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Smart meters generate high-resolution data that can feed forecasting models for a range of distribution network applications. However, the literature remains fragmented across forecasting methods, data structures, and operational use cases. No comprehensive taxonomy links forecasting model characteristics to the distinct requirements of grid flexibility, microgrid management, and congestion control. This limits the ability of researchers and system operators to choose models according to data availability, forecast horizon, and grid function. This article develops a taxonomy of smart-meter forecasting models for distribution-level operation. The taxonomy maps model categories, data inputs, forecast horizons, and application domains into a structured classification framework. A systematic literature review of 27 peer-reviewed journal articles published from 2017 to 2024 was conducted. Models were classified by mathematical structure, data requirements, forecast horizon, and relevance to grid flexibility, microgrid energy management, and congestion control. The taxonomy organises forecasting approaches into classical time series, conventional machine learning, deep learning, hybrid and ensemble, and probabilistic forecasting categories. Six tables support the review method, model classification, smart-meter data mapping, application suitability analysis, full taxonomy, and research gap assessment. The taxonomy provides a structured decision-support tool for selecting and designing forecasting models for specific distribution grid tasks. It highlights gaps in cross-domain transferability, real-time deployment, benchmarking, data integration, and operational validation.

Microgrids need real-time energy management that integrates accurate load and solar forecasts with efficient battery dispatch while preserving battery lifetime. Forecast uncertainty can increase unnecessary grid imports, battery cycling, and curtailment when operational decisions rely on single-source prediction. This paper proposes a real-time forecasting and dispatch framework combining a hybrid load–solar prediction model with a degradation-aware battery scheduling strategy. The objective is to improve short-horizon operational decisions while reducing avoidable battery ageing. A hybrid forecasting model uses distinct methods for load and solar components. The dispatch strategy minimises operational cost while incorporating a degradation penalty derived from a cycle-life model. The framework is tested on representative microgrid data against persistence, statistical, and non-degradation dispatch benchmarks. The hybrid forecaster reduces prediction error compared to single-model approaches, and the degradation-aware dispatch extends estimated battery life by 8.6% while maintaining near-optimal economic performance. The framework also reduces unnecessary charge–discharge reversals during periods of volatile photovoltaic output. The integrated real-time system offers a practical, computationally light solution for microgrid operators, balancing cost, accuracy, and battery health. Its design supports deployment in campus, community, and commercial microgrids with limited computational infrastructure.

Foundation models are transforming artificial intelligence by enabling pre-training, adaptation, and reuse across heterogeneous tasks. Their application in energy systems is emerging rapidly, creating a need for systematic synthesis across forecasting, control, and decision-support settings. This systematic review identifies, categorizes, and evaluates the use of foundation models in energy-system forecasting, grid-edge control, and smart-grid decision support. It focuses on how large language models, pre-trained transformers, multi-modal models, and emerging time-series foundation models are adapted for energy tasks. A PRISMA-guided search of peer-reviewed literature from 2017 to 2025 resulted in 30 included studies. Models were classified by architecture, fine-tuning method, data modality, and application domain. The review finds that large language models dominate decision-support applications, while pre-trained transformers and time-series foundation models are most developed in forecasting. Grid-edge control applications remain nascent, with most studies addressing planning, diagnostics, or dispatch rather than closed-loop physical deployment. Foundation models hold significant promise for smart grids, but critical gaps remain in real-time control, safety assurance, privacy, and evaluation. A research agenda is proposed to guide rigorous, trustworthy, and operationally relevant deployment.

Transactive energy and peer-to-peer trading are key enablers of distributed energy markets. Optimization models drive these systems, but a systematic taxonomy linking models to prosumer flexibility, EV coordination, and grid constraints is lacking. This article develops a taxonomy of transactive energy and P2P trading optimization models. It classifies models according to their treatment of prosumer flexibility, EV integration, and local network constraints. A structured literature review of 27 articles published between 2017 and 2025 was conducted. Models were categorized across market, optimization, flexibility, EV, and network-integration dimensions. The taxonomy organizes models by market mechanism, optimization method, and depth of grid and flexibility integration. It reveals significant gaps in holistic multi-vector coordination. The taxonomy provides a systematic framework for comparing optimization models in local energy markets. It also identifies research needs for integrated, grid-aware, flexibility-oriented transactive systems.

Smart-grid optimization increasingly uses artificial intelligence, automated control, and data-driven market coordination to manage low-carbon electricity systems. These developments create new opportunities for flexibility, but they also raise unresolved questions about equity, accountability, and justice. Existing reviews often examine energy justice, smart-grid optimization, algorithmic governance, and flexibility markets as separate domains. This fragmentation limits understanding of how optimization models and market rules may reproduce or reduce social inequalities. This article critically reviews the literature on justice-aware smart-grid optimization and develops a structured future research agenda. It focuses on how energy equity, algorithmic governance, and low-carbon flexibility markets can be integrated into future smart-grid research and policy. A targeted review of 26 peer-reviewed articles published between 2017 and 2025 integrates perspectives from energy justice, smart-grid studies, AI governance, and electricity market design. The review identifies conceptual tensions, methodological gaps, and governance challenges rather than presenting new empirical data. The review shows that justice is often operationalized narrowly in optimization, while broader concerns about recognition, participation, and accountability remain underdeveloped. It also finds that flexibility markets can distribute benefits unevenly unless market design explicitly addresses barriers to access and agency. A more interdisciplinary and justice-centered approach is needed to guide smart-grid optimization. The proposed agenda calls for stronger integration between technical modeling, energy justice theory, participatory governance, and regulatory innovation.

Peer-to-peer solar trading can empower prosumers but poses congestion risks for distribution networks. A bilevel framework that separates market functions from network oversight can enable secure, efficient transactive energy. This article proposes a conceptual bilevel transactive energy framework that coordinates P2P solar trading, EV charging flexibility, and active congestion management. The framework is intended as an architectural contribution rather than a mathematical optimization model. The framework is designed conceptually, with an upper market layer and a lower network operation layer. Information flows, decision logics, and validation pathways are described without mathematical formulation. The framework defines clear roles for market participants and the DSO, integrates EV flexibility as a congestion-mitigation resource, and includes a feedback mechanism from network to market. Five tables summarize the architecture, component functions, and validation plan. The proposed conceptual architecture can guide future software implementations, regulatory sandbox designs, and field pilots. It bridges the gap between market innovation and grid reliability.

Reaching carbon neutrality by 2050 will require transmission system operators to accommodate unprecedented levels of variable solar and wind generation while preserving system reliability. To address this systemic constraint, Réseau de Transport d’Électricité (RTE), together with the research community, introduced Learn To Run A Power Network (L2RPN), a crowd-sourced competition designed to accelerate the integration of intermittent renewable sources into grid operations. L2RPN relies on 16 years of weekly scenarios (832 in total) modeled on a 118-node network under realistic operational constraints, framing real-time grid management as a Markov Decision Process. The six competing teams approached the problem by designing autonomous agents that combined heuristics, optimization, data scaling, supervised learning, and reinforcement learning techniques. We deliver a comprehensive assessment of all six teams’ performances under the competition’s stringent framework. Furthermore, we conduct an extensive examination of the publicly released winning solution, which ensures consistent decision-making across scenarios, performs real-time multimodal actions in less than 5 seconds, and achieves efficient topology control through action-space reduction and a neural policy that predicts beneficial grid actions with over 80% accuracy. Concurrently, we trained a neural alert module using 315,000 samples generated from leading agents, obtaining 93.9% recall in identifying hazardous states and enabling agents to anticipate potential failures. Ultimately, this study highlights both the potential and current limitations of AI in real-time grid operation, while establishing a transparent foundation for developing more robust and trustworthy systems to support the energy transition.

The rising priority placed on energy conservation, together with the adoption of renewable sources, has grown essential to sustainable practices across today’s university campuses. This research introduces an intelligent green energy management framework (SGEMS) that integrates machine learning predictions with reinforcement learning (RL) to enhance energy utilization and photovoltaic output on an eco-conscious campus. Leveraging historical records from three campus structures, the study developed a forecasting engine capable of projecting near-term electricity demand and solar yield. When paired with RL, the XGBoost method achieved markedly better accuracy in estimating consumption and production levels than competing algorithms, with root mean square errors (RMSEs) of 14.72, mean absolute errors (MAEs) of 12.00, and mean absolute percentage errors (MAPE) of 2.18%. The work also describes an accessible, browser-based dashboard that enables live energy oversight and supports timely decision-making, empowering facility managers to anticipate deficits and regulate consumption more intelligently. Overall, the presented methodology provides an adaptable blueprint that enables campuses to reduce dependence on utility grids, improve efficiency, and serve as a reference model for subsequent eco-friendly campus programs.

Recent advances in residential automation technologies and the widespread adoption of distributed energy resources (DERs) have dramatically amplified the need for sophisticated energy management strategies that harmonize operational reliability with cost-effectiveness in smart residential microgrids (SRMG). When smart homes fail to coordinate effectively or exchange energy, irregularities in the SRMG load profile emerge, potentially breaching critical technical thresholds. This work presents a fully decentralized architecture for SRMG that accommodates heterogeneous smart homes that actively participate in peer-to-peer (P2P) energy trading. This architecture targets the simultaneous minimization of load profile variability across the microgrid, reduction of household electricity bills, and preservation of occupant comfort levels enabled by P2P exchanges. At the household level, the home energy management (HEM) system is engineered to curtail overall energy expenditure by leveraging available DER capacity to enable P2P transactions and maintain continuous bidirectional links with the SRMG operator (SRMGO). Real-time, ongoing data interchange between SRMGO and HEM units proves indispensable for achieving superior load-profile smoothing in a decentralized setting. The proposed mechanism achieves roughly a 4.25% decrease in load-profile deviations while keeping energy costs unchanged, thereby confirming that decentralized P2P energy exchanges markedly improve both microgrid load balancing and household expenditure stability. Simulations conducted with the general algebraic modeling system (GAMS) software confirm that incorporating P2P energy mechanisms within a decentralized structure satisfactorily meets the SRMG’s technical constraints and the monetary priorities of participating smart homes.

Microgrids provide an effective strategy for delivering energy while keeping expenses to a minimum. Within residential microgrids, individuals with diverse personality traits can collaborate in multiple ways. Additionally, the distinct decision styles arising from these personality differences can have a substantial impact on the success of their joint activities. The present study explores patterns of collaboration among neighbors with a range of personality traits within residential microgrids that form part of a smart-city ecosystem, thereby supporting energy exchange and reducing the total cost of energy provision. The methodology introduced in this work accounts for uncertainties arising from social conduct and organizes joint action through a game-theoretic structure. That structure works toward achieving an efficient and fair division of energy across participating neighbors, simultaneously driving down expenditures, encouraging cooperative behavior, and applying disincentives for non-cooperative conduct in line with each person’s behavioral profile. The suggested technique was tested on a residential benchmark microgrid using the GAMS CPLEX optimizer; two contrasting cases were examined to evaluate how energy storage affects trading decisions. By establishing mean values for discount and penalty rates, the method becomes better suited to diverse microgrid topologies. The analysis further addresses the tension between the utility company’s prices and the most advantageous rates from the neighbors’ perspective. Simulation results indicate that collaborative behavior enables neighbors to meet surplus requirements and can reduce the microgrid’s aggregate operating costs by up to 80%.

Each server in data centers contains multiple fans and various heat-generating components. Factors such as fan speeds, component layout, and workload conditions affect airflow and temperature distribution within the server. Consequently, server thermal management is regarded as a Multi-Input Multi-Output (MIMO) nonlinear control system that cannot be effectively analyzed or controlled using conventional linear system techniques. This study implements a multi-fan thermal control approach for servers by applying a weighting mechanism that assigns distinct weights to each cooling fan, enabling asynchronous adjustment of multiple fans. This is combined with the Evolutionary Strategy (ES) algorithm and carefully designed fitness functions. The proposed system can identify near-optimal weighting combinations within a small number of search iterations, tailored to various workload scenarios. By asynchronously adjusting the fans according to these weightings, the system achieves near-optimal energy efficiency while still satisfying the server’s thermal requirements—specifically, the maximum allowable temperatures for the CPU and PCIe components. Compared with traditional synchronous fan modulation, this asynchronous multi-fan control method reduces the total fan power consumption by an average of 43.1%. It requires searching only 14.2% of all possible weighting combinations across the eight experimental workload scenarios. Moreover, the probability that each identified near-optimal weighting corresponds to the global optimal solution is 47.5%. Experimental results confirm that the proposed asynchronous multi-fan control system successfully meets thermal specifications while delivering near-optimal energy savings for the server. Therefore, the developed approach proves practical and highly effective for substantial energy reduction, without requiring the development of mathematical thermal models or the analysis of extensive datasets. In the future, by fine-tuning the system’s code parameters, it could potentially be adapted to different server architectures. 

The integration of hydrokinetic turbine (HKT) systems relies heavily on power converters, which enable precise control of turbine rotational speed to regulate active power delivered to the grid while providing reactive power compensation. This paper introduces an advanced model predictive control (MPC) strategy designed for maximum power point tracking and output frequency regulation in an HKT system interfaced through a back-to-back converter. The setup comprises a horizontal propeller turbine, a permanent magnet synchronous generator, a rectifier, and an inverter. The proposed control scheme was implemented in MATLAB and validated via simulations, with performance assessed through the system’s state variables. A comparison with a conventional proportional-integral (PI) control method demonstrates that MPC delivers superior transient performance and shorter settling times for high-efficiency operation, while remaining straightforward to implement. Specifically, the MPC controller exhibited a settling time that was 4 times faster than that of the PI controller. 

Precise prediction of load profiles—most notably the accurate identification of peak demand—remains difficult owing to the highly variable and unpredictable patterns of energy use. This work evaluates three forecasting approaches: Long Short-Term Memory (LSTM), Support Vector Regression (SVR), and a hybrid ensemble combining SVR, Gated Recurrent Units (GRU), and Linear Regression (LR) to generate 24-hour-ahead load forecasts. Individual load categories—household (HH), heat pump (HP), and electric vehicle (EV)—were treated together using full one-year consumption records. The research focuses on improving the reliability of load-profile predictions by comparing LSTM, SVR, and the proposed ensemble across HH, HP, and EV demand patterns. An innovative correction procedure is presented that refreshes the forecast every 8 hours, thereby improving forecast trustworthiness. Results underscore the value of deep learning techniques for better anticipating electricity demand, with particular strength in recognizing peak events, which in turn supports smoother and more cost-effective power system management. Both graphical assessments and quantitative validation metrics were analyzed, covering model behavior during off-peak hours, peak hours, and across the complete daily cycle. Across the tested approaches, LSTM delivered marginally stronger overall metrics in the majority of cases and demonstrated especially good performance at detecting peaks. That said, the hybrid model slightly outperformed LSTM when predicting EV-specific power demand, achieving an on-peak mean absolute percentage error (MAPE) of 21.45%, compared with 29.24% (SVR) and 22.02% (LSTM). Even so, graphical inspection clearly illustrated LSTM’s superior peak-capturing ability. This advantage was also evident in the on-peak mean absolute percentage error (MAPE) and symmetric mean absolute percentage error (SMAPE) values, where LSTM improved by roughly 3–5% relative to both SVR and the hybrid model. In the final application, LSTM was used to forecast next-day load profiles based on real measurements collected from four separate grid areas and consistently demonstrated excellent peak-detection capability, with MAPE below 10% across most of those datasets.

This research proposes a novel, low-power routing protocol tailored for IoT networks that merges intelligent data-aggregation clustering with optimized path selection to overcome critical data-handling challenges in smart urban environments. The protocol relies on a layered architecture that includes Data Fusion Heads (DFH), intermediate relay DFHs, and the marine predators algorithm—an effective nature-inspired optimizer equipped with a purpose-built fitness function. This function simultaneously refines multiple criteria: the geographic clustering density of Sensor Nodes (SNs) inside each data fusion group (DFG), the total hop distance toward the sink, intra-group node proximity, residual battery level (RE), Average Scale of Building Occlusions (ASBO), and the rotation interval of the Primary DFH (PDFH). One of the protocol’s core advancements is the systematic application of in-network data fusion, which drastically reduces duplicate packet forwarding and markedly improves the accuracy and usefulness of collected information across all DFGs. By aggregating readings from multiple sensors, the volume of outbound traffic is greatly reduced, yielding substantial improvements in energy efficiency. Two complementary forwarding strategies are supported: single-hop forwarding, where already fused payloads travel directly to the sink, and multi-hop forwarding, in which an appropriate PDF relay is dynamically elected using a weighted relay selection metric that incorporates remaining energy (RE), sink distance, and ASBO. By integrating robust fault-tolerance procedures, intelligent redundancy elimination, and efficient fusion mechanisms, the protocol achieves markedly greater dependability and sustains strong operation under adverse or unpredictable network conditions. Detailed simulation experiments, along with side-by-side benchmarking against contemporary methods, confirm the protocol’s clear advantage across essential performance indicators—namely, network longevity, total energy expenditure, data throughput, and mean end-to-end latency. Compared with leading recent approaches such as the Particle Swarm Optimization-based energy-efficient clustering protocol (PSO-EEC), linearly decreasing inertia weight PSO (LDIWPSO), Optimized Fuzzy Clustering Algorithm (OFCA), and Novel PSO-based Protocol (NPSOP), the presented solution delivers outstanding gains. Concretely, network lifetime is prolonged by 299% versus PSO-EEC, 264% versus LDIWPSO, 306% versus OFCA, and 249% versus NPSOP. Energy usage drops by 254% relative to PSO-EEC, 264% relative to LDIWPSO, 247% relative to OFCA, and 253% relative to NPSOP. Throughput rises by 67% over PSO-EEC, 59% over LDIWPSO, 53% over OFCA, and 50% over NPSOP. By skillfully combining data aggregation with carefully tuned routing decisions, this protocol establishes a new benchmark for power-efficient organization and communication within IoT-based data fusion groups, delivering a dependable and adaptable framework suitable for diverse smart-city and IoT scenarios.

Energy efficiency in industrial systems remains a major challenge, as conventional data-driven models are often constrained by limited precision and insufficient data. Integrating the physical laws that govern energy systems can enhance performance and ensure greater physical plausibility; however, such models often struggle with loss computation and often overlook dynamic interactions among subsystems, leading to excessive simplification and reduced real-world utility. Accordingly, this study explores a theoretical framework for constructing a conservation-law-guided neural network to improve energy-efficiency forecasting in industrial processes. The framework embeds physical principles directly into floating nodes built upon a long short-term memory (LSTM) architecture, enabling the model to capture relationships among process variables more effectively. At the same time, gradient aggregation enhances both fluidity and interpretability. Performance was assessed through two large-scale industrial case studies—vinyl chloride monomer production and detergent powder manufacturing—where the proposed model achieved notable gains in prediction accuracy and overall reliability, delivering test prediction improvements of 12.2% and 5.87% compared to previously published approaches. Compared with methods that rely on network architecture modifications, the proposed model demonstrated superior reliability and reproducibility in energy-efficiency forecasts. Furthermore, the model effectively identified energy inefficiencies, resulting in a 4.21% decrease in energy use and a corresponding reduction of 377.35 tons in carbon emissions.

Effectively integrating renewable energy sources into electricity markets is essential to aligning real-time supply with demand and reducing revenue losses from curtailment. Storage technologies offer an effective means to overcome this issue by boosting grid reliability and operational efficiency, while also enhancing market liquidity and dampening price fluctuations. In short-term electricity markets, participants face a wide range of choices, each with distinct risks and benefits, and well-designed trading strategies are crucial for profit maximization. This research investigates the optimization of trading activities in the day-ahead and balancing markets of the Irish electricity system over the period 2019–2022, using quantile-based forecasting techniques. By applying three trading strategies subject to realistic operational constraints, the study assesses different approaches, raises trading frequency, and incorporates flexible timestamp-based orders. The results highlight substantial profit opportunities from concurrent participation in both day-ahead and balancing markets, particularly with larger battery storage capacities. Even though higher trading volumes entail greater costs and compressed profit margins, the adoption of adaptive dual-market strategies proves instrumental in maximizing returns and overcoming market-specific obstacles. Lastly, a scenario-based analysis assesses the economic feasibility of four commercial battery storage systems, demonstrating that larger battery units deliver shorter payback periods. 

In today’s rapidly advancing landscape of information and communication technologies, metropolitan areas face a pressing challenge: water shortages combined with the urgent need for smart, sustainable water distribution systems. Deploying an Internet of Things (IoT)-driven monitoring solution can substantially strengthen oversight of water supply networks and help address common distribution problems such as leaks, pipe bursts, unauthorized withdrawals, tank overflows, and pump dry-running. Nevertheless, the proliferation of sensors and communication endpoints across smart urban environments is placing considerable strain on conventional networks, leading to higher latency and greater power consumption. This research outlines several practical approaches to minimize both delay and energy consumption in IoT-supported water distribution infrastructure in intelligent cities. A range of IoT-compatible network topologies is introduced, each shaped by specific hydraulic design features, surface land cover characteristics, and prevailing wireless propagation conditions. After assessing these layout options together with all critical communication metrics, the most suitable network structure for ongoing surveillance of a municipal water supply system in an Indian metropolitan setting is selected. As a practical illustration, the study develops and evaluates an IoT architecture tailored to a secondary-tier metropolitan city in India—namely, Kochi. Using location-specific conditions and the prevailing land-use/land-cover distribution, detailed delay and energy performance models for the IoT-enabled distribution network are constructed and examined. The paper also describes algorithms for classifying nodes and assigning tasks across edge and fog layers, supported by quantitative assessments of the resulting delays and energy consumption. These models yield approximate values for overall network latency and power consumption. Drawing on the derived outcomes and the associated state transition representations, the optimal positioning of fog computing nodes relative to edge devices and the central cloud platform can be determined. Moreover, evaluations across multiple configurations show that increasing the number of operational states in the transition diagram can deliver energy savings of up to 40%. The proposed techniques offer a roadmap for realizing low-latency, energy-conscious IoT communication infrastructures customized to particular geographic and functional contexts. 

Effective energy management plays a vital role in improving greenhouse (GH) performance and advancing sustainability. This study introduces an innovative multi-objective optimization strategy specifically developed for GH energy management, aiming to reduce reliance on grid energy while maximizing the battery state of charge (SOC) over a defined time period. The optimization framework incorporates decision variables, including network power, battery power, and battery energy, while respecting constraints on battery capacity, initial energy levels, and the minimum and maximum energy limits of the battery storage system. The performance of a smart energy management system (EMS) is assessed by comparing it against conventional optimization techniques. Critical hyperparameters for the optimization process—such as plateau time, prediction time, and optimization time—are identified using the ellipse optimization technique. By modeling the GH as a microgrid, the study examines relevant energy management metrics and load profiles. Simulations performed in MATLAB Simulink (R2021b) reveal substantial improvements, with the smart EMS reducing the objective function by more than 50% relative to traditional EMS approaches. Furthermore, the proposed EMS demonstrates strong robustness under varying load power and irradiation patterns. Even in partial-shading scenarios, the system remains adaptable, with a maximum increase in the objective function of only 0.35553%. Matching the photovoltaic (PV) system output power to realistic field conditions further confirms the EMS’s practical effectiveness. These results highlight the smart EMS’s capability to optimize energy use in GH settings, paving the way for more sustainable energy management solutions. This work advances energy optimization techniques in agricultural contexts, supporting greater resource efficiency and environmental responsibility.

This investigation explores ways to enhance the performance of ground source heat pump (GSHP) systems and energy piles as part of sustainable urban planning, with special attention to conditions in Southeast Asia. By drawing on stable geothermal heat, GSHPs provide an environmentally friendly alternative to conventional HVAC systems, capitalizing on the near-constant underground temperatures for efficient space heating and cooling. A key factor in boosting system effectiveness lies in thoroughly understanding how heat migrates through soil—particularly the soft marine clay deposits widespread in Southeast Asia. The research uses a one-dimensional finite-difference numerical model to assess the influence of soil thermal conductivity and density on operational outcomes. Results demonstrate that GSHP installations, especially those coupled with energy piles, can dramatically reduce electricity demand and cut greenhouse gas emissions, offering meaningful relief from the urban heat island effect in crowded metropolitan areas. Although the persistently warm and humid regional climate may pose challenges to sustained performance, the work calls for expanded research—including real-world field testing and more refined simulation methods—to fine-tune GSHP arrangements and unlock the full potential of shallow geothermal resources in city settings. These findings on subsurface thermal processes and optimized system configurations help advance climate-resilient, resource-efficient urban infrastructure. 

The mitigation of negative environmental impacts and the socioeconomic benefits associated with renewable energy systems encourage greater incorporation of distributed energy systems into conventional power grids. A novel form of the sharing economy is emerging, involving the shared use of energy resources to lower transaction costs via cloud-based platform services. In light of challenges arising from outdated infrastructure and diverse approaches to renewable energy integration, Distributed Energy and Micro Grids (DE-MG) represent an effective solution to enhance the quality of energy supply services. However, constraints related to microgrid scalability, budget adherence, and security can complicate implementation. Consumers achieve more favorable pricing for renewable energy allocation via cloud-based Peer-to-Peer (P2P) networks. The core goal is to minimize the daily energy expenditure for microgrid power in commercial buildings. In the proposed approach, cloud-based P2P combined with peer-Multi-Agent System (p-MAS) optimization methods is employed to reduce system peak loads and to integrate Demand Response (DR) with an Energy Management System (EMS) within a commercial MG. To address gaps in understanding how different power market structures and individual decision-making behaviors influence local interactions and overall market results, cloud-based P2P for Modeling Leveraging Agents (MLA) is applied to perform bill calculations. Finally, a performance metric is developed to assess cost and reliability, enabling evaluation of the societal benefits of cloud-based P2P energy exchange models. Comparisons are presented between the proposed cloud-based P2P model and an existing P2P energy exchange model across various pricing scenarios and resource categories. The key application of a distributed P2P model for power trading in a microgrid is to reduce electricity costs while improving the grid’s environmental reliability.

The usefulness of Hybrid Energy Storage Systems (HESSs) has been demonstrated across various application areas, including Charging Stations (CSs), grid services, and microgrids. HESSs combine two or more individual Energy Storage Systems (ESSs) to merge the advantages of each technology and enhance overall system performance. In this study, we introduce a novel power management controller, termed the Hybrid Controller, designed for efficient charging and discharging of the HESS while accounting for its State of Charge (SoC) and the fluctuating supply and load conditions. The Hybrid Controller improves HESS utilization by minimizing energy exchange with the grid, thereby prioritizing and maximizing the use of available Photovoltaic (PV) power. The effectiveness of the proposed approach was evaluated through simulations using several performance indicators, namely Autarky, charge/discharge cycles, and Self-Consumption (SC). For this purpose, we defined 24 scenarios across different locations in Germany. 

Platforms based on digital twins are gaining widespread use in overseeing energy infrastructure, particularly within smart grid environments. Recent progress in artificial intelligence opens up new possibilities to build greater user confidence by strengthening predictive and prescriptive analysis capabilities and refining how users interact with these systems. This study introduces an innovative framework and architectural design for energy digital twins that combines data-driven techniques with knowledge-based elements. Machine learning-driven data integration and analysis are fused into a knowledge graph that captures asset condition information, forecasting results, and relevant domain expertise. This structure underpins a retrieval-augmented generation strategy, which powers an advanced conversational virtual assistant grounded in a large language model. The assistant delivers practical decision-making support for asset oversight and maintenance activities. Key parts of the suggested architecture have been aligned with readily available commercial tools to create a working prototype. This prototype was applied in a practical scenario involving a portion of the high-voltage energy network located in central Italy.

Amid worsening worldwide environmental degradation and escalating carbon outputs from the construction sector, school campuses—serving as the main settings for young people’s education and daily life—hold significant importance in promoting designs that prioritize low-carbon approaches, enhanced energy conservation, and eco-friendly surroundings. This study examines the renovation of Yezhai Middle School in Qianshan, Anhui Province, as a specific example, investigating methods to improve airflow patterns and to implement facade-based energy conservation measures for hilly-terrain school facilities during upgrades to pre-existing structures. Within the broader push toward intelligent urban systems, embedding cutting-edge tools and eco-conscious methods into communal facilities has emerged as a central priority. By conducting airflow modeling and upgrading building exteriors for greater efficiency, the analysis demonstrates nonlinear increases in airflow velocity with increasing elevation, alongside notable influences of surface texture on fluctuations in wind conditions. Incorporating expanded polystyrene (EPS) boards, along with added thermal barriers, during exterior upgrades reduces heating demand in colder months and air conditioning demand in warmer months. The updated exteriors successfully block chilly drafts while minimizing unwanted solar heat absorption, resulting in energy savings of roughly 24%. These findings offer valuable technical guidance and real-world insights for implementing low-carbon, efficiency-focused upgrades across additional educational and civic structures, supporting the building sector’s shift toward sustainable, lower-emission practices within intelligent urban frameworks.

Cities undoubtedly play a crucial part in efforts to mitigate climate change. Many urban areas worldwide have pledged to pursue sustainability shifts by adopting environmentally friendly approaches. In recent years, Europe has seen a notable increase in smart city initiatives and progress toward building more sustainable urban environments. Currently, the idea of Positive Energy Districts (PEDs) is motivating districts and municipalities to move away from conventional practices and strive for greater carbon neutrality. This article examines five municipalities—Maia, Reykjavik, Kifissia, Kladno, and Lviv—that are participating in an active Horizon 2020 initiative. The main goal is to explore how these cities identified and chose the 10 solutions they plan to replicate. Data were gathered via conversations, interviews, and the replication roadmaps prepared by each city. It should be emphasized that every city faces unique conditions regarding political backing, funding availability, technical capabilities, and stakeholder engagement—circumstances that apply universally when introducing innovative, efficient measures. Municipalities throughout Europe and other regions may encounter comparable challenges, so this article also presents the experiences of these five Fellow Cities as they advance toward PED implementation.

Improving energy efficiency in the petrochemical sector is vital for reducing energy use and mitigating environmental harm. A reliable model for energy efficiency can provide important guidance for decision-making on operational process adjustments. In real-world settings, however, the varying sampling frequencies common in petrochemical facilities make conventional methods for estimating energy efficiency less dependable and more challenging to apply to multirate datasets. To address this, the present study introduces a multi-channel convolutional neural network framework that leverages transfer learning of model parameters to enhance energy efficiency forecasting despite irregular sampling rates. The multi-channel design enables the extraction of distinct temporal patterns by applying convolutions across the time axis of the data. At the same time, transfer learning enables the model to adapt and capture novel input behaviors after initial training. The effectiveness of this approach for both predicting energy efficiency and evaluating potential savings was demonstrated through its implementation in a vinyl chloride monomer manufacturing scenario. Results indicate that the suggested model surpasses standard benchmark approaches and conventional CNN architectures in predictive precision and consistency, achieving an R² value of 0.97. Incorporating transfer learning helps maintain robust performance without major declines and improves the model’s flexibility for continuous, real-time energy monitoring. In addition, an analysis of energy discrepancies identified substantial conservation opportunities, resulting in an average annual reduction in energy use of 7.25% and a corresponding decrease of 5,709 tons in CO₂ emissions.

The increasing deployment of wind power plays a key role in addressing climate change. Nevertheless, the intermittent nature of wind generation often leads to curtailment, resulting in substantial financial losses for wind farm operators. Battery energy storage systems (BESS) can help alleviate curtailment by providing local backup power. However, dedicating storage primarily to this supporting function can greatly diminish its profitability in energy markets. Optimal BESS operation requires carefully balancing local curtailment mitigation with participation in market bidding, yet achieving this in practice remains difficult owing to the intricate coordination required and the uncertainty of both electricity prices and wind output. This study explores a combined bidding strategy for a co-located wind-plus-battery setup operating in the spot market and the Regulation Frequency Control Ancillary Service markets. We introduce an innovative deep reinforcement learning method that separates the overall market engagement into two interconnected Markov decision processes—one for each asset. This structure allows the BESS to capture surplus wind energy that would otherwise be curtailed while simultaneously participating in joint bidding across markets to optimize total operational income. Validation was performed using real-world wind farm data. The proposed coordinated bidding approach outperformed a conventional optimization-based reference case, delivering roughly 25% higher revenue and achieving 2.3 times greater reduction in wind curtailment. Findings demonstrate that participating jointly in multiple markets substantially enhances the economic viability of wind-battery installations relative to engaging in individual markets in isolation. Additional simulations indicate that charging the BESS with otherwise curtailed wind power yields further economic benefits. Effective deployment of the developed algorithm is expected to promote greater co-location of renewable generation and storage facilities, thereby realizing broader advantages for the power system.

This paper introduces a novel hybrid artificial intelligence technique designed to enhance energy management efficiency within smart grids, with a particular focus on Renewable Energy Communities (RECs). The proposed approach integrates a Time Delay Neural Network (TDNN) to predict upcoming values of key energy variables in the community. These forecasts then feed into a stochastic Model Predictive Control (MPC) framework, which optimizes community operations through effective Battery Energy Storage System (BESS) control strategies. Evaluation on a public dataset for a 24-hour prediction horizon yielded Mean Absolute Errors (MAE) of 1.60 kW for photovoltaic generation, 2.15 kW for total energy consumption, and 0.30 kW for common services. When integrated with stochastic MPC, these predictions yielded higher financial returns than those from alternative methods. Overall, the system increased total income by 18.72% compared to the same management approach without any forecasting component.

Urban areas account for 65% of total energy use and generate 70% of global CO2 emissions. Strong and targeted measures are essential to steer cities toward climate neutrality by 2050, with strategies designed by and for their inhabitants. In this context, the “100 climate-neutral cities Mission” sets the deadline for achieving climate neutrality at 2030. This paper, developed under the H2020 GRETA project—GReen Energy Transition Actions (GA101022317), explores energy communities and climate city contracts as central instruments for realizing the challenging vision of citizen-focused, climate-neutral urban environments. To meet this goal, the paper is arranged in the following sequence: (1) a refreshed review of European and Italian rules governing energy communities; (2) a concise presentation of the concept and core elements of climate city contracts; (3) a choice and evaluation of real-world energy community examples; (4) an outline of pilot climate city contracts that have already been established. The outcomes deliver enhanced understanding of EU-level energy community approaches and of the contractual mechanisms capable of securing mutual obligations among involved parties while promoting direct citizen involvement in the energy system. These insights support the rollout of the GRETA project’s Italian case study, including a description of its early citizen participation efforts.

Global efforts are increasingly focused on achieving fully sustainable energy systems by mid-century to comply with the Paris Agreement targets. This study introduces a novel approach for the optimal operation of the Egyptian power grid, aiming to maximize renewable energy integration while minimizing voltage fluctuations and overall power losses. The optimization is implemented using the Corona Virus Optimization (CVO) algorithm. The proposed CVO method is benchmarked against the Teaching and Learning-Based Optimization (TLBO) algorithm for voltage deviation, power losses, and renewable energy share. The study uses real-world data on electricity demand, solar irradiation, and wind speed for representative winter and summer days. A 2020 Egyptian grid model is developed, simulated, and optimized using DIgSILENT software. Results demonstrate that CVO outperforms TLBO in enabling the grid to operate with the highest achievable share of renewable energy. This research contributes toward Egypt’s national objectives of reaching 20% and 42% renewable energy penetration by 2022 and 2035, respectively.

This study introduces an improved bidirectional multi-input DC–DC converter (MIPC) tailored for hybrid energy storage systems (HESS) in DC microgrid environments. Traditional batteries exhibit limited charge and discharge rates, which can impose high current stresses, thereby reducing their operational lifespan. Supercapacitors (SCs), due to their high power density, respond rapidly to sudden load variations, mitigating these limitations; however, they are unsuitable for long-term energy storage. Combining batteries with SCs in a HESS leverages their complementary properties, creating an efficient energy storage solution. The proposed MIPC links the HESS to the DC microgrid, enabling independent regulation of battery and SC power as well as efficient energy transfer among storage units. A unified control strategy was developed to manage both charging and discharging of the HESS. A model predictive control (MPC) scheme demonstrated superior DC bus voltage restoration during step changes in photovoltaic (PV) generation and load conditions, compared with conventional proportional-integral (PI) controllers. The MPC reduces current stress, prolongs battery lifespan, and improves overall system performance, ensuring rapid DC grid voltage stabilization. Simulation and experimental tests under varying PV output and load profiles confirmed enhanced DC-link voltage regulation.