Abstract The direct synthetic organic use of electricity is currently experiencing a renaissance. More synthetically oriented laboratories working in this area are exploiting both novel and more traditional concepts, paving the way to broader applications of this niche technology. As only electrons serve as reagents, the generation of reagent waste is efficiently avoided. Moreover, stoichiometric reagents can be regenerated and allow a transformation to be conducted in an electrocatalytic fashion. However, the application of electroorganic transformations is more than minimizing the waste footprint, it rather gives rise to inherently safe processes, reduces the number of steps of many syntheses, allows for milder reaction conditions, provides alternative means to access desired structural entities, and creates intellectual property (IP) space. When the electricity originates from renewable resources, this surplus might be directly employed as a terminal oxidizing or reducing agent, providing an ultra‐sustainable and therefore highly attractive technique. This Review surveys recent developments in electrochemical synthesis that will influence the future of this area.
Abstract Urban overheating is documented for more than 400 major cities in the world. Numerous experimental data show that the magnitude of the average temperature increase may exceed 4-5 C, while at the peak may exceed 10 C. Increased ambient temperatures cause a serious impact on the cooling energy consumption, peak electricity demand, heat related mortality and morbidity, urban environmental quality, local vulnerability and comfort. Synergies between urban heat island and heat waves increase further the amplitude of urban overheating The present paper reviews and reports the recent progress and knowledge on the specific impact of current and projected urban overheating in energy, peak electricity demand, air quality, mortality and morbidity and urban vulnerability. In parallel, it discusses new findings related to the characteristics and the magnitude of urban overheating, and reports and analyse the recent knowledge on the synergies between urban heat island and heat waves.
Electricity networks are vulnerable to weather damage, with severe events often leading to faults and power outages. Timely forecasts of fault occurrences, ranging from nowcasts to several days ahead, can enhance preparedness, support faster response, and reduce outage durations. To be operationally useful, such forecasts must quantify uncertainty, enabling risk-informed resource allocation. We present a novel probabilistic framework for forecasting fault counts that captures typical and extreme events. Non-extreme faults are modeled linearly interpolating estimates from multiple additive quantile regressions, while extreme events are described through a discrete generalized Pareto distribution. To incorporate the impact of weather fluctuations, we use ensemble numerical weather predictions, which helps to quantify uncertainty in the forecasts. This approach is designed to provide reliable fault predictions up to four days ahead. We evaluate the model through numerical experiments and apply it to historical fault data from two electricity distribution networks in Great Britain. The resulting forecasts demonstrate substantial improvements over business-as-usual and alternative modeling approaches. A practitioner trial conducted with Scottish Power Energy Networks from October 2024 to March 2025 further demonstrates the operational value of the forecasts. Engineers found them sufficiently reliable to inform decision-making, offering benefits to both network operators and electricity consumers.
In this paper, based on the developed statistical-thermodynamic model, which is based on data on the local structure of the compound and taking into account the striction interaction caused by the large sizes of the Ba and K cations, the formation of ferroelectric phases in BaTiO3 and KNbO3 perovskites has been studied. Based on the modified eight-minimum model, it has been possible to qualitatively identify the factors that determine the features of the thermodynamic behavior of these crystals and to reproduce the process of formation of the whole set of phase states observed in BaTiO3 and KNbO3.
Saket Mathur, Victoria Bishop, Andrew Swindle
et al.
Day to day energy production is shifting towards renewable energy sources as these sources become more economically viable while being less polluting to operate; solar energy has become one of the major sources of renewable energy. However, it currently relies on ultra-pure silicon ingots to produce commercial silicon photovoltaics, which prevents the cost of electricity being produced to compete with non-renewable energy production. A viable low-cost alternative for silicon based cells would be dye-sensitized solar cells (DSSCs), which are easier and cheaper to manufacture as they do not require expensive and delicate raw materials to make. Moreover, they could be made semi-flexible which allows for a greater variety of applications. A DSSC consists of three components, a photo-electrode, an electrolyte and a counter-electrode. When exposed to incident light, the complex photosensitizers in the photoelectrode release electrons which are transported to the external load, leaving the photoelectrode in an oxidized state. The electrons are collected by the counter electrode and used to reduce the electrolyte. This charged electrolyte then reduces the positively charged photoelectrode, allowing the process to begin again. To improve the efficiency of this process, we explore the use of bismuth sulfide (Bi2S3) and titanium oxide (TiO2) composite as photoelectrode material and investigate their impact on the efficiency of DSSC.
Synne Krekling Lien, Bjørn Ludvigsen, Harald Taxt Walnum
et al.
This article describes a dataset of hourly sub-metered energy use data from 48 school buildings located in Oslo, owned and managed by Oslobygg KF. The dataset consists of 1 comma-delimited file per building, each containing meta data about the building, time series data containing energy use measurements and local weather data. The length of the dataset varies by building, covering between 1 and 11 years of raw data. Raw data for each building was downloaded in 2023 from Oslobygg KF’s energy management system, “Energinet.” Only buildings with sufficiently reliable sub-metered heating data were included. This process included manual selection, quality-control, relabelling and cleaning to ensure consistency and accuracy. The dataset includes buildings with both electric heating (electric boilers and/or heat pumps) and district heating. All buildings have sub-metered heating data, and some also include sub-meters for domestic hot water heating and photovoltaic electricity generation. The data set can be used for several research and engineering purposes, including benchmarking and validation of building simulations, heating disaggregation, energy use time series classification, forecasting of energy loads and flexibility, grid planning and other modelling activities.
Computer applications to medicine. Medical informatics, Science (General)
In many countries, declining demand in energy-intensive industries such as cement, steel, and aluminum is leading to industrial overcapacity. Although industrial overcapacity is traditionally envisioned as problematic and resource-wasteful, it could unlock energy-intensive industries' flexibility in electricity use. Here, using China's aluminum smelting industry as a case study, we evaluate the system-level cost-benefit of retaining energy-intensive industries overcapacity for flexible electricity use in decarbonized energy systems. We find that overcapacity can enable aluminum smelters to adopt a seasonal operation paradigm, ceasing production during winter load peaks that are exacerbated by heating electrification and renewable seasonality. This seasonal operation paradigm could reduce the investment and operational costs of China's decarbonized electricity system by 23-32 billion CNY/year (11-15% of the aluminum smelting industry's product value), sufficient to offset the increased smelter maintenance and product storage costs associated with overcapacity. It may also provide an opportunity for seasonally complementary labor deployment across the aluminum smelting and thermal power generation sectors, offering a potential pathway for mitigating socio-economic disruptions caused by industrial restructuring and energy decarbonization.
A key challenge in combinatorial auctions is designing bid formats that accurately capture agents' preferences while remaining computationally feasible. This is especially true for electricity auctions, where complex preferences complicate straightforward solutions. In this context, we examine the XOR package bid, the default choice in combinatorial auctions and adopted in European day-ahead and intraday auctions under the name "exclusive group of block bids". Unlike parametric bid formats often employed in US power auctions, XOR package bids are technology-agnostic, making them particularly suitable for emerging demand-side participants. However, the challenge with package bids is that auctioneers must limit their number to maintain computational feasibility. As a result, agents are constrained in expressing their preferences, potentially lowering their surplus and reducing overall welfare. To address this issue, we propose decision support algorithms that optimize package bid selection, evaluate welfare losses resulting from bid limits, and explore alternative bid formats. In our analysis, we leverage the fact that electricity prices are often fairly predictable and, at least in European auctions, tend to approximate equilibrium prices reasonably well. Our findings offer actionable insights for both auctioneers and bidders.
The uptake of battery electric vehicles (BEVs) is increasing to reduce greenhouse gas emissions in the transport sector. The rapid adoption of BEVs depends significantly on the coordinated charging/discharging infrastructure. Without it, uncontrolled and erratic charging patterns could lead to increased power losses and voltage fluctuations beyond acceptable thresholds. BEV charge scheduling presents a multi-objective optimization (MOO) challenge, demanding a balance between minimizing network impact and maximizing the benefits for electric vehicle charging station (EVCS) operators and BEV owners. In this paper, we develop an MOO framework incorporating a carbon emission program and a dynamic economic dispatch problem, allowing BEV users to respond by charging and discharging through grid-to-vehicle (G2V) and vehicle-to-grid (V2G) technologies according to the optimal electricity price and compensation. Furthermore, we integrate dynamic economic dispatch with time-of-use tariffs to obtain optimal market electricity prices and reduce total costs over 24 hours. Our experimental results on a sample network show that the proposed scheduling increases participation in V2G services by over 10%, increases EVCS benefits by over 20%, and reduces network losses. Furthermore, increased rates of charging/discharging, coupled with more significant carbon revenue benefits for BEV users and EVCS, contribute to better offsetting battery degradation costs.
The rapid growth of distributed energy resources (DERs), including rooftop solar and energy storage, is transforming the grid edge, where distributed technologies and customer-side systems increasingly interact with the broader power grid. DER aggregators, entities that coordinate and optimize the actions of many small-scale DERs, play a key role in this transformation. This paper presents a hybrid Mean-Field Control (MFC) and Mean-Field Game (MFG) framework for integrating DER aggregators into wholesale electricity markets. Unlike traditional approaches that treat market prices as exogenous, our model captures the feedback between aggregators' strategies and locational marginal prices (LMPs) of electricity. The MFC component optimizes DER operations within each aggregator, while the MFG models strategic interactions among multiple aggregators. To account for various uncertainties, we incorporate reinforcement learning (RL), which allows aggregators to learn optimal bidding strategies in dynamic market conditions. We prove the existence and uniqueness of a mean-field equilibrium and validate the framework through a case study of the Oahu Island power system. Results show that our approach reduces price volatility and improves market efficiency, offering a scalable and decentralized solution for DER integration in wholesale markets.
Alternatives to traditional fossil-based energy generation are required to combat climate change and air pollution. Solar power has become increasingly appealing due to its infinite supply, ability to mitigate climate change, and non-polluting nature. The semi-transparent photovoltaics have a portion of the cell that allows light to pass through while the rest of the cell generates electricity. Energy analysis on semi-transparent photovoltaic is needed to determine their performance. The studied semi-transparent photovoltaic systems consist of ten modules combined in one panel and two separate panels. The system consists of 2x10 pieces 165 Wp Solarwatt Vision modules with 3,3 kWp capacity. The installation site’s location is the latitude 47.5946° N, 19.3619° E. The energy production of semi-transparent photovoltaics: The highest energy production per year was found in 2018 with a value of 3.18 MWh, followed by 2019, 2021, 2020, and 2017 with values of 2.76 MWh, 2.5 MWh, 2.31 MWh, and 1.68 MWh, respectively. The highest monthly energy production is found in April, May, June, and July, with values of 445618 Wh, 459812 Wh, 442955 Wh, and 496671 Wh, respectively. The further plan is to study PAR components under the modules.
The current state of intelligent target recognition approaches for Synthetic Aperture Radar (SAR) continues to experience challenges owing to their limited robustness, generalizability, and interpretability. Currently, research focuses on comprehending the microwave properties of SAR targets and integrating them with advanced deep learning algorithms to achieve effective and resilient SAR target recognition. The computational complexity of SAR target characteristic-inversion approaches is often considerable, rendering their integration with deep neural networks for achieving real-time predictions in an end-to-end manner challenging. To facilitate the utilization of the physical properties of SAR targets in intelligent recognition tasks, advancing the development of microwave physical property sensing technologies that are efficient, intelligent, and interpretable is imperative. This paper focuses on the nonstationary nature of high-resolution SAR targets and proposes an improved intelligent approach for analyzing target characteristics using time-frequency analysis. This method enhances the processing flow and calculation efficiency, making it more suitable for SAR targets. It is integrated with a deep neural network for SAR target recognition to achieve consistent performance improvement. The proposed approach exhibits robust generalization capabilities and notable computing efficiency, enabling the acquisition of classification outcomes of the SAR target characteristics that are readily interpretable from a physical standpoint. The enhancement in the performance of the target recognition algorithm is comparable to that achieved by the attribute scattering center model.