Search Results (860)

The growing complexity of distribution-level virtual power plants (VPPs) demands a rethinking of how flexible demand is modeled, aggregated, and dispatched under uncertainty. Traditional optimization frameworks often rely on deterministic or homogeneous assumptions about end-user behavior, thereby overestimating controllability and underestimating risk. In this paper, we propose a behavior-aware, two-stage stochastic dispatch framework for VPPs that explicitly models heterogeneous user participation via integrated behavioral economics and social interaction structures. At the behavioral layer, user responses to demand response (DR) incentives are captured using a Prospect Theory-based utility function, parameterized by loss aversion, nonlinear gain perception, and subjective probability weighting. In parallel, social influence dynamics are modeled using a peer interaction network that modulates individual participation probabilities through local contagion effects. These two mechanisms are combined to produce a high-dimensional, time-varying participation map across user classes, including residential, commercial, and industrial actors. This probabilistic behavioral landscape is embedded within a scenario-based two-stage stochastic optimization model. The first stage determines pre-committed dispatch quantities across flexible loads, electric vehicles, and distributed storage systems, while the second stage executes real-time recourse based on realized participation trajectories. The dispatch model includes physical constraints (e.g., energy balance, network limits), behavioral fatigue, and the intertemporal coupling of flexible resources. A scenario reduction technique and the Conditional Value-at-Risk (CVaR) metric are used to ensure computational tractability and robustness against extreme behavior deviations. Full article

Under the background of energy transition, the Integrated Energy System (IES) of the park has become a key carrier for enhancing the consumption capacity of renewable energy due to its multi-energy complementary characteristics. However, the high proportion of wind and solar resource access and the fluctuation of diverse loads have led to the system facing dual uncertainty challenges, and traditional optimization methods are difficult to adapt to the dynamic and complex dispatching requirements. To this end, this paper proposes a new dynamic energy management method based on Deep Reinforcement Learning (DRL) and constructs an IES hybrid integer nonlinear programming model including wind power, photovoltaic, combined heat and power generation, and storage of electric heat energy, with the goal of minimizing the operating cost of the system. By expressing the dispatching process as a Markov decision process, a state space covering wind and solar output, multiple loads and energy storage states is defined, a continuous action space for unit output and energy storage control is constructed, and a reward function integrating economic cost and the penalty for renewable energy consumption is designed. The Deep Deterministic Policy Gradient (DDPG) and Deep Q-Network (DQN) algorithms were adopted to achieve policy optimization. This study is based on simulation rather than experimental validation, which aligns with the exploratory scope of this research. The simulation results show that the DDPG algorithm achieves an average weekly operating cost of 532,424 yuan in the continuous action space scheduling, which is 8.6% lower than that of the DQN algorithm, and the standard deviation of the cost is reduced by 19.5%, indicating better robustness. Under the fluctuation of 10% to 30% on the source-load side, the DQN algorithm still maintains a cost fluctuation of less than 4.5%, highlighting the strong adaptability of DRL to uncertain environments. Therefore, this method has significant theoretical and practical value for promoting the intelligent transformation of the energy system. Full article

In recent years, frequent typhoon-induced disasters have significantly increased the risk of power grid outages, posing severe challenges to the secure and stable operation of distribution grids with high penetration of distributed photovoltaic (PV) systems. Furthermore, during post-disaster recovery, the dual uncertainties of distributed PV output and the charging/discharging behavior of flexible resources such as electric vehicles (EVs) complicate the configuration and scheduling of mobile energy storage systems (MESS). To address these challenges, this paper proposes a two-stage robust optimization framework for dynamic recovery of distribution grids: Firstly, a multi-stage decision framework is developed, incorporating MESS site selection, network reconfiguration, and resource scheduling. Secondly, a spatiotemporal coupling model is designed to integrate the dynamic dispatch behavior of MESS with the temporal and spatial evolution of disaster scenarios, enabling dynamic path planning. Finally, a nested column-and-constraint generation (NC&CG) algorithm is employed to address the uncertainties in PV output intervals and EV demand fluctuations. Simulations on the IEEE 33-node system demonstrate that the proposed method improves grid resilience and economic efficiency while reducing operational risks. Full article

Hydroelectricity, one of the oldest and most potent forms of renewable energy, not only provides low-cost electricity for the grid but also preserves nature through flood control and irrigation support. Forecasting hydroelectricity generation is vital for utilizing alleviating resources effectively, optimizing energy production, and ensuring sustainability. This paper provides an innovative approach to hydroelectricity generation forecasting (HGF) of a 138 MW hydroelectric power plant (HPP) in the Eastern Mediterranean by taking electricity productions from the remaining upstream HPPs on the Ceyhan River within the same basin into account, unlike prior research focusing on individual HPPs. In light of tuning hyperparameters such as number of trees and learning rates, this paper presents a thorough benchmark of the state-of-the-art tree-based machine learning models, namely categorical boosting (CatBoost), extreme gradient boosting (XGBoost), and light gradient boosting machines (LightGBM). The comprehensive data set includes historical hydroelectricity generation, meteorological conditions, market pricing, and calendar variables acquired from the transparency platform of the Energy Exchange Istanbul (EXIST) and MERRA-2 reanalysis of the NASA with hourly resolution. Although all three models demonstrated successful performances, LightGBM emerged as the most accurate and efficient model by outperforming the others with the highest coefficient of determination (R²) (97.07%), the lowest root mean squared scaled error (RMSSE) (0.1217), and the shortest computational time (1.24 s). Consequently, it is considered that the proposed methodology demonstrates significant potential for advancing the HGF and will contribute to the operation of existing HPPs and the improvement of power dispatch planning. Full article

With the rapid development of distributed energy and electric vehicles (EVs), the limited hosting capacity of distribution networks has severely impacted their economic dispatch and safe operation. To address these challenges, in this work, an optimal planning model considering the uncertainty of wind and solar power output is proposed, aiming to determine the location and capacity of electric vehicle charging stations (EVCSs). The model seeks to minimize the total costs, voltage fluctuations, and network losses, subject to constraints such as EV user satisfaction and grid company satisfaction. A multi-objective heat exchange optimization algorithm under Gaussian mutation (MOTEO-GM) is employed to validate the model on an extended IEEE-33 bus system and a real-world case in the University Town area of Chenggong District, Kunming City. Simulation results indicate that, in the test system, voltage fluctuations and system power losses are decreased by 43.05% and 37.47%, respectively, significantly enhancing the economic operation of the distribution grid. Full article

Myanmar’s electricity supply relies mainly on hydropower and gas-fired generation, yet rural electrification remains limited, with national access at approximately 60%. The National Electrification Plan (NEP) aims for universal access via nationwide grid expansion, but progress in remote areas is constrained by financial limits and suspended external funding. This study evaluates the techno-economic feasibility of decentralized microgrids as an alternative to conventional grid extension under current budgetary conditions. We integrate a terrain-adjusted MV line-cost model with (i) PLEXOS capacity expansion and chronological dispatch for centralized supply and (ii) HOMER Pro optimization for PV–diesel–battery microgrids. Key indicators include LCOE, NPC, CAPEX, OPEX, reliability (ASAI/max shortage), renewable fraction, and unserved energy. Sensitivity analyses cover diesel, PV, and battery prices, as well as discount rate variations. The results show microgrids are more cost-effective in terrain-constrained regions such as Chin State, particularly when accounting for transmission and delayed generation costs, whereas grid extension remains preferable in flat, accessible regions like Nay Pyi Taw. Diesel price is the dominant cost driver across both regions, while battery cost and discount rate affect Chin State more, and PV cost is critical in Nay Pyi Taw’s solar-rich context. These findings provide evidence-based guidance for rural electrification strategies in Myanmar and other developing countries facing similar financial and infrastructural challenges. Full article

This study investigates the configuration of an energy storage system (ESS) and the optimization of energy management strategies for diesel-electric hybrid ships, with the goal of enhancing fuel economy and reducing emissions. An integrated mathematical model of the diesel generator set and the battery-based ESS is established. A rule-based energy management strategy (EMS) is proposed, in which the ship operating conditions are classified into berthing, maneuvering, and cruising modes. This classification enables coordinated power allocation between the diesel generator set and the ESS, while ensuring that the diesel engine operates within its high-efficiency region. The optimization framework considers the number of battery modules in series and the upper and lower bounds of the state of charge (SOC) as design variables. The dual objectives are set as lifecycle cost (LCC) and greenhouse gas (GHG) emissions, optimized using the Multi-Objective Coati Optimization Algorithm (MOCOA). The algorithm achieves a balance between global exploration and local exploitation. Numerical simulations indicate that, under the LCC-optimal solution, fuel consumption and GHG emissions are reduced by 16.12% and 13.18%, respectively, while under the GHG-minimization solution, reductions of 37.84% in fuel consumption and 35.02% in emissions are achieved. Compared with conventional algorithms, including Multi-Objective Particle Swarm Optimization (MOPSO), Non-dominated Sorting Dung Beetle Optimizer (NSDBO), and Multi-Objective Sparrow Search Algorithm (MOSSA), MOCOA exhibits superior convergence and solution diversity. The findings provide valuable engineering insights into the optimal configuration of ESS and EMS for hybrid ships, thereby contributing to the advancement of green shipping. Full article

Integrated Hydrogen–Energy Systems (IHES) have attracted widespread attention; however, distributed energy sources such as photovoltaics (PV) and wind turbines (WT) within these systems exhibit significant uncertainty and intermittency, posing key challenges to scheduling complexity and system instability. As a core mechanism for the integrated operation of IHES, electricity price regulation can promote the absorption of renewable energy, optimize resource allocation, and enhance operational economy. Nevertheless, uncertainties in IHES hinder the formulation of accurate electricity prices, which easily lead to delays in scheduling responses and an increase in cumulative operating costs. To address these issues, this study develops lifespan models for Proton Exchange Membrane Electrolyzers (PEMELs) and Proton Exchange Membrane Fuel Cells (PEMFCs), constructs dynamic equations for the demand side and response side, and proposes a fuzzy-weighted dynamic pricing strategy. Simulation results show that, compared with fixed pricing, the proposed dynamic pricing strategy reduces economic indicators by an average of 15.3%, effectively alleviates energy imbalance, and optimizes the energy supply of components. Additionally, it reduces the lifespan degradation of PEMELs by 21.59% and increases the utilization rate of PEMFCs by 54.8%. Full article

Achieving net-zero emissions in arid and high-solar-yield regions demands innovative, cost-effective, and scalable energy technologies. This study conducts a comprehensive techno-economic analysis and assessment of a novel hybrid photovoltaic–thermal solar collector (U.S. Patent No. 11,431,289) that integrates a reverse flat plate collector and mini-concentrating solar thermal elements. The system was tested in Qatar and Germany and simulated via a System Advising Model tool with typical meteorological year data. The system demonstrated a combined efficiency exceeding 90%, delivering both electricity and thermal energy at temperatures up to 170 °C and pressures up to 10 bars. Compared to conventional photovoltaic–thermal systems capped below 80 °C, the system achieves a heat-to-power ratio of 6:1, offering an exceptional exergy performance and broader industrial applications. A comparative financial analysis of 120 MW utility-scale configurations shows that the PVT + ORC option yields a Levelized Cost of Energy of $44/MWh, significantly outperforming PV + CSP ($82.8/MWh) and PV + BESS ($132.3/MWh). In addition, the capital expenditure is reduced by over 50%, and the system requires 40–60% less land, offering a transformative solution for off-grid data centers, water desalination (producing up to 300,000 m³/day using MED), district cooling, and industrial process heat. The energy payback time is shortened to less than 4.5 years, with lifecycle CO₂ savings of up to 1.8 tons/MWh. Additionally, the integration with Organic Rankine Cycle (ORC) systems ensures 24/7 dispatchable power without reliance on batteries or molten salt. Positioned as a next-generation solar platform, the Hassabou system presents a climate-resilient, modular, and economical alternative to current hybrid solar technologies. This work advances the deployment readiness of integrated solar-thermal technologies aligned with national decarbonization strategies across MENA and Sub-Saharan Africa, addressing urgent needs for energy security, water access, and industrial decarbonization. Full article

Under China’s dual-carbon strategic objectives, virtual power plants (VPPs) actively participate in the coupled electricity–carbon market through the optimized scheduling of distributed energy resources, simultaneously stabilizing grid operations and reducing carbon emissions. Photovoltaic (PV) generation, a cornerstone resource within VPP systems, introduces significant challenges in scheduling due to its inherent output variability. To increase the accuracy in the characterization of the PV output uncertainty, a weighted probability distribution of solar irradiance, based on historical irradiance data, is newly proposed. The leveraging rejection sampling technique is applied to generate solar irradiance scenarios that are consistent with the proposed weighted solar irradiance probability model. Further, a confidence interval-based filtering mechanism is applied to eliminate extreme scenarios, ensuring statistical credibility and enhancing practicability in actual dispatch scenarios. Based on the filtered scenarios, a novel dispatch strategy for the VPP operation in the electricity–carbon market is proposed. Numerical case studies verify that scenarios generated by the weighted solar irradiance probability model are capable of closely replicating historical PV characteristics, and the confidence interval filter effectively excludes improbable extreme scenarios. Compared to conventional normal distribution-based methods, the proposed approach yields dispatch solutions that are more closely aligned with the optimal dispatch of the historical irradiance data, demonstrating the improved accuracy in the probabilistic modelling of the PV output uncertainty. Consequently, the obtained dispatch strategy shows the improved capability to ensure the market revenue of the VPP considering the fluctuations of the PV output. Full article

The large-scale integration of new power loads, such as electric vehicles and energy storage devices, has led to challenges including insufficient regulation capacity and low resource coordination efficiency in low-voltage distribution transformer areas. To address these issues, this paper proposes an optimal configuration model for flexible interconnected distribution transformer areas based on load aggregation. First, a flexible interconnection architecture is constructed using multi-port power electronic conversion devices, enabling mutual power support and voltage stabilization between adjacent areas. Second, a load aggregator scheduling model is established to quantitatively assess the dispatchable potential of electric vehicle charging loads. On this basis, a multi-objective optimization configuration model is formulated with the objectives of minimizing the comprehensive cost of the system and minimizing the average peak-valley difference of substation transformer loads. Case study results demonstrate that the proposed model significantly improves both economic efficiency and operational reliability. Compared to the traditional independent operation mode, the coordinated optimization scheme reduces the comprehensive system cost by 29.6% and narrows the average load peak-valley difference by 50.8%. These findings verify the synergistic effectiveness of flexible interconnection and load aggregation technologies in enhancing equipment utilization, reducing distribution losses, and improving power supply resilience. Full article

With the large-scale integration of distributed resources into the distribution network, such as wind/solar power and electric vehicles (EVs), the uncertainties have rapidly increased in the operation optimization of the distribution network. In this context, it is of great practical interest to ensure the security and economic operation of the distribution network. This paper addresses this issue and makes the following contributions. Firstly, a two-stage stochastic rolling optimization framework for active–reactive power scheduling is established. In the first stage, it dispatches the active power of distributed resources. In the second stage, it optimizes the reactive power compensation based on the first-stage scheduling plan. Secondly, the simulation-based Rollout method is proposed to obtain the improved active power dispatching policy for cost optimization in the first stage. Meanwhile, the aggregated power of EVs can be determined based on the mobility and charging demand of EVs. Thirdly, based on the aggregated power of EVs, a scenario-based second-order cone programming is applied to perform the rolling optimization of reactive power compensation for voltage performance improvement in the second stage. The numerical results demonstrate that this method can effectively improve the economic operation of the distribution network while enhancing its operational security by leveraging the charging elasticity of EVs. Full article

Electric arc furnace technology is a key factor in the sustainable transformation of the iron and steel industry. This study compares two discrete-time multi-objective optimization models—integer and mixed-integer linear programming—that integrate unit commitment with economic and environmental dispatch. After evaluating both approaches, the integer linear programming model is used, due to its reasonable calculation time, to assess demand-side management potentials under real-world processes and day-ahead market conditions. The model is applied to various scenarios with differing energy price dynamics, CO₂ pricing, EAF utilization levels, and weighting of the objective functions. Results indicate cost savings of up to 6.95% and CO₂ emission reductions of up to 10.86%, though these are subject to a non-linear trade-off between economic and environmental goals. Due to process constraints and market structures, EAFs’ flexibility in energy carrier use (switch between electricity and natural gas) is limited to 3.07%. Additionally, lower furnace utilization does not necessarily increase flexibility, as downstream process requirements restrict scheduling options. The study underscores the importance of green electrification, with up to 36% CO₂ savings when using 100% renewable electricity. Overall, unlocking industrial flexibility requires technical solutions, supportive market incentives, and regulatory frameworks for effective industrial decarbonization. Full article

As electric grids decarbonize and distributed energy resources (DERs) become increasingly prevalent, interconnection assessments must evolve to reflect operational variability and control flexibility. This paper highlights key modeling limitations observed in practice and reviews approaches for modeling uncertainty. It then introduces a Probabilistic Deliverability Assessment (PDA) framework designed to complement and extend existing procedures. The framework integrates scenario-based AC optimal power flow (AC OPF), corrective dispatch, and optional multi-temporal constraints. Together, these form a structured methodology for quantifying DER utilization, deliverability, and reliability under uncertainty in load, generation, and topology. Outputs include interpretable metrics with confidence intervals that inform siting decisions and evaluate compliance with reliability thresholds across sampled operating conditions. A case study on Puerto Rico’s publicly available bulk power system model demonstrates the framework’s application using minimal input data, consistent with current interconnection practice. Across staged fossil generation retirements, the PDA identifies high-value DER sites and regions requiring additional reactive power support. Results are presented through mean dispatch signals, reliability metrics, and geospatial visualizations, demonstrating how the framework provides transparent, data-driven siting recommendations. The framework’s modular design supports incremental adoption within existing workflows, encouraging broader use of AC OPF in interconnection and planning contexts. Full article

With the gradual shift towards the use of electric vehicles (EV), electricity demand is expected to increase especially in energy communities. Therefore, it is important to investigate how energy is generated as the provenance of electricity supply is directly linked to climate change. There are only a few studies that investigated the internet of energy and energy provenance, but this area of research is important to prevent the rebound effect of CO₂ emission due to the lack of a transparent approach that verifies the source of electricity consumed for charging EVs. The energy system is a complex network, which results in difficulty verifying the source of electricity as related to the generation of energy. Identifying the provenance of electricity is challenging since electricity is a non-physical element. Moreover, the volatility of a Renewable Energy Source (RES), such as solar and wind power farms, in relation to the complex electricity distribution system makes tracking and tracing challenging. Disruptive technologies, such as Distributed Ledger Technologies (DLT), have been previously adopted to trace the end-to-end stages of products. Likewise, artificial intelligence (AI) can be adopted for the optimization, control, dispatching, and management of energy systems. Therefore, this study develops a decentralized intelligent framework enabled by AI-based DLT and smart contracts deployed to accelerate the development of the internet of energy towards energy provenance in energy communities. The framework supports the tracing and tracking of RES type and source consumed for charging EVs. Findings from this study will help to accelerate the production, trading, distribution, sharing, and consumption of RES in energy communities. Full article