Hasil untuk "Electricity"

María Castrillo Melguizo, Jaime Iglesias Blanco, Álvaro López García

The environmental impact of electricity consumption is commonly assessed through its carbon footprint (CF), while water-related impacts are often overlooked despite the strong interdependence between energy and water systems. This is particularly relevant for electricity-intensive activities such as data center (DC) operations, where both carbon emissions and water use occur largely off-site through electricity consumption. In this work, we present Wattnet, an open-source tool that jointly assesses the CF and water footprint (WF) of electricity consumption across Europe with high temporal resolution. Wattnet implements an electricity flow-tracing methodology that accounts for local generation mixes, as well as for cross-border electricity imports and exports at a 15-minute resolution. Operational and life-cycle impact factors are used to quantify and compare local (generation-based) and global (consumption-based) footprints for multiple European regions during 2024. The results demonstrate that neglecting electricity flows and temporal variability can lead to significant misestimations of both CF and WF, particularly in countries with high levels of electricity trade or hydropower dependence. Furthermore, the joint analysis reveals trade-offs between decarbonisation and water use, highlighting the prominent role of reservoir-based hydropower in increasing WF even in low-carbon systems. Wattnet facilitates informed decision-making for workload scheduling and energy-aware operation of DCs, while also enhancing transparency regarding the environmental impacts of electricity consumption for end users and policymakers.

Saud Alghumayjan, Ming Yi, Bolun Xu

This paper proposes a few-shot classification framework based on Large Language Models (LLMs) to predict whether the next day will have spikes in real-time electricity prices. The approach aggregates system state information, including electricity demand, renewable generation, weather forecasts, and recent electricity prices, into a set of statistical features that are formatted as natural-language prompts and fed to an LLM along with general instructions. The model then determines the likelihood that the next day would be a spike day and reports a confidence score. Using historical data from the Texas electricity market, we demonstrate that this few-shot approach achieves performance comparable to supervised machine learning models, such as Support Vector Machines and XGBoost, and outperforms the latter two when limited historical data are available. These findings highlight the potential of LLMs as a data-efficient tool for classifying electricity price spikes in settings with scarce data.

Changhai Yang, Ding Li, Yuxuan Wang et al.

With the increasing share of renewable energy, green power parks face challenges such as high electricity purchasing costs and fluctuations in power quality. To address these issues, this paper proposes an integrated optimization method based on power quality responsibility modeling and a differentiated reward–penalty pricing mechanism (DRPPM). First, an integrated operation model of “source–grid–load–storage” is established. Within the pressure–state–response (PSR) framework, power quality deviations are quantified and mapped into economic costs. Then, a differentiated reward–penalty pricing mechanism is designed to dynamically adjust power quality deviations through a continuous function, guiding users toward adaptive energy consumption behavior. Finally, a green power park in Gansu Province dominated by wind and photovoltaic generation is used as a case study with four typical simulation scenarios. The results show that the proposed mechanism reduces the park’s electricity purchasing cost and increases the green power consumption ratio by up to 74.9%. Meanwhile, it effectively improves power quality indicators such as frequency, voltage, and harmonics. The study verifies the comprehensive advantages of the proposed framework in terms of economy, energy efficiency, and stability, providing a reference for low-carbon and efficient operation of high-energy-consumption green power parks.

Zhen Li, Ji Liu, Yoshimichi Ohki et al.

Abstract Surface flashover is a gas–solid interface insulation failure that significantly jeopardises the secure operation of advanced electronic, electrical, and spacecraft applications. Despite the widespread application of numerous material modification and structure optimisation technologies aimed at enhancing surface flashover performance, the influence mechanisms of the present technologies have yet to be systematically discussed and summarised. This review aims to introduce various material modification technologies while demonstrating their influence mechanisms on flashover performances by establishing relationships among ‘microscopic structure‐mesoscopic charge transport‐macroscopic insulation failure’. Moreover, it elucidates the effects of chemical structure on surface trap parameters and surface charge transport concerning flashover performance. The review categorises and presents structure optimisation technologies that govern electric field distribution. All identified technologies highlight that achieving a uniform tangential electric field and reducing the normal electric field can effectively enhance flashover performance. Finally, this review proposes recommendations encompassing mathematical, chemical, evaluation, and manufacturing technologies. This systematic summary of current technologies, their influence mechanisms, and associated advantages and disadvantages in improving surface insulation performance is anticipated to be a pivotal component in flashover and future dielectric theory.

Paweł Kelm, Rozmysław Mieński, Irena Wasiak

In accordance with the European common rules for the internal electricity market, suppliers offer end users contracts with dynamic energy prices. To reduce energy costs, prosumers must manage their installations with energy storage devices (ESSs). The authors propose a novel control strategy with a relatively simple simulation-based algorithm that effectively reduces daily energy costs by managing the ESS charging and discharging schedule under different types of dynamic energy tariffs. The algorithm operates in a running window mode to ensure ongoing control updates in response to the changing conditions of the prosumer’s installation operation and dynamically changing energy prices. A feature of the control system is its ability to regulate the power exchanged with the supply network in response to an external signal from a superior control system or a network operator. This feature allows the control system to participate in regulatory services provided by the prosumer to the DSO. The effectiveness of the proposed control algorithm was verified in the PSCAD V4 Professional environment and with the MS Excel SOLVER for Office 365 optimisation tool. The results showed good accuracy with respect to the cost reduction algorithm and confirmed that the additional regulatory service can be effectively implemented within the same prosumer ESS control system.

Danijela Nikolić, Saša Jovanović, Nebojša Jurišević et al.

In modern agriculture today, the cultivation of agricultural products cannot be imagined without greenhouses. This paper presents an energy optimization of a solar greenhouse with a photovoltaic system (PV) and a ground-source heat pump (GSHP). The PV system generates electricity, while the GSHP is used for heating and cooling. A greenhouse is designed with an Open Studio plug-in in the Google SketchUp environment, the EnergyPlus software (8.7.1 version) was used for energy simulation, and the GenOpt software (2.0.0 version) was used for optimization of the azimuth angle and PV cell efficiency. Results for different solar greenhouse orientations and different photovoltaic module efficiency are presented in the paper. The obtained optimal azimuth angle of the solar greenhouse was −8°. With the installation of a PV array with higher module efficiency (20–24%), it is possible to achieve annual energy savings of 6.87–101.77%. Also, with the PV module efficiency of 23.94%, a concept of zero-net-energy solar greenhouses (ZNEG) is achieved at optimal azimuth and slope angle. Through the environmental analysis of different greenhouses, CO₂ emissions of PV and GSHP are calculated and compared with electricity usage. Saved CO₂ emission for a zero-net-energy greenhouse is 6626 kg CO₂/year. An economic analysis of installed renewable energy systems was carried out: with the total investment of 19,326 € for ZNEG, the payback period is 8.63 years.

Wenyang Deng, Dongliang Xiao, Mingli Chen et al.

As distributed photovoltaic and shared energy storage systems expanded on the user side, developing an energy-sharing mechanism across different regions became crucial for fully utilizing local renewable energy resources and maximizing the system’s overall economic performance. This paper established a multi-regional energy operator (MREO) model considering shared energy storage, and a two-layer trading and optimization framework based on a master–slave game was developed. Initially, a trading system was devised to evaluate the interests of the power grid, MREO, and end-users. Next, an optimization model was formulated to capture the dynamic interactions between MREO decisions and user responses. The top-layer model was managed by MREO and focused on energy sharing among regions, which is used to set flexible electricity prices according to regional demand and optimize the use of shared energy storage. Meanwhile, the bottom-layer model addressed user demand response, allowing users to modify their energy consumption and select more advantageous trading areas based on information provided by the MREO. Simulation results confirmed that the proposed model accurately evaluated each party’s income, iteratively balanced their interests, and increased economic returns for both users and MREO. Additionally, the proposed approach supported greater local photovoltaic energy consumption, reduced grid load fluctuations, and fostered mutually beneficial outcomes for all stakeholders.

Zhen Zhu, Hongcai Zhang, Yonghua Song

The participation of electric vehicle (EV) aggregators in real-time electricity markets offers promising revenue opportunities through price-responsive energy arbitrage. A central challenge in economic bidding lies in quantifying the marginal opportunity value of EVs' charging and discharging decisions. This value is implicitly defined and dynamically shaped by uncertainties in electricity prices and availability of EV resources. In this paper, we propose an efficient bidding strategy that enables EV aggregators to generate market-compliant bids based on the underlying marginal value of energy. The approach first formulates the EV aggregator's power scheduling problem as a Markov decision process, linking the opportunity value of energy to the value function. Building on this formulation, we derive the probability distributions of marginal opportunity values across EVs' different energy states under stochastic electricity prices. These are then used to construct closed-form expressions for marginal charging values and discharging costs under both risk-neutral and risk-averse preferences. The resulting expressions support a fully analytical bid construction procedure that transforms marginal valuations into stepwise price-quantity bids without redundant computation. Case studies using real-world EV charging data and market prices demonstrate the effectiveness and adaptability of the proposed strategy.

Alzubaidi Laith H., Thakur Gaurav, G Vijayalakshmi et al.

This review explores Peer-to-Peer (P2P) Energy Trading Platforms in Local Energy Markets, drawing insights from three key studies. These markets, using P2P trading, efficiently distribute electricity within communities. Research assesses P2P trading’s impact in a Norwegian neighbourhood, comparing it to scenarios without local markets. It also examines integrating PV systems, batteries, and electric vehicles on grid operations. Findings reveal minimal grid impacts with PVs alone, but adding batteries increases voltage fluctuations and losses. However, P2P trading benefits end-users with cost savings and supports Distribution System Operator operations. The paper surveys global P2P energy trading projects, emphasising communication and control networks within local Microgrids. It discusses the transition from passive consumers to prosumers in power networks and introduces the concept of a federated power plant, combining virtual power plants and P2P transactions among prosumers to address challenges and unlock additional value. This review fills research gaps, shedding light on P2P energy trading’s multifaceted aspects in local markets and its transformative potential for the energy sector.

Pavel Dvurechensky, Caroline Geiersbach, Michael Hintermüller et al.

We present a novel model of a coupled hydrogen and electricity market on the intraday time scale, where hydrogen gas is used as a storage device for the electric grid. Electricity is produced by renewable energy sources or by extracting hydrogen from a pipeline that is shared by non-cooperative agents. The resulting model is a generalized Nash equilibrium problem. Under certain mild assumptions, we prove that an equilibrium exists. Perspectives for future work are presented.

Grégoire Dutot, Margaux Zaffran, Olivier Féron et al.

Electricity price forecasting (EPF) plays a major role for electricity companies as a fundamental entry for trading decisions or energy management operations. As electricity can not be stored, electricity prices are highly volatile which make EPF a particularly difficult task. This is all the more true when dramatic fortuitous events disrupt the markets. Trading and more generally energy management decisions require risk management tools which are based on probabilistic EPF (PEPF). In this challenging context, we argue in favor of the deployment of highly adaptive black-boxes strategies allowing to turn any forecasts into a robust adaptive predictive interval, such as conformal prediction and online aggregation, as a fundamental last layer of any operational pipeline. We propose to investigate a novel data set containing the French electricity spot prices during the turbulent 2020-2021 years, and build a new explanatory feature revealing high predictive power, namely the nuclear availability. Benchmarking state-of-the-art PEPF on this data set highlights the difficulty of choosing a given model, as they all behave very differently in practice, and none of them is reliable. However, we propose an adequate conformalisation, OSSCP-horizon, that improves the performances of PEPF methods, even in the most hazardous period of late 2021. Finally, we emphasize that combining it with online aggregation significantly outperforms any other approaches, and should be the preferred pipeline, as it provides trustworthy probabilistic forecasts.

Sonja H. M. Germscheid, Benedikt Nilges, Niklas von der Assen et al.

This work studies synergies arising from combining industrial demand response and local renewable electricity supply. To this end, we optimize the design of a local electricity generation and storage system with an integrated demand response scheduling of a continuous power-intensive production process in a multi-stage problem. We optimize both total annualized cost and global warming impact and consider local photovoltaic and wind electricity generation, an electric battery, and electricity trading on day-ahead and intraday market. We find that installing a battery can reduce emissions and enable large trading volumes on the electricity markets, but significantly increases cost. Economically and ecologically-optimal operation of the process and battery are driven primarily by the electricity price and grid emission factor, respectively, rather than locally generated electricity. A parameter study reveals that cost savings from the local system and flexibilizing the process behave almost additively.

Jamiu O. Oladigbolu, Asad Mujeeb, Yusuf A. Al-Turki et al.

In Saudi Arabia, the energy sector is presently the most significant contributor to carbon emissions, followed by the transportation sector, which contributes about 26% of the gross greenhouse gas emissions. The adoption of electric vehicles (EVs) in the transportation sector worldwide is one way to bring about a global green solution that can support the decarbonization of the environment, which now constitutes a new electric power demand for the utility grid network. To preserve the environment, and reduce the pressure on the existing grid network, we propose the utilization of EV charging stations (EVCSs) in off-grid locations. It is essential to have an alternative stand-alone renewables-based electrification framework to secure the charging demand needed for the electric vehicles. The present study performs a techno-economic investigation of a novel off-grid scheme that combines renewable energy resources to provide clean electricity for EV charging stations. The optimized system for the EVCS is compared with the alternative option of grid extension using economic criteria evaluation metrics and distance limitations. The optimization and comparative analysis results reveal that the option of an optimum stand-alone hybrid charging station is an economical, sustainable, and eco-friendly alternative to the option of grid expansion.

Sylvester William Chisale, Samuel Eliya, John Taulo

Interrupted power supply and poor access to electricity (15%) have been persistent problems in Malawi for decades. Diversification of resources is required to solve the challenges. Therefore, this study is aimed at conducting a techno-economic analysis of a hybrid system to ensure electricity reliability, bill reduction, and reduced grid demand at a school. The study investigated six hybrid system scenarios that had various combinations of the grid, diesel, solar PV, wind, biogas, and battery. The HOMER Pro model was used to determine the best system combination. Six scenarios were examined further using the CRITIC-PROMETHEE II approaches. The best system configuration included grid, solar PV, and biogas electricity. Biogas generation is mainly from human excreta. The study also investigated the effect of the 2022 inflation rate on the financial performance of the system, which had shown a sharp increase in capital, replacement, O&M costs, and payback time. The proposed system’s levelized cost of electricity is 0.095 $/kWh, which is less than Malawi’s grid’s levelized cost of 0.11 $/kWh. Environmentally, the system could help to reduce greenhouse emissions, including those from the sewage system. Therefore, schools and governments should invest in alternative energy generation.

Stuart D. C. Walsh, Laura Easton, Changlong Wang et al.

Australia has ambitions to become a major global hydrogen producer by 2030. The establishment of Australia’s and the world’s hydrogen economy, however, will depend upon the availability of affordable and reliable hydrogen storage. Geological hydrogen storage is a practical solution for large scale storage requirements ensuring hydrogen supply can always meet demand, and excess renewable electricity can be stored for later use, improving electricity network reliability. Hosting thick, underground halite (salt) deposits and an abundance of onshore depleted gas fields, Australia is well placed to take advantage of geological hydrogen storage options to support its ambition of building a global hydrogen hub export industry. Using the Bluecap modelling software, we identify regions in Australia that are potentially profitable for large scale hydrogen production and storage. We use the results of this work to suggest high-potential regions for hydrogen development, supporting policymaker and investor decisions on the locations of new infrastructure and hydrogen projects in Australia.

Rene Aid, Anupama Kowli, Ankur A. Kulkarni

Utilities and transmission system operators (TSO) around the world implement demand response programs for reducing electricity consumption by sending information on the state of balance between supply demand to end-use consumers. We construct a Bayesian persuasion model to analyse such demand response programs. Using a simple model consisting of two time steps for contract signing and invoking, we analyse the relation between the pricing of electricity and the incentives of the TSO to garble information about the true state of the generation. We show that if the electricity is priced at its marginal cost of production, the TSO has no incentive to lie and always tells the truth. On the other hand, we provide conditions where overpricing of electricity leads the TSO to provide no information to the consumer.

Zhiqiang Zhao, Yining Liu, Zhixin Zeng et al.

In most electricity theft detection schemes, consumers' power consumption data is directly input into the detection center. Although it is valid in detecting the theft of consumers, the privacy of all consumers is at risk unless the detection center is assumed to be trusted. In fact, it is impractical. Moreover, existing schemes may result in some security problems, such as the collusion attack due to the presence of a trusted third party, and malicious data tampering caused by the system operator (SO) being attacked. Aiming at the problems above, we propose a blockchain-based privacy-preserving electricity theft detection scheme without a third party. Specifically, the proposed scheme uses an improved functional encryption scheme to enable electricity theft detection and load monitoring while preserving consumers' privacy; distributed storage of consumers' data with blockchain to resolve security problems such as data tampering, etc. Meanwhile, we build a long short-term memory network (LSTM) model to perform higher accuracy for electricity theft detection. The proposed scheme is evaluated in a real environment, and the results show that it is more accurate in electricity theft detection within acceptable communication and computational overhead. Our system analysis demonstrates that the proposed scheme can resist various security attacks and preserve consumers' privacy.

Gunther Gust, Alexander Schlüter, Stefan Feuerriegel et al.

With the global effort to reduce carbon emissions, clean technologies such as electric vehicles and heat pumps are increasingly introduced into electricity distribution networks. These technologies considerably increase electricity flows and can lead to more coincident electricity demand. In this paper, we analyze how such increases in demand coincidence impact future distribution network investments. For this purpose, we develop a novel model for designing electricity distribution networks, called the distribution network reconfiguration problem with line-specific demand coincidence (DNRP-LSDC). Our analysis is two-fold: (1) We apply our model to a large sample of real-world networks from a Swiss distribution network operator. We find that a high demand coincidence due to, for example, a large-scale uptake of electric vehicles, requires a substantial amount of new network line construction and increases average network cost by 84 % in comparison to the status quo. (2) We use a set of synthetic networks to isolate the effect of specific network characteristics. Here, we show that high coincidence has a more detrimental effect on large networks and on networks with low geographic consumer densities, as present in, e. g., rural areas. We also show that expansion measures are robust to variations in the cost parameters. Our results demonstrate the necessity of designing policies and operational protocols that reduce demand coincidence. Moreover, our findings show that operators of distribution networks must consider the demand coincidence of new electricity uses and adapt investment budgets accordingly. Here, our solution algorithms for the DNRP-LSDC problem can support operators of distribution networks in strategic and operational network design tasks.

Lyubov N. Shishelina

The author attempts to trace the development of the eastern direction of Hungary’s foreign policy, which received the name «Opening to the East» with the return in 2010 to the Hungarian policy of Viktor Orban’s cabinet. The reason for the study of this aspect was the recent visit of the Hungarian Prime Minister to Moscow on February 1, 2022. In our country, especially after the crisis of 2014, these visits are seen as a breakthrough event, a success of politics and diplomacy. The European Union does not share such a degree of pragmatism of Viktor Orban’s foreign policy, seeing in his trips primarily a political component, which forces him to precede and conclude visits to Moscow with a cascade of meetings with leading Western politicians. The same thing happens before and after the visits of the Russian leader to Budapest. In Hungary, relations with Moscow are viewed exclusively from a pragmatic standpoint, since their economic component allows to achieve real results in overcoming the crisis phenomena affecting the daily lives of citizens (gas, gasoline, electricity, etc.). In addition, the Russian direction is a component of a broader direction of «Opening to the east»” policy. This program, where the Turk element is a significant factor, also separately includes relations with specific Muslim republics of central Russia, with the states of the Caucasus and Central Asia, with Turkey, China, etc.

Xi Zhu, Yi Yin, Suman Peng et al.

Abstract In this study, characteristics of charge injection under extra high electric field (above 100 kV/mm) in cross‐linked polyethylene (XLPE) were investigated by experiments of conduction current and space charge. The results show that current density from low electric field to sample breakdown corresponds to space charge limited current (SCLC) theory. More specifically, Schottky current is similar to experiment current before 100 kV/mm, while the J–E curve conforms to a modified SCLC theory after 100 kV/mm. Besides, the non‐linear coefficient of J–E curve from 100 kV/mm to extra high electric field is smaller than theoretical value, and the injection depth of space charge is restricted as the field becomes higher than 100 kV/mm, which may be caused by the negative differential mobility of charge. Driven by extra high electric field, charge collides with lattice of dielectric and scatters. As a result, mean free time of charge decreases and charge mobility is reduced with the increased field. Consequently, considering the decrease in charge mobility, a mobility‐limited charge injection equation is proposed, and the validity of the proposed equation under extra high electric field is demonstrated by space charge simulation.