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Active distribution networks increasingly rely on stochastic optimization to manage uncertainties arising from renewable generation, flexible demand, distributed storage, electric vehicles, and local energy markets. These uncertainties affect voltage quality, congestion, operational cost, reliability, and the value of flexibility. This systematic review categorizes and evaluates uncertainty-aware stochastic optimization techniques for demand response, DER coordination, and flexibility provision in active distribution networks. It focuses on methodological transparency, thematic synthesis, and identification of research gaps. A PRISMA-guided systematic review of 31 peer-reviewed journal articles published from 2017 to 2023 was conducted. A structured search, screening, eligibility assessment, and data extraction process was used to organize the evidence. The review maps dominant stochastic frameworks, including two-stage stochastic programming, chance-constrained optimization, robust-stochastic formulations, and distributionally robust optimization. Five synthesis tables are used across the full manuscript to summarize study selection, article characteristics, method taxonomies, application mappings, and research gaps. Stochastic optimization supports secure and efficient operation of active distribution grids, but important gaps remain in scalability, real-world validation, interoperability, and regulatory integration. The review outlines an agenda for more adaptive, transparent, and implementation-ready optimization methods.

Active distribution networks face growing renewable curtailment as distributed photovoltaic and wind generation increasingly encounter voltage, congestion, and flexibility constraints. Battery storage scheduling under uncertainty has become a central operational strategy for absorbing otherwise curtailed energy and improving distribution-level renewable integration. Despite substantial growth in optimization-based storage studies, the literature remains fragmented across stochastic programming, chance-constrained optimization, robust optimization, and distributionally robust formulations. No critical review has systematically compared these paradigms for storage scheduling and curtailment reduction specifically in active distribution networks. This critical review assesses and contrasts stochastic and robust optimization models for battery storage scheduling aimed at reducing renewable curtailment in active distribution networks. It evaluates how these approaches represent uncertainty, network constraints, operational objectives, and implementation readiness. A structured critical review of 27 peer-reviewed journal articles published between 2017 and 2024 was conducted. The analysis focuses on mathematical formulations, uncertainty modeling, storage representation, curtailment objectives, network constraints, and operational applicability. Stochastic models offer strong expected-performance reasoning but often depend on scenario quality and can become computationally demanding. Robust models provide stronger protection against adverse uncertainty realizations but may become conservative when uncertainty sets are poorly calibrated, while gaps remain in real-time scheduling, multi-service operation, degradation modeling, and field validation. The choice between stochastic and robust optimization depends on operator risk preference, data availability, computational resources, and distribution network complexity. Hybrid, data-driven, and implementation-oriented methods offer promising future pathways for reducing renewable curtailment through battery storage scheduling.

Residential prosumers with rooftop photovoltaic systems and behind-the-meter batteries are changing the operating conditions of low-voltage and medium-voltage distribution feeders. When these resources respond independently to retail tariffs, their aggregate behavior can intensify reverse power flows, transformer loading, and voltage excursions. This paper proposes a bilevel optimization model that coordinates residential demand response, rooftop photovoltaic generation, and battery storage under distribution-feeder congestion constraints. The objective is to align prosumer economic decisions with distribution system operator requirements for congestion relief and voltage compliance. The upper-level problem represents the distribution system operator and minimizes feeder congestion, voltage deviations, and coordination costs. The lower-level problem represents residential prosumers that minimize their individual energy costs through load shifting, photovoltaic self-consumption, and battery dispatch. The proposed formulation produces coordinated schedules that reduce peak feeder loading and voltage violations relative to uncoordinated operation. Battery arbitrage and flexible demand are redirected from purely price-driven operation toward feeder-aware congestion mitigation. The bilevel coordination framework provides an operational method for managing residential distributed energy resources without replacing prosumer autonomy. It enables distribution system operators to procure flexibility while maintaining economically rational prosumer behavior.

Renewable-rich microgrids face operational complexity from variable generation, storage dynamics, flexible loads, and the need for human oversight. A layered architecture can manage this complexity by separating functions while preserving coordination. Existing microgrid control architectures often entangle forecasting, scheduling, demand response, and supervisory control. This can make energy management systems brittle, difficult to maintain, and insufficiently accommodating of operator override. This article proposes a layered architecture that cleanly separates forecasting, battery scheduling, demand response, and operator override functions. The aim is to support modular design, resilient operation, and transparent human supervisory control. The architecture is developed conceptually using established ideas from microgrid energy management, hierarchical control, renewable integration, storage scheduling, demand response, and human-in-the-loop supervision. No experiments are conducted, and no quantitative results are presented. The architecture comprises four layers with defined functions, inputs, outputs, decision horizons, and interfaces. A validation plan is reserved for the second part of the article, while this part establishes the architecture logic and the forecasting layer. The proposed architecture offers a blueprint for building modular, resilient, and human-centred microgrid energy management systems. It is intended as a conceptual reference model for future implementation, testing, and refinement.

Day-ahead distribution-grid operation requires anticipating customer demand, renewable generation, network loading, and available demand-side flexibility. Smart-meter data provide a practical basis for identifying recurring consumption patterns and embedding those patterns into operational scheduling. This article presents an original forecasting-optimization decision-support system for day-ahead distribution-grid operation. The system integrates smart-meter clustering, renewable prediction, and flexible load scheduling to support distribution system operators. The proposed system processes smart-meter profiles into consumer clusters, predicts day-ahead photovoltaic and wind generation from weather-driven features, and schedules flexible loads through constrained optimization. The decision-support workflow is designed to produce feeder-level schedules, expected voltage conditions, congestion indicators, and operator-facing recommendations. The clustering module identifies consumer groups with different peak timing, ramping behavior, and flexibility availability. The integrated DSS is structured to reduce peak loading, improve voltage profiles, and lower operational cost compared with a baseline day-ahead plan without forecast-informed scheduling. The proposed DSS offers a practical framework for data-driven distribution-grid operation. It supports renewable integration by translating forecasts and customer flexibility into actionable schedules for day-ahead planning.

Smart-grid optimization increasingly integrates digital twins, reinforcement learning, and cyber-physical resilience analytics into a shared research space. This convergence reflects a shift from isolated grid-control studies toward data-intensive, adaptive, and resilience-oriented infrastructure analytics. This bibliometric review maps the research landscape at the intersection of these three domains. It quantifies publication trends, identifies thematic clusters, and interprets emerging fronts in smart-grid optimization research. A structured bibliometric protocol was applied to 28 peer-reviewed journal articles published between 2017 and 2025. Co-word analysis, citation analysis, thematic clustering, and citation burst interpretation were used to organize the evidence base. The analysis reveals rising publication activity after 2019 and three dominant thematic clusters. Digital twin research centers on simulation and cyber-physical representation, reinforcement learning emphasizes adaptive grid control, and cyber-resilience analytics focuses on vulnerability, disruption, and recovery. The field is moving toward integrated frameworks that combine digital twins and reinforcement learning for proactive, cyber-resilient grid optimization. These fronts suggest a growing need for real-time, explainable, and safety-aware analytics.

Cyber-physical attacks on smart grids can compromise the measurements, forecasts, and control signals used in dispatch. A resilient decision-support system must detect anomalies, explain their operational meaning, and support secure re-dispatch before manipulated data causes unsafe operation. This article proposes a cyber-resilient decision-support system that integrates anomaly detection, explainable alerts, and robust optimization for smart-grid dispatch. The system is designed as an operator-facing architecture rather than a standalone detection model. The system combines machine learning-based anomaly detection, explainable artificial intelligence-driven alert generation, and robust dispatch re-optimization. Validation is structured around false data injection, voltage measurement manipulation, and load manipulation attacks on an IEEE test feeder. The proposed decision-support system identifies cyber-physical anomalies, converts model outputs into actionable explanations, and updates dispatch decisions under attack-induced uncertainty. Compared with non-resilient dispatch and detection-only benchmarks, it reduces unsafe voltage excursions and avoidable line-flow stress. The integrated architecture improves cyber-resilience by connecting detection, explanation, and mitigation in one operational workflow. It supports faster operator interpretation while maintaining dispatch feasibility under uncertain measurements.

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.

Distribution grids face increasing failure risks from extreme weather, cyber-attacks, aging assets, and high penetrations of distributed energy resources. Traditional contingency planning often depends on limited deterministic scenarios, which can miss compound and low-probability events. This article proposes an original generative-AI decision-support system for distribution-grid contingency planning. The system combines synthetic failure scenario generation, natural-language operator prompts, and risk-aware optimization to support resilient operational decision-making. The DSS generates synthetic line, transformer, load, weather, and cyber-contingency scenarios using generative models trained on historical and simulated grid states. Operator prompts are translated into structured scenario filters, optimization constraints, and explanation requests, while a risk-aware solver recommends reconfiguration and load-shedding actions. Simulation on a realistic distribution feeder shows that synthetic augmentation expands contingency coverage beyond deterministic N-1 analysis. The proposed DSS reduces expected load not served, lowers tail-risk exposure, and decreases operator workload relative to deterministic and non-augmented stochastic baselines. The integrated DSS improves contingency planning by linking generative scenario discovery, interactive human–AI exploration, and optimization-based response selection. Governance requirements related to model reliability, operator trust, privacy, and safe deployment are identified.

Demand response can provide valuable grid flexibility, but its effectiveness depends on respecting consumer comfort, aligning with prosumer preferences, and supporting decarbonization goals through low-carbon signals. Integrating these aspects within a human-in-the-loop optimization framework remains an operational challenge for aggregators managing heterogeneous flexible loads. This article proposes an optimization model that dispatches flexible residential and building-cluster loads while incorporating thermal comfort constraints, prosumer willingness-to-accept, and carbon intensity signals. The framework allows a human operator to review optimized schedules, adjust objective weights, and override selected dispatch decisions before execution. The model is formulated as a mixed-integer linear program with a weighted-sum objective capturing energy cost, carbon emissions, comfort deviations, and preference adherence. A realistic residential-cluster case study with weather-driven thermal dynamics, appliance flexibility, time-varying tariffs, and carbon intensity data validates the model. The proposed dispatch reduces feeder peak demand by 18.6% and carbon emissions by 11.9% relative to a non-carbon-aware baseline while maintaining the comfort violation index below 0.035 degree-hours per occupied interval. Operator overrides occur in 6.8% of dispatch decisions and primarily adjust HVAC curtailment during extreme outdoor-temperature periods and high-price events. The proposed framework offers a practical decision-support tool for demand response aggregators and grid operators. It balances competing operational objectives while retaining human oversight for exceptional comfort, reliability, and participation-risk conditions.

Low-carbon flexibility markets are becoming important tools for coordinating distributed energy resources, electric vehicles, storage, and responsive demand. Their practical value depends on how well they manage renewable forecast errors, uncertain prosumer behavior, and distribution-network constraints. This article develops a risk-aware optimization model for local flexibility market clearing under renewable forecast uncertainty, prosumer non-compliance, and feeder operating limits. The purpose is to reduce severe cost, carbon, and reliability outcomes rather than optimizing only average performance. The proposed model combines probabilistic renewable uncertainty, statistical prosumer compliance behavior, linearized distribution-network limits, and a conditional value-at-risk representation. The clearing problem is structured as a tractable optimization model suitable for commercial solvers. The model procures low-carbon flexibility while limiting exposure to delivery shortfalls and network infeasibility. The case study compares the proposed risk-aware clearing approach with deterministic and risk-neutral alternatives. The framework provides a practical risk-management tool for flexibility market operators. It supports reliable low-carbon dispatch while accounting for imperfect forecasts, incomplete prosumer delivery, and feeder constraints.

Community microgrids can empower local energy sharing, but the equitable allocation of costs and benefits from shared resources like batteries remains a challenge. Decentralized peer-to-peer trading adds flexibility, yet requires a fair framework to ensure broad participation. Existing frameworks rarely integrate P2P trading with shared battery access and explicit justice-aware cost allocation in a single, coherent design. This gap is especially important where technical efficiency, social legitimacy, and community governance must operate together. This article proposes a decentralized optimization framework that links a P2P trading layer, a battery sharing layer, and a justice-aware cost allocation layer for community microgrids. The framework is designed as a conceptual architecture rather than an empirical or simulation-based study. The framework is constructed conceptually, defining the roles, rules, and interactions of each layer. Decision rules are articulated qualitatively, and evaluation scenarios outline a validation pathway without quantitative simulations. The framework provides a modular architecture where prosumers trade energy directly, a community battery is shared according to contribution-based rules, and costs are allocated using fairness-driven formulas. Three tables summarize the trading mechanisms, justice principles, and evaluation scenarios. The proposed framework can guide the design of equitable and efficient community microgrids, and provides a foundation for future empirical testing and policy development. It emphasizes that decentralized energy systems should be evaluated not only by efficiency, but also by fairness, participation, and social accountability.

Extreme events increasingly threaten distribution grids by damaging feeders, interrupting communication, and isolating critical loads. Prosumers with local flexibility, EVs, and controllable network topology can provide emergency support, but their coordination is operationally complex. This article proposes a multi-agent optimization model that coordinates prosumer local flexibility, EV backup power, and dynamic network reconfiguration. The objective is to sustain critical load supply during extreme events while respecting device, feeder, and communication constraints. The model represents prosumers, EV aggregators, and the distribution system operator as interacting agents. A consensus-based distributed optimization procedure decomposes the resilience coordination problem into local operating decisions while enforcing feeder-level feasibility. The proposed model restores critical load faster than a non-coordinated baseline and approaches centralized optimization performance with lower communication burden. EV backup power is dispatched selectively to preserve mobility needs while supporting critical nodes. The model provides a scalable optimization approach for emergency coordination in active distribution grids. It shows that local flexibility, EV backup power, and dynamic reconfiguration are mutually reinforcing resilience resources when operated through coordinated multi-agent control.

This paper introduces and assesses two innovative ordinal classification approaches for forecasting wind speed across three horizons: 1h, 4h, and 8h. To tackle the issue, wind speed values are categorized into four discrete classes, which are vital for effective wind farm operations. Each class conveys key information about wind farm output, ranging from very low wind speeds to extreme wind speeds and their associated generation scenarios, thereby supporting operational decisions by wind farm managers. Ordinal classifiers prove more appropriate than nominal ones for this task. The central goal of the study is to evaluate recently developed ordinal classifiers for wind speed forecasting, with particular emphasis on extreme wind conditions that frequently lead to turbine shutdowns. The analysis relies on hourly wind speed records from a wind farm in Spain, along with predictor variables derived from the European Centre for Medium-Range Weather Forecasts Reanalysis v5 (ERA5). The proposed techniques consist of an Artificial Neural Network (ANN) employing the Cumulative Link Model as its ordinal output mechanism (MLP-CLMO), which prioritizes overall effectiveness, and an ANN refined through a soft labeling strategy grounded in triangular distributions (MLP-TO), which performs particularly well for extreme class predictions. The findings show that both methods outperform other nominal and ordinal techniques across evaluation metrics that properly address the data’s imbalance and ordinal characteristics. MLP-CLMO outperforms MLP-TO across general and ordinal performance, whereas MLP-TO shows stronger performance in predicting the extreme class.

A virtual power plant (VPP) serves as a promising solution by aggregating distributed energy resources (DERs) and fulfilling the prosumer’s requirements for power availability, quality, and reliability. This study presents the optimized scheduling of an interconnected VPP within a multi-area framework, with tie-line connections and various renewable resources. The scheduling process begins with a day-ahead (hourly) interval, then advances to an hour-ahead interval (intra-hour and real-time), specifically using 15-minute and 5-minute time steps for the proposed VPP in the multi-area setting. The problem features two primary objective functions: net profit (to be maximized) and emissions (to be minimized). Given the involvement of renewables in the energy portfolio and the inherent complexity of the problem, the suggested multi-area VPP is evaluated using an advanced nature-inspired metaheuristic algorithm. Additionally, the formulation is extended to a multi-objective framework, with various scheduling approaches implemented to simultaneously lower emissions and enhance overall profit margins by boosting cumulative system profits. Moreover, an extensive numerical assessment across different optimization horizons demonstrates fast convergence and low computational time in achieving the optimal solution. 

In large cities with evolving infrastructure, optimizing water supply systems is critical. However, tackling these issues becomes considerably more difficult without a thorough understanding of the network layout and water-use patterns. This paper introduces a methodology based on geospatial data analysis to address two primary objectives. The first is the definition of service zones for infrastructure facilities to improve overall system manageability. The second is the development of an approach to determine the optimal locations for devices that collect and transmit hydraulic network parameters, ensuring their placement aligns with both water supply sources and the areas they serve. The research utilizes data from the water supply network of a city with a population of more than half a million, applying hierarchical clustering with Ward’s method to examine spatial distribution. Four territorial clusters were distinguished, each exhibiting distinct characteristics related to consumer density and water consumption volumes. The resulting cluster boundaries were compared with the system’s current service scheme, demonstrating consistency with the actual infrastructure. Clustering quality was additionally assessed using the silhouette coefficient, which confirmed the high precision and dependability of the selected method. The study illustrates the value of visualizing cluster boundaries in evaluating the even placement of pressure sensors throughout the urban water supply network. The findings indicate that combining geographic data with water consumption details not only supports efficient infrastructure planning and resource distribution but also lays the foundation for the digital transformation of the hydraulic network—an essential element of sustainable progress in contemporary smart cities.

Designing multi-floor arrangements for biodiesel production facilities that utilize empty fruit bunch (EFB) feedstock represents a computationally intensive, large-scale problem that requires the concurrent assessment of cost, hazard mitigation, and building integrity constraints. The present work proposes a Mixed-Integer Nonlinear Programming (MINLP) formulation tailored to multi-level plant layouts, jointly determining optimal equipment materials, vertical floor allocations, sectional positioning, and passive safeguarding features based on explicit safety quantification. Developed within the General Algebraic Modeling System (GAMS) environment, the model is tackled through a staged decomposition consisting of three sequential phases—(i) choice of construction materials together with load-bearing evaluation, (ii) intra-section multi-floor arrangement, and (iii) full-plant sectional coordination—because solving the full monolithic MINLP violates academic solver license restrictions and does not reach convergence under realistic time constraints. In contrast, the sequential procedure consistently delivers solutions featuring small optimality gaps (overall CPU usage: 22.97 min). Applied to a 1,000 t/d EFB processing scenario involving 57 equipment units distributed across seven functional sections, the resulting layout achieves a 28.9% reduction in total capital expenditure, a 51.97% decrease in required land area, and—when separations follow Dow’s Fire and Explosion Index (F&EI) guidelines including credit for installed devices—a 18.8% drop in Maximum Probable Property Damage (MPPD) before protective measures and a 69.0% reduction once protection is accounted for. This hierarchical decomposition technique shows that highly complex facility layout tasks incorporating formal safety limits can be addressed in a tractable and effective manner, supplying a generalizable methodology suitable for diverse multi-objective design problems encountered in the chemical processing and renewable energy domains.

This research addresses the optimization of scheduling strategies for virtual power plants (VPPs), as demonstrated in Portugal’s national New Generation Storage (NGS) initiative, aiming to maximize societal benefits and boost the effectiveness of energy transactions in today’s advanced power networks. VPPs play a crucial role in sustainable urban energy ecosystems by combining various distributed energy resources (DERs), such as renewable generation units and battery storage installations. The underlying optimization challenge is expressed as a Mixed-Integer Linear Programming (MILP) model and tackled via a parallelized simulated annealing (SA) heuristic running on high-performance computing (HPC) platforms. The parallel design greatly enhances the speed of searching the solution landscape, thereby allowing the framework to cope with the growing intricacy associated with expanded DER fleets and increasingly complex dispatch requirements. The developed technique aligns closely with smart city aspirations by enabling flexible, high-efficiency energy management, supporting real-time pricing schemes, and extending the durability of essential components, such as energy storage units. Comprehensive simulation experiments reveal the method’s strong performance in reducing computation time, maintaining high-quality solutions, and scaling effectively to larger problem sizes, thereby supporting instantaneous operational decisions in competitive energy markets. In addition, the improved synchronization among DERs strengthens overall grid reliability, increases market agility, and fosters the emergence of robust, decarbonized urban energy landscapes. This investigation emphasizes the decisive impact of powerful computing resources in overcoming the complexities of contemporary energy infrastructures, illustrating the potential of cutting-edge algorithmic approaches and HPC systems to deliver scalable, responsive, and environmentally sound energy management solutions tailored to smart urban settings. Ultimately, the outcomes pave the way for energy systems that are both socially inclusive and ecologically sustainable, fully consistent with the essential aims of urban resilience and long-term sustainable progress.

This study introduces a coordinated control approach to address the challenge of volt-var optimization (VVO), incorporating load tap-changing transformers, step-voltage regulators, and switched-capacitor banks, along with a photovoltaic (PV)-integrated microgrid. The harmony search (HS) metaheuristic algorithm was applied to efficiently compute the globally optimal operating parameters for these devices, ensuring robust performance across fluctuating operating conditions. The key aims of the volt-var control strategy were to achieve substantial reductions in active power losses, peak demand, and voltage deviations throughout the distribution system, while simultaneously maintaining bus voltages within acceptable regulatory limits under all possible load scenarios. The optimization problem is a mixed-integer nonlinear program with discrete decision variables, binary variables representing capacitor bank switching states (on/off), integer variables for voltage regulator tap positions, continuous variables for the microgrid’s injected current, and nonlinear network power-flow constraints. The proposed methodology was tested and validated on the standard IEEE 13-node distribution test feeder. Time-series simulations used realistic daily load profiles from the main grid, along with the microgrid’s PV output, both resolved at 15-minute intervals. All power flow computations were carried out with OpenDSS (version 9.5.1.1, released in 2022), an established open-source distribution system analysis tool. The developed control framework supports both real-time operation and long-term planning studies. Simulation results showed that the integrated volt-var coordination significantly reduced total active power losses, lowered peak demand, and effectively mitigated voltage variations across the network.

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.

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 paper introduces a benchmark comparison of distributed optimization algorithms based on dual decomposition for model predictive control problems with constraint coupling. Such problems can be viewed as multiple subsystems linked through constraints on shared, limited resources. In a dual decomposition approach, the allocation and use of these resources are coordinated by repeatedly calculating their associated prices and distributing those values to the participating subsystems. After a short overview of model predictive control, various architectures and communication structures suitable for distributed implementations are outlined. The centralized control problem is then decomposed into several subproblems by introducing dual variables. A range of distributed optimization methods is examined, including the recently developed quasi-Newton dual ascent algorithm. Additionally, an epigraph reformulation of the bundle, combined with a line search technique, is proposed for the quasi-Newton dual ascent method; these enhancements improve its numerical stability and accelerate convergence relative to the earlier trust-region approach. Lastly, the quasi-Newton dual ascent algorithm is benchmarked against the subgradient method, the bundle trust method, and the alternating direction method of multipliers across a broad set of test problems. All benchmark instances are openly accessible on GitHub. 

Swift technological progress has heightened the importance of managing charging demand and refining travel routes for electric vehicle (EV) fleets, particularly in urban settings. Sensors based on IoT, embedded in charging stations and vehicles, supply live information about driver charging routines, road congestion, vehicle positioning, and surrounding conditions—greatly strengthening forecasting and optimization techniques. This continuous stream of IoT data powers the combined deep learning architecture, GNN-ViGNet, to predict upcoming EV charging requirements. An analysis of readings from 400,000 IoT devices distributed across charging points and EVs in Texas was performed to reveal typical charging behavior. These devices continuously log key indicators, including user charging preferences, current traffic levels, and diverse external conditions. Data refinement was achieved through Frequency-Aware Dynamic Range Scaling, together with sophisticated preprocessing techniques including Categorical Encoding. Ultimately, the GNN-ViGNet framework delivered a prediction accuracy of 98.9%. Beyond conventional metrics such as Root-Mean-Square Error (RMSE) and Mean Squared Error (MSE), the research introduced the Forecast Accuracy Rate (FAR) and the Charging Load Variation Index (CLVI) to provide a more comprehensive evaluation of predictive capability. The work introduces both an advanced forecasting system and a novel hybrid Coati–Northern Goshawk Optimization (Coati–NGO) technique designed for route planning. Real-time route modifications become possible thanks to live IoT feeds that include congestion updates, vehicle coordinates, and remaining battery capacity. The Coati–NGO algorithm combines the broad search strength of Coati Optimization (COA) with the refined exploitation strength of Northern Goshawk Optimization (NGO). Compared against Particle Swarm Optimization (919 km) and the Firefly Algorithm (914 km), it substantially reduced the total distance traveled to 511 km. Moreover, this combined method exhibited quicker convergence, consistently locating high-quality solutions by the 100th iteration. Overall, the presented EV fleet management system leverages continuous IoT inputs to improve charging network performance, reduce operating expenses, and enhance operational effectiveness—delivering an adaptable, realistic framework suitable for city-wide electric mobility.

This paper presents a two-stage approach that leverages energy savings from optimal network reconductoring to determine the appropriate sizing and placement of electric vehicle (EV) charging loads in residential areas of urban distribution networks. In the initial stage, the Flower Pollination Algorithm (FPA) was used to reduce annual energy losses in the radial distribution system through optimal reconductoring. A multi-objective function was created to simultaneously minimize the investment cost, peak power loss, and annual energy loss cost across various load factors. Outcomes from the flower pollination algorithm were benchmarked against those from the particle swarm optimization algorithm. In the subsequent stage, a straightforward heuristic was introduced to allocate and size the EV charging demand at each node, using a share of the annual energy savings realized from the optimal reconductoring. The quantity of electric cars, electric bikes, and electric scooters that could be supported at individual nodes was calculated without violating voltage or branch current limits. The proposed method was tested through simulations on the 123-bus and 51-bus radial distribution networks to confirm its validity and performance.

As electric vehicles (EVs) steadily gain popularity on public roads, commercial EV charging has become a routine business service. As this trend continues, charging station operators are actively exploring strategies to boost the profitability of their services or to distribute limited resources more effectively. Dynamic pricing has proven effective for increasing revenue in markets with varying customer demand. This paper introduces a Markov Decision Process (MDP)-based framework for dynamic pricing that maximizes either revenue or utilization for charging station operators. The proposed method is implemented through a Monte Carlo Tree Search (MCTS) algorithm. It is tested in simulation across multiple scenarios derived from a real-world dataset of EV charging sessions. Results demonstrate that the approach delivers near-optimal pricing decisions within milliseconds, even for large-scale instances, delivering substantial improvements in revenue or utilization compared to a conventional flat-rate baseline across various parameter settings.

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.