Hasil untuk "Electricity"

A. Andrae, Tomas Edler

This work presents an estimation of the global electricity usage that can be ascribed to Communication Technology (CT) between 2010 and 2030. The scope is three scenarios for use and production of consumer devices, communication networks and data centers. Three different scenarios, best, expected, and worst, are set up, which include annual numbers of sold devices, data traffic and electricity intensities/efficiencies. The most significant trend, regardless of scenario, is that the proportion of use-stage electricity by consumer devices will decrease and will be transferred to the networks and data centers. Still, it seems like wireless access networks will not be the main driver for electricity use. The analysis shows that for the worst-case scenario, CT could use as much as 51% of global electricity in 2030. This will happen if not enough improvement in electricity efficiency of wireless access networks and fixed access networks/data centers is possible. However, until 2030, globally-generated renewable electricity is likely to exceed the electricity demand of all networks and data centers. Nevertheless, the present investigation suggests, for the worst-case scenario, that CT electricity usage could contribute up to 23% of the globally released greenhouse gas emissions in 2030.

K. Branker, M. Pathak, Joshua M. Pearce

Alexandra Lüth, J. Zepter, P. Crespo del Granado et al.

Deployment of distributed generation technologies, especially solar photovoltaic, have turned regular consumers into active contributors to the local supply of electricity. This development along with the digitalisation of power distribution grids (smart grids) is setting the scene to a new paradigm: peer-to-peer electricity trading. The design of the features and rules on how to sell or buy electricity locally, however, is in its early stages for microgrids or small communities. Market design research focuses predominantly on established electricity markets and not so much on incentivising local trading. This is partially because concepts of local markets carry distinct features: the diversity and characteristics of distributed generation, the specific rules for local electricity prices, and the role of digitalisation tools to facilitate peer-to-peer trade (e.g. Blockchain). As different local or peer-to-peer energy trading schemes have emerged recently, this paper proposes two market designs centred on the role of electricity storage. That is, we focus on the following questions: What is the value of prosumer batteries in P2P trade?; What market features do battery system configurations need?; and What electricity market design will open the economical potential of end-user batteries? To address these questions, we implement an optimisation model to represent the peer-to-peer interactions in the presence of storage for a small community in London, United Kingdom. We investigate the contribution of batteries located at the customer level versus a central battery shared by the community. Results show that the combined features of trade and flexibility from storage produce savings of up to 31% for the end-users. More than half of the savings comes from cooperation and trading in the community, while the rest is due to battery’s flexibility in balancing supply-demand operations.

M. Ahmadi, Mahyar Ghazvini, M. Sadeghzadeh et al.

Negative environmental impact of fossil fuel consumption highlight the role of renewable energy sources and give them a unique opportunity to grow and improve. Among renewable energy sources solar energy attract more attention and many studies have focused on using solar energy for electricity generation. Here, in this study, solar energy technologies are reviewed to find out the best option for electricity generation. Using solar energy to generate electricity can be done either directly and indirectly. In the direct method, PV modules are utilized to convert solar irradiation into electricity. In the indirect method, thermal energy is harnessed employing concentrated solar power (CSP) plants such as Linear Fresnel collectors and parabolic trough collectors. In this paper, solar thermal technologies including soar trough collectors, linear Fresnel collectors, central tower systems, and solar parabolic dishes are comprehensively reviewed and barriers and opportunities are discussed. In addition, a comparison is made between solar thermal power plants and PV power generation plants. Based on published studies, PV‐based systems are more suitable for small‐scale power generation. They are also capable of generating more electricity in a specific area in comparison with CSP‐based systems. However, based on economic considerations, CSP plants are better in economic return.

M. Auffhammer, Patrick Baylis, Catherine Hausman

Zhang Yang, Li Ce, Li Lian

I. Staffell, S. Pfenninger

Wind and solar power have experienced rapid cost declines and are being deployed at scale. However, their output variability remains a key problem for managing electricity systems, and the implications of multi-day to multi-year variability are still poorly understood. As other energy-using sectors are electrified, the shape and variability of electricity demand will also change. We develop an open framework for quantifying the impacts of weather on electricity supply and demand using the Renewables.ninja and DESSTINEE models. We demonstrate this using a case study of Britain using National Grid's Two Degrees scenario forwards to 2030.

P. Cramton

Electricity markets are designed to provide reliable electricity at least cost to consumers. This paper describes how the best designs satisfy the twin goals of short-run efficiency—making the best use of existing resources—and long-run efficiency—promoting efficient investment in new resources. The core elements are a day-ahead market for optimal scheduling of resources and a real-time market for security-constrained economic dispatch. Resources directly offer to produce per their underlying economics and then the system operator centrally optimizes all resources to maximize social welfare. Locational marginal prices, reflecting the marginal value of energy at each time and location, are used in settlement. This spot market provides the basis for forward contracting, which enables participants to manage risk and improves bidding incentives in the spot market. There are important differences in electricity markets around the world, reflecting different economic and political settings. Electricity markets are undergoing a transformation as the resource mix transitions from fossil fuels to renewables. The main renewables, wind and solar, are intermittent, have zero marginal cost, and lack inertia. These challenges can be met with battery storage and improved demand response. However, good governance is needed to assure the market rules adapt to meet new challenges.

Jinliang Zhang, Yi-Ming Wei, Dezhi Li et al.

Abstract Short term electricity load forecasting is one of the most important issue for all market participants. Short term electricity load is affected by natural and social factors, which makes load forecasting more difficult. To improve the forecasting accuracy, a new hybrid model based on improved empirical mode decomposition (IEMD), autoregressive integrated moving average (ARIMA) and wavelet neural network (WNN) optimized by fruit fly optimization algorithm (FOA) is proposed and compared with some other models. Simulation results illustrate that the proposed model performs well in electricity load forecasting than other comparison models.

D. Newbery, M. Pollitt, Robert A. Ritz et al.

This paper presents a set of policy recommendations for the market design of a future European electricity system characterized by a dominant share of intermittent renewable energy supply (RES), in line with the stated targets of European governments. We discuss the market failures that need to be addressed to accommodate RES in liberalized electricity markets, review the evolution of the EU's RES policy mechanisms, and summarize the key market impacts of RES to date. We then set out economic principles for market design and use these to develop our policy recommendations. Our analysis covers the value of interconnection and market integration, electricity storage, the design of RES support mechanisms, distributed generation and network tariffs, the pricing of electricity and flexibility as well as long-term contracting and risk management.

Milkias Berhanu Tuka, Seid Endris Ali

The increasing demand for electric power, coupled with rapid urbanization, necessitates a reliable and high-quality electricity supply to meet consumer expectations. However, existing passive distribution systems are inadequate to address the escalating power requirements, resulting in challenges such as increased power losses and suboptimal voltage profiles. In the base case scenario, the total active and reactive power losses were substantial, and many buses exhibited voltage magnitudes that fell outside acceptable limits. This study investigates the optimal placement and sizing of distributed generation (DG) resources to improve the performance of distribution feeders. A multi-objective optimization framework, utilizing a Genetic Algorithm (GA), was developed to minimize power losses and enhance voltage profiles. Load flow analysis was conducted using the Backward/Forward Sweep (BFS) method, allowing for precise evaluation of the distribution feeder under various DG configurations. Consequently, the study successfully enhanced the system through optimal DG allocation. Additionally, a comparative analysis was conducted to assess the performance of the proposed GA algorithm against other optimization techniques. The results indicate that, in nearly all cases, the GA method outperforms PSO by reducing system power losses and improving the voltage profile more effectively.

D. Kalathil, Chenye Wu, K. Poolla et al.

The sharing economy has upset the market for housing and transportation services. Homeowners can rent out their property when they are away on vacation, car owners can offer ridesharing services. These sharing economy business models are based on monetizing under-utilized infrastructure. They are enabled by peer-to-peer platforms that match eager sellers with willing buyers. Are there compelling sharing economy opportunities in the electricity sector? What products or services can be shared in tomorrow’s smart grid? We begin by exploring sharing economy opportunities in the electricity sector, and discuss regulatory and technical obstacles to these opportunities. We then study the specific problem of a collection of firms sharing their electricity storage. We characterize equilibrium prices for shared storage in a spot market. We formulate storage investment decisions of the firms as a non-convex non-cooperative game. We show that under a mild alignment condition, a Nash equilibrium exists, it is unique, and it supports the social welfare. We discuss technology platforms necessary for the physical exchange of power, and market platforms necessary to trade electricity storage. We close with synthetic examples to illustrate our ideas.

Mert Temiz, N. Javani

Abstract The current study deals with a potential solution for the replacement of fossil fuel based energy resources with a sustainable solar energy resource. Electrical energy demand of a small community is investigated where a floating photovoltaic system and integrated hydrogen production unit are employed. Data are taken from Mumcular Dam located in Aegean Region of Turkey. PvSyst software is used for the simulation purposes. Furthermore, the obtained results are analyzed in the HOMER Pro Software. Photovoltaic (PV) electricity provides the required load and excess electricity to be used in the electrolyzer and to produce hydrogen. Saving lands by preventing their usage in conventional PV farms, saving the water due to reducing evaporation, and compensating the intermittent availability of solar energy are among the obtained results of the study for the considered scenario. Stored hydrogen is used to compensate the electric load through generating electricity by fuel cell. Floating PV (FPV) system decreases the water evaporation of water resources due to 3010 m2 shading area. FPV and Hydrogen Systems provides %99.43 of the electricity demand without any grid connection or fossil fuel usage, where 60.30 MWh/year of 211.94 MWh/year produced electricity is consumed by electric load at $0.6124/kWh levelized cost of electricity (LCOE).

Ailing Yang, Weide Li, Xuan Yang

Abstract Short-Term Electricity Load Forecasting (STLF) has become one of the hot topics of energy research as it plays a crucial role in electricity markets and power systems. Few researches aim at selecting optimal input features (Feature Selection, FS) when forecasting model is established, although more and more intelligent hybrid models are developed to forecast real-time electricity load. In fact, a good FS is a key factor that influence prediction accuracy. Based on the idea of selecting optimal input features, a hybrid model, AS-GCLSSVM, is developed to forecast electricity load in this research, which combines ACF (AutoCorrelation Function) and LSSVM (Least Squares Support Vector Machines). ACF is applied to select the informative input variables, and LSSVM is for prediction. The parameters in LSSVM are optimized by GWO (Grey Wolf Optimization Algorithm) and CV (Cross Validation). The proposed model is to forecast the half-hour electricity load of the following week. Experimental results show that it is an effective approach that can improve the forecasting accuracy remarkably, compared with the benchmark models.

Weibiao Qiao, Zhe Yang

Abstract Wavelet transform (WT), as a data preprocessing algorithm, has been widely applied in electricity price forecasting. However, this deterministic-based algorithm does not present stable performance owing to the experiential selection of its orders and layers. For determining the selection of WT’s orders and layers in U.S. electricity prices forecasting, this paper designs a crossover experiment with 240 schemes of WT parameter selection and forecasts each scheme with stacked autoencoder (SAE) and long short-term memory (LSTM), generating a novel hybrid model WT-SAE-LSTM. The results show that the proposed model outperforms other AI models, such as back propagation neural network et al., in forecasting accuracy. The best performance of WT-SAE-LSTM in residential, commercial, and industrial electricity price cases obtained by five order four layers, five order four layers, and four order seven layers, where the MAPE is 0.8606%, 0.4719%, and 0.4956%, respectively. Additionally, the difference between the proposed forecasting model and the forecasting result of Energy Information Administration (U.S.) is small. This paper determines the optimal orders and layers of WT in U.S. electricity prices forecasting, which provides an effective reference for the application of WT in other forecasting scenarios and for electricity market participants.

Filipe Neves, Armando A. Soares, Abel Rouboa

Due to the growing global population, rising energy demands, and the environmental impacts of fossil fuel use, there is an urgent need for sustainable energy sources. Biomass conversion technologies have emerged as a promising solution, particularly supercritical water gasification (SCWG), which enables efficient energy recovery from wet and dry biomass. This systematic review, following PRISMA 2020 guidelines, analyzed 51 peer-reviewed studies published between 2015 and 2025. The number of publications has increased over the decade, reflecting rising interest in SCWG for energy production. Research has focused on six biomass feedstock categories, with lignocellulosic and wet biomasses most widely studied. Reported energy efficiencies ranged from ~20% to >80%, strongly influenced by operating conditions and system integration. Integrating SCWG with solid oxide fuel cells, organic Rankine cycles, carbon capture and storage, or solar input enhanced both energy recovery and environmental performance. While SCWG demonstrates lower greenhouse gas emissions than conventional methods, many studies lacked comprehensive life cycle or economic analyses. Common limitations include high energy demand, modeling simplifications, and scalability challenges. These trends highlight both the potential and the barriers to advancing SCWG as a viable biomass-to-energy technology.

Longjian Piao, Laurens de Vries, Mathijs de Weerdt et al.

Future energy markets for low voltage AC and DC distribution systems will facilitate prosumer participation in the market. To comply with market regulations and grid constraints, a tailored market design reflecting (DC) operational requirements is needed. Our previous work identified a locational energy market design. However, its real-life implementation faces challenges due to uncertainties in system operation, prosumer preferences, and bidding strategies. This article tests the market design under uncertain scenarios. To this end, we develop an agent-based model that simulates typical electric vehicle user preferences and bidding strategies, influenced by varying degrees of range anxiety. The market design is tested in challenging scenarios with a high share of solar panels and electric vehicles, modelled using the high-resolution Pecan Street database. Simulations indicate that the proposed market design maintains both economic efficiency and system reliability under real-life uncertainties. This in turn indicates the practical feasibility of locational energy markets in helping to integrate renewable generation sources and bidirectional power flows.

Arash Khojaste, Jonathan Pearce, Golbon Zakeri et al.

Data centers are becoming a major consumer of electricity on the grid, with cooling accounting for about 40\% of that energy. As electricity prices vary throughout the day and year, there is a need for cooling strategies that adapt to these fluctuations to reduce data center cooling costs. In this paper, we present a model for electricity price-aware cooling scheduling using a Markov Decision Process(MDP) framework to reliably estimate the cooling system operational costs and facilitate investment-phase decision-making. We utilize Quantile Fourier Regression (QFR) fits to classify electricity prices into different regimes while capturing both daily and seasonal patterns. We simulate 14 years of operation using historical electricity price and outdoor temperature data, and compare our model against heuristic baselines. The results demonstrate that our approach consistently achieves lower cooling costs. This model is useful for grid operators interested in demand response programs and data center investors looking to make investment decisions.

Fazil Kaytez

Abstract Electricity consumption is on the rise in developing countries. Most of the research studies in energy demand forecasting aim to provide that sufficient electricity is produced to meet future needs. A reliable forecasting model is necessary for accurate investment planning of electricity generation and distribution. The main goal of this study is to develop effective and realistic solutions for electricity consumption forecasting in Turkey. This paper proposes a hybrid model based on least-square support vector machine and an autoregressive integrated moving average. This hybrid approach’s forecast results are compared with multiple linear regression approach, a single autoregressive integrated moving average model, official forecasts and similar studies in literature. Also, it is applied to forecast the future net electricity consumption for Turkey until 2022. The study results indicate that the proposed model can generate more realistic and reliable forecasts. It can also be stated that it responds better to some unexpected reactions in the time series.

Mucahit Aydin

In this study, the relationship between renewable and non-renewable electricity consumption and economic growth was examined using data from the 1980–2015 period for 26 OECD countries. The relationship between the variables was examined using two different panel causality approaches in order to make a comparison. Time domain Granger causality tests cannot examine the causality relation at different frequencies. However, frequency domain Granger causality tests examine the causality at different frequencies. While Dumitrescu-Hurlin (2012) panel causality test is time domain causality test, the causality test developed by Croux and Reusens (2013) is the frequency domain causality test. According to the Dumitrescu-Hurlin panel causality test results, bidirectional causality has been determined between non-renewable electricity consumption and economic growth. On the contrary, the Croux and Reusens test results show that there is a bidirectional temporary, and permanent causality between economic growth and renewable-non-renewable electricity consumption. According to this results, for 26 OECD countries, where the feedback hypothesis is valid, policies need to be assessed not only in terms of economic growth, but also in terms of improving electricity energy supply security and environmental quality. Finally, policy-makers should promote the renewable electricity consumption to ensure energy security, reduce energy dependence, and encourage economic growth.