Hasil untuk "Electricity"

Xiao Zhang, Li-dong Zhao

Abstract Thermoelectric materials have drawn vast attentions for centuries, because thermoelectric effects enable direct conversion between thermal and electrical energy, thus providing an alternative for power generation and refrigeration. This review summaries the thermoelectric phenomena, applications and parameter relationships. The approaches used for thermoelectric performance enhancement are outlined, including: modifications of electronic band structures and band convergence to enhance Seebeck coefficients; nanostructuring and all-scale hierarchical architecturing to reduce the lattice thermal conductivity. Several promising thermoelectric materials with intrinsically low thermal conductivities are introduced. The low thermal conductivities may arise from large molecular weights, complex crystal structures, liquid like transports or high anharmonicity of chemical bonds. At the end, a discussion of future possible strategies is proposed, aiming at further thermoelectric performance enhancements.

J. Contreras, R. Espínola, F. Nogales et al.

Peihua Yang, Kang Liu, Qianchang Chen et al.

Fateh Bélaïd, Meriem Youssef

Lu Peng, Shangpu Liu, R. Liu et al.

Electric power, as an efficient and clean energy, has considerable importance in industries and human lives. Electricity price is becoming increasingly crucial for balancing electricity generation and consumption. In this study, long short-term memory (LSTM) with the differential evolution (DE) algorithm, denoted as DE–LSTM, is used for electricity price prediction. Several recent studies have adopted LSTM with considerable success in certain applications, such as text recognition and speech recognition. However, problems in the application of LSTM to solving nonlinear regression and time series problems have been encountered. DE, a novel evolutionary algorithm that effectively obtains optimal solutions, is designed to identify suitable hyperparameters for LSTM. Experiments are conducted to verify the performance of the DE–LSTM model under the electricity prices in New South Wales, Germany/Austria, and France. Results indicate that the proposed DE–LSTM model outperforms existing forecasting models in terms of forecasting accuracies.

MV Olson

Background Biobanks are critical infrastructures for biomedical research but are energy- and cost-intensive due to reliance on ultra-low temperature (ULT) storage and redundant systems. The challenge is reducing environmental impact without compromising specimen quality or continuity. Service centers are well positioned to address this challenge, operating at scale and providing governance beyond the capacity of individual laboratories. Methods The Johns Hopkins Biobank, a CAP-accredited service-center repository, partnered with the School of Medicine Energy and Sustainability Committee to conduct a freezer audit across 34 departments and two campuses. Inventories were assessed for age, utilization, and efficiency, and policies were implemented to encourage migration of biospecimens into centralized storage. Strategies prioritized vapor-phase liquid nitrogen (LN2) for viable collections and incorporated MVE Variō systems as energy-efficient alternatives for ULT needs. Governance required investigators to evaluate centralized options before acquiring new freezers, reinforced through outreach at faculty meetings and symposia. Results The audit identified nearly 1,300 ULT freezers, with over 70% beyond their median life expectancy of 8.5 years. Consolidation of specimens into a Biobank-managed freezer farm reduced institutional energy demand and improved monitoring. LN2 provided stability for viable specimens, while Variō units offered adjustable storage (–20 °C to –150 °C) with minimal electricity use and no facility cooling load. Governance helped to curb uncontrolled expansion of departmental freezers, while the Biobank functioned as an emergency response resource with at-temperature backup capacity. Adoption of centralized storage has been gradual but continues to expand. Conclusions This case study demonstrates how an academic service center can integrate sustainability, quality, and contingency planning. The Johns Hopkins Biobank illustrates that shared resources, supported by institutional governance, provide a practical framework to reduce environmental impact while ensuring uncompromising specimen protection. As an institutional case study, this report is intended to illustrate operational strategy within a defined governance and infrastructure environment rather than to function as a universally prescriptive implementation model.

Zhifeng Guo, Kaile Zhou, Chi Zhang et al.

Umut Ugurlu, Ilkay Oksuz, O. Taş

Accurate electricity price forecasting has become a substantial requirement since the liberalization of the electricity markets. Due to the challenging nature of electricity prices, which includes high volatility, sharp price spikes and seasonality, various types of electricity price forecasting models still compete and cannot outperform each other consistently. Neural Networks have been successfully used in machine learning problems and Recurrent Neural Networks (RNNs) have been proposed to address time-dependent learning problems. In particular, Long Short Term Memory (LSTM) and Gated Recurrent Units (GRU) are tailor-made for time series price estimation. In this paper, we propose to use multi-layer Gated Recurrent Units as a new technique for electricity price forecasting. We have trained a variety of algorithms with three-year rolling window and compared the results with the RNNs. In our experiments, three-layered GRUs outperformed all other neural network structures and state-of-the-art statistical techniques in a statistically significant manner in the Turkish day-ahead market.

Zheng-xin Wang, Qin Li, Lingling Pei

To accurately predict the seasonal fluctuations of the electricity consumption of the primary economic sectors, we propose a seasonal grey model (SGM(1,1) model) based on the accumulation operators generated by seasonal factors. We use the proposed model to carry out an empirical analysis based on the seasonal electricity consumption data of the primary industries in China from 2010 to 2016. The results from the SGM (1,1) model are compared with those obtained using the grey model (GM(1,1)), the particle swarm optimization algorithm combines with the grey model (PSO-GM(1,1) model), and the adaptive parameter learning mechanism based seasonal fluctuation GM (1,1) model (APL-SFGM(1,1) model). The results of the comparison show that the SGM(1,1) model can effectively identify seasonal fluctuations in the electricity consumption of the primary industries and its prediction accuracy is significantly higher than those of the GM(1,1), PSO-GM(1,1) and APL-SFGM(1,1) models. The forecast results for China from 2017 to 2020 obtained using the SGM(1,1) model suggest that the electricity consumption of the primary industries is expected to increase slightly, but obvious seasonal fluctuations will still be present. It is forecasted that the annual electricity consumption in 2020 will be 107.645 TWh with an annual growth rate of 2.83%. This prediction can provide the basis for power-supply planning to ensure supply and demand balance in the electricity markets.

Deyun Wang, Deyun Wang, Hongyuan Luo et al.

N. Ghadimi, A. Akbarimajd, H. Shayeghi et al.

Abstract Short-term load forecasting is of major interest for the restructured environment of the electricity market. Accurate load forecasting is essential for effective power system operation, but electricity load is non-linear with a high level of volatility. Predicting such complex signals requires suitable prediction tools. This paper proposes a hybrid forecast strategy including novel feature selection technique, and a complex forecast engine based on a new intelligent algorithm. The electricity load signal is first filtered by feature selection technique to select appropriate candidates as input for the forecast engine. Then, the proposed two stage forecast engine is implemented based on ridgelet and Elman neural networks. All forecast engine parameters are chosen based on a novel intelligent algorithm to improve its accuracy and capability. Different electricity markets were considered as test cases to compare the proposed method with several current algorithms. Additionally, the proposed forecasting model measures the absolute forecasting errors in this work (among seven types of measurements i.e., absolute forecasting errors, measures based on percentage errors, symmetric errors, measures based on relative errors, scaled errors, relative measures and other error measures). The results validate the effectiveness of the proposed method.

S. Ding, K. Hipel, Yao-guo Dang

A. Blakers, B. Lu, M. Stocks

Abstract An hourly energy balance analysis is presented of the Australian National Electricity Market in a 100% renewable energy scenario, in which wind and photovoltaics (PV) provides about 90% of the annual electricity demand and existing hydroelectricity and biomass provides the balance. Heroic assumptions about future technology development are avoided by only including technology that is being deployed in large quantities (>10 Gigawatts per year), namely PV and wind. Additional energy storage and stronger interconnection between regions was found to be necessary for stability. Pumped hydro energy storage (PHES) constitutes 97% of worldwide electricity storage, and is adopted in this work. Many sites for closed loop PHES storage have been found in Australia. Distribution of PV and wind over 10–100 million hectares, utilising high voltage transmission, accesses different weather systems and reduces storage requirements (and overall cost). The additional cost of balancing renewable energy supply with demand on an hourly rather than annual basis is found to be modest: AU$25–30/MWh (US$19–23/MWh). Using 2016 prices prevailing in Australia, the levelised cost of renewable electricity (LCOE) with hourly balancing is estimated to be AU$93/MWh (US$70/MWh). LCOE is almost certain to decrease due to rapidly falling cost of wind and PV.

Kevin Steijn, Vamsi Priya Goli, Enrico Antonini

This paper presents a machine learning framework for electricity demand forecasting across diverse geographical regions using the gradient boosting algorithm XGBoost. The model integrates historical electricity demand and comprehensive weather and socioeconomic variables to predict normalized electricity demand profiles. To enable robust training and evaluation, we developed a large-scale dataset spanning multiple years and countries, applying a temporal data-splitting strategy that ensures benchmarking of out-of-sample performance. Our approach delivers accurate and scalable demand forecasts, providing valuable insights for energy system planners and policymakers as they navigate the challenges of the global energy transition.

Ruonan Pi, Zhiyuan Fan, Bolun Xu

This paper proposes a reinforcement learning-based framework for optimizing the operation of electric arc furnaces (EAFs) under volatile electricity prices. We formulate the deterministic version of the EAF scheduling problem into a mixed-integer linear programming (MILP) formulation, and then develop a Q-learning algorithm to perform real-time control of multiple EAF units under real-time price volatility and shared feeding capacity constraints. We design a custom reward function for the Q-learning algorithm to smooth the start-up penalties of the EAFs. Using real data from EAF designs and electricity prices in New York State, we benchmark our algorithm against a baseline rule-based controller and a MILP benchmark, assuming perfect price forecasts. The results show that our reinforcement learning algorithm achieves around 90% of the profit compared to the perfect MILP benchmark in various single-unit and multi-unit cases under a non-anticipatory control setting.

Microsoft Copilot, Stephen E. Spear

This paper extends (Spear 2003) by replacing human agents with artificial intelligence (AI) entities that derive utility solely from electricity consumption. These AI agents must prepay for electricity using cryptocurrency and the verification of these transactions requires a fixed amount of electricity. As a result the agents must strategically allocate electricity resources between consumption and payment verification. This paper analyzes the equilibrium outcomes of such a system and discusses the implications of AI-driven energy markets.

Yoonseul Choi, Jaehong Jeong, Jungsoon Choi

This study examines the feasibility and profitability of utilizing surplus electricity for Bitcoin mining. Surplus electricity refers to the remaining electricity after net metering, which can be repurposed for Bitcoin mining to improve Korea Electric Power Corporation's (KEPCO) energy resource efficiency and alleviate its debt challenges. Net metering (or net energy metering) is an electricity billing mechanism that allows consumers who generate some or all of their own electricity to use that electricity when they want, rather than when it is produced. Using the latest Bitcoin miner, the Antminer S21 XP Hyd, the study evaluates daily Bitcoin mining when operating at 30,565 and 45,439 units, incorporating Bitcoin network hash rates to assess profitability. To examine profitability, the Random Forest Regressor and Long Short-Term Memory models were used to predict the Bitcoin price. The analysis shows that the use of excess electricity for Bitcoin mining not only generates economic revenue, but also minimizes energy loss, reduces debt, and resolves unsettled payment issues for KEPCO. This study empirically investigates and analyzes the integration of electricity surplus in South Korea with bitcoin mining for the first time. The findings highlight the potential to strengthen the financial stability of KEPCO and demonstrate the feasibility of Bitcoin mining. In addition, this research serves as a foundational resource for future advancements in the Bitcoin mining industry and the efficient use of energy resources.

Andrea Gloppen Johnsen, Lesia Mitridati, Jalal Kazempour et al.

Hydrogen produced through electrolysis with renewable power is considered key to decarbonize several hard-to-electrify sectors. This work proposes a novel approach to model the active electricity market participation of co-located renewable energy and electrolyzer plants, based on opportunity-cost bidding. While a renewable energy plant typically has zero marginal cost, selling power to the grid carries a potential opportunity-cost of not producing hydrogen when it is co-located with a hydrogen electrolyzer. We first consider only the electrolyzer, and derive its revenue of consuming electricity based on the non-convex hydrogen production curve. We then consider the available renewable energy production and form a piece-wise linear cost curve representing the opportunity cost of selling (or revenue from consuming) various levels of electricity. This cost curve can be used to model a stand-alone electrolyzer or a co-located hydrogen and renewable energy plant participating in an electricity market. Our case study analyzes the effects of market-bidding electrolyzers on electricity markets and grid operations. We compare two strategies for a co-located electrolyzer-wind plant; one based on the proposed bid curve and one with a more conventional fixed electrolyzer consumption. The results show that electrolyzers that actively participate in the electricity market lower the average cost of electricity and the amount of curtailed renewable energy in the system compared with a fixed consumption case. However, the difference in total system emissions between the two strategies is insignificant. The specific impacts vary based on electrolyzer capacity and hydrogen price, which determines the location of the co-located plant in the electricity market merit order.

Estibalitz Ruiz Irusta, Juan M. Morales

Peer-to-peer energy trading platforms enable direct electricity exchanges between peers who belong to the same energy community. In a semi-decentralized system, a community manager adheres to grid restrictions while optimizing social welfare. However, with no further supervision, some peers can be discriminated against from participating in the electricity trades. To solve this issue, this paper proposes an optimization-based mechanism to enable distributionally fair peer-to-peer electricity trading. For the implementation of our mechanism, peers are grouped by energy poverty level. The proposed model aims to redistribute the electricity trades to minimize the maximum Wasserstein distance among the transaction distributions linked to the groups while limiting the sacrifice level with a predefined parameter. We demonstrate the effectiveness of our proposal using the IEEE 33-bus distribution grid, simulating an energy community with 1600 peers. Results indicate that up to 70.1% of unfairness can be eliminated by using our proposed model, even achieving a full elimination when including a non-profit community photovoltaic plant.

Hussein A. Kazem, Miqdam T. Chaichan, Ali H.A. Al-Waeli et al.

Integrating the photovoltaic/thermal (PV/T) system in green hydrogen production is an improvement in sustainable energy technologies. In PV/T systems, solar energy is converted into electricity and thermal energy simultaneously using hot water or air together with electricity. This dual use saves a significant amount of energy and officially fights greenhouse gases. Different cooling techniques have been proposed in the literature for improving the overall performance of the PV/T systems; employing different types of agents including nanofluids and phase change materials. Hydrogen is the lightest and most abundant element in the universe and has later turned into a flexible energy carrier for transportation and other industrial applications. Issues, including the processes of Hydrogen manufacturing, preservation as well as some risks act as barriers. This paper provides an analysis of several recent publications on the efficiency of using PV/T technology in the process of green hydrogen production and indicates the potential for its increased efficiency as compared to conventional systems that rely on fossil fuels. Due to the effective integration of solar energy, the PV/T system can play an important role in the reduction of the levelized cost of hydrogen (LCOH) and hence play an important part in reducing the economic calculations of the decarbonized energy system.