Hasil untuk "Electricity"

Darshini R., Akshay Kumar, Maria Anu Vensuslaus et al.

Pufan Qi

This paper examines the impact of electric vehicles on total annual electricity consumption across 58 counties in California from 2010 to 2021. Employing a log linear model to analyze the relationship between electricity consumption and EV ownership, alongside a linear log model with an instrumental variable approach, the study finds that annual per capita electricity consumption increased by 0.23% for each additional electric vehicle per 10,000 residents over the 12 years period. The analysis identifies partisanship, measured as the annual percentage of voter registration for the Democratic Party by county, as a robust instrumental variable. Specifically, a 1% increase in Democratic voter registration corresponds to the adoption of approximately two additional EVs per 10,000 residents.

Shiliang Zhang, Sabita Maharjan, Kai Strunz et al.

Geographic data is vital in understanding, analyzing, and contextualizing energy usage at the regional level within electricity systems. While geospatial visualizations of electricity infrastructure and distributions of production and consumption are available from governmental and third-party sources, these sources are often disparate, and compatible geographic datasets remain scarce. In this paper, we present a comprehensive geographic dataset representing the electricity system in Norway. We collect data from multiple authoritative sources, process it into widely accepted formats, and generate interactive maps based on this data. Our dataset includes information for each municipality in Norway for the year 2024, encompassing electricity infrastructure, consumption, renewable and conventional production, main power grid topology, relevant natural resources, and population demographics. This work results in a formatted geographic dataset that integrates diverse informational resources, along with openly released interactive maps. We anticipate that our dataset will alleviate software incompatibilities in data retrieval, and facilitate joint analyses on regional electricity system for energy researchers, stakeholders, and developers.

Xin Lu

Accurate prediction of electricity prices plays an essential role in the electricity market. To reflect the uncertainty of electricity prices, price intervals are predicted. This paper proposes a novel prediction interval construction method. A conditional generative adversarial network is first presented to generate electricity price scenarios, with which the prediction intervals can be constructed. Then, different generated scenarios are stacked to obtain the probability densities, which can be applied to accurately reflect the uncertainty of electricity prices. Furthermore, a reinforced prediction mechanism based on the volatility level of weather factors is introduced to address the spikes or volatile prices. A case study is conducted to verify the effectiveness of the proposed novel prediction interval construction method. The method can also provide the probability density of each price scenario within the prediction interval and has the superiority to address the volatile prices and price spikes with a reinforced prediction mechanism.

Jieyu Xie, Xingying Chen, Kun Yu et al.

In the context of rapid growth in renewable energy installations and increasingly severe consumption issues, this paper designs a 100% green electricity supplied zero-carbon integrated energy station. It aims to analyze its configuration focusing on the following three core features: zero carbon emissions, 100% green electricity supply, and a centralized–distributed system structure. It discusses equipment selection and provides models for configuring upstream green electricity resources, power generation, energy storage, transformer, and energy conversion. The study examines the synergy between lithium-ion battery storage and modular molten salt thermal storage, along with the virtual energy storage characteristics formed by thermal load inertia, supported by mathematical models. Based on the data from a green electricity system in an Eastern Chinese city and typical load profiles, the paper validates a specific configuration for a 100% green electricity supplied zero-carbon integrated energy station, confirming model accuracy and calculating the required scale of upstream green electricity resources. It proves that establishing an electro-thermal storage synergy system is crucial for addressing the significant fluctuations in renewable energy output. It also argues that leveraging thermal load inertia to create virtual storage can reduce the investment in energy storage system construction.

Caterina Conigliani, Martina Iorio, Salvatore Monni

According to the UN's Sustainable Development Agenda, to effectively achieve sustainable development, strategies for building economic growth should also address social needs, including access to essential services. Sustainable integrated management of water resources for both primary use and energy production is crucial, especially in territories such as the Amazonian State of Pará, where a primary good like fresh water is also the main source of electricity. However, the territorial transformations occurring in Pará over installing new hydroelectric plants have jeopardised local development. This was mainly caused by the top-down approach underlying national strategic projects that have paid little attention to local needs, thus paving the way for detrimental conditions for implementing the UN's 2030 Agenda. This paper aims to analyse the relationship between a municipality's level of development and quality of life and the most relevant key determinants of sustainable development in Pará. To this end, we consider a spatial regression analysis, with particular attention devoted to the role of access to both energy and water. The presence of significant spillover effects implies that providing public services on a geographically broad basis could induce self-reinforcing benefits.

Franko Pandžić, Ivan Sudić, Tomislav Capuder et al.

Abstract The cost for covering active power losses makes a significant item in transmission system operators (TSO) annual budgets, and still it received limited attention in the existing literature. The focus of accurate power loss forecasting and procurement is of high increase during the past 2 years due to spikes in electricity prices, making the cost of covering the active power losses a dominant factor of TSO operational costs. This paper presents practical aspects of the highly accurate models for transmission loss forecast in the day ahead time frame for the Croatian transmission system. The contributions are two‐fold: 1) Practical insights into usable TSO data are provided, filling a critical research gap and a foundational literature review is established on transmission loss forecasting. 2) A novel method utilizing only electricity transit data as input which outperforms existing practices is presented. For this, several algorithms such as gradient boosted decision tree model (XGB), support vector regressors, multiple linear regression and fully connected feedforward artificial neural networks are developed, and implemented and validated on data obtained from the Croatian TSO. The results show that the XGB model outperforms current TSO model by 32% for 4 months of comparison and TSCNET's commercial solution by 25% during a year‐long testing period. The developed XGB model is also implemented as a software tool and put into everyday operation with the Croatian TSO.

Shaukat Ali Jawaid, Masood Jawaid

There are numerous well-established parameters to judge and evaluate the standard of a medical journal like quality of its contents and geographic distribution of manuscripts, it attracts for publication, indexation and coverage in important indexes and databases like Medline, Web of Sciences by Clarivate known for its Impact Factor (IF), PubMed Central, Scopus etc., journal visibility and readership, timely regular publication. Impact Factor is one of the criteria and not the only criteria, which should be used to judge the standard of a journal. However, too much importance being given to the  Impact Factor by the regulatory bodies in Pakistan as well as by medical institutions, asking those doing PhD to publish their research work in Impact Factor journals has created a crisis like situation not only for the researchers but also made the life of the IF journal editors miserable. They are under tremendous pressure to accommodate more and more papers by the authors anxious for early publication to meet certain deadlines for the completion of their research project and award of degrees while the editors on their part are faced with a dilemma due to human resource and financial resource constraints. In an environment where political stability remains in short supply most of the time, law and order situation is unpredictable, not to forget the frequent breakdown of electricity and inefficient internet service, it is not possible to either increase the frequency of publications or plan some other measures which all call for additional investment. Finding trained human resource and then retaining those remains a constant problem.

Simone Balmelli, Francesco Moresino

We address the problem of charging plug-in electric vehicles (PEVs) in a decentralized way and under stochastic dynamics affecting the real-time electricity tariff. The model is formulated as a Nash equilibrium seeking problem, where players wish to minimize the costs for charging their own PEVs. For finite PEVs populations, the Nash equilibrium does not correspond to the social optimum, i.e., to a control strategy minimizing the total electricity costs at the aggregate level. We accordingly introduce some taxes/incentives on the price of electricity for charging PEVs and show that it is possible to tune them so that (a) the social optimum is reached as a Nash equilibrium, (b) in correspondence with this equilibrium, players do not pay any net total tax, nor receive any net total incentive.

David Fellner, Thomas I. Strasser, Wolfgang Kastner et al.

The changes in the electric energy system toward a sustainable future are inevitable and already on the way today. This often entails a change of paradigm for the electric energy grid, for example, the switch from central to decentralized power generation which also has to provide grid-supporting functionalities. However, due to the scarcity of distributed sensors, new solutions for grid operators for monitoring these functionalities are needed. The framework presented in this work allows to apply and assess data-driven detection methods in order to implement such monitoring capabilities. Furthermore, an approach to a multi-stage detection of misconfigurations is introduced. Details on implementations of the single stages as well as their requirements are also presented. Furthermore, testing and validation results are discussed. Due to its feature of being seamlessly integrable into system operators' current metering infrastructure, clear benefits of the proposed solution are pointed out.

Peeter Raesaar, Eeli Tiigimägi, Juhan Valtin

Kun Liu, Feng Gao

Abstract With the breaking down of information barriers between energy flow with manufacturing flow, the coordination between them is an effective way to relieve the dual pressure from energy and environment industrial integrated energy system. The authors develop a scenario‐based coordination model for energy flow and manufacturing flow to make full use of the flexibilities of energy supply and production process to reduce energy cost. To capture the flexibility in the energy flow, the authors enhance an electricity‐steam‐product gas–gas storage coupling energy flow model considering multi‐uncertainties. The authors also develop a batch process model to formulate the flexibility in the production process. Based on the batch energy consumption constraints, the energy flow model and the batch process model are integrated as a coordination model. To ensure the feasibility of hard budget constraints under all possible random realisations, we add all‐scenario‐feasibility robust constraints which are infinite‐dimensional constraints into the model. To solve the model, a vertex scenario set based on the characteristics of convex optimisation is constructed to equivalently convert infinite‐dimensional constraints to finite‐dimensional constraints. In this way, the coordination model is transformed to a mixed integer linear programming and can be solved using CPLEX. Finally, numerical test based on a real iron and steel plant is analysed. The results show that coordination between energy with manufacturing flow is effective to reduce the energy cost and carbon emission. Compare with only optimising energy flow, the coordination model can reduce total cost about 221.6 thousand RMB and 304.44t coal every day.

Nafise Rezaei, Roozbeh Rajabi, Abouzar Estebsari

The participation of consumers and producers in demand response programs has increased in smart grids, which reduces investment and operation costs of power systems. Also, with the advent of renewable energy sources, the electricity market is becoming more complex and unpredictable. To effectively implement demand response programs, forecasting the future price of electricity is very crucial for producers in the electricity market. Electricity prices are very volatile and change under the influence of various factors such as temperature, wind speed, rainfall, intensity of commercial and daily activities, etc. Therefore, considering the influencing factors as dependent variables can increase the accuracy of the forecast. In this paper, a model for electricity price forecasting is presented based on Gated Recurrent Units. The electrical load consumption is considered as an input variable in this model. Noise in electricity price seriously reduces the efficiency and effectiveness of analysis. Therefore, an adaptive noise reducer is integrated into the model for noise reduction. The SAEs are then used to extract features from the de-noised electricity price. Finally, the de-noised features are fed into the GRU to train predictor. Results on real dataset shows that the proposed methodology can perform effectively in prediction of electricity price.

Alejandro Gutierrez-Alcoba, Roberto Rossi, Belen Martin-Barragan et al.

Electric road systems (ERS) are roads that allow compatible vehicles to be powered by grid electricity while in transit, reducing the need for stopping to recharge electric batteries. We investigate how this technology can affect routing and delivery decisions for hybrid heavy good vehicles (HGVs) travelling on a ERS network to support the demand of a single product faced by a set of retailers in the network. We introduce the Electric Roads Routing Problem, which accounts for the costs of electricity and fuel on a ERS network, consumption that are affected by the battery level of the vehicle in each step of the journey, the routing decisions and the variable weight of the vehicle, which depends on vehicle load and delivery decisions. In particular, we study a stochastic demand version of the problem, formulating a mathematical programming heuristic and proving its effectiveness. We use our model on a realistic instance of the problem, showcasing the different strategies that a vehicle may follow depending on fuel costs in relation to the costs of electricity.

Tong Zhang, Christopher Williams, Reza Ahmadian et al.

As the demand for electricity and the need for power systems flexibility grow, it is crucial to exploit more reliable and clean sources of energy to produce electricity when needed most. Tidal lagoons generate renewable electricity by creating an artificial head difference between water levels on the seaside, driven by tides, and water levels inside the basin, controlled by flow through the structure. Depending on the level of seawater, power generation from a tidal lagoon can be controlled, i.e. shifting power generation in time. This paper aims to investigate the operation of a tidal lagoon in response to fluctuating electricity prices. By developing an optimal operation model of a tidal lagoon, its schedule in the day-ahead wholesale electricity market was optimized to achieve maximum revenue. The Swansea Bay tidal lagoon was used as a case study. It was demonstrated that by exploiting the flexibility offered by the tidal lagoon, it can achieve a higher revenue in the day-ahead market, although their total electricity generation is reduced.

N K MEENA, RAM SINGH, S M FEROZE et al.

The PV solar device for pumping water from underground and from other source of water for irrigation has been recognized as very new initiative. Three year socio-economic study sponsored by Ministry of Human Resource under higher education scheme has been conducted in two districts of Rajasthan applying standard methodology to assess comparative advantages of PV solar device for irrigation of kinnow orchard. Hence, study found that solar irrigation system has enhanced the returns of farm and played a partial catalyst role to enhance the income of the farm. Therefore, the economic as well as environment benefits need to realize for popularization of the solar device for betterment of farming society which would reduce the dependency on electricity of farmers for irrigation specially and other works depend on electricity generally. Hence, provision of incentives on solar devices should be made to the farmers.

Wills Adam, Banister Carsen, Pellissier Mathieu et al.

This work explores the importance of renewable resource temporal distribution for solar and wind energy deployment in Arctic communities to meet building and ancillary loads. An analysis of ten years of historic weather data was performed for six locations in the Canadian Arctic to assess renewable resource variation. Simulations of similar capacity solar and wind generation systems were then coupled with the historic data to compare and contrast generation potential. This analysis highlighted the importance of considering hourly, daily, monthly, and year-to-year renewable generation when deploying solar and wind to the Arctic. As many northern communities in Canada have local electricity generation and distribution systems, and no connection to the continental grid, managing grid interactions effectively is crucial to the success of deployment, integration, and operation. The results for the solar energy analysis showed high consistency of production year-to-year. The results for the wind energy analysis showed that the annual outputs have significantly less variation than the year-to-year output of individual months for all the locations under study. For the high latitude locations studied, solar energy can still provide useful electricity generation output, but the more pronounced bias of the annual output to the summer months can leave several months with little or no output. The use of additional renewable sources is crucial in beginning to transition some electricity generating capacity within Arctic communities from being solely reliant on fossil fuels.

Moein Shamoushaki, Mehdi Aliehyaei, Marc A. Rosen

Energy, exergy, and exergoeconomic evaluations of various geothermal configurations are reported. The main operational and economic parameters of the cycles are evaluated and compared. Multi-objective optimization of the cycles is conducted using the artificial bee colony algorithm. A sensitivity assessment is carried out on the effect of production well temperature variation on system performance from energy and economic perspectives. The results show that the flash-binary cycle has the highest thermal and exergy efficiencies, at 15.6% and 64.3%, respectively. The highest generated power cost and pay-back period are attributable to the simple organic Rankine cycle (ORC). Raising the well-temperature can increase the exergy destruction rate in all configurations. However, the electricity cost and pay-back period decrease. Based on the results, in all cases, the exergoenvironmental impact improvement factor decreases, and the temperature rises. The exergy destruction ratio and efficiency of all components for each configuration are calculated and compared. It is found that, at the optimum state, the exergy efficiencies of the simple organic Rankine cycle, single flash, double flash, and flash-binary cycles respectively are 14.7%, 14.4%, 12.6%, and 14.1% higher than their relevant base cases, while the pay-back periods are 10.6%, 1.5% 1.4%, and 0.6% lower than the base cases.

Cheng Cheng, Andrew Blakers, Matthew Stocks et al.

Japan has committed to carbon neutrality by 2050. Emissions from the electricity sector amount to 42% of the total. Solar photovoltaics (PV) and wind comprise three quarters of global net capacity additions because of low and falling prices. This provides an opportunity for Japan to make large reductions in emissions while also reducing its dependence on energy imports. This study shows that Japan has 14 times more solar and offshore wind resources than needed to supply 100% renewable electricity. A 40 year hourly energy balance model is presented of Japan's electricity system using historical data. Pumped hydro energy storage, high voltage interconnection and dispatchable capacity (hydro, biomass and hydrogen energy) are included to balance variable generation and demand. Differential evolution is used to find the least-cost solution under various constraints. The levelized cost of electricity is found to be USD 86 per MWh for a PV-dominated system, and USD 110 per MWh for a wind-dominated system. These costs can be compared with the average system prices on the spot market in Japan of USD 102 per MWh. In summary, Japan can be self-sufficient for electricity supply at competitive costs.

Jinghuan Ma, Jie Gu, Zhijian Jin

This paper presents a perspective of functional analysis to analyze electric load and electricity pricing in the $L_2$ space and its isomorphic vector space, which also yields the general algebraic model of load and pricing associated with time course. It clarifies law of interaction between payment and load associated with time course and necessity of pricing to sufficiently convey information on how load results in supply cost. It describes classic paradigm of modeling in a general algebraic form and formally proves the ineffectiveness of classic integral-based pricing in modeling. It consequently introduces to: describe electricity supply cost by a mapping defined on an isomorphic space of the original space of load constituted by orthonormal basis that quantifies the dynamism of load, named the space of dynamism; a pricing model defined on the space of dynamism that sufficiently conveys the information; a further derived computational model of pricing based on the Fourier series; simple examples to demonstrate use of the proposed pricing and its effectiveness in distinguishing loads and reflecting supply cost associated with time course, which theoretically completes the incentive to reshape loads.