feature graph attention layer, it can model the time dependence and dynamic evolution of load sequences by learning the amount of load variation at different time points, while representing the correlation of different variable features of load sequences through the graph attention layer. Comparative experiments show that the prediction errors of the proposed model have decreased by 5–26% compared to other advanced methods in the UC Irvine Machine Learning Repository Electricity Load Chart public dataset. [ABSTRACT FROM AUTHOR]

This paper aims to reconsider prosumers' role in the power markets in the early stages of their decentralization, accounting for rising self-supply trends, security threats, and economic and regulatory barriers. The development of prosumerism envisages finding the ratio between retail market sales under the feed-in tariff and the net billing mechanism. Within the methodology section, the indicator of prosumer efficiency for electricity generation (EUR/kWh) is proposed based on average consumption/production ratios and consumption/delivery incentives. To support household prosumers, the mentioned incentives on the renewable energy market consider the self-supply cost of electricity, the levelized cost of electricity for small-scale green energy facilities (solar photovoltaic and wind), and transaction costs. This paper evaluates prosumer efficiency under three consumption/production ratio scenarios for Ukrainian households (self-consumption of 40%, 20%, and 100% of green electricity annually generated by a household and selling the leftovers via the feed-in tariff) for 2023. The gradual movement from fixed tariffs for households toward market-based prices promotes the emergence of new related market players and their consolidation in the market. Participation in the organized power market segments is relevant for day-ahead market prices above 130 EUR/MWh, disregarding the households' tariff rate. The low price caps inhibit the prosumer's participation in the market, while the transition from the feed-in tariff to net billing significantly promotes their development only under high price caps. [ABSTRACT FROM AUTHOR]

Clustering-based reactive voltage partitioning is successful in reducing grid cascading faults, by using clustering methods to categorize different power-consuming entities in the power grid into distinct regions. In reality, each power-consuming entity has different electrical characteristics. Additionally, due to the irregular and uneven distribution of the population, the distribution of electricity consumption is also irregular and uneven. However, the existing method neglects the electrical difference among each entity and the irregular and uneven density distribution of electricity consumption, resulting in poor accuracy and adaptability of these methods. To address these problems, an enhanced density peak model-based power grid reactive voltage partitioning method is proposed in this paper, called EDPVP. First, the power grid is modeled as a weighted reactive network to consider entity electrical differences. Second, the novel local density and density following distance are designed to enhance the density peak model to address the problem that the traditional density peak model cannot adapt to weighted networks. Finally, the enhanced density peak model is further equipped with an optimized cluster centers selection strategy and an updated remaining node assignment strategy, to better identify irregular and uneven density distribution of electricity consumption, and to achieve fast and accurate reactive voltage partition. Experiments on two real power grids demonstrate the effectiveness of the EDPVP. [ABSTRACT FROM AUTHOR]

This paper investigates the relationship between geothermal energy consumption, economic growth, and foreign direct investments in countries where geothermal energy production is possible. Panel Fourier Granger causality and panel Fourier Toda–Yamamoto causality tests (2020–2021) were applied, which can take into account smooth transitional structural breaks with trigonometric functions using quarterly data for the period 2016 Q1–2020 Q3. Data were obtained from the International Energy Agency (IEA), Federal Reserve Economic Data (FRED), and the OECD official website. According to the results obtained based on panels, there is one-way causality from economic growth to geothermal energy and one-way causality from geothermal energy consumption to foreign direct investments. The results obtained based on individual countries indicate that one-way causality from foreign direct investment to geothermal energy consumption was found for Mexico and Portugal, and one-way causality from geothermal energy consumption to economic growth was found for Italy and Mexico. On the other hand, causality from economic growth to geothermal energy consumption was observed for Germany, Japan, and USA. No significant results were found for Turkey and New Zealand, and it is understood that the macroeconomic structures of these countries are not affected by geothermal energy. The difference in the results reveals that the application recommendations on this subject should also be different. [ABSTRACT FROM AUTHOR]

Bearing in mind European Green Deal assumptions regarding a significant reduction of green house emissions, electricity generation from Renewable Energy Sources (RES) is more and more important nowadays. Besides this, accurate and reliable electricity generation forecasts from RES are needed for capacity planning, scheduling, managing inertia and frequency response during contingency events. The recent three years have proved that Machine Learning (ML) models are a promising solution for forecasting electricity generation from RES. In this review, the 8-step methodology was used to find and analyze 262 relevant research articles from the Scopus database. Statistic analysis based on eight criteria (ML method used, renewable energy source involved, affiliation location, hybrid model proposed, short term prediction, author name, number of citations, and journal title) was shown. The results indicate that (1) Extreme Learning Machine and ensemble methods were the most popular methods used for electricity generation forecasting from RES in the last three years (2020–2022), (2) most of the research was carried out for wind systems, (3) the hybrid models accounted for about a third of the analyzed works, (4) most of the articles concerned short-term models, (5) the most researchers came from China, (6) and the journal which published the most papers in the analyzed field was Energies. Moreover, strengths, weaknesses, opportunities, and threats for the analyzed ML forecasting models were identified and presented in this paper. [ABSTRACT FROM AUTHOR]

The development of renewable energy in China has made remarkable achievements, but the problem of renewable energy consumption has become increasingly prominent. This paper establishes a power market trading system for renewable energy, with the aim of promoting large-scale renewable energy consumption and increasing the enthusiasm of renewable energy producers and users to participate in market transactions. First, according to the power generation cost, the backup cost of renewable energy power plants and the possible quotation strategies of other renewable energy producers, a quotation model of renewable energy producers is established. In the clearing of the spot market by renewable energy producers, the independent market operator conducts the first-stage clearing of the electricity market with the goal of maximizing social welfare. After the announcement of the clearing results, the renewable energy producers that did not win the bid will revise their quotations and carry out the second stage clearing to realize the consumption of renewable energy. In this paper, the particle swarm algorithm combined with the CPLEX solver is used to solve the problem, and finally, different scenarios are analyzed through example analysis. The results show that, compared with the conventional power market trading mechanism, the energy abandonment rate of the power market trading mechanism for renewable energy proposed in this paper drops from 8.2% to 2.1%, and the profit margin of renewable energy producers increase by 6.6%. It is demonstrated that the proposed electricity market mechanism can effectively promote the consumption of renewable energy and increase the income of renewable energy producers. [ABSTRACT FROM AUTHOR]

Demand response management (DRM) plays a crucial role in the prospective development of smart grids. The precise estimation of electricity demand for individual houses is vital for optimizing the operation and planning of the power system. Accurate forecasting of the required components holds significance as it can substantially impact the final cost, mitigate risks, and support informed decision-making. In this paper, a forecasting approach employing neural networks for smart grid demand-side management is proposed. The study explores various enhanced artificial neural network (ANN) architectures for forecasting smart grid consumption. The performance of the ANN approach in predicting energy demands is evaluated through a comparison with three statistical models: a time series model, an auto-regressive model, and a hybrid model. Experimental results demonstrate the ability of the proposed neural network framework to deliver accurate and reliable energy demand forecasts. [ABSTRACT FROM AUTHOR]

Power load prediction is fundamental for ensuring the reliability of power grid operation and the accuracy of power demand forecasting. However, the uncertainties stemming from power generation, such as wind speed and water flow, along with variations in electricity demand, present new challenges to existing power load prediction methods. In this paper, we propose an improved Convolutional Neural Network–Bidirectional Long Short-Term Memory (CNN-BILSTM) model for analyzing power load in systems affected by uncertain power conditions. Initially, we delineate the uncertainty characteristics inherent in real-world power systems and establish a data-driven power load model based on fluctuations in power source loads. Building upon this foundation, we design the CNN-BILSTM model, which comprises a convolutional neural network (CNN) module for extracting features from power data, along with a forward Long Short-Term Memory (LSTM) module and a reverse LSTM module. The two LSTM modules account for factors influencing forward and reverse power load timings in the entire power load data, thus enhancing model performance and data utilization efficiency. We further conduct comparative experiments to evaluate the effectiveness of the proposed CNN-BILSTM model. The experimental results demonstrate that CNN-BILSTM can effectively and more accurately predict power loads within power systems characterized by uncertain power generation and electricity demand. Consequently, it exhibits promising prospects for industrial applications. [ABSTRACT FROM AUTHOR]

The demand for different energy goods and services is a fundamental component in a country's economic structure for development. Understanding it is vital in designing economic policies, such as taxes, that can improve the welfare of the population. A comprehension of the distributional effects of elasticities and the application of them to simulate household responses to price changes, as well as a calculation of the welfare impacts on poor and rich households in Mexico, should inform policy design. This paper uses the Household Income and Expenditure Survey (ENIGH) from 1996 to 2018 to estimate the demand of Mexican households for fuels, specifically electricity, liquefied petroleum gas, and gasoline. A Quasi Ideal Quadratic Demand System (QUAIDS) is employed to analyse the effects of removing energy subsidies and introducing a carbon tax. The results indicate that welfare losses would be regressive concerning electricity price increases, while changes in gasoline prices would be progressive. Redistributing the tax revenues accrued by removing energy subsidies and imposing the carbon tax would have more progressive effects on the economy of Mexican households, with welfare gains of up to 350% for the poorest households in the case of electricity consumption taxes. [ABSTRACT FROM AUTHOR]

This paper takes a low-energy building in Changchun, China, as an object to test and study the characteristics of two heating modes, AC/DC (Alternative current/Direct current) switching and AC/DC synthesis, from the perspectives of temperature change, irradiation intensity, power generation, electricity consumption, etc. Firstly, the experimental research was conducted under two heating cable modes by establishing mathematical models and a test rig, and it was found that the photoelectric conversion efficiency on sunny, cloudy, and overcast days was 18%, 14.5%, and 12%, respectively. A simulation model was established by TRNSYS to run an ultra-low-energy building throughout the year. It was found that the highest and lowest monthly power generation occurred in February and July, respectively. The annual power generation of the system was 6614 kWh, and the heating season power generation was 3293.42 kWh. In the current research, the DC electricity consumption was slightly higher than the AC electricity consumption. Under conditions of similar radiation intensity and power generation, the indoor temperature of the AC/DC synthesis cable heating mode were 1.38% higher than the AC/DC switching heating able mode, and the electricity consumption were 10.9% and 4.76% higher, respectively, than those of the AC switching heating cable mode. This is of great significance for clean-energy heating, energy savings, and emissions reduction in northern China. [ABSTRACT FROM AUTHOR]

Urban carbon emissions are an important area for addressing climate change, and it is necessary to establish scientific and effective carbon emission prediction models to formulate reasonable emission reduction policies and measures. In this paper, a novel model based on Lasso regression, an ARIMA model, and a BPNN is proposed. Lasso regression is used to screen the key factors affecting carbon emissions, and the ARIMA model is used to extract the linear components of the carbon emission sequences, while the BPNN is used to predict the residuals of the ARIMA model. The final result is the sum of that from the ARIMA model and the BPNN. The carbon peak, carbon neutralization time, and emissions were analyzed under different scenarios. Taking Suzhou City as an example, the results show that the electricity consumption of the whole population is one of the key drivers of carbon emissions; the carbon emission prediction accuracy and stability of the ARIMA-BPNN combined model are better than those of the single model, which improves the reliability as well as the accuracy of the model's prediction. However, under the constraints of the current policies, the goal of achieving carbon peaking by 2030 in Suzhou City may not be realized as scheduled. This novel carbon emission prediction model built was validated to provide a scientific basis for low-carbon urban development. This study presents an important reference value for predicting carbon emissions and formulating emission reduction measures in other cities. [ABSTRACT FROM AUTHOR]

Alternative solvents based on aqueous solutions of amino acids have been recently developed as possible substitutes for Mono Ethanol Amine (MEA) for CO2 removal from flue gas streams. The potassium taurate solvent has the advantages of degradation resistance, low toxicity and low energy requirements for its regeneration. With any type of solvent, CO2 removal applied to a power production plant decreases the revenues obtained from selling electricity because of the energy requirements. Operating the CO2 removal section in flexible mode avoids significant effects on the profits of the power plant, while accomplishing environmental regulations. This work is the first journal paper focusing on the application in flexible mode of the potassium taurate system for treating a flue gas stream from a 500 MW coal-fired power plant. Techno-economic evaluations are performed to determine the best operating conditions considering the variation in the electricity demand and its price, and different values of carbon tax. In the summer period, with high electricity prices and demands, carbon tax values between 45 EUR/tCO2 and 60 EUR/tCO2 favor CO2 absorption in the flexible mode, without periods of full CO2 emissions during the day. [ABSTRACT FROM AUTHOR]

Non-technical losses (NTL) have been a growing problem over the years, causing significant financial losses for electric utilities. Among the methods for detecting this type of loss, those based on Artificial Intelligence (AI) have been the most popular. Many works use the electricity consumption profile as an input for AI models, which may not be sufficient to develop a model that achieves a high detection rate for various types of energy fraud that may occur. In this paper, using actual electricity consumption data, additional statistical and temporal features based on these data are used to improve the detection rate of various types of NTL. Furthermore, a model that combines both the electricity consumption data and these features is developed, achieving a better detection rate for all types of fraud considered. [ABSTRACT FROM AUTHOR]

This paper addresses the optimization of contracted electricity demand (CD) for commercial and industrial entities, focusing on cost reduction within the Brazilian time-of-use electricity tariff scheme. Leveraging genetic algorithms (GAs), this study proposes a practical approach to determining the optimal CD profile, considering the complex dynamics of energy demand on a city-like load. The methodology is applied to a case study at the Federal University of Pará, Brazil, where energy efficiency and demand response initiatives as well as renewable energy projects are underway. The findings highlight the significance of tailored demand management strategies in achieving energy-related cost reduction for large-scale consumers, with implications for economic efficiency in energy consumption. [ABSTRACT FROM AUTHOR]

The establishment of near-autonomous micro-grids in commercial or public building complexes is gaining increasing popularity. Short-term storage capacity is provided by means of large battery installations, or, more often, by the employees' increasing use of electric vehicle batteries, which are allowed to operate in bi-directional charging mode. In addition to the above short-term storage means, a long-term storage medium is considered essential to the optimal operation of the building's micro-grid. The most promising long-term energy storage carrier is hydrogen, which is produced by standard electrolyzer units by exploiting the surplus electricity produced by photovoltaic installation, due to the seasonal or weekly variation in a building's electricity consumption. To this end, a novel concept is studied in this paper. The details of the proposed concept are described in the context of a nearly Zero Energy Building (nZEB) and the associated micro-grid. The hydrogen produced is stored in a high-pressure tank to be used occasionally as fuel in an advanced technology hydrogen spark ignition engine, which moves a synchronous generator. A size optimization study is carried out to determine the genset's rating, the electrolyzer units' capacity and the tilt angle of the rooftop's photovoltaic panels, which minimize the building's interaction with the external grid. The hydrogen-fueled genset engine is optimally sized to 40 kW (0.18 kW/kWp PV). The optimal tilt angle of the rooftop PV panels is 39°. The maximum capacity of the electrolyzer units is optimized to 72 kW (0.33 kWmax/kWp PV). The resulting system is tacitly assumed to integrate to an external hydrogen network to make up for the expected mismatches between hydrogen production and consumption. The significance of technology in addressing the current challenges in the field of energy storage and micro-grid optimization is discussed, with an emphasis on its potential benefits. Moreover, areas for further research are highlighted, aiming to further advance sustainable energy solutions. [ABSTRACT FROM AUTHOR]

This paper offers a theoretical and experimental examination of the concurrent production of electricity and heat using photovoltaic thermal (PV/T) technology. The efficiency performance of the PV/T system is meticulously analyzed using MATLAB/Simulink software environments. Notably, the proposed PV/T system shows reliable performance, and its validity is confirmed through experimental validation on a test stand with a single PV/T panel positioned at a 45-degree angle to the horizontal and a 0-degree azimuth angle. The measurements were conducted during the summer season, and two models were suggested to calculate the overall efficiency of the PV/T system. The variance between the results obtained from the two models was minimal, below 5%. For the examined panel type, the following average values were derived: electrical efficiency = 12.01%, thermal efficiency = 47.21%, and overall efficiency = 59.23%. [ABSTRACT FROM AUTHOR]

Over time, several relationships have been defined between electricity consumption and a region's social and economic variables, with income as the main factor. This paper uses multiple correspondence analysis to identify the categories of dwellings and, from a graphical point of view (positioning maps), the effects of the different characteristics that influence the electricity consumption of households in rural areas of Cundinamarca, Colombia. In this analysis, the consumption of residential users responded mainly to what they can afford or acquire based on their income, consumption habits, and the characteristics of the technology. Furthermore, this study highlights the implications of these findings for policymakers and energy providers, providing valuable insights for developing targeted strategies to promote energy efficiency and sustainability in rural areas. This research contributes to a deeper understanding of the dynamics of electricity consumption and highlights the importance of tailoring energy-related interventions to the specific socio-economic context of rural communities, in this case in Cundinamarca. [ABSTRACT FROM AUTHOR]

This paper investigates the microclimate in a conference room with thermal stratification, taking as a case study the chapel of Villa San Saverio, now the seat of the "Scuola Superiore" of the University of Catania (Italy). Surveys of the former chapel were conducted to monitor air temperature and relative humidity. Subsequently, the investigation relied on numerical simulations of a simplified computational fluid dynamics (CFD) model built with the DesignBuilder v7.0 software and validated by comparison with measured values. Simulations were then carried out considering three different scenarios: the current state without any HVAC system and two possible HVAC system configurations providing both air conditioning and ventilation. The results show that, from a comfort perspective, a lightweight radiant floor heating system, assisted by an appropriate ventilation system for air renewal placed at the floor level near the occupants, is preferable to floor-level fan coils and high ventilation channels. Furthermore, this was also confirmed by a preliminary energy analysis of the two HVAC options, where the ventilation effectiveness of the winter period, the temperature of the water the emitters are fed, the consequent COP value of the heat pump, and the electricity consumption were taken into consideration. [ABSTRACT FROM AUTHOR]

With the rapid development of smart grids and distributed energy sources, the home energy management system (HEMS) is becoming a hot topic of research as a hub for connecting customers and utilities for energy visualization. Accurate forecasting of future short-term residential electricity demand for each major appliance is a key part of the energy management system. This paper aims to explore the current research status of household-level short-term load forecasting, summarize the advantages and disadvantages of various forecasting methods, and provide research ideas for short-term household load forecasting and household energy management. Firstly, the paper analyzes the latest research results and research trends in deep learning load forecasting methods in terms of network models, feature extraction, and adaptive learning; secondly, it points out the importance of combining probabilistic forecasting methods that take into account load uncertainty with deep learning techniques; and further explores the implications and methods for device-level as well as ultra-short-term load forecasting. In addition, the paper also analyzes the importance of short-term household load forecasting for the scheduling of electricity consumption in household energy management systems. Finally, the paper points out the problems in the current research and proposes suggestions for future development of short-term household load forecasting. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to uncover how the process of managing electricity in buildings based on the Internet of Things occurs. In particular, the work seeks to depict the factors affecting electricity consumption and management, as well as the application of the Internet of Things in energy management. A systematic literature review is used to examine the breadth of the electric energy management literature, encompassing bibliometric and thematic analysis based on an established procedure. The findings show the evolution of this field within key research networks with a few papers covering important elements of energy management, such as energy use, consumption and monitoring, assessment, and planning, in an integrative manner. Within this field, lacking in theory and practice, the originality of the work is the assembly of electric energy management into a conceptual framework based on real-time consumption and the Internet of Things (IoT). Indeed, the framework brings together the breadth of factors affecting consumption, energy use, and improvements that have been dispersed across the literature into one place. This framework, therefore, represents a stage towards an integrative view of IoT electric energy management and subsequent enhancement of theory and energy efficiency adoption. [ABSTRACT FROM AUTHOR]

Solar-heating technology is a promising solution to help China achieve the "3060 double carbon" target as soon as possible. Seasonal thermal storage (STS) can effectively solve the mismatch problem of solar-heating systems between the supply and demand of thermal energy. Due to the instability of solar radiation resources and the heat demand, it is necessary to analyze the dynamic response characteristics and operation strategy optimization of the system in different operation stages. Yet, related studies are still scarce. The aim of this paper is to study the switching mechanism of the operation modes and the transitive relation of the system energy in different operation stages based on a pilot solar-heating system with STS in Huangdicheng, northern China. The impacts of different heating strategies on the system performance were also analyzed with a dynamic simulated method in TRNSYS. The results showed that the solar fraction of the system reached 89.4% in the third year, which was 3.6% higher than that in the first year. The quality–quantity heating operation strategies are effective ways to improve the discharge efficiency of the STS and the system performance without a heat pump. The electricity consumption of the pump on the heating side could be significantly reduced by 44.6% compared with the quality control. Ultimately, the findings in this paper are valuable for the optimization of the operation of solar-heating systems. [ABSTRACT FROM AUTHOR]

Electric vehicles are growing in popularity as a form of transportation, but are still underused for several reasons, such as their relatively low range and the high costs associated with manufacturing and maintaining batteries. Many studies using several approaches have been conducted on electric vehicles. Among all studied subjects, here we are interested in the use of machine learning to efficiently manage the energy consumption of electric vehicles, in order to develop intelligent electric vehicles that make quick unprogrammed decisions based on observed data allowing minimal electricity consumption. Our interest is motivated by the adequate results obtained using machine learning in many fields and the increasing but still insufficient use of machine learning to efficiently manage the energy consumption of electric vehicles. From this standpoint, we have built this comprehensive survey covering a broad variety of scientific papers in the field published over the last few years. According to the findings, we identified the current trend and revealed future perspectives. [ABSTRACT FROM AUTHOR]

In this paper, balancing electricity production using renewable energy such as wind power, PV cells, hydropower, and CHP (combined heat and power) with biomass is carried out in relation to electricity consumption in primarily one major region in Sweden, SE-3, which contains 75% of the country's population. The time perspective is hours and days. Statistics with respect to power production and consumption are analyzed and used as input for power-balance calculations. How long periods are with low or high production, as well as the energy for charge and discharge that is needed to maintain a generally constant power production, is analyzed. One conclusion is that if the difference in production were to be completely covered with battery capacity it would be expensive, but if a large part of the difference were met by a shifting load it would be possible to cover the rest with battery storage in an economical way. To enhance the economy with battery storage, second-life batteries are proposed to reduce the capital cost in particular. Batteries are compared to hydrogen as an energy carrier. The efficiency of a battery system is higher than that of hydrogen plus fuel cells, but in general much fewer precious materials are needed with an H2/fuel-cell system than with batteries. The paper discusses how to make the energy system more robust and resilient. [ABSTRACT FROM AUTHOR]

This paper explores the potential of rooftop solar PV to meet the electricity demand in the urban areas of Abha city, Saudi Arabia (KSA), minimising imports from the grid. A localised energy system for Abha is proposed that considers two types of loads: (i) residential loads with a monthly aggregated energy consumption of 172,440 MWh and an electric demand of 239.5 MW, and (ii) commercial loads with a monthly aggregated energy consumption of 179,280 MWh and an electric demand of 249 MW. The grid currently supplies this load. This paper proposes a PV development planning tool for residential and commercial areas to calculate the total PV production for each type of load to achieve a balanced energy area, considering (i) the number of buildings, (ii) the type of load, (iii) the peak load, and (iv) the total PV array area in m2 per building. The results of the modelling study using real data demonstrate that the anticipated total PV production in residential and commercial areas is sufficient to meet local peak demand, and there is an excess of power that can either be stored locally or exported to the grid. [ABSTRACT FROM AUTHOR]

Accurate electricity demand forecasting is essential for global energy security, reducing costs, ensuring grid stability, and informing decision making in the energy sector. Disruptions often lead to unpredictable demand shifts, posing greater challenges for short-term load forecasting. Understanding electricity demand patterns during a pandemic offers insights into handling future disruptions. This study aims to develop an effective forecasting model for daily electricity peak demand, which is crucial for managing potential disruptions. This paper proposed a hybrid approach to address scenarios involving both government intervention and non-intervention, utilizing integration methods such as stepwise regression, similar day selection-based day type criterion, variational mode decomposition, empirical mode decomposition, fast Fourier transform, and neural networks with grid search optimization for the problem. The electricity peak load data in Thailand during the year of the COVID-19 situation is used as a case study to demonstrate the effectiveness of the approach. To enhance the flexibility and adaptability of the approach, the new criterion of separating datasets and the new criterion of similar day selection are proposed to perform one-day-ahead forecasting with rolling datasets. Computational analysis confirms the method's effectiveness, adaptability, reduced input, and computational efficiency, rendering it a practical choice for daily electricity peak demand forecasting, especially in disrupted situations. [ABSTRACT FROM AUTHOR]

Global energy consumption is growing rapidly, with the frequency and intensity of extreme events constantly increasing, posing a long-term threat to power supply and consumption. Therefore, analyzing the spatiotemporal characteristics of electricity consumption and quantitatively assessing the impact of extreme events on electricity consumption are of great significance. Based on fine-grained electricity consumption data from Europe for the years 2019–2022, this paper employs a data mining perspective and four methods including Z-score, Isolation Forest, Local Outlier Factor, and Autoencoder to detect abnormal electricity consumption during extreme events. Additionally, it combines indicators such as elastic loss, vulnerability, and duration to measure the impact of extreme events on electricity consumption. It is found that low temperatures could lead to abrupt changes in electricity consumption, with Northern Europe being more significantly affected by low temperatures. The COVID-19 pandemic had the most significant impact on electricity consumption in Europe, with the middle part of Europe being the hardest hit during the first wave of the pandemic. Electricity anomalies during the pandemic period were related to national pandemic control policies and exhibited some lag. High temperatures persisted for a longer duration in the middle part of Europe. [ABSTRACT FROM AUTHOR]

This paper presents the concept of using electric vehicles (EVs) as a countermeasure to deal with the negative effects of power rationing when electricity demands become difficult to meet due to unfavorable electrical system operating conditions. At present, an energy storage is widely used to maintain the stability of electricity supply in facilities whose main source of energy is renewable energy sources (RESs). However, we must not forget that electric vehicles are also electricity storage facilities, but they are not always available due to their mobility. With properly developed strategies, they can be used in electricity management processes, for example, by reducing their consumption during charging using smart charging technology, or by providing electricity from their batteries using vehicle-to-building (V2B) technology. Thus, this article presents a research methodology that treats electric vehicles as a remedy for eliminating power constraints. It consists of five main steps, including two algorithms for deciding how to deploy EVs during power rationing periods. An efficiency factor for eliminating these constraints was also introduced. The results showed that the use of smart charging or V2B technology in EVs can reduce the number of potential hours in which certain power levels are exceeded by up to several tens of percent. This means that in the future, with the significant development of electromobility, such a way of dealing with power constraints could be an alternative to conventional solutions like diesel generators. [ABSTRACT FROM AUTHOR]

Fruit and vegetable processing comes 6th in terms of energy consumption in the agri-food industry. At the same time, 88.4% of the industry's final energy consumption structure is thermal energy, which depends heavily on electricity consumption. In addition, fruit and vegetable processing has a significant impact on the environment due to consumption of significant amounts of water. Reducing these three indicators simultaneously would increase the efficiency of the process while improving environmental protection. This paper proposes neural models of thermal energy, electricity and water consumption for selected major fruit- and vegetable-processing plants in Poland. These models were the basis for formulating a multi-criteria optimization task. Optimization of thermal energy, electricity and water consumption was carried out using genetic algorithms. The optimization results in the sense of Pareto can be the basis for the use of sustainable technology in selected fruit- and vegetable-processing plants. [ABSTRACT FROM AUTHOR]

As the scale of electricity consumption grows, the peak electricity consumption prediction of campus buildings is essential for effective building energy system management. The selection of an appropriate model is of paramount importance to accurately predict peak electricity consumption of campus buildings due to the substantial variations in electricity consumption trends and characteristics among campus buildings. In this paper, we proposed eight deep recurrent neural networks and compared their performance in predicting peak electricity consumption for each campus building to select the best model. Furthermore, we applied an attention approach capable of capturing long sequence patterns and controlling the importance level of input states. The test cases involve three campus buildings in Incheon City, South Korea: an office building, a nature science building, and a general education building, each with different scales and trends of electricity consumption. The experiment results demonstrate the importance of accurate model selection to enhance building energy efficiency, as no single model's performance dominates across all buildings. Moreover, we observe that the attention approach effectively improves the prediction performance of peak electricity consumption. [ABSTRACT FROM AUTHOR]

Over the last decade, a constantly growing trend of the popularization of self-service automated stores has been observed. Vending machines have been expanded into fully automated stores, the offer of which is comparable to small, conventional stores. One of the basic reasons for the popularization of modern automated stores is the reduction in a store's energy consumption while ensuring a comparable range of products offered. The research into possibilities of reducing greenhouse gases emission is important in terms of the environment and climate protection. The research presented in the paper concerns the development of a model for determining electricity consumption, operating costs and CO2 emission of an automated store designed and developed by Delfin company. In the developed model, the potential location of the store, prevailing climatic conditions and expected product sales are taken into account. Estimated energy demand for the store is the information of key importance for the potential investors and the manufacturer of the automated store. It is worth emphasizing that the average annual electrical energy consumption evaluated for a grocery store of an area of 70 m2 amounted to approximately 38.4 MWh, while for an automated store of an area of 9 m2 and a comparable product range, the electricity consumption was approximately 10.1 MWh, i.e., 74% smaller. [ABSTRACT FROM AUTHOR]