Electricity Demand Research Articles

Occupant-driven end use load models for demand response and flexibility service participation of residential grid-interactive buildings

As demand response becomes increasingly used as a tool to support improved grid flexibility, it is important to consider that there are many potential types of energy end uses that may be used to support such flexibility. Residential appliances, often accounting for 30 % or more of residential energy use, are a currently untapped source of demand flexibility, particularly when aggregated together across homes. To date there has been very limited analysis of residential appliances for use as grid-interactive loads. As such, this research uses disaggregated energy end use data for 564 households, to model the electricity demand flexibility potential of the use of residential dishwashers, clothes washers, clothes dryers, ovens, and ranges (oven + stovetop) on both weekdays and weekends. This includes both at the building level, as well as aggregated to the grid level, specifically the Midcontinent Independent System Operator (MISO) region. This study was divided into two parts. Part 1 focuses on determining appliance-level loads, and Part 2, which involves aggregation to the grid. Findings suggest that among the studied appliances, clothes dryers provide the greatest demand reduction potential for most times of the day, followed by dishwashers and clothes washers. The maximum potential reduction for clothes dryers is found to be approximately at 11:00 a.m. and this potential sustains throughout most of the daytime period. When considering the willingness of households to participate, based on a survey of households in the Midwest region, clothes dryers still have the most potential for demand reduction. The availability of appliances for load modulation on weekdays and weekends indicates similar load reduction potential for all appliances. Overall, the results of this study suggest that there is an opportunity for shifting appliance usage to optimize grid efficiency and enhance demand response strategies.

Using Wind Energy to Carry out Basketball Special Physical Fitness Training—Calculation and Research on Aerodynamic Efficiency of Integrated Wind Turbine in Stadiums and Gymnasiums

This study investigates the feasibility of using wind energy for basketball special physical fitness training by combining wind turbines in stadiums and gyms. The investigation focuses on measuring the aerodynamic efficiencies of the combined wind turbine systems. It starts with an analysis of wind energy utilization for fitness training, which signifies the potential benefit of harnessing the renewable energy in sports facilities. The investigation into the aerodynamic efficiency of the combined wind turbine systems, considers factors such as speed of wind, size of turbine, and energy output. Numerical simulation and measurements are conducted to determine the optimal design and placement of wind turbines in stadiums and gymnasiums. The results demonstrate the potential of wind energy to supplement traditional power sources in sports facilities, and provide sustainable energy solutions while boosting fitness and environmental awareness. The study reveals that with an average speed of 8 m/s, a properly integrated wind turbine system in a stadium or gymnasium can generate approximately 11-23% of the facility's electricity demand. This translates to an annual energy production of 510-1200 kWh per turbine, depending on the specific location and design considerations. The aerodynamic efficacy of the integrated wind turbine system is estimated to be around 40-50%, indicating a significant potential for energy savings and environmental impact reduction.

Educational Station for the Generation and Use of Green Hydrogen

Globally, electrical energy predominantly derives from fossil fuels such as coal, oil, and natural gas, which significantly emit greenhouse gases, thereby accelerating ecosystem degradation and climate change. In response, recent decades have witnessed a surge in the development of clean, sustainable electricity generation technologies such as wind and solar photovoltaic systems. These renewable sources are expected to replace fossil fuel-based methods but are limited by their dependency on variable weather conditions, which cannot align with electricity demand. Periods when energy production exceeds demand highlight the critical role of innovative storage solutions. Notably, surplus energy can be utilized for hydrogen production via water electrolysis, producing “Green Hydrogen”, which generates no polluting emissions. This shift towards sustainable technologies necessitates novel educational approaches. It is essential to revise curricula to include these technologies, ensuring students are well-prepared for the evolving energy sector. This article discusses the design and construction of a green hydrogen educational station built with commercial materials for laboratory use, embodying the integration of renewable energy technologies into academic programs and promoting hands-on learning experiences in energy management and sustainability.

Charging Strategies for Electric Vehicles from Renewable Hybrid Systems

Objective: The aim of this study is to analyze charging strategies for electric vehicles (EVs) in a hybrid system connected to the conventional electricity grid, to optimize the use of renewable energies. Theoretical Framework: This paper presents the concepts of hybrid systems, photovoltaic power generation, and electric vehicle energy demand. It emphasizes the importance of integrating renewable energy into the transportation sector to reduce greenhouse gas emissions and promote sustainability. Method: The research was based on obtaining load curves and photovoltaic generation in different climatic conditions. The energy available for charging electric vehicles was calculated, considering the limitations of the hybrid system. Different charging power configurations were studied. Results and Discussion: The results demonstrate the feasibility of recharging EVs with renewable energy and highlight the operational flexibility provided by storage systems. Different climate scenarios affect energy availability for charging, highlighting the importance of adaptive strategies. Research Implications: This research contributes to developing sustainable strategies in the transportation sector by providing insights into integrating electric vehicles and renewable energy. Practical implications include reducing carbon emissions and decreasing dependence on fossil fuels. Originality/Value: This study stands out for its specific analysis of EV charging strategies in a hybrid context, providing new approaches for the efficient use of renewable energy in transportation. The integration of storage systems and adaptation to climatic conditions are innovative aspects that add value to the research.

Electrification as a factor in replacing hydrocarbon fuel

The article looks at the role of electrification that is expanding as a key factor in transitioning to a low-carbon economy. The study is based on a systemic approach and aims to provide a comprehensive understanding of the necessary transformations in energy production and consumption directed at phasing out fossil fuels and advancing the climate agenda. The novelty of the study resides in the development of conceptual foundations of the fuel-saving aspect of electrification which makes it possible to not only significantly reduce the negative environmental effects in regions, but also to ensure intensive economic growth through the application of advanced energy technologies and minimization of the use of a scarce energy carrier - natural gas - in the power industry and industry. The article clarifies the conceptual apparatus, defines the characteristics and patterns of the new stage of electrification, assesses the impact of electrification on the economic growth and environmental safety of the region, gives recommendations on optimizing the technological structure of the electric power industry in the context of the changing requirements for the parameters of electricity supply. The authors propose a methodology for accounting for the environmental factor in the electrification of the region, which includes ranking and selecting facilities to be electrified according to the minimum “electric fuel replacement ratio”; using the associated energy saving effect for financial compensation of the environmental burden, as well as introducing electricity demand management programs that improve the environmental situation in the region. The article defines the sequence of the implementation of the fuel-saving aspect of electrification and formulates a set of progressive solutions that would invigorate the relevant projects.

Benefits and Limitations of Encapsulating Platinum Electrocatalysts with Niobium Oxide

The development of energy infrastructure based on renewable sources is vital to reduce our reliance on fossil fuels. Technologies such as hydrogen fuel cells and water electrolyzers are able to bridge the gap between the intermittency issues associated with green energy sources such as wind and solar, and the demand for reliable electricity by being able to convert between electrical and chemical energy. Nanostructured and mesoporous platinum (Pt) prepared by electrodeposition or sputter coating have been shown to have increased performance in hydrogen fuel cells compared to the industry standard of Pt nanoparticles supported on carbon.1,2 Over the lifetime of a fuel cell Pt nanostructures and nanoparticles are, however, known to restructure and lose their surface area by mechanisms such as Ostwald ripening, dissolution, and coalescence. Previously, thin film Pt catalysts have been shown to be stabilized by a nanoscale film of niobium oxide.3 Herein, nanoscale thin films of niobium oxide are investigated as a stabilizing material through use by encapsulating nanostructured Pt catalysts.Nanostructured Pt catalysts were electrodeposited onto Pt-coated Si wafer substrates. Subsequently, niobium oxide films with thicknesses of 0.5 nm, 3 nm, and 4.5 nm were deposited onto the Pt catalyst using atomic layer deposition. X-ray fluorescence and conductive atomic force microscopy techniques were used to confirm the presence and coverage of the niobium oxide layer on the Pt catalyst. The performance of the encapsulated and un-encapsulated Pt catalysts as oxygen reduction reaction catalysts over the course of up to a thousand degradation cycles were evaluated using electrochemical techniques. The morphology of the catalyst before and after the degradation testing was also assessed using scanning electron microscopy techniques. Electrochemical impedance spectroscopy indicated that increasing the thickness of the niobium oxide layer increased the resistance of the catalyst towards the oxygen reduction reaction. The combined results of the analyses performed by electrochemical techniques and electron microscopy measurements revealed a range of film thicknesses for the niobium oxide that successfully stabilized the electrochemically active surface area and surface morphology of the Pt catalyst. Paul, M. T. Y.; Gates, B. D. Mesoporous Platinum Prepared by Electrodeposition for Ultralow Loading Proton Exchange Membrane Fuel Cells. Sci Rep 2019, 9, 4161.Debe, M. K. Tutorial on the Fundamental Characteristics and Practical Properties of Nanostructured Thin Film (NSTF) Catalysts. Electrochem. Soc. 2013, 160, F522.Eastcott, J.; Gates, B. D. Nanoscale Thin Films of Niobium Oxide on Platinum Surfaces: Creating a Platform for Optimizing Material Composition and Electrochemical Stability. Can. J. Chem. 2017, 96, 260–266.

Influence of Microstructure on Defect Chemistry - Transport - Chemical Strain Coupling of (Pr,Ce)O2-δ Nanoparticles

Solid oxide fuel/electrolysis cells (SOFC/SOECs) are promising and environmentally-friendly technologies for meeting ever-increasing electricity and energy storage demands. Nanostructuring of SOFC/SOEC electrodes can enable improvement of the reaction kinetics and reduction of the operating temperature, owing to factors including the enlarged surface area, short diffusion lengths, and unique surface & interface properties. While the kinetic benefit is apparent, the effect of nanostructuring on other device-relevant physico-chemical properties cannot be neglected. Especially, the coefficients of chemical expansion (CCEs) are an important metric for mixed ionic-electronic conductors that can undergo changes in oxygen stoichiometry with variations in the surrounding atmosphere, temperature, and voltages during processing/operation. Chemical strains can cause cracking of monolithic parts during fabrication and delamination of electrodes from electrolytes in operation. CCEs are typically measured on bulk, polycrystalline materials with large particle or grain sizes. Therefore, it is important to understand the coupled transport and chemical expansion behavior in nano-ionic materials with large interface/surface density. This work aims to uncover the role of microstructure and nanoscale effects on the chemical strain and transport properties, through the lens of defect chemistry, with the goal of ultimately developing design principles for zero-strain materials with improved device lifetime and performance.Pr0.2Ce0.8O2- δ (PCO20) was selected as a model system and prepared as nanoparticles by a surfactant-free, low temperature co-precipitation method. (Pr,Ce)O2-δ compositions are valuable model oxide ion conductors to study, since both Pr and Ce can undergo changes of valence states under different temperature and pO2 regimes to accommodate oxygen stoichiometry excursions. Prepared nanoparticle samples were first in-situ sintered in the dilatometer at three different temperatures (600 ⁰C, 725 ⁰C, 850 ⁰C) to induce and evaluate microstructure evolution. The chemical strain and electronic/ionic conductivity were then studied on stable microstructures with different average particle sizes at lower temperatures by simultaneous in-situ dilatometry and electrochemical impedance measurement (EIS) upon changing pO2 at different isotherms (550 to 400 ⁰C). At each isotherm, all the samples expand as pO2 decreases. From the conductivity vs. pO2 measurements, the maxima at certain pO2 indicate the dominant polaron mechanism of the electronic transport, consistent with the measured conductivity activation energy. However, samples with lower sintering temperatures exhibit less pO2 dependence of the conductivity, which implies a transition in the mixed ionic/electronic conduction behavior toward higher ionic transference numbers as particle size decreases. Correspondingly, the chemical strains associated with losing oxygen monotonically decrease with the decrease of the sintering temperature, and hence the particle size. The chemical strain vs. oxygen stoichiometry dependence and corresponding CCEs were quantified as function of particle size and temperature by combining thermogravimetric analysis (TGA) with the dilatometry results and in-situ X-ray diffraction. The particle size, chemical composition, valence states, and morphology of samples after CCEs measurements were also compared using XRD, SEM, and S/TEM-EELS, with the latter indicating the expected differences between bulk and surface defect chemistry. Implications of the results for size-dependent defect chemistry and its influence on chemical strains and redox CCEs will be further discussed. Keywords: redox, chemical expansion, defect chemistry, mixed ionic electronic conduction, ceria, chemo-mechanics, nanoparticle, thermogravimetric analysis, dilatometry, impedance

Scaling Considerations for the Commercialization of the Electrochemical CO2 Reduction Reaction

The Electrochemical CO2 Reduction Reaction (ECO2RR) is an attractive method to produce CO2-based chemicals. Formate is an ideal target compound due to its high atom efficiency, low electrical demand, and potential as a building block for carbon-negative products. In recent years, low temperature ECO2RR electrocatalyst research has developed rapidly, reporting impressive efficiencies (>90 % FE) at industrial relevant current densities (>200 mA/cm2). While these are essential metrics, there is less focus on the commercialization of these systems, such as electrode stability and process scalability. For industrial feasibility, ECO2RR electrolyzers must be >1 meter high, stable for 5 years, and operate under elevated temperatures and fluctuating pressures. This presents challenges to typical gas diffusion electrodes used in ECO2RR, which rely on a fine pressure gradients for efficient gas transport to the catalyst layer. Furthermore, high product concentrations and high CO2 utilization are essential for achieving an overall, energy efficient process. At Avantium’s Volta group, we industrialize ECO2RR processes. This presentation will demonstrate the scaling potential of these processes with examples from our pre-pilot units. Overall, we believe that ECO2RR will be a key technology to de-fossilize the chemical industry and valorize CO2 as the feedstock of the future. Figure 1

Paving the way for low-carbon hydrogen supply chain deployment by exploring the potential of renewable energies and multisectoral hydrogen demand: Case study of France

France has set ambitious targets for hydrogen production in its National Roadmap, aiming to install at least 6.5 GW of electrolyzer capacity and produce 700,000 tons of hydrogen annually by 2030. The country is focusing on producing renewable or low-carbon hydrogen primarily through electrolysis. However, it faces significant barriers in rapidly scaling up renewable energy infrastructure and may need to consider import strategies to address potential shortages. Addressing these challenges requires investigating whether the availability of renewable energy for the production of electrolytic hydrogen could become a limiting factor for hydrogen adoption and potentially act as a bottleneck in its market integration. The methodology merges forecasts from the public and private sectors to address both renewable and non-renewable electricity production and the energy needed for rising hydrogen demand. The approach developed involves estimating France’s renewable energy supply up to 2050 and determines how much of this energy can be allocated to hydrogen production to ensure it remains carbon-free and genuinely renewable. Unlike many existing roadmaps that take a more general approach, the innovative part of this study is developing a territorial perspective to conduct a detailed analysis of potential mismatches between hydrogen supply and demand.Three distinct sources of electricity are considered for the electrolyzers, which could be connected to the grid or directly to renewable power plants: low-carbon electricity from the French grid, renewable electricity from re-powered solar and wind farms, and renewable electricity from newly installed power plants. Total electricity demand is projected to rise from 475 TWh/y in 2020 to 754 TWh/y in 2050, with the share of renewable energy increasing from 19% in 2020 to 69% in 2050.The study evaluates the demand for hydrogen in two key sectors, industry, which is heavily dependent on hydrogen, and mobility, which currently has a more modest contribution. Hydrogen demand is expected to increase from nearly 310 ktons per day in 2025 to over 2650 ktons per day by 2050.Given an average specific consumption of 55 kWh of electricity per kg of hydrogen produced, the total electricity demand for electrolytic hydrogen production is projected to grow from 17 TWh/year in 2025 to 146 TWh/year in 2050.It can be concluded that allocating the entire anticipated production from re-powered solar and on-shore wind farms in the coming years will not be sufficient to meet the electricity demand required for electrolytic hydrogen production. To prevent renewable energy from becoming a bottleneck for hydrogen market integration and to avoid the need for hydrogen imports, it is crucial to allocate 5% to 10% of the projected renewable output from newly installed plants to address the increasing hydrogen demand. This result is key to creating an optimal design model for hydrogen supply chains.

Evaluating planning and operational policies for urban public electric vehicle charging infrastructure

It is expected that public electric vehicle charging demands will gradually increase in Korea. In this study, we propose a macroscopic indicator of the level of service (LOS) of urban public fast charging infrastructure to quantitively evaluate the joint impacts of possible planning and operational policies on it. The indicator is defined as the reliability-based capacity, the number of electric vehicles that can be charged within the maximum targeted weighted delay between wait and detour. We consider three combinable policies: changing the distribution of chargers in the planning stage; assigning the optimal charging station to each charging event in real-time and evenly distributing daily charging demands in the operational phase. For multiple policy combinations and various demand cases, we compare their LOS levels, calculated by Monte Carlo simulations that can efficiently address the complex joint impact of stochastic demands. The simulation experiments are designed to explicitly discover the impacts of policies along with varying charging demands. The results show that certain combinations of proposed policies can effectively and efficiently improve LOS.

Energy Management System for an Industrial Microgrid Using Optimization Algorithms-Based Reinforcement Learning Technique

The climate crisis necessitates a global shift to achieve a secure, sustainable, and affordable energy system toward a green energy transition reaching climate neutrality by 2050. Because of this, renewable energy sources have come to the forefront, and the research interest in microgrids that rely on distributed generation and storage systems has exploded. Furthermore, many new markets for energy trading, ancillary services, and frequency reserve markets have provided attractive investment opportunities in exchange for balancing the supply and demand of electricity. Artificial intelligence can be utilized to locally optimize energy consumption, trade energy with the main grid, and participate in these markets. Reinforcement learning (RL) is one of the most promising approaches to achieve this goal because it enables an agent to learn optimal behavior in a microgrid by executing specific actions that maximize the long-term reward signal/function. The study focuses on testing two optimization algorithms: logic-based optimization and reinforcement learning. This paper builds on the existing research framework by combining PPO with machine learning-based load forecasting to produce an optimal solution for an industrial microgrid in Norway under different pricing schemes, including day-ahead pricing and peak pricing. It addresses the peak shaving and price arbitrage challenges by taking the historical data into the algorithm and making the decisions according to the energy consumption pattern, battery characteristics, PV production, and energy price. The RL-based approach is implemented in Python based on real data from the site and in combination with MATLAB-Simulink to validate its results. The application of the RL algorithm achieved an average monthly cost saving of 20% compared with logic-based optimization. These findings contribute to digitalization and decarbonization of energy technology, and support the fundamental goals and policies of the European Green Deal.

A cradle-to-customer life cycle assessment case study of UK vertical farming

Global population rise, increased rural to urban migration, climate change and conflict have placed strain on global trade and food supply chains. Vertical farming (VF) is a relatively novel method for cultivating many fresh fruits and vegetables, and often is claimed to have lower environmental impacts than field cultivation. However, to date there are few studies utilising primary data which have evaluated the environmental impact VF may have. This study utilised Life Cycle Assessment (LCA) to evaluate the environmental impact of lettuce production in a commercial vertical farm in the UK. This was compared with data from the literature on the impacts of lettuce in field cultivation. Scenarios were also examined to evaluate the impact of VF systems with varying electricity sources, and waste, water and nutrient management options. The VF was found to have a similar or lower Climate Change impact than field cultivation, depending on energy source used. VF had a slightly higher environmental impact than field cultivation in some other environmental impact categories such as freshwater eutrophication potential and acidification potential. Electricity demand and the medium used for ‘plugs’ in the VF were found to be the main hotspots of the system. The results of this study provide insights on the environmental impact and resource efficiency of VF, feasibility for larger scale deployment, and provide further empirical evidence to support the claims made previously in the literature.

Oahu as a case study for island electricity systems relying on wind and solar generation instead of imported petroleum fuels

A macro-scale electricity model was used to evaluate whether wind and solar generation, combined with short- and long-duration energy storage, could cost-effectively provide 100% of Oahu's electricity. A least-cost renewable electricity system was developed with 100% of hourly averaged demand met, based on 14 consecutive years of hourly wind and solar resource and electricity demand data. The system used current asset costs for wind generation, solar generation, short-duration battery storage, and long-duration hydrogen energy storage. Our results indicate that Oahu could transition to an electricity system reliant on wind and solar generation and battery and hydrogen storage with electricity costs lower than today's electricity costs. For Oahu, a least-cost wind-solar-battery electricity system that would have met 100% of hourly averaged demand would have a system cost of $0.2458 per kWh. In comparison, from October 2022 through September 2023, electricity generation costs from the petroleum-dominated electricity system on Oahu were between $0.2126/kWh and $0.2987/kWh. This wind-solar-battery system would require 332 km2 (21.4%) of Oahu's land. Moreover, including hydrogen storage as a second form of energy storage led to reductions in system cost and land use. At current asset costs, a least-cost wind-solar-battery‑hydrogen electricity system that would have met 100% of hourly averaged demand would have a system cost of $0.1673/kWh and require 265 km2 (17.1%) of Oahu's land. This case study indicates that other isolated regions with similar resource profiles and a reliance on imported petroleum fuels could realize a decrease in electricity system costs when switching to renewables in conjunction with short- and long-duration energy storage, at current asset costs, while meeting demand in full over extended periods of renewable resource variability.

Public Acceptance Regarding Photovoltaic Solar in Myanmar Using the Theory of Planned Behavior

This study investigates public acceptance of photovoltaic (PV) solar energy in Myanmar using the Theory of Planned Behavior (TPB), focusing on various demographic groups in 2023. The 337 respondents across different regions provided a comprehensive overview of public attitudes. The survey examined subjective norms, attitudes, perceived behavioral control, willingness to pay, and intention to adopt (PV) solar technology. Results indicated a high level of awareness, with 98% familiar with (PV) solar and 95% knowledgeable about its panels. However, adoption rates were low at 20%. Subjective norms positively influenced attitudes, as 81% observed their social circles using (PV) solar and 80% found it convenient. Environmental concerns were minimal, with 75% not worried about (PV) Solar’s impact and 85% recognizing its carbon reduction benefits. The initial cost was a significant barrier, with 80% finding it too expensive, despite 96% believing in future cost savings. Perceptions of self-efficacy showed strong support for (PV) solar as a solution for electricity demand (90%) and interest in rooftop solar panels (87%). From an aesthetic perspective, only 14% described solar panels as unattractive. The study recommends financial incentives, education campaigns, and improved (PV) solar access, with government and stakeholder support to boost acceptance and adoption in Myanmar.

Construction of Utility Tunnel power supply and distribution system

As an underground structure that can accommodate multiple municipal pipelines, the utility tunnel can not only coordinate the planning, construction, and management of various municipal pipelines, but also achieve intensive development of urban underground space. In theory, any municipal pipeline can be included in the utility tunnel, thereby eradicating phenomena such as "road zippers" and "aerial spider webs" in the urban construction process. As a massive underground structure, the utility tunnel has its own electricity demand. Only by constructing a safe and stable power supply and distribution system inside the pipe gallery can it effectively ensure personal safety and the reliability of pipeline operation.

An Improved MGM (1, n) Model for Predicting Urban Electricity Consumption

The MGM (1, n) model has the characteristics of less data required, simple modeling, and high prediction accuracy. It has been successfully applied to short-term forecasting across various economic, social, and technological domains, yielding promising outcomes. There is insufficient attention paid to the interpolation coefficient of the model. The interpolation coefficients determine the extent of model fitting, which, in turn, impacts its prediction accuracy. This study made some improvements to the interpolation coefficients and proposed an improved MGM (1, n) model. IMGM (1, n) model and MGM (1, n) model were employed to compare the performance of the improved MGM (1, n) model. Upon a series of comparisons and analyses, it was concluded that the improved MGM (1, n) model has higher fitting and prediction accuracy than the other two forecasting methods. The method was used to forecast the short-term electricity consumption of Linfen City. The findings revealed that by 2030, the electricity demand in Linfen City is projected to be 563.7 billion kWh.

Maintenance Practices of High-Voltage Cross-Linked Polyethylene (XLPE) Cable Buffer Layers

As urbanization accelerates globally, the demand for electricity continues to rise, especially within high-voltage power transmission systems. Cross-linked polyethylene (XLPE) cables have become crucial due to their exceptional insulation properties and mechanical characteristics. However, the buffer layers of XLPE cables may undergo aging and faults caused by various factors, including electrical aging, thermal aging, chemical impacts, and improper design. This paper synthesizes existing literature to provide an in-depth analysis of the material properties, aging mechanisms, fault causes, design principles, and maintenance practices related to XLPE cable buffer layers. Through finite element method (FEM) simulations, it evaluates the electric field distribution across XLPE cables under different operating voltages and the contact state between the buffer layer and the metal sheath. Additionally, this study explores the recyclability and environmental impact of XLPE materials, as well as how improvements in material properties and detection technologies can enhance cable reliability and safety. The findings offer comprehensive theoretical and practical guidance to the power industry, aiding in optimizing cable design, improving system reliability, and reducing failure rates, while also suggesting future research directions.

Support for Advanced Nuclear Power Growing in the United States

Global clean energy and decarbonization goals are forcing governments around the world to take a hard look at their energy resource portfolios and future plans for meeting increasing electricity demand. While the last several years have seen a significant uptick in the deployment of solar, both terrestrial and offshore wind, and battery storage here in the United States, nuclear energy is once again being considered a critical energy generation source. Nuclear power, and in particular small modular nuclear reactors, are being considered as carbon‐free power generation sources to strategically meet new loads.

Performance evaluation of multivariate deep-time convolution neural architectures for short-term electricity forecasting: Findings and failures

Deep learning (DL) models hold great promise in enhancing the decision-making abilities of electricity market participants and system operators in the short term, as they excel at capturing the intricate interdependencies and uncertainties in electricity consumption. Nevertheless, currently, the adoption of DL models for electricity prediction is not keeping up with the advancements in DL research. Within this framework, the aims of this study are to examine the probabilistic, multivariate, and multihorizon time series prediction of deep time convolution neural (DTCN) models. The effectiveness of DTCN in forecasting electricity consumption over daily, weekly and monthly time horizons is emperically assessed. We analyze both probabilistic and deterministic predictions. The evaluation data comprises real residential electricity consumption, average temperature and humidity with hourly granularity, collected over one year from a residential building. For evaluation, we considered accuracy and uncertainties, sensitivity to hyperparameters, the impact of exogeneous variables, as well as computational efficiency. Results show that the probabilistic models offer greater advantages when it comes to forecasting on multihorizons, especially for diverse datasets with non-uniform time series models. Unlike traditional models, the DTCN models demonstrated superior performance in capturing complex patterns and reducing forecast errors in short-term scenarios. However, their performance can be influenced by the sensitivity of hyperparameters. These findings can act as a benchmark for quantitatively assessing the performance of novel multivariate DL models in predicting electricity demand.

Impact of cap-and-trade mechanism on investment decision of new electric power system.

Under the background of cap-and-trade mechanisms, this article constructs a game model of the electricity supply chain, which is dominated by electricity generators and followed by electricity sellers, taking into account the situation of electricity generators investing in renewable energy and energy storage under the grandfathering mechanism (GM) and benchmarking mechanism (BM). By comparing the equilibrium solutions in different cases, the research finds that (1) compared with the grandfathering mechanism, the benchmarking mechanism has more investment in renewable energy and higher energy storage quality; (2) in the consumer market, compared with GM, the electricity price and the electricity demand are higher under BM; (3) an increase in the renewable energy preference coefficient or carbon price will lead to an increase in the electricity wholesale price, renewable energy investment, energy storage quality, electricity price, and electricity demand. Further, an increase in the energy storage cost coefficient or renewable energy investment cost coefficient will result in lower electricity wholesale price, renewable energy investment, energy storage quality, electricity price, and electricity demand; and (4) the profit of the generator under GM is higher than that under BM when the total carbon quota is larger, while the profit of the generator under BM is higher than that under GM when the unit carbon quota is larger.