Hasil untuk "Electricity"

T. Razykov, C. Ferekides, D. Morel et al.

M. Beaudin, H. Zareipour, Anthony Schellenberglabe et al.

A. Conejo, M. Carrión, J. Morales

P. Denholm, M. Hand

H. Lund, W. Kempton

J. Koomey

The direct electricity used by data centers has become an important issue in recent years as demands for new Internet services (such as search, music downloads, video-on-demand, social networking, and telephony) have become more widespread. This study estimates historical electricity used by data centers worldwide and regionally on the basis of more detailed data than were available for previous assessments, including electricity used by servers, data center communications, and storage equipment. Aggregate electricity use for data centers doubled worldwide from 2000 to 2005. Three quarters of this growth was the result of growth in the number of the least expensive (volume) servers. Data center communications and storage equipment each contributed about 10% of the growth. Total electricity use grew at an average annual rate of 16.7% per year, with the Asia Pacific region (without Japan) being the only major world region with growth significantly exceeding that average. Direct electricity used by information technology equipment in data centers represented about 0.5% of total world electricity consumption in 2005. When electricity for cooling and power distribution is included, that figure is about 1%. Worldwide data center power demand in 2005 was equivalent (in capacity terms) to about seventeen 1000 MW power plants.

Frank Sensfuß, M. Ragwitz, M. Genoese

James H Williams, A. DeBenedictis, R. Ghanadan et al.

Peng Zhou, B. Ang, Hui Wang

F. Schweppe

J. Johnson

R. Jones, A. Fuertes, K. Lomas

This paper aims to investigate the socio-economic, dwelling and appliance related factors that have significant or non-significant effects on domestic electricity consumption. To achieve this aim, a comprehensive literature review of international research investigating these factors was undertaken. Although papers examining the factors affecting electricity demand are numerous, to the authors’ knowledge, a comprehensive analysis taking stock of all previous findings has not previously been undertaken. The review establishes that no less than 62 factors potentially have an effect on domestic electricity use. This includes 13 socio-economic factors, 12 dwelling factors and 37 appliance factors. Of the 62 factors, four of the socio-economic factors, seven of the dwelling factors, and nine of the appliance related factors were found to unambiguously have a significant positive effect on electricity use. This paper contributes to a better understanding of those factors that certainly affect electricity consumption and those for which effects are unclear and require further research. Understanding the effects of factors can support both the implementation of effective energy policy and aid prediction of future electricity consumption in the domestic sector.

Chase Humiston, Mehmet Cetin, Anderson Rodrigo de Queiroz

Battery Energy Storage Systems (BESS) can benefit from price volatility in electricity markets, but frequent cycling increases degradation and reduces long-term value. This study develops a rolling-horizon dispatch framework in which battery operation is fully price-driven, while degradation is evaluated separately to isolate the effect of degradation model choice. A 48 h look-ahead window is solved repeatedly and advanced by 24 h, with only the first 24 h of decisions implemented and remaining capacity carried forward. Degradation is assessed using three widely used model classes: Linear-Calendar (LC), Energy-Throughput (ET), and Cycle-Based rainflow (CB) models. The framework is applied to Electric Reliability Council of Texas (ERCOT) 15 min real-time prices for 2024 (Houston Zone). LC and ET result in limited annual capacity loss (≈2%) and modest economic impact, while the CB model predicts substantially higher degradation and large negative valuation. Sensitivity analysis shows that CB-based results are highly dependent on parameter calibration. Overall, the results highlight the strong influence of degradation modeling choices on BESS valuation under rolling-horizon operation.

LIU Donglin, ZHOU Xia, DAI Jianfeng, XIE Xiangpeng, TANG Yi, LI Juanshi

Integrated energy systems in buildings are an effective means to achieve low-carbon buildings. To further tap into their demand-side flexibility adjustable potential and carbon reduction potential, and reasonably allocate the interests of various entities in the building integrated energy system, a bi-level optimization scheduling strategy for building integrated energy system considering virtual energy storage in buildings under Stackelberg game framework is proposed. First, the thermal inertia of the cooling and heating system inside the building and the flexibility of the cooling and heating load are considered to leverage the virtual energy storage function of the building and improve system flexibility in the game model. Then, the genetic algorithm is used to solve the upper-level pricing model of energy operators, updating the purchase and sale electricity prices set by upper-level leaders, while the CPLEX solver is used to solve the lower-level problem, optimizing equipment output, demand response, and electricity trading plans. Finally, the proposed model is verified by case studies that it can effectively improve the economic performance and low-carbon characteristics of building integrated energy systems.

Xinyi Zhu, Sheng Zhou, Fucong Xu et al.

Abstract The widespread integration of photovoltaic (PV) power, energy storage systems, and other demand‐side resources highlights the importance of optimal dispatching for the PV‐storage‐load virtual power plant (VPP). However, the fluctuation of the PV power generation and the uncertainty of the electricity prices exacerbate the economic operation risks of the VPP. To address these challenges, an optimal dispatching strategy for the PV‐storage‐load VPP is proposed, with due consideration given to the dual uncertainties of electricity prices and PV power output. Firstly, the conditional value‐at‐risk theory is employed to quantify the uncertainty risk of VPP revenue caused by electricity price fluctuations. Secondly, in view of the asymmetric fluctuation intervals of PV power output, a quantification method for PV uncertainty and dispatch robustness is developed using the confidence gap decision theory. Furthermore, by combining the regulation reserve model of multi‐type flexible resources, a robust optimization model for the PV‐storage‐load VPP is constructed with the objective of maximizing comprehensive operational revenue, which includes the provision of upward and downward reserve services. Finally, case studies based on a PV‐storage‐load VPP in a Chinese province are conducted to validate the effectiveness and superiority of the proposed model. The simulation results indicate that the proposed robust optimization strategy effectively reflects the relationship between the uncertainty of PV power output and the risk preference of decision‐maker, mitigates the fluctuation risks of electricity prices to ensure the stability of the power system, and enhances the economic efficiency and flexibility of the PV‐storage‐load VPP operation.

Mohammad A. Salahuddin, Khorshed Alam

Leonova Yu.Yu., Negadaev V.A.

The increase in the cost of energy resources makes it necessary to take into account measures to reduce non-production losses of electricity when building an electricity supply network. It is possible to reduce power losses by increasing the power factor. Despite a significant amount of research on the optimization of methods for selecting reactive power compensation devices, their placement and management, the installation costs of such devices are often determined approximately, without taking into account the nature of the dependencies between cost and their parameters. This article presents the results of research on the dependence of the cost of reactive power compensation devices on their parameters: reactive power, permissible current and capacitance. To clarify the nature of the relationship between the cost of capacitor banks and the value of reactive power, regression equations describing this relationship (in prices of 2023) are defined. The method of correlation analysis is used to find the regression equations. Based on the value of the Fisher criterion, a regression equation was chosen that best describes the relationship between the specified parameters of capacitor banks. The use of a deflator index to predict the cost of capacitor banks is proposed. To determine the values of the reactive power of a capacitor bank, at which it is advisable to turn on capacitors through a step-up transformer, the cost of reactive power compensation methods at reactive power values of 50 kVAr and above and a comparison of reactive power compensation methods using a step-up transformer and without its use was carried out. Based on the results of the study, conclusions were drawn: on the existence of a relationship between the cost of capacitor banks and reactive power, described by a polynomial regression equation; on the influence of voltage values on the cost of a unit of capacitance of capacitor banks. It is noted that if the required reactive power exceeds the value of 300 kVAr, it is advisable to consider switching on static capacitors using a step-up transformer.

Yu-Shu Wu, Philip H. Winterfeld

Enhanced or engineered geothermal systems (EGSs), or non-hydrothermal resources, are highly notable among sustainable energy resources because of their abundance and cleanness. The EGS concept has received worldwide attention and undergone intensive studies in the last decade in the US and around the world. In comparison, hydrothermal reservoir resources, the ‘low-hanging fruit’ of geothermal energy, are very limited in amount or availability, while EGSs are extensive and have great potential to supply the entire world with the needed energy almost permanently. The EGS, in essence, is an engineered subsurface heat mining concept, where water or another suitable heat exchange fluid is injected into hot formations to extract heat from the hot dry rock (HDR). Specifically, the EGS relies on the principle that injected water, or another working fluid, penetrates deep into reservoirs through fractures or high-permeability channels to absorb large quantities of thermal energy by contact with the host hot rock. Finally, the heated fluid is produced through production wells for electricity generation or other usages. Heat mining from fractured EGS reservoirs is subject to complex interactions within the reservoir rock, involving high-temperature heat exchange, multi-phase flow, rock deformation, and chemical reactions under thermal-hydrological-mechanical (THM) processes or thermal-hydrological-mechanical-chemical (THMC) interactions. In this paper, we will present a THM model and reservoir simulator and its application for simulation of hydrothermal geothermal systems and EGS reservoirs as well as a methodology of coupling thermal, hydrological, and mechanical processes. A numerical approach, based on discretizing the thermo-poro-elastic Navier equation using an integral finite difference method, is discussed. This method provides a rigorous, accurate, and efficient fully coupled methodology for the three (THM) strongly interacted processes. Several programs based on this methodology are demonstrated in the simulation cases of geothermal reservoirs, including fracture aperture enhancement, thermal stress impact, and tracer transport in a field-scale reservoir. Results are displayed to show geomechanics’ impact on fluid and heat flow in geothermal reservoirs.

Pablo Catota-Ocapana, Cesar Minaya-Andino, Paul Astudillo et al.

In recent years, agriculture has significantly evolved with the integration of technology, enabling the development of new cultivation techniques that respond to the growing demand for food and the need to conserve natural resources. In this context, we conducted a comprehensive review of models of intelligent control for managing nutrients in hydroponic systems by analyzing studies from the last five years. The selection of articles was based on the guidelines of PRISMA and research questions, focusing on control techniques based on fuzzy Logic, Artificial Intelligence and artificial Vision. These models are essential to automatically adjust the concentrations of nutrients, adapting to the needs of the plants at each stage of their growth. The review results highlight essential advances but also identify significant challenges, such as the need for precise sensors, the management of large volumes of data, and adapting the models to different crops and conditions. Despite these challenges, the benefits include a more efficient use of nutrients, a reduction in the consumption of water, and increased crop yields. Continuous research in this field is essential to improve the sustainability and productivity of hydroponic systems, offering new opportunities for agriculture in the future. The findings of this review provide a solid basis for evaluating the effectiveness of the control models and their application in real agricultural scenarios.

Jeremy Proz, Martin Huber

Collusion and capacity withholding in electricity wholesale markets are important mechanisms of market manipulation. This study applies a refined machine learning-based cartel detection algorithm to two cartel cases in the Italian electricity market and evaluates its out-of-sample performance. Specifically, we consider an ensemble machine learning method that uses statistical screens constructed from the offer price distribution as predictors for the incidence of collusion among electricity providers in specific regions. We propose novel screens related to the capacity-withholding behavior of electricity providers and find that including such screens derived from the day-ahead spot market as predictors can improve cartel detection. We find that, under complete cartels - where collusion in a tender presumably involves all suppliers - the method correctly classifies up to roughly 95% of tenders in our data as collusive or competitive, improving classification accuracy compared to using only previously available screens. However, when trained on larger datasets including non-cartel members and applying algorithms tailored to detect incomplete cartels, the previously existing screens are sufficient to achieve 98% accuracy, and the addition of our newly proposed capacity-withholding screens does not further improve performance. Overall, this study highlights the promising potential of supervised machine learning techniques for detecting and dismantling cartels in electricity markets.