Search Results (7,514)

This paper presents two forecast-driven energy trading methodologies for a grid-connected photovoltaic-battery system participating in the day-ahead electricity market. Both methodologies use bidirectional long short-term memory neural networks with attention to forecast electricity prices, but they differ in the way the resulting forecasts are converted into operational decisions. The first method uses 24- to 48 h-ahead price forecasts within a mixed-integer linear programming rolling-horizon optimizer to compute the revenue-maximizing schedule for the following day. The second method uses an online twin delayed deep deterministic policy gradient controller that outputs a complete 24 h charge–discharge schedule once per day, using state information that includes battery state, recent price history, forecast prices, and forecast photovoltaic production. The control models are trained using historical data from 2019 to 2022, validated chronologically on 2023 data, and tested on the 2024 annual horizon, while the price forecaster is trained and validated on non-2024 data and evaluated on the held-out 2024 test period. In the realistic execution setting, schedules are planned using forecast photovoltaic production and implemented against actual photovoltaic production, while the day-ahead omniscience benchmark uses actual next-day prices and actual PV production as ideal scheduling inputs. The BiLSTM-MILP framework achieves EUR 10,928.7 over the 2024 test horizon, corresponding to 82.67% of the day-ahead omniscience benchmark. The online BiLSTM-TD3 controller achieves EUR 10,884.9, corresponding to 82.34% of the same benchmark and 99.60% of the BiLSTM-MILP revenue, while outperforming a rule-based baseline by 34.9%. These results show that online deep reinforcement learning can approach the performance of explicit mathematical optimization in day-ahead PV-battery trading while substantially improving over simple rule-based operation. Overall, the results indicate that BiLSTM-based forecasts can support both optimization-based and reinforcement-learning-based day-ahead control for the examined PV-battery system. Full article

The emergence of citizen energy communities under the European Clean Energy Package is creating new opportunities for neighboring households to collectively reduce electricity costs through local energy sharing. This paper presents a distributed multi-agent energy management system for a two-household residential energy community in which each household is equipped with photovoltaic generation and a battery energy storage system operating under realistic hourly-varying electricity prices. Each household is managed by an independent Deep Q-Learning agent that learns a cost-optimal charging and discharging policy using only local observations. In parallel, a coordination agent, implemented on the SPADE platform with XMPP-based messaging, oversees real-time peer-to-peer energy transfers between households, enabling energy exchange whenever one household has surplus generation and another faces a deficit. The two households are deliberately configured with complementary profiles: one has higher PV generation capacity while the other has higher energy consumption. This setup creates natural opportunities for local energy sharing between them. Performance is assessed through a three-level evaluation framework: (i) individual household economics (cost reduction, battery management, grid exchanges), (ii) coordination efficiency (transfer frequency, direction, and volume), and (iii) aggregate community performance, which isolates the added value of peer-to-peer sharing beyond what each household achieves through individual BESS optimization. Numerical experiments using GEFCom2014 solar generation data, synthetic residential load profiles calibrated following documented consumption patterns, and day-ahead price signals representative of the Spanish electricity market demonstrate that both Deep Q-Learning agents independently learn effective charge/discharge strategies aligned with price signals and PV availability. They also show that the coordination layer further reduces community grid dependence by routing surplus energy locally rather than exchanging it with the main grid at less favorable rates. The results confirm that a well-engineered integration of decentralized reinforcement learning with a lightweight coordination protocol can deliver measurable economic benefits in realistic residential energy communities without requiring centralized training, shared data, or complex multi-agent reinforcement learning architectures. Full article

The dominance of inexpensive ferrites and high-performance rare-earth-based magnets on the global market causes a significant performance gap between these materials. Fe${}_2$P-based materials are promising rare-earth-free candidates to bridge this gap, offering high magnetization and uniaxial anisotropy. In this study, density functional theory was employed to systematically analyze the influence of Si and Co substitution on the phase stabilities of such Fe${}_{2-y}$Co${}_y$P${}_{1-x}$Si${}_x$ compounds. At 0 K, Si substitution destabilizes the compounds; however, this trend is reversed at elevated temperatures, where Si significantly enhances phase stability. In contrast, Co substitution reduces competition energies at 0 K but promotes instability with increasing temperature. For quaternary Fe${}_{2-y}$Co${}_y$P${}_{1-x}$Si${}_x$ compounds, the combined presence of Si and Co leads to a pronounced expansion of the stability range of the hexagonal crystal structure, in reasonable agreement with available experimental observations. Starting from temperatures above 1000 K, several quaternary compounds exhibit negative competition energies, indicating thermodynamic stability. Among all investigated compositions, Fe${}_{1.84}$Co${}_{0.16}$P${}_{0.84}$Si${}_{0.16}$ stands out, combining particularly low competition energies with a previously reported mean-field Curie temperature of 557 K and a high magnetic hardness factor. These results identify Fe${}_{1.84}$Co${}_{0.16}$P${}_{0.84}$Si${}_{0.16}$ as a highly promising rare-earth-lean hard magnetic material for future applications. Full article

We address the growing urgency of climate action and China’s pivotal role in advancing carbon capture, utilization, and storage (CCUS) toward its “dual carbon” goals. This study examines factors influencing CCUS development in China using structural equation modeling (SEM), identifying five critical determinants: resources, environment, market, technology, and government–society dimensions. Empirical data from expert surveys underscore the need for integrated policy measures, including fiscal incentives, standardized evaluation, international cooperation, and energy infrastructure upgrades. The findings enable effective decarbonization and provide a transferable framework for emerging economies. Full article

The sustainable transition of power systems is currently hindered by fragmented carbon pricing systems and insufficient cross-market synergies. Considering this, we herein construct a system dynamics model of carbon tax regulation under conditions integrating electricity markets, carbon emission trading (CET) markets, and tradable green certificate (TGC) markets using Vensim PLE 7.3.5 software. We also propose a price-matching mechanism and implementation pathway for carbon taxation and CET to advance low-carbon sustainable development. The simulation results show that the introduction of a carbon tax at an initial rate of 50 CNY per ton significantly improves renewable energy investment returns. Moreover, effective coordination between the carbon tax and CET reduces carbon emissions from the power system, delivering benefits in terms of both environmental and socio-economic sustainability. We further identify a dynamic coordination scheme consisting of a carbon tax with an initial rate of 50 CNY per ton, which is appropriate when the CET prices stabilize at approximately 60 CNY per ton. An initial rate of 30 CNY per ton is more suitable when the CET prices rise above 100 CNY per ton. These findings verify the optimal matching rules for carbon tax intensity under different carbon allowance price levels, and they also provide quantitative policy tools and empirical support for the scenario-based regulation of carbon pricing systems to achieve sustainable energy transition goals. Full article

The rapid expansion of solar photovoltaics is intensifying competition for land and highlighting the need for scalable energy solutions that can be integrated into existing power systems without displacing agricultural activity. Once the technical and agronomic viability of agrivoltaic configurations has been demonstrated at field scale, a critical next step toward their market consolidation is the assessment of their deployment potential at regional scales from an energy systems and grid integration perspective. This study presents a GIS-based framework to evaluate the large-scale implementation of vertically integrated agrivoltaic systems, using vineyard landscapes in the Region of Murcia (southeastern Spain) as a representative case study. The analysis combines high-resolution land-use data, crop distribution, regulatory constraints on grid connection distances, and existing electrical infrastructure to quantify installable capacity, energy production, self-consumption potential, and grid accessibility. Results indicate that vertically mounted bifacial PV systems could reach up to 7.06 GWp, generating approximately 11.84 TWh/year, while revealing a pronounced spatial mismatch between optimal agrivoltaic production sites and current grid connection points. This distance-dependent distribution highlights the need for differentiated deployment strategies, balancing local self-consumption, grid reinforcement, and centralized injection. Beyond the specific case examined, the proposed approach provides a transferable framework for energy system planning, supporting grid-aware agrivoltaic deployment in diverse regions and regulatory contexts. Full article

The increasing deployment of distributed energy storage systems (ESSs) presents new opportunities to enhance power system flexibility and enable innovative market participation models. However, many small-scale energy storage system assets remain underutilized due to fragmented ownership, uncertainty in market prices and revenue opportunities, as well as regulatory and operational constraints, and heterogeneous decision making behaviors. To address these challenges, this paper proposes an enhanced energy storage as a service (ESaaS) framework that enables distributed ESS owners to lease idle storage capacity to a centralized platform for coordinated participation in multiple grid support services. The proposed platform aggregates the distributed ESS capacity and allocates it across several value streams. Unlike conventional approaches that assume fully rational agents, this work incorporates behavioral decision making dynamics using prospect theory (PT), which captures loss aversion, asymmetric risk perception, and the subjective valuation of uncertain outcomes. The interaction between the ESaaS operator and ESS owners is formulated as a bi-level optimization problem, where the upper level determines leasing prices and operational strategies across multiple services while the lower-level models ESS owner participation decisions. Prospect theory is integrated at both decision layers to capture the behavioral preferences of the ESaaS operator and ESS owners under uncertainty. The resulting mixed-integer bi-level model is solved using a modified reformulation-and-decomposition approach that incorporates a nested column-and-constraint generation (NC&CG) method to ensure computational tractability. Numerical studies demonstrate that behavioral decision modeling significantly influences pricing strategies and the overall profitability of both the ESaaS platform and the participating energy storage system owners. Full article

Large Language Models (LLMs) offer opportunities for processing heterogeneous information streams relevant to energy-market decision-making, but their practical role in forecasting-oriented analytical workflows remains uncertain. This paper presents an exploratory feasibility study of LLM use across four energy-market tasks: geopolitical event monitoring for Dutch Title Transfer Facility (TTF) market context using Global Database of Events, Language, and Tone (GDELT)-based data, structured summarisation of commodity-intelligence articles, prompt-engineered solar-power and grid-load forecasting for Austria, and a short-horizon exploratory TTF price-estimation case. The study is positioned as a pilot investigation and hybrid workflow blueprint rather than as a statistically conclusive forecasting benchmark. A four-layer reference architecture was devised, including structured market data, semi-structured news intelligence, web-scraping concepts, and implemented Twitter/X and GDELT monitoring layers. The empirical cases indicate that LLMs are most useful for text-heavy reasoning, event-context integration, source triage, and structured interpretation. In the 20-article summarisation corpus, Gemini 1.5 Pro achieved higher commodity-direction accuracy than GPT-4, while GPT-4 showed stronger output-format stability. In selected solar case checks, OpenAI models produced plausible generation curves close to the Fraunhofer ISE Energy Charts reference, while Energy Charts remained more accurate for aggregate load estimation in the available benchmark comparison. The two-day TTF experiment illustrated that LLMs can incorporate qualitative geopolitical context into short-horizon reasoning, but it did not establish reliable price-forecasting capability. The Twitter/X monitoring layer is retained as a documented negative pathway, showing the limitations of informal social-media scraping for reproducible market intelligence. Full article

Post-disaster reconstruction programmes create an irreversible window for embedding or foreclosing residential energy efficiency at scale. This study examines the structural determinants of per capita residential electricity consumption (K_MES) across all 81 provinces of Türkiye over 2013–2022 using a balanced province-year panel. We develop two complementary panel models, both estimated by two-way fixed effects (province + year) with cluster-robust standard errors, and supported by GLS-AR(1) and random-effects GLS robustness checks. Note that K_MES measures the electricity component of residential energy use only; we, therefore, also estimate the building-stock model with a constructed total-energy dependent variable that combines residential electricity (H_MES) and natural-gas consumption (X_DG) in kWh-equivalent units. Model 1 isolates the macroeconomic transmission channel through which exchange-rate volatility shapes residential electricity demand. Because the USD/TRY rate has no cross-sectional variation, its identifying power in two-way fixed effects comes from its interaction with province-level natural-gas-heating exposure (sh_gas × EV_DA). The interaction is robustly negative across all full-sample specifications (β ≈ −0.022, p < 0.01), indicating that provinces with greater gas-heating penetration are buffered against currency-depreciation pass-through into electricity demand. Provincial GDP carries the dominant direct macro coefficient (β ≈ 0.27–0.29, p < 0.01), establishing income elasticity rather than the exchange rate as the headline aggregate driver. Model 2 decomposes the building stock by structural system, filler material, heating system, and heating fuel. The dominant predictors are the share of electric heating (β ≈ 1.16–1.27, p < 0.01) and the share of AC-only heating (β ≈ −1.0 to −1.13, p < 0.05), with a total-energy specification reaching R² = 0.92. In the comparative subsample of the eleven Kahramanmaraş-affected provinces, masonry construction emerges as the dominant pre-disaster predictor of per capita electricity consumption (β = 14.04, p < 0.05), revealing structurally distinct stock characteristics that pre-date the February 2023 earthquake. Two re-framings are required. First, since the panel covers 2013–2022, the disaster-province estimates capture pre-disaster structural heterogeneity rather than post-disaster market rupture. Second, the macroeconomic mechanism that prior work attributed to the exchange-rate level is more accurately understood as a fuel-mix-mediated exposure channel. The combined evidence implies that mandatory building-code enforcement and natural-gas grid extension are complementary policy levers in the 488,000-unit Turkish Housing Development Administration reconstruction programme: gas grid expansion reduces the macroeconomic vulnerability of residential energy demand, while masonry-replacement construction standards address the largest pre-disaster structural determinant of energy intensity in the affected region. Full article

Public data openness (PDO) is critical for advancing digital government initiatives and sustainable development. This study investigates the impact and underlying mechanisms of PDO on carbon emission efficiency (CEE) using a staggered difference-in-differences (DID) approach. The results reveal that the PDO significantly improves CEE. Mechanism analysis demonstrates that PDO enhances CEE by facilitating digital technology innovation, improving capacity utilization, and fostering industrial structure upgrading. The positive effect of PDO on CEE exhibits heterogeneity across the dimensions of data themes, human capital, green finance development, and land marketization. Furthermore, the Broadband China Strategy (BCS) and the New Energy Demonstration City (NEDC) policy amplify PDO’s positive effect on CEE. This study quantitatively evaluates the economic and environmental effects of data resource openness and sharing, offering insights into deepening data infrastructure development and unleashing data’s potential to promote sustainable development. Full article

The geographic and economic contexts play a major role in decision-making when it comes to municipal solid waste management. In the present study, simulations are carried out using the Waste and Resource Assessment Tool for the Environment (WRATE) software academic version 3.0.1, based on the Ecoinvent database (version 2) to assess the greenhouse gas emissions released by 1 ton of municipal solid waste with a typical composition characterizing upper middle-income countries, with an organic fraction of approximately 50% by weight. The variation over time (2000 to 2022) with no intended transformation in the management strategy is first analyzed, then several transformations are applied by varying the waste management routes (open dumping, landfilling, recycling and composting) as well as the energy recovery integration. The results are then discussed based on the waste categories and the performed operations (landfilling, recycling, transportation, treatment and recovery). The results revealed that the most promising scenario includes limited open dumping that does not exceed 10%, landfilling with at least 20% energy recovery, and major fractions addressed to composting and recycling. Overall, this scenario returns a negative carbon footprint with a value of approximately−0.35 tons of CO₂-Eq/ton of MSW. Results are mostly applicable to countries with similar waste composition and infrastructure levels; preconditions include source segregation, compost markets, and landfill gas infrastructure. Full article

Background: Blockchain and distributed ledger technologies are increasingly proposed to strengthen traceability, governance, visibility, and coordination in mining and mineral supply chains, but mining-specific evidence remains fragmented. Methods: We conducted a systematic mapping review of peer-reviewed articles indexed in Scopus and Web of Science to examine application contexts, functional roles, technical architectures, evidence types, and adoption constraints of blockchain-enabled systems in these settings. Results: The review shows that blockchain is used across five functional domains: traceability and provenance; governance and secure data control; operational monitoring and inspection; energy and market coordination; and sustainability and environmental surveillance. Permissioned and consortium-based architectures predominated and were commonly combined with sensors, external storage, identity mechanisms, and smart contracts. Evidence was strongest for technical feasibility under simulated, experimental, comparative, or bounded pilot conditions, whereas durable economic, social, and governance outcomes remained less substantiated. Conclusions: Blockchain is most credible in mining contexts when it supports controlled coordination, auditable recordkeeping, and process integrity. Its practical value depends on reliable physical-to-digital data capture, workable governance arrangements, interoperability, and validation under real institutional and operational conditions. Full article

Modern power systems are undergoing a structural transformation driven by the rapid integration of renewable energy sources, distributed energy resources, electrification, and increasing operational uncertainty. These developments expose the limitations of traditional centralized energy management and rule-based automation in highly distributed, data-intensive, and dynamically coupled energy infrastructures. In response, recent advances in artificial intelligence offer new opportunities for improving prediction, coordination, and adaptive control. This paper develops a reference architecture for Autonomous Energy Systems based on the integration of generative AI, agentic AI, digital twins, and distributed cyber–physical energy infrastructures. Rather than treating forecasting, control, simulation, and market coordination as separate research tracks, the paper organizes them within a common architectural perspective. Generative AI is positioned as a source of scenario intelligence, synthetic data generation, and uncertainty-aware forecasting, while agentic AI is framed as a bounded decision layer for perception, reasoning, planning, and coordinated action under operational constraints. The paper further clarifies the distinction between agentic AI, conventional multi-agent systems, and multi-agent reinforcement learning in energy applications. Representative application domains are discussed, including self-healing power grids, autonomous energy markets, and digital twin training environments. Major open challenges are identified in relation to scalability, physical consistency, safety verification, sim-to-real transfer, cybersecurity, interoperability with legacy infrastructures, and governance. The paper concludes by outlining a research agenda for the staged and safe development of increasingly autonomous energy systems. Full article

Electric vehicle (EV) adoption is rising rapidly, creating growing challenges for charging infrastructure planning, energy demand management, and grid stability. However, most existing studies rely on single-domain data, such as behavioral charging sessions or station metadata, which limits their ability to capture the joint effects of user behavior, charger characteristics, and market context. To address this gap, this study proposes a multi-domain machine learning framework for EV charger-type prediction by integrating behavioral, infrastructure, and market-level data. Behavioral charging logs are transformed into structured event-token sequences and modeled using XLM-RoBERTa (Cross-lingual Language Model–RoBERTa), which is used here as a transformer-based sequence encoder to capture long-range dependencies in charging behavior. Structured infrastructure and market features are modeled using LightGBM and TabNet. The study contributes a unified multi-domain framework, a systematic comparison of transformer and tabular-learning models, and a broader evaluation through ablation analysis, cross-validation, confusion matrix analysis, and confidence calibration. The results show that multi-domain fusion consistently improves performance over single-domain learning. XLM-RoBERTa achieved the best overall performance on the fused dataset, with 98.76% accuracy and 97.86% weighted F1-score, while TabNet demonstrated stronger calibration and deployment reliability. Full article

To address the output volatility of distributed photovoltaics, the low utilization efficiency of energy storage resources, and the challenge of optimal revenue for PV-storage virtual power plants (VPPs) in multi-market environments, this paper proposes a three-stage stochastic optimal operation strategy for PV-storage VPPs under multi-market synergy and develops a benefit allocation model based on the Nash–Harsanyi bargaining game. A Monte Carlo simulation was adopted to capture the uncertainties of market electricity prices and PV power output, and the stochastic dual-dynamic-programming (SDDP) algorithm was employed to solve the three-stage optimization framework consisting of day-ahead bidding, real-time optimization, and real-time frequency regulation. Bargaining power was quantified from four dimensions—the marginal contribution rate, PV prediction accuracy, energy storage capacity, and utilization rate—to establish a fair and reasonable internal benefit allocation mechanism. Case studies verified that the proposed method improved the single-day market revenue by up to 20.79% compared with traditional operation modes, achieved a near-zero curtailment rate for distributed PV, and maintained frequency regulation performance scores above 0.4 at all times. The benefits of all investment entities in the alliance increased by 3.36–99.43%, significantly enhancing the multi-market profitability of PV-storage VPPs and the stability of alliance cooperation. Full article