Search Results (312)

Due to the rapidly increasing number of studies conducted using SFO in recent years, a comprehensive and systematic review of the existing literature has become necessary. SFO is a bio-inspired metaheuristic optimization algorithm developed based on the sun-tracking behavior of sunflower plants. Owing to its simple mathematical structure and flexible search capability, SFO has been increasingly applied to various engineering and AI problems. This review study presents a systematic and comprehensive analysis of SFO-based studies published in the literature. The literature search was performed using the Scopus database, and a total of 192 studies were included in the final evaluation process. The reviewed studies were classified into eight major application domains, including engineering design, energy systems, machine learning, image processing, communication systems, robotics, forecasting, and multi-objective optimization. In addition, the distributions of standard, hybrid, and modified SFO approaches were comparatively analyzed. The temporal evolution of SFO studies, hybridization tendencies, application diversity, strengths, limitations, and future research directions were also systematically evaluated. The findings indicate that hybrid and modified SFO structures have become increasingly dominant in recent years, particularly in AI and data-driven optimization applications. Overall, this review provides a broad understanding of the current state and future research potential of SFO-based optimization studies. Full article

This paper presents a comprehensive evaluation of control strategies for a highly energy-efficient plus-energy terraced housing complex equipped with photovoltaic generation, modulating ground-source heat pumps, electrical and thermal energy storage systems, and activation of building thermal mass. The study combines long-term monitoring data, annual simulations, and hardware-in-the-loop (HiL) experiments to assess modulating heat-controlled operation (HC), PV-controlled (PVC), and predictive control strategies, including simple predictive control (SPC) and model predictive control (MPC). The simulation results show that the baseline HC operation already achieves a high load cover factor (LCF), defined as the fraction of total electrical demand covered by local PV generation (direct use + battery discharge) of 65.6% and a seasonal performance factor (SPF) of the central heat pumps of 5.8. PVC increases LCF (71.0%) by shifting heat pump operation toward PV-rich periods but leads to elevated storage temperatures up to 5 K and a reduced SPF of 4.8. MPC further enhances LCF by 4–7 percentage points in simulated and HiL environments. However, its real-world performance is strongly influenced by forecast quality and the limited controllability of the heat pump system. In addition, building thermal mass activation is investigated as a complementary flexibility option. Simulation and monitoring results demonstrate that moderate room temperature set-point (2 K) increases during PV availability significantly improve LCF from 20% to 55% while maintaining thermal comfort. Overall, the findings indicate that in highly efficient plus-energy buildings, robust rule-based strategies combined with thermal mass activation can achieve a large share of the attainable benefits, while the added complexity of MPC must be carefully weighed against practical limitations. Full article

The growing penetration of renewable energy has intensified building load fluctuations, substantially increasing balancing costs. Electric vehicles (EVs) in building clusters often have considerable idle parking time beyond essential charging needs, enabling them to provide significant flexibility while meeting scheduled demands. This EV flexibility can balance intra-day load deviations and enable arbitrage in day-ahead electricity markets. However, conventional model-based approaches are fundamentally limited by their dependence on forecasting accuracy under high uncertainty from renewable generation and EV behavior. To address this, we propose a novel bi-level online optimization framework. The upper level employs a Lyapunov optimization-based algorithm that operates without predictions, making real-time decisions on total EV charging power to balance supply-demand mismatches. The lower level introduces novel flexibility metrics for individual EVs—encompassing temporal, volumetric, and cross-day dimensions—and optimizes power allocation by minimizing flexibility loss. Furthermore, we model EV flexibility as virtual queues and rigorously derive mathematical bounds on their limits, providing theoretical support for managing flexibility reserves. Rigorous analysis validates the framework’s feasibility, and comprehensive simulations demonstrate its superiority over benchmark algorithms, achieving significant cost reductions under various uncertainty scenarios. Full article

Accurate short-term forecasting of residential electricity demand is increasingly important for smart distribution systems, particularly in the context of demand-side management and flexibility-oriented grid operation. In this study, a high-resolution forecasting framework is proposed in which household electricity demand is classified into fixed, shiftable, and adjustable load categories and forecasted together with total load. A one-minute-resolution synthetic residential load dataset is generated using the Centre for Renewable Energy Systems Technology (CREST) demand model for households with two to five occupants over a 31-day winter period in January. The appliance-level demand data are grouped according to operational characteristics and integrated into a representative four-bus distribution feeder. Minute-level power flow analysis is then performed to calculate technical losses, which are incorporated into the forecasting dataset together with meteorological variables (temperature, wind speed, and solar irradiance) and temporal descriptors. Using this multi-input structure, random forest (RF), support vector machine (SVM), feed-forward neural network (FFNN), and long short-term memory (LSTM) models are comparatively evaluated for the prediction of fixed, shiftable, adjustable, and total residential loads. Model performance is assessed using root mean square error (RMSE) and Pearson correlation coefficient (R), while mean absolute error (MAE) is additionally reported for the final test set. The results show that the LSTM model provided the most consistent overall forecasting performance, particularly for shiftable, adjustable, and total load estimation, while RF yielded competitive results for fixed-load correlation and short-window forecasting in Buses 1 and 2. In contrast, SVM and FFNN exhibited weaker generalization performance across several load categories. The proposed framework provides a practical foundation for the development of dynamic pricing mechanisms that consider load-type-based controllability levels. Overall, the findings demonstrate that integrating load categorization with meteorological, temporal, and technical loss information provides a robust and reproducible framework for smart grid applications such as demand-side management, peak load mitigation, and flexibility-aware residential load analysis. Full article

Buildings are the primary energy consumption layer of Renewable Energy Communities (RECs) and a key target for net-zero policy under the EPBD recast. This scoping review applies the PRISMA-ScR framework to map Digital Twin (DT) architectures for building-scale and community-scale energy management in REC configurations. A Scopus search yielded a final analytical corpus of 102 studies, coded through an eight-dimensional thematic matrix covering lifecycle phases, digitalization objectives, enabling technologies, DT capability dimensions, and data realism. DT is the dominant enabling technology (55.9%), followed by IoT (23.5%) and machine learning (22.5%). Research is concentrated in the Planning and Design phase (77.5%) and markedly underrepresented in Implementation and Commissioning (16.7%). Notably, only 10.8% of studies integrate real-time operational data, exposing a significant gap between simulation-based research and the deployment conditions required under current EPBD mandates. The evidence base supports building energy monitoring, demand forecasting, and flexible grid operation but remains limited for retrofit verification, standardized net-zero KPIs, and operational workflows in existing stock. Critical DT capability gaps persist in Data Services (7.8%) and User Experience (18.6%). Overall, DT architectures show genuine potential for grid-interactive, net-zero building management, yet the field presents unresolved structural challenges for large-scale real-world deployment. Full article

Under international climate governance frameworks, including the Paris Agreement, the global decarbonization process has accelerated, imposing more stringent requirements on power system flexibility and low-carbon operation. Against this backdrop, pumped storage power stations, characterized by high flexibility and rapid response capability, serve as large-scale energy storage solutions that can replace thermal power for peak shaving, thereby enhancing renewable energy integration and delivering significant carbon reduction benefits in multi-energy complementary systems. A carbon reduction calculation model is developed within the framework of the Chinese Certified Emission Reduction (CCER) trading mechanism to quantify the annual contributions of pumped storage to carbon reduction. Using a Fractional-Order Gray Model (FGM) optimized via Particle Swarm Optimization (PSO), future carbon market prices are forecasted, facilitating a robust economic evaluation. The findings reveal that, over its lifecycle, pumped storage could achieve a total carbon reduction of approximately 23.27 million tons of CO₂, yielding approximately 7.981 billion CNY in carbon reduction value, with an initial 7-year CCER inclusion period contributing 254.0787 million CNY in carbon credits. It provides critical economic and policy insights, supporting the design of advanced power systems that position pumped storage as a central regulatory asset in carbon reduction strategies. Full article

The high-penetration integration of offshore renewable energy introduces significant challenges, including high volatility, randomness, and insufficient energy accommodation, which place higher demands on the planning and operation of offshore integrated energy systems. To address these issues, this paper proposes an offshore multi-energy coupled DC microgrid system integrating wind, photovoltaic, tidal current, and wave energy, together with flexible loads such as seawater desalination and power-to-hydrogen. A hybrid forecasting model based on EMD-PCA-LSTM is developed to improve prediction accuracy under uncertain conditions. On this basis, a two-stage optimization framework considering both economic efficiency and operational risk is established. At the planning level, a joint operation–planning model incorporating Conditional Value-at-Risk (CVaR) is formulated to determine the optimal capacity configuration by minimizing the total annualized cost and risk cost. At the operational level, a multi-time-scale rolling optimization model is constructed to enhance system adaptability under renewable fluctuations. Case study results demonstrate that the proposed method significantly improves renewable energy accommodation, reduces the curtailment rate to 0.7%, and effectively balances economic performance and operational stability. The proposed framework provides a practical and efficient approach for capacity allocation and optimal operation of offshore multi-energy coupled systems. Full article

This paper presents a comprehensive review of artificial intelligence (AI) applications in photovoltaic-integrated buildings, focusing on energy forecasting, advanced control strategies, and pathways toward net-zero energy performance. Net-zero energy buildings are defined as systems that balance annual energy consumption with on-site renewable generation, requiring efficient and adaptive energy management. The review analyzes state-of-the-art AI-based forecasting methods for photovoltaic power generation and building energy demand, demonstrating the superior performance of machine learning and deep learning models in capturing nonlinear and time-dependent patterns. In parallel, advanced control strategies, including model predictive control (MPC), reinforcement learning (RL), and hybrid approaches, are evaluated in terms of their performance, limitations, and practical applicability. The results show that accurate forecasting alone is insufficient, and its integration with control strategies is essential for optimal system operation. Hybrid approaches combining model-based and data-driven methods emerge as the most effective solution for complex and dynamic environments. The role of real-time energy management systems in enabling adaptive and coordinated operation is also highlighted. Finally, key challenges related to data quality, model interpretability, and system integration are identified, along with future research directions. Overall, AI-driven energy management systems have strong potential to transform photovoltaic-integrated buildings into intelligent, flexible, and sustainable energy systems. Full article

With the large-scale deployment of distributed photovoltaics (PVs) on the user side, integrated PV-storage systems have become a critical means to reduce electricity costs and enhance energy flexibility. However, the volatility of PV output and the dynamic nature of time-of-use (TOU) pricing render the economic viability of such systems highly dependent on the coordinated optimization of capacity configuration and operational strategies. To address this, a bi-level optimization model is developed. The upper level maximizes the equivalent annual economic benefit by determining the installed capacities of PV and storage, explicitly incorporating power-sensitive operation and maintenance costs. The lower level, formulated as a mixed-integer programming problem, minimizes the daily net electricity cost by optimizing charging/discharging schedules and grid interaction. The model is solved through an iterative hierarchical approach combining the chaotic sparrow search algorithm (CSSA) and the CPLEX solver. A case study using actual data from an industrial park demonstrates that, compared with scenarios without PV-storage and with PV only, the joint PV-storage configuration reduces total electricity costs by 17.3% and 4.5%, respectively. Furthermore, the asymmetric impacts of PV forecast errors on operational economics are quantitatively analyzed: when PV output is underestimated, the failure to pre-reserve accommodation capacity leads to an increase in electricity procurement costs of RMB 1927.84 compared with the ideal scenario. To mitigate this, a risk-aware fault-tolerant scheduling strategy is proposed, which reserves a 5% accommodation margin through conservative biasing, reducing the additional cost caused by forecast errors by 20.14% and significantly enhancing the system’s economic robustness under forecast uncertainty. Full article

High penetration of wind and photovoltaic (PV) generation increases renewable uncertainty and real-time balancing pressure in active distribution networks. To address this problem, this paper proposes a two-stage robust optimization method for day-ahead and real-time scheduling of a flexibly interconnected multi-microgrid (MMG) system enabled by a flexible interconnection device (FID). The proposed framework jointly optimizes power purchase from the upper-level distribution network, micro-gas turbine output, energy storage system (ESS) operation, and FID-based bidirectional power exchange, thereby coordinating local temporal flexibility and inter-microgrid spatial flexibility. A polyhedral uncertainty set is used to model wind and PV forecast errors, and the problem is solved by the column-and-constraint generation (C&CG) algorithm. Case studies on a two-microgrid system show that, compared with independent operation under traditional robust optimization, the proposed method reduces real-time balancing cost, wind and PV curtailment, and total operating cost by 98.96%, 95.84%, and 0.59%, respectively. Sensitivity analysis further verifies the economy–robustness trade-off under different uncertainty budgets and forecast deviation levels. Full article

The rapid growth of renewable energy is driving a profound transformation of electricity grids toward architectures characterized by high shares of inverter-based generation, increased decentralization, and extensive digitalization. While wind and solar technologies have matured at the component level, their large-scale integration introduces technical, operational, and institutional challenges that extend beyond conventional power-system design paradigms. This review provides an integrated synthesis of the technologies, control strategies, and management processes that enable renewable energy integration into emerging electricity grids. Key challenges are analyzed across multiple timescales: fast frequency and voltage dynamics in low-inertia systems (milliseconds to seconds), forecasting, optimization, and automated control (real-time to near-real-time), and long-term planning of transmission, storage, and flexibility resources (years to decades). The synthesis covers grid-forming and grid-following inverter control, with quantitative comparison across short-circuit-ratio regimes; HVDC and HVAC transmission technologies; energy storage systems, including emerging electrochemical and mechanical solutions; smart-grid digitalization through EMS, SCADA, and digital twins; artificial intelligence and machine-learning deployments at major transmission system operators; sector coupling involving hydrogen and carbon capture; and cybersecurity considerations. Real-world case studies are used to illustrate practical lessons, with explicit attention to the brownfield–greenfield distinction between modernization of legacy systems and the design of new networks in developing regions. The review concludes by identifying key research and development priorities for achieving reliable, resilient, and economically efficient high-renewable energy systems. Full article

The growing energy demand in rural areas such as the ejido Boca del Cerro, located in Tenosique, Tabasco (Mexico), near the Usumacinta River, calls for sustainable energy solutions such as microgrids. This study proposes an energy management system combining renewable energy forecasting and fuzzy control for a simulated small autonomous rural microgrid scenario designed to supply a fixed priority load of 5 kW and a variable flexible load ranging from 1 to 10 kW. Three LSTM architectures (vanilla, stacked, and bidirectional) are compared for predicting solar irradiance, wind speed, and river flow. The vanilla model is optimized using Hyperband to improve prediction accuracy, particularly for flow rate, which is rarely addressed in similar studies. Forecasts feed into models of photovoltaic, wind, and hydro systems within the microgrid. Energy dispatch is managed through fuzzy logic control. The fuzzy controller supports load prioritization, battery charge/discharge management, and surplus energy redirection to an absorbing load. The final vanilla LSTM achieved RMSE values of 25.741, 0.302, and 12.644 for solar irradiance, wind speed, and river flow, respectively, with NSE values above 0.949 in all cases. These results indicate high forecasting accuracy for solar irradiance and river flow, with limited improvement for wind speed. Overall, the proposed EMS enables effective energy flow management, while the integration of hydrokinetic turbines with AI-based forecasting represents a novel contribution. Full article

Electric power distribution systems have been undergoing a transformation that can be attributed to factors such as the deregulation of the electric power supply industry, growing public concern over energy security and the environmental impact of energy generation and utilization, and technological advancements that have given impetus to concerted efforts to modernize the power grid in the framework of smart grid initiatives. The traditionally passive distribution network is increasingly becoming active due to the steady increase in the amount of distributed energy resources being integrated into the network. This has, in turn, given rise to a higher need for flexibility resources that can be used to handle the increased uncertainty caused by stochastic and intermittent distributed resources, such as variable renewable power generation. The provision of demand-side flexibility has largely been the purview of large industrial and commercial energy consumers. This article discusses the role that the aggregator can play in facilitating the provision of flexibility resources by small-scale consumers and prosumers and presents a case study on small-scale renewable generation and residential demand forecasting, which form an integral part of demand flexibility aggregation. Full article

Wind power plays an increasingly important role in the global energy transition. However, its power output exhibits significant uncertainty due to rapid variations in meteorological conditions. Existing forecasting methods still face challenges in large-scale wind farm cluster scenarios. In such cases, spatial heterogeneity and temporal asynchrony among wind farms cannot be fully characterized, which limits the overall prediction accuracy. To address these issues, this study proposes a novel hierarchical and adaptive collaborative forecasting framework for wind farm clusters by integrating meteorology-driven dynamic clustering with deep learning-based prediction. First, a multidimensional feature system is constructed by jointly considering static wind farm attributes and dynamic meteorological variation trends. Based on a sliding time window, real-time meteorological similarity among wind turbines is evaluated, allowing meteorological data to actively drive the formation and continuous evolution of adaptive subcluster structures. Subsequently, a deep learning model is developed to perform short-term power forecasting at the dynamic subcluster level. This approach enables the framework to flexibly capture spatio-temporal heterogeneity while maintaining robust prediction capability under varying cluster structures. Experimental results based on real-world wind farm cluster data demonstrate that the proposed method achieves superior accuracy and robustness compared with conventional whole-farm forecasting and static clustering approaches. The proposed framework enhances forecasting reliability, thereby supporting renewable energy integration and sustainable low-carbon power systems. Full article

Brent crude oil prices are strategically important due to their sensitivity to geopolitical developments, financial market stress, and global monetary conditions. This study examines whether strategic risk indicators improve the forecasting performance of Brent crude oil returns within an integrated econometric and machine learning framework. Monthly data from January 2001 to December 2025 are employed, using the Global Geopolitical Risk Index (GPR), the CBOE Volatility Index (VIX), and the U.S. 10-year Treasury yield (DGS10) as key explanatory variables. Methodologically, the analysis first estimates benchmark econometric models, including ARIMAX (AutoRegressive Integrated Moving Average with Explanatory Variable) and ARIMAX-gjrGARCH (Glosten-Jagannathan-Runkle Generalized Autoregressive Conditional Heteroscedasticity, and then implements machine learning models, namely XGBoost (eXtreme Gradient Boosting), LightGBM (Light Gradient Boosting Machine), and Random Forest, to capture potential nonlinear relationships. Using sMAPE (Symmetric Mean Absolute Percentage Error), forecast performance is assessed over multiple forecast horizons under a rolling-origin framework. Across several forecasting horizons and train-test split configurations, the empirical results consistently show that machine learning techniques, especially LightGBM, offer superior out-of-sample forecasting accuracy. These findings suggest that the dynamics of Brent crude oil returns are influenced by complex and nonlinear relationships between macro-financial conditions, financial uncertainty, and geopolitical risk. The study concludes that flexible data-driven forecasting frameworks offer stronger predictive performance than benchmark econometric models under strategic risk conditions and provide useful implications for energy market risk management and policy decision-making. Full article