This paper studies how platform design shapes strategic behavior in decentralized electricity trading. We develop a finite-horizon dynamic game in which photovoltaic- and battery-equipped players ("prosumers") trade on a platform that maps aggregate imports and exports into internal buy and sell prices. We establish existence of a perfect conditional epsilon-equilibrium and characterize a Cournot-like market-power mechanism in an observable-types benchmark of the game: because the producer price is decreasing in aggregate exports, strategic prosumers withhold supply and underutilize storage relative to the price-taking benchmark. To quantify these effects, we use a multi-agent computational framework that exploits the differentiable structure of the platform's clearing rule to compare planner, price-taking, and strategic outcomes under alternative pricing mechanisms. In our baseline calibration, strategic play raises grid settlement cost by about 6 percent relative to price-taking. The magnitude of the distortion depends strongly on platform design: some designs can largely eliminate strategic incentives, while increased competition in storage ownership sharply reduces withholding, with most of the distortion disappearing once storage is split across more than three owners. We also find that information disclosure can improve competitive coordination but also increase the market power effects. Despite these distortions, the platform remains highly valuable overall, reducing a passive consumer's annual electricity bill by roughly 40 percent relative to exclusive grid settlement, with strategic behavior clawing back only about 8 percent of that saving. The results show that pricing rules, information disclosure, and ownership structure determine how much of the gains from decentralized electricity trading are realized.
This paper introduces a new stochastic diffusion process to model the electricity production from natural gas sources (as a percentage of total electricity production) in the United States. The method employs trend function analysis to generate fits and forecasts with both conditional and unconditional estimated trend functions. Parameters are estimated using the maximum likelihood (ML) method, based on discrete sampling paths of the variable "electricity production from natural gas sources in the United States" with annual data from 1990 to 2021. The results show that the proposed model effectively fits the data and provides dependable medium-term forecasts for 2022-2023.
Predicting the behaviour of the Earth’s ionosphere is crucial for the ground-based and spaceborne technologies that rely on it. This paper presents a novel way of inferring ionospheric electron density profiles and electron temperature profiles using machine learning. The analysis is based on the Nearest Neighbour (NNB) and Radial Basis Function (RBF) regression models. Synthetic data sets used to train and validate these two inference models are constructed using the International Reference Ionosphere (IRI 2020) model with randomly chosen years (1987–2022), months (1–12), days (1–31), latitudes (−60 to 60°), longitudes (0, 360°), and times (0–23 h), at altitudes ranging from 95 to 600 km. The NNB and RBF models use the constructed ionosonde-like profiles to infer complete ISR-like profiles. The results show that the inference of ionospheric electron density profiles is better with the NNB model than with the RBF model, while the RBF model is better at inferring the electron temperature profiles. A major and unexpected finding of this research is the ability of the two models to infer full electron temperature profiles that are not provided by ionosondes using the same truncated electron density data set used to infer electron density profiles. NNB and RBF models generally over- or underestimate the inferred electron density and electron temperature values, especially at higher altitudes, but they tend to produce good matches at lower altitudes. Additionally, maximum absolute relative errors for electron density and temperature inferences are found at higher altitudes for both NNB and RBF models.
The pathway towards carbon neutrality in regions with a relatively light industrial structure and scarce renewable energy resources presents a challenge when balancing energy efficiency improvements with the expansion of renewable energy. Therefore, this study investigates the effectiveness of carbon pricing and carbon quota control as regional carbon abatement policies. The findings demonstrate that carbon taxes are less effective than carbon emission quota control in economic growth and carbon abatement due to their weaker impact on energy efficiency enhancement and structural transition in the energy and industrial sectors. Moreover, stricter carbon pricing, determined by carbon emission goals, leads to greater reduction in sectoral carbon intensity but slower GDP growth caused by the accelerated decline of manufacturing and infrastructure industries compared to carbon intensity quota policies. In addition, carbon pricing derived from carbon emission and intensity quota policies increases reliance on domestically imported electricity, which is constrained by the availability of renewable energy resources.
Oscar A. Martínez-Martínez, Oscar A. Martínez-Martínez, Claudia V. Zamudio-Lazarín
et al.
IntroductionIn middle-income countries such as Mexico, housing adequacy remains a central challenge, particularly in relation to the physical conditions of dwellings and the provision of essential services, including potable water, drainage, and electricity. This study examines how the quality of living spaces, access to basic services, and conditions of vulnerability are perceived across municipalities with different levels of marginalization.MethodsThe study involved a literature review, the development of a sampling strategy based on the Municipal Marginalization Index (IMM), and the conduct of 235 semi-structured interviews. The data were coded using thematic analysis and interpreted through an analytical framework that integrates material and subjective dimensions of habitability. The analysis focused on physical dwelling conditions, such as construction materials, structural stability, and service provision, as well as subjective perceptions of comfort, safety, and vulnerability.ResultsThe findings indicate that in highly marginalized areas, households face chronic water scarcity, infrastructural fragility, and exposure to environmental risks. In these contexts, comfort is often associated with minimal protection, and safety is linked to community support networks. In less marginalized municipalities, improved construction conditions and greater access to basic services contribute to higher perceived stability, although concerns regarding water availability and neighborhood security persist.DiscussionThe results show that marginalization shapes both material housing conditions and the lived experience of habitability. These insights highlight the need for in-situ upgrading strategies, community-centered service provision, and neighborhood-scale planning to improve living conditions in marginalized urban contexts.
Abstract Single-walled carbon nanotubes (SWCNTs) have attracted attention for use in thermoelectric generators (TEGs) that convert thermal energy into electricity. SWCNTs, which are lightweight, flexible, and nontoxic materials, are perfectly suited for TEGs as self-contained power sources for Internet of Things (IoT) sensors. However, the generation of electricity by TEGs requires not only a heat source to create a temperature gradient within the TEGs but also a cold source. In this study, we prepared SWCNT-TEGs consisting of p–i–n junction SWCNT films on a polyimide sheet that automatically generated a temperature gradient within the SWCNT-TEGs by uniform heating without a cold source. The output voltage and current of the SWCNT-TEG increased with increasing temperature. At a heating temperature of 370 K, the SWCNT-TEG exhibited an output voltage of 2.3 mV, a short-circuit current of 13.7 µA, and a maximum output power of 7.7 nW. The self-generated temperature gradient under uniform heating was attributed to the higher optical absorption of the SWCNT film than that of the polyimide sheet, which increased the temperature of the SWCNT film. The results of this study indicate that a simple attachment of SWCNT-TEGs to a heat source can sustainably power an IoT sensor as self-contained power sources.
Badreddine Ayadi, Karim Kriaa, Ahmed Mohsin Alsayah
et al.
Abstract This study proposes and investigates a novel solar power tower-based tri-generation system producing electricity, hydrogen, and green ammonia through integrated thermodynamic cycles. The plant couples a Steam Rankine Cycle (SRC), a Vanadium–Chlorine Thermochemical Water Splitting Cycle (TWSC), and a Haber–Bosch reactor. Concentrated solar energy is stored in a heat transfer fluid and utilized to drive the SRC for power generation and supply high-temperature heat to the TWSC for hydrogen production, which, combined with nitrogen, is converted to ammonia. Comprehensive thermodynamic and economic models are developed, validated, and applied to assess system feasibility. Parametric analyses reveal that higher receiver temperatures and turbine inlet pressures increase power output but reduce hydrogen and ammonia yields, while hydrogen storage fraction significantly influences product distribution and cost. Dynamic simulations using real solar data demonstrate seasonal performance variations, with summer months offering peak outputs. A tri-objective optimization via the Grey Wolf algorithm balances ammonia production rate, exergy efficiency, and levelized cost of products, yielding optimal values of 0.154 kg/s, 61.7%, and 35.4 $/GJ, respectively. Results confirm that the proposed solar-driven system offers an efficient, low-carbon pathway for simultaneous renewable electricity generation, hydrogen production, and sustainable ammonia synthesis.
Slawek Smyl, Boris N. Oreshkin, Paweł Pełka
et al.
Power systems operate under uncertainty originating from multiple factors that are impossible to account for deterministically. Distributional forecasting is used to control and mitigate risks associated with this uncertainty. Recent progress in deep learning has helped to significantly improve the accuracy of point forecasts, while accurate distributional forecasting still presents a significant challenge. In this paper, we propose a novel general approach for distributional forecasting capable of predicting arbitrary quantiles. We show that our general approach can be seamlessly applied to two distinct neural architectures leading to the state-of-the-art distributional forecasting results in the context of short-term electricity demand forecasting task. We empirically validate our method on 35 hourly electricity demand time-series for European countries. Our code is available here: https://github.com/boreshkinai/any-quantile.
Rubén Rodríguez-Vilches, Francisco Martín-Martínez, Álvaro Sánchez-Miralles
et al.
The emergence of distributed generation and the electrification of demand have opened the possibility for prosumers to participate in electricity markets, receiving economic benefits on their bills and contributing to the reduction of carbon emissions, aligning with United Nations Sustainable Development Goal 7. Consumers and prosumers can participate through implicit and explicit demand flexibility and (collective) self-consumption. This study analyses the potential markets in which prosumers can participate and indicates whether these are currently open. The markets studied include day-ahead, intraday, ancillary services, adequacy services, constraint management, and local flexibility markets. Additionally, collective self-consumption is analysed as a service through which prosumers can participate in the electricity market. Previous studies are usually focused on a single market or in a single country, making impossible a complete comparison. This analysis has been done in Spain, Italy, Croatia, and the United Kingdom as representative countries to obtain a methodology to assess countries' openness to prosumer participation in electricity markets, comparing regulatory frameworks and assigning scores based on their prosumer inclusion across various markets. This work updates current literature reviews with the changes and a new description of local market designs in Spain. This methodology can be used to compare other countries' grade of openness. The results of this study show that the analysed countries can be categorised into three groups: almost open, partially open, and closed markets. Analysing the differences, recommendations on the following steps to foster user participation are suggested for each group.
As a critical component of modern infrastructure, data centers account for a huge amount of power consumption and greenhouse gas emission. This paper studies the electricity purchase strategy for a data center to lower its energy cost while integrating local renewable generation under uncertainty. To facilitate efficient and scalable decision-making, we propose a two-layer hierarchy where the lower layer consists of the operation of all electrical equipment in the data center and the upper layer determines the procurement and dispatch of electricity. At the lower layer, instead of device-level scheduling in real time, we propose to exploit the inherent flexibility in demand, such as thermostatically controlled loads and flexible computing tasks, and aggregate them into virtual batteries. By this means, the upper-layer decision only needs to take into account these virtual batteries, the size of which is generally small and independent of the data center scale. We further propose an online algorithm based on Lyapunov optimization to purchase electricity from the grid with a manageable energy cost, even though the prices, renewable availability, and battery specifications are uncertain and dynamic. In particular, we show that, under mild conditions, our algorithm can achieve bounded loss compared with the offline optimal cost, while strictly respecting battery operational constraints. Extensive simulation studies validate the theoretical analysis and illustrate the tradeoff between optimality and conservativeness.
This working paper presents a comprehensive study on the development and analysis of various electricity market models, focusing on continuous, discrete, and fractional-order approaches. The continuous model captures the ongoing interactions between power producers and consumers using differential equations, providing insights into long-term trends and steady-state behaviors. The discrete model, suitable for analyzing scenarios where market events occur at specific time intervals, incorporates memory effects to account for historical behaviors and decisions, offering a realistic representation of short-term market dynamics. The fractional-order model introduces fractional calculus to capture memory effects and hereditary properties, enhancing the model's realism and predictive capability by reflecting the influence of past states on current market behavior.
BackgroundRevealing the full potential of digital public health (DiPH) systems requires a wide-ranging tool to assess their maturity and readiness for emerging technologies. Although a variety of indices exist to assess digital health systems, questions arise about the inclusion of indicators of information and communications technology maturity and readiness, digital (health) literacy, and interest in DiPH tools by the society and workforce, as well as the maturity of the legal framework and the readiness of digitalized health systems. Existing tools frequently target one of these domains while overlooking the others. In addition, no review has yet holistically investigated the available national DiPH system maturity and readiness indicators using a multidisciplinary lens.
ObjectiveWe used a narrative review to map the landscape of DiPH system maturity and readiness indicators published in the gray literature.
MethodsAs original indicators were not published in scientific databases, we applied predefined search strings to the DuckDuckGo and Google search engines for 11 countries from all continents that had reached level 4 of 5 in the latest Global Digital Health Monitor evaluation. In addition, we searched the literature published by 19 international organizations for maturity and readiness indicators concerning DiPH.
ResultsOf the 1484 identified references, 137 were included, and they yielded 15,806 indicators. We deemed 286 indicators from 90 references relevant for DiPH system maturity and readiness assessments. The majority of these indicators (133/286, 46.5%) had legal relevance (targeting big data and artificial intelligence regulation, cybersecurity, national DiPH strategies, or health data governance), and the smallest number of indicators (37/286, 12.9%) were related to social domains (focusing on internet use and access, digital literacy and digital health literacy, or the use of DiPH tools, smartphones, and computers). Another 14.3% (41/286) of indicators analyzed the information and communications technology infrastructure (such as workforce, electricity, internet, and smartphone availability or interoperability standards). The remaining 26.2% (75/286) of indicators described the degree to which DiPH was applied (including health data architecture, storage, and access; the implementation of DiPH interventions; or the existence of interventions promoting health literacy and digital inclusion).
ConclusionsOur work is the first to conduct a multidisciplinary analysis of the gray literature on DiPH maturity and readiness assessments. Although new methods for systematically researching gray literature are needed, our study holds the potential to develop more comprehensive tools for DiPH system assessments. We contributed toward a more holistic understanding of DiPH. Further examination is required to analyze the suitability and applicability of all identified indicators in diverse health care settings. By developing a standardized method to assess DiPH system maturity and readiness, we aim to foster informed decision-making among health care planners and practitioners to improve resource distribution and continue to drive innovation in health care delivery.
Abstract Triboelectric nanogenerator (TENG) is a promising technology, which can convert biokinetic energy into electricity and be utilized as self‐powered sensors and power sources for wearable electronics. The existing designs of conventional TENGs require complex fabrication processes and device structures, and they need to be attached on human body for wearable application, which is uncomfortable and may lead to malfunction under intense body moment. Here, a direct current TENG is proposed by utilizing natural human body, basketball, shoes, and ground floor. A unidirectional peak voltage and current output up to 700 V and 23 µA can be generated when a player plays a basketball, which can lighten up an array of 240 LEDs, and charge a 100 µF capacitor to 3.2 V in 1 min. The output of TENG is utilized to identify different movements of a basketball player using machine learning algorithm with an accuracy up to 96.7%. Moreover, the human body enabled direct current TENG (HBDC‐TENG) is used as a self‐powered sensor and an energy harvester for a wireless sensing system, which can collect human motion and environmental information, and transmit them wirelessly. The HBDC‐TENG has a great significance for self‐powered wearable electronics, providing a viable solution for human motion status and ambient environment monitoring.
Electric apparatus and materials. Electric circuits. Electric networks, Physics
Electricity demand forecasting is a well established research field. Usually this task is performed considering historical loads, weather forecasts, calendar information and known major events. Recently attention has been given on the possible use of new sources of information from textual news in order to improve the performance of these predictions. This paper proposes a Long and Short-Term Memory (LSTM) network incorporating textual news features that successfully predicts the deterministic and probabilistic tasks of the UK national electricity demand. The study finds that public sentiment and word vector representations related to transport and geopolitics have time-continuity effects on electricity demand. The experimental results show that the LSTM with textual features improves by more than 3% compared to the pure LSTM benchmark and by close to 10% over the official benchmark. Furthermore, the proposed model effectively reduces forecasting uncertainty by narrowing the confidence interval and bringing the forecast distribution closer to the truth.
Konstantin Hopf, Hannah Hartstang, Thorsten Staake
Forecasting electricity demand plays a critical role in ensuring reliable and cost-efficient operation of the electricity supply. With the global transition to distributed renewable energy sources and the electrification of heating and transportation, accurate load forecasts become even more important. While numerous empirical studies and a handful of review articles exist, there is surprisingly little quantitative analysis of the literature, most notably none that identifies the impact of factors on forecasting performance across the entirety of empirical studies. In this article, we therefore present a Meta-Regression Analysis (MRA) that examines factors that influence the accuracy of short-term electricity load forecasts. We use data from 421 forecast models published in 59 studies. While the grid level (esp. individual vs. aggregated vs. system), the forecast granularity, and the algorithms used seem to have a significant impact on the MAPE, bibliometric data, dataset sizes, and prediction horizon show no significant effect. We found the LSTM approach and a combination of neural networks with other approaches to be the best forecasting methods. The results help practitioners and researchers to make meaningful model choices. Yet, this paper calls for further MRA in the field of load forecasting to close the blind spots in research and practice of load forecasting.
Currently, gas furnaces are common heating systems in Europe. Due to the efforts for decarbonizing the complete energy sector, heat pumps should continuously replace existing gas furnaces. At the same time, the electrification of the heating sector represents a significant challenge for the power grids and their operators. Thus, new approaches are required to estimate the additional electricity demand to operate heat pumps. The electricity required by a heat pump to produce a given amount of heat depends on the Seasonal Performance Factor (SPF), which is hard to model in theory due to many influencing factors and hard to measure in reality as the heat produced by a heat pump is usually not measured. Therefore, we show in this paper that collected smart meter data forms an excellent data basis on building level for modeling heat demand and the SPF. We present a novel methodology to estimate the mean SPF based on an unpaired dataset of heat pump electricity and gas consumption data taken from buildings within the same city by comparing the distributions using the Jensen-Shannon Divergence (JSD). Based on a real-world dataset, we evaluate this novel method by predicting the electricity demand required if all gas furnaces in a city were replaced by heat pumps and briefly highlight possible use cases.
The Internet of Energy (IoE) is a topic that industry and academics find intriguing and promising, since it can aid in developing technology for smart cities. This study suggests an innovative energy system with peer-to-peer trading and more sophisticated residential energy storage system management. It proposes a smart residential community strategy that includes household customers and nearby energy storage installations. Without constructing new energy-producing facilities, users can consume affordable renewable energy by exchanging energy with the community energy pool. The community energy pool can purchase any excess energy from consumers and renewable energy sources and sell it for a price higher than the feed-in tariff but lower than the going rate. The energy pricing of the power pool is based on a real-time link between supply and demand to stimulate local energy trade. Under this pricing structure, the cost of electricity may vary depending on the retail price, the number of consumers, and the amount of renewable energy. This maximizes the advantages for customers and the utilization of renewable energy. A Markov decision process (MDP) depicts the recommended power to maximize consumer advantages, increase renewable energy utilization, and provide the optimum option for the energy trading process. The reinforcement learning technique determined the best option in the renewable energy MDP and the energy exchange process. The fuzzy inference system, which takes into account infinite opportunities for the energy exchange process, enables Q-learning to be used in continuous state space problems (fuzzy Q-learning). The analysis of the suggested demand-side management system is successful. The efficacy of the advanced demand-side management system is assessed quantitatively by comparing the cost of power before and after the deployment of the proposed energy management system.
We propose a method for the fault passage indication of earth faults in resonant-earthed networks, based on phase current measurements alone. This is particularly relevant for electricity distribution systems at medium-voltage levels. The method is based on the relative magnitudes of the phasor changes in the phase currents due to the fault. It is tested for various network types and operation configurations by simulating the network in <span style="font-variant: small-caps;">pscad</span> and using the simulated currents as the input for an implementation of the method in <span style="font-variant: small-caps;">matlab</span>. In over-compensated networks, the method shows reliable detection of the fault passage, with good selectivity and sensitivity for both homogeneous and mixed (cable and overhead line) feeders. However, for the less common under-compensated systems, it has limitations that are described further in this study. The method has good potential for being cost effective since it requires only current measurements, from a single location, at a moderate sampling rate.