The pattern of electricity use in an individual domestic dwelling is highly dependent upon the activities of the occupants and their associated use of electrical appliances. This paper presents a high-resolution model of domestic electricity use that is based upon a combination of patterns of active occupancy (i.e. when people are at home and awake), and daily activity profiles that characterise how people spend their time performing certain activities. One-min resolution synthetic electricity demand data is created through the simulation of appliance use; the model covers all major appliances commonly found in the domestic environment. In order to validate the model, electricity demand was recorded over the period of a year within 22 dwellings in the East Midlands, UK. A thorough quantitative comparison is made between the synthetic and measured data sets, showing them to have similar statistical characteristics. A freely downloadable example of the model is made available and may be configured to the particular requirements of users or incorporated into other models.
Significance Life-cycle assessments commonly used to analyze the environmental costs and benefits of climate-mitigation options are usually static in nature and address individual power plants. Our paper presents, to our knowledge, the first life-cycle assessment of the large-scale implementation of climate-mitigation technologies, addressing the feedback of the electricity system onto itself and using scenario-consistent assumptions of technical improvements in key energy and material production technologies. Decarbonization of electricity generation can support climate-change mitigation and presents an opportunity to address pollution resulting from fossil-fuel combustion. Generally, renewable technologies require higher initial investments in infrastructure than fossil-based power systems. To assess the tradeoffs of increased up-front emissions and reduced operational emissions, we present, to our knowledge, the first global, integrated life-cycle assessment (LCA) of long-term, wide-scale implementation of electricity generation from renewable sources (i.e., photovoltaic and solar thermal, wind, and hydropower) and of carbon dioxide capture and storage for fossil power generation. We compare emissions causing particulate matter exposure, freshwater ecotoxicity, freshwater eutrophication, and climate change for the climate-change-mitigation (BLUE Map) and business-as-usual (Baseline) scenarios of the International Energy Agency up to 2050. We use a vintage stock model to conduct an LCA of newly installed capacity year-by-year for each region, thus accounting for changes in the energy mix used to manufacture future power plants. Under the Baseline scenario, emissions of air and water pollutants more than double whereas the low-carbon technologies introduced in the BLUE Map scenario allow a doubling of electricity supply while stabilizing or even reducing pollution. Material requirements per unit generation for low-carbon technologies can be higher than for conventional fossil generation: 11–40 times more copper for photovoltaic systems and 6–14 times more iron for wind power plants. However, only two years of current global copper and one year of iron production will suffice to build a low-carbon energy system capable of supplying the world's electricity needs in 2050.
А.H. Tkachuk, A.A. Humeniuk, O.O. Dobrzhansky
et al.
The article examines modern approaches to improving the energy efficiency of electric drives in the context of implementing the concept of smart energy networks (Smart Grid). Particular attention is given to the integration of electric drives as active participants in the energy balance, capable not only of consuming energy but also of adaptively regulating their operating modes in accordance with network parameters, load conditions, and the state of renewable energy sources. The study emphasizes the feasibility of using intelligent control systems that ensure high-quality regulation and reduced energy consumption under dynamically changing external conditions. Methods of energy consumption optimization based on adaptive control of variable-frequency drives are analyzed. The principles of using load forecasting algorithms are considered, enabling the formation of optimal operating profiles in advance and preventing peak overloads in the grid. The potential of regenerative operating modes, which allow excess energy during braking or speed reduction to be returned to the grid or local storage systems, is highlighted. This approach improves the overall efficiency of electric drive systems and reduces power losses. The results of simulation modeling performed in MATLAB/Simulink, using adaptive regulators and load models, confirm the effectiveness of the proposed strategies. It has been established that the application of intelligent control algorithms reduces the electricity consumption of electric drives compared to traditional control methods, increases the power factor, and decreases harmonic distortion levels in the grid by 25–30 %. Additionally, it is demonstrated that the use of adaptive regulators ensures system stability even under varying motor parameters and external disturbances. The practical implementation of such solutions is feasible in a wide range of applications: industrial production lines, electric transport systems, and integrated energy complexes with renewable sources. This opens new prospects for the development of energy-efficient Smart Grid systems with a high level of flexibility, reliability, and self-recovery capability after disturbances. The proposed approaches contribute to shaping a new paradigm of electric drives focused on minimizing energy losses and enhancing the overall efficiency of modern power systems.
Jorge Sánchez Canales, Alice Lixuan Xu, Chiara Fusar Bassini
et al.
Researchers and electricity sector practitioners frequently require the supply curve of electricity markets and the price elasticity of supply for purposes such as price forecasting, policy analyses or market power assessment. It is common practice to construct supply curves from engineering data such as installed capacity and fuel prices. In this study, we propose a data-driven methodology to estimate the supply curve of electricity market empirically, i.e. from observed prices and quantities without further modeling assumptions. Due to the massive swings in fuel prices during the European energy crisis, a central task is detecting periods of stable supply curves. To this end, we implement two alternative clustering methods, one based on the fundamental drivers of electricity supply and the other directly on observed market outcomes. We apply our methods to the German electricity market between 2019 and 2024. We find that both approaches identify almost identical regimes shifts, supporting the idea of stable supply regimes stemming from stable drivers. Supply conditions are often stable for extended periods, but evolved rapidly during the energy crisis, triggering a rapid succession of regimes. Fuel prices were the dominant drivers of regime shifts, while conventional plant availability and the nuclear phase-out play a comparatively minor role. Our approach produces empirical supply curves suitable for causal inference and counterfactual analysis of market outcomes.
An increasing share of consumers care about the carbon footprint of their electricity. This paper analyzes a method to integrate consumer carbon preferences in the electricity market-clearing by introducing consumer-based carbon costs and a carbon allocation mechanism. Specifically, consumers submit not only bids for power but also assign a cost to the carbon emissions incurred by their electricity use. The carbon allocation mechanism then assigns emissions from generation to consumers to minimize overall carbon costs. Our analysis starts from a previously proposed centralized market clearing formulation that maximizes social welfare under consideration of generation costs, consumer utility, and consumer carbon costs. We then derive an equivalent equilibrium formulation that incorporates a carbon allocation problem and gives rise to a set of carbon-adjusted electricity prices for both consumers and generators. We prove that the carbon-adjusted prices are higher for low-emitting generators and consumers with high carbon costs. Further, we prove that this new paradigm satisfies the same desirable market properties as standard electricity markets based on locational marginal prices, namely revenue adequacy and individual rationality, and demonstrate that a carbon tax on generators is equivalent to imposing a uniform carbon cost on consumers. Using a simplified three-bus system and the RTS-GMLC system, we illustrate that consumer-based carbon costs contribute to greener electricity market clearing both through generation redispatch and demand reductions.
Osei-Agyeman Yeboah, Nicholas Mensah Amoah, Kwadwo Antwi-Wiafe
The high price of energy due to the green energy policy will cause adjustments across the U.S. economy is predicted in the present computable general equilibrium with specific factors model. This includes energy input, especially electricity with capital and labor to produce manufacturing and service goods. 2022 labor, energy, and sector-specific capital input data on U.S. manufacturing, service, and agricultural sectors is applied to specific factors of the computable general equilibrium model. The model, which assumes constant returns, full employment, competitive pricing, and perfect labor mobility across industries hypothesizes a range of price changes due to project potential adjustments in factor prices and outputs. The U.S manufacturing sector is revealed to have a higher degree of noncompetitive pricing for energy factor inputs, but not on labor and capital as advocates for green energy tout by the new technology. The policy has virtually no significant impact on the service and agricultural sectors. The high price of green energy will cause an elastic decrease in all energy inputs. The output from energy-intensive manufacturing only rises in the long run by 4 % while service and agriculture fall. Clear winners are the owners of energy resources through their price-searching behavior. This includes the government, which owns a large share of hydrocarbon reserves.
N. Yalaoui, L. Dessaint, M. Reza Dehbozorgi
et al.
The availability and cost of fossil fuels, natural disasters, aging infrastructure, climate change, and rising electricity consumption have affected today’s power grids. One of the most practical solutions for achieving green and reliable energy is the use of microgrids. The stability of microgrids dominated by electronic converters presents several challenges. Among the problems encountered are the absence of physical inertia, delay in detecting islanding, and loss of stability associated with the transition between operating modes and variations of the load power. To overcome these challenges, this study presents a new robust control strategy based on active disturbance rejection control (ADRC). It is suitable for both islanded and connected operation modes with a single control, without an islanding detection algorithm or Phase-Locked Loop (PLL). The effectiveness of the control strategy is demonstrated through simulations and a comparative analysis with conventional droop control. Flexibility of the transition is also ensured. The proposed control strategy is successfully validated using a TI C2000 DSP TMS320F28335 microcontroller.
Distinguishing between ships and corner reflectors is challenging in radar observations of the sea. Traditional identification methods, including high resolution range profiles, polarization decomposition, and polarization modulation, improve radial range resolution to the target by transmitting signals with a large bandwidth. The latter two methods use polarization to improve target identification. Single-carrier pulse signals, often used in civil marine radars owing to their low hardware cost, pose challenges in identifying ships and corner reflectors owing to their low range resolution and pulse compression gain. This article proposes a novel method for identifying ships and corner reflectors using polarization modulation in civil marine radars. This approach aims to fully exploit the target identification potential of the narrowband signal joint polarization modulation technology. Through constructing the polarization-range 2D images, the method differentiates between ships and corner reflectors through their unique polarization scattering characteristics. The process involves calculating the average Pearson correlation coefficient between each polarization image and the range image, which serves as the correlation feature parameter. A support vector machine is then employed to achieve accurate target identification. Electromagnetic simulations show that by increasing the device bandwidth to 2～6 times the original signal bandwidth (2 MHz), civil marine radar can achieve a comprehensive identification rate of 90.18%～92.31% at a Signal to Noise Ratio (SNR) of 15 dB and a sampling rate of 100 MHz. The study also explores the influence of missing 50% of pitch angle and azimuth angle data in the training set, finding that identification rates in all four cases exceed 85% when the SNR is above 15 dB. Comparisons with the polarization decomposition method under the same narrowband observation conditions show that when the SNR is 15 dB or higher and the device bandwidth is increased sixfold, the average identification rate of the proposed method improves by 22.67%. This strongly supports the effectiveness of the proposed method. In addition, two cases with different polarization scattering characteristics are constructed in the anechoic chamber using dihedral and trihedral setups. Five sets of measured data show that when the SNR of the echo is 8～12 dB, the experiments demonstrate strong intra-class aggregation and clear inter-class separability. These results effectively support the electromagnetic simulation findings.
Abstract The increasing amount of distributed renewable energy (DRE) is participating in grid‐connected operation as an important unit of the virtual power plant (VPP) aggregation. VPP also contains a variety of flexible resources such as demand response (DR), energy storage (ES), and fuel cell (FC). How to achieve efficient energy utilization while reducing carbon emissions and resisting the risk of failure caused by extreme weather has attracted widespread attention. In this article, a cooperative game‐based low‐carbon scheduling model for multi‐VPPs under the consideration of typhoon‐induced grid outage risks is proposed. First, a cooperative game mechanism for multi‐VPPs is constructed. And a bi‐level model of multi‐VPPs low‐carbon scheduling is built under the framework of electricity‐carbon trading markets. Second, the bi‐level scheduling model is linearized based on the Strong Duality Theorem and Karush‐Kuhn‐Tucker (KKT) condition. Then, the dispatch scheme of each VPP under the cooperative game form is obtained. Finally, simulations are performed to verify the validity of the proposed model. The results show that the economic and low‐carbon performance of multi‐VPPs can be improved by applying the cooperative game, which can also enhance the power system ability of resisting line faults.
We address the need for forecasting methodologies that handle large uncertainties in electricity prices for continuous intraday markets by incorporating parameter uncertainty and using a broad set of covariables. This study presents the first Bayesian forecasting of electricity prices traded on the German intraday market. Endogenous and exogenous covariables are handled via Orthogonal Matching Pursuit (OMP) and regularising priors. The target variable is the IDFull price index, with forecasts given as posterior predictive distributions. Validation uses the highly volatile 2022 electricity prices, which have seldom been studied. As a benchmark, we use all intraday transactions at the time of forecast to compute a live IDFull value. According to market efficiency, it should not be possible to improve on this last-price benchmark. However, we observe significant improvements in point measures and probability scores, including an average reduction of $5.9\,\%$ in absolute errors and an average increase of $1.7\,\%$ in accuracy when forecasting whether the IDFull exceeds the day-ahead price. Finally, we challenge the use of LASSO in electricity price forecasting, showing that OMP results in superior performance, specifically an average reduction of $22.7\,\%$ in absolute error and $20.2\,\%$ in the continuous ranked probability score.