Search Results (211)

Modern power systems increasingly integrate renewable energy sources (RESs), electric mobility, and dynamic market participation. Dynamic electricity pricing, reflecting real-time market conditions, is increasingly important for prosumers worldwide, enabling flexible and efficient energy management. The growing adoption of electric vehicles (EVs) and bidirectional charging technologies (V2G, V2H) allows EVs to act as mobile battery energy storage systems (mBESSs). This study presents a Python 3.11-based application for simulating and analyzing energy flows in residential systems with photovoltaic (PV) installations, EVs acting as mBESS, and optional stationary battery energy storage systems (BESSs), using real 2024 data on consumption, PV production, and market prices. The energy management system (EMS) employs a rule-based algorithm to optimize energy use and economic benefits, adjusting dispatch between PV systems, the grid, mBESSs, and BESSs based on price coefficients $\alpha$ and $\beta$. Simulation scenarios were developed based on two EV availability patterns: Profile 1, representing users unavailable during standard working hours, and Profile 2, representing users with intermittent availability for brief excursions. The results demonstrate substantial electricity cost reductions: For a Nissan Leaf e+ with Profile 1, annual costs decrease by approximately 20% compared to a system without EVs. With PV generation and Profile 2, costs drop by 57% relative to the baseline, while adding a stationary BESS further reduces costs by nearly 95%. It should be noted that the results were obtained assuming zero energy costs for propulsion. Therefore, the economic benefits reported here represent an upper-bound estimate and would be lower under real-world driving conditions. These findings highlight that coordinated EMS operation with EVs as mBESSs, supported by optional BESSs, can maximize economic performance and provide prosumers with a practical framework for flexible and efficient energy management. Full article

To address the coupled challenges of renewable power volatility, high operating cost, and electrolyzer degradation in grid-connected wind–PV hydrogen production systems, this paper proposes a hierarchical day-ahead scheduling strategy under time-of-use (TOU) electricity prices. The upper layer performs price-responsive economic dispatch to coordinate renewable utilization, battery operation, grid transactions, and aggregate hydrogen-production power with the objective of minimizing lifecycle operating cost. The lower layer introduces a health-aware non-uniform rotation mechanism to allocate the aggregate power command among electrolyzer units, thereby reducing fluctuation exposure and balancing lifetime consumption across the array. Practical constraints, including multi-state electrolyzer operation, unit-commitment logic, battery state-of-charge dynamics, hydrogen storage limits, and system power balance, are explicitly considered. A case study of a wind–PV hydrogen production project in Northern China shows that the proposed strategy shifts electricity purchases to valley-price periods and promotes electricity export during peak-price periods. Compared with the benchmark strategy, hydrogen production during low wind–PV generation periods increases from 342,000 to 381,000 Nm³, the share of fluctuating operating time decreases from 62.5% to 12.5%, and the average daily start–stop frequency declines from 8.0 to 4.8. Consequently, the degradation penalty is reduced by about 40%, and lifecycle operating cost decreases by 27.3%. Full article

To improve the economic efficiency and reliability of off-grid renewable energy hydrogen production systems, this paper proposes an integrated optimal variable temperature operation strategy for multi-electrolyzer systems. This paper develops a unified optimization model that deeply integrates the electro-thermal characteristics and dynamic operational states of multiple alkaline water electrolyzers. By actively regulating the operating temperature and optimizing power allocation, the strategy significantly improves economic efficiency under fluctuating power inputs. Furthermore, a collaborative dispatch principle is introduced to ensure balanced aging across the electrolyzer cluster. Simulation results based on real-world wind and solar data demonstrate that compared to traditional rule-based methods, the proposed strategy increases the monthly net profit by up to 14.6% and significantly reduces the frequency of cold and hot starts by 51.21% and 89.41%, respectively. This research provides an efficient and reliable technical framework for the collaborative management of large-scale green hydrogen infrastructure. Full article

Food delivery logistics sustainability includes environmental impact, economic efficiency, and service quality. Traditional logistics models mainly rely on fixed ''pickup buffer'' policies (such as a set 10 min wait). These systems do not account for the changing nature of restaurant operations and delivery conditions, leading to higher operating costs, driver idle time, and poorer food quality. To move delivery systems from reactive decision-making to proactive, dynamically forecasted operations, an adaptive control mechanism is needed. In on-demand food delivery, this offers a clear path to sustainability through better dispatch accuracy, order prep, and pickup coordination. To resolve these bottlenecks, this study examines how a smart logistics framework based on a dynamic Gradient Boosting Regressor (GBR) and policy-sensitive GBR can provide more accurate estimates of drivers' waiting times in light of contextual factors such as rush hour, time of day, and operational constraints. In last-mile food delivery, the proposed method aims to reduce operational costs, improve scheduling effectiveness, and maximize resource utilization by moving beyond static, predefined waiting periods to adaptive, context-aware decisions. The developed framework analyzes a proprietary dataset of 368,250 instant orders from a major Saudi Arabian logistics provider to evaluate the efficacy of static thresholds versus a proposed predictive, dynamic machine-learning-based approach. After rigorous data cleaning and temporal-logic adjustments, a ''High-Fidelity Ground-Truth'' subset of 1842 verified orders is used to simulate policy performance. This 99.5% reduction is necessitated by the widespread absence of the ''Order Ready'' timestamp in operational logs, which is the critical target variable for supervised learning; comparative analysis confirms the subset remains representative of the parent population’s spatiotemporal dynamics. The baseline analysis reveals severe inefficiencies in the static model, with a 61.67% violation rate for driver wait times, particularly in Riyadh (p < 0.001) and during late-night operations. The simulation results demonstrate that the dynamic policy reduces the ''Buffer Miss Rate'' (premature driver arrivals) from 59.08% to 7.32%, resulting in a 68.5% reduction in total operational waste costs. Full article

Korea’s rising shares of variable renewable energy (VRE) and inflexible baseload increases the need for fast-responding and cost-effective flexibility. Most studies on power-to-heat (P2H) emphasize district-heating (DH) economics or load shifting, leaving the system-level impacts of its reserve provision capability unclear. We develop a mixed-integer linear programming model for reserve-constrained unit commitment (RCUC) that co-optimizes the power and DH systems. In addition, the model incorporates a P2H system capable of providing multiple reserve services. Reserve requirements are divided into static and dynamic terms, with the dynamic term represented as a piecewise-linear approximation of short-term VRE variability derived from weather-based generation profiles and evaluated at the scheduled VRE output. Using a 2030 winter week for Korea, we compare five cases: no EB; EB as load only; and EB contributing only to the secondary/regulation reserve requirement, only to the primary reserve requirement, or both. Under the KRW 1000/kWh curtailment-penalty case, EB as load reduces system operating cost compared to the baseline, and enabling reserve provision yields additional cost savings, with the largest benefit observed when primary reserve is provided. EB operation also shifts dispatch from coal and gas toward nuclear, VRE, and pumped storage, while reducing renewable curtailment. Overall, enabling P2H to contribute to reserve procurement, particularly in the primary reserve, delivers substantially greater value than representing P2H solely as a controllable load for energy shifting. Full article

With the integration of renewable energy into traction power supply systems at a high proportion and penetration level, the intermittency and randomness of renewable energy output significantly intensify the fluctuation characteristics of traction loads, posing severe challenges to the stable operation and precise dispatch of the system. To effectively address the dynamic tracking and anti-disturbance issues arising from the dual uncertainties of source and load, this paper proposes a dual-timescale two-layer optimization dispatch strategy based on Model Predictive Control (MPC). In the upper-layer optimization, with the objective of optimal system economic operation, a multi-step rolling optimization method is adopted to formulate a long-timescale baseline dispatch plan, fully considering the temporal correlation of photovoltaic and wind power outputs and the periodic characteristics of traction loads. In the lower-layer optimization, aimed at smoothing power fluctuations and correcting prediction deviations, the technical advantages of supercapacitors—high power density and fast response—are utilized to perform real-time tracking and dynamic compensation of the upper-layer baseline plan. This effectively reduces the impact of prediction errors on control accuracy, achieves smooth control of tie-line power, and enhances overall system stability. Case study results based on an actual railway traction power supply system demonstrate that the proposed method can fully leverage the coordinated and complementary characteristics of the hybrid energy storage system, effectively suppress power fluctuations from renewable energy output and traction loads, and achieve economic operation objectives while ensuring system disturbance rejection performance, thereby validating the effectiveness and practicality of the strategy. Full article

Electricity demand forecasting is essential for optimizing energy management and planning in microgrids and institutional contexts. The purpose of this article is to demonstrate how flexibility characterization can serve as a structural foundation for prediction, providing a contextualized framework that surpasses the limitations of traditional approaches. Representative trajectories (A–D), derived from entropy and variability metrics, were consolidated from historical user data and used as the basis for modeling. Two complementary approaches were implemented: ARIMA models, which capture endogenous dynamics, and ARX models, which extend this capacity by incorporating exogenous cyclical variables (hour, day of the week, month) and lagged predictors. A systematic grid search was conducted to identify optimal parameter configurations, followed by validation through rolling forecasts with a 24-h horizon, relevant for operators of microgrids, institutional managers, and energy planners. Performance was evaluated using MAE, RMSE, MAPE, and SMAPE, ensuring comparability across trajectories. Results show that ARIMA consistently achieved lower error rates in stable trajectories (A and C), with SMAPE values around 2.0%, while ARX provided substantial improvements in irregular ones (B and C), reducing SMAPE from 3.7–5.9% to approximately 2.2–2.6%. In highly irregular profiles (D), all models converged to similar accuracy (SMAPE ≈ 9.0%). When applied to individual users, predictive errors varied more widely depending on trajectory assignment: stable users showed SMAPE values around 3–4%, while irregular users exhibited much higher errors, exceeding 18–21%. Unlike conventional methods that treat characterization and prediction as separate processes, this study integrates both into a unified framework, enabling forecasts to capture stability, cyclicity, and adaptability. The methodology was further applied to individual users by assigning them to representative trajectories and adjusting predictions through baseline scaling. Overall, the findings demonstrate that embedding forecasts within characterized trajectories transforms prediction into a contextualized analysis of flexibility, enabling accurate short-term forecasts and supporting practical applications in energy planning, demand management, and economic dispatch. The framework has been designed to support electricity demand forecasting across multiple contexts, from microgrids and institutional systems to larger territorial and national scales. Through contextual calibration, the methodology ensures adaptability and broader relevance for energy forecasting and demand-side management. Full article

Metaheuristic optimization algorithms often suffer from structural imbalance between exploration and exploitation, leading to premature convergence and performance degradation in high-dimensional or constrained problems. To address this issue, a symmetry-enhanced Improved Enterprise Development Optimization Algorithm (IEDOA) is proposed. The algorithm establishes a dynamic symmetry between global exploration and local exploitation through three coordinated strategies: a performance-feedback-based adaptive activity selection mechanism, a multi-elite-guided structural evolution strategy, and a lifecycle-aware exploration mechanism inspired by technological scheduling dynamics. The proposed symmetric regulation framework improves population diversity while preserving convergence stability, thereby enhancing search efficiency in complex landscapes. To validate its performance, IEDOA is evaluated on CEC2017 (30/50 dimensions) and CEC2022 (10/20 dimensions) benchmark suites and compared with several advanced metaheuristic algorithms. Experimental results demonstrate superior convergence accuracy, robustness, and scalability. Statistical analyses using the Wilcoxon signed-rank and Friedman tests further confirm its significant performance advantages. To demonstrate practical applicability, IEDOA is applied to a grid-connected microgrid economic dispatch problem involving renewable generation units, controllable generators, and energy storage systems under 24 h operational constraints. Simulation results show that the proposed method achieves lower operational costs and smaller performance variance across independent runs. Overall, IEDOA provides an effective symmetric optimization framework for complex engineering systems characterized by nonlinearity, multi-constraints, and high dimensionality. Full article

The operation of the gas–steam–electricity multi-energy coupling system in iron and steel enterprises faces critical challenges: conflicts between energy efficiency and economic objectives, insufficient scheduling accuracy, and low energy utilization caused by source–load fluctuations. To address these issues, this paper proposes a hybrid scheduling model based on condition awareness and multi-objective optimization. The model integrates three key components. First, an energy fluctuation prediction technology based on working condition changes is developed. By acquiring real-time production signals and gas flow data, combined with a condition definition management module, it enables automatic identification and tracking of equipment operation status. A working condition sample curve superposition method is used to calculate energy medium imbalances, generating visual prediction curves for key parameters such as blast furnace, coke oven, and converter gas holder levels, achieving an average prediction accuracy of ≥95%. Second, a peak-shifting and valley-filling scheduling model for gas holders is designed, leveraging time-of-use electricity prices. During valley price periods, power purchases are increased and surplus gas is stored; during peak price periods, gas power generation is increased to reduce purchased electricity. A nonlinear model capturing the load–efficiency relationship of boilers and generators is established to dynamically optimize scheduling strategies. This reduces the proportion of peak hour power purchases by 10.3%, energy costs by 3.12%, and system energy consumption by 2.16%. Third, a multi-period and multi-medium energy optimization scheduling model is formulated as a mixed-integer nonlinear programming (MINLP) problem, with dual objectives of minimizing operating cost and energy consumption. Constraints include energy supply–demand balance, equipment operating limits, gas holder capacity, and generator ramp rates. The Pareto optimal solution set is obtained using the AUGMECON2 method and efficiently computed with the IPOPT solver. Application results demonstrate that the model achieves zero gas emissions, a dispatching instruction accuracy of 95%, and a 0.8% increase in the proportion of peak–valley-level self-generated power, outperforming comparable technologies. It provides technical support for the safe, efficient, and economic operation of multi-energy systems in iron and steel enterprises. Full article

This paper proposes a safety-constrained Transformer–PPO framework for low-carbon economic dispatch with settable incentive-based demand response (DR) in wind–PV integrated electro–thermal–hydrogen industrial-park energy systems. Hydrogen is modeled as exogenous hydrogen-domain demand and is satisfied through electrolyzer production and hydrogen inventory dynamics. A causal Transformer captures long-horizon multi-energy coupling and intertemporal constraints and is trained with PPO under uncertainty. A dual-layer safety mechanism combines dual-variable (Lagrange multiplier) updates for statistical constraints with an execution-layer quadratic-programming action projection to enforce hard physical constraints, including operating limits, ramping, battery SOC, hydrogen inventory bounds, and energy balance. Baseline–verification–settlement rules and budget-ledger states are embedded to ensure verifiable response quantities and settlement outcomes that are traceable and independently recompilable. Case studies on a real industrial-park scenario in Inner Mongolia show reduced peak-hour maximum grid purchase demand and constraint violations, together with lower total cost, carbon cost, and curtailment penalties versus MILP, PPO-MLP, and Transformer–PPO without safety mechanisms. Full article

As wind power penetration increases, understanding its potential to exercise unilateral market power is critical. This dynamic is particularly relevant in systems like the Colombian wholesale electricity market, which is characterized by a strong dependence on reservoir-based hydropower and a concentrated oligopolistic structure. However, evaluating the threshold where a renewable generator transitions from a price-taker to a price-setter remains challenging. This article explores this strategic transition and its market implications. By isolating a wind agent’s actions against a competitive hydro-thermal fringe using a discretized bi-level approach, we analyze how physical capacity withholding strategies might evolve under varying wind availability and system stress. The findings suggest that wind market power operates across three dynamic regimes: (i) a defensive “Price-Support” strategy during low demand, where capacity may be withheld to prevent price collapses; (ii) a “Scarcity Creation” tipping point during peak demand (observed around a 20% wind availability factor), which appears to incentivize fractional withholding to force expensive thermal dispatch; and iii) a return to “Volume Maximization” when abundant wind renders manipulation economically suboptimal. Ultimately, these results indicate that renewable market power is highly transient and conditional on meteorological profiles, suggesting that regulators could benefit from shifting toward predictive, weather-driven market surveillance. Full article

To overcome the limitations of static operation modes in traditional cogeneration and the intermittency of renewable energy, this paper proposes a scenario-assisted adaptive robust optimization framework with a dispatch resolution for Integrated Energy Systems (IES). A closed-loop cascading mechanism is established, integrating biomass co-firing, Carbon Capture and Storage (CCS), and Power-to-Gas (P2G) technologies, where captured ${\mathrm{CO}}_2$ reacts with green hydrogen to produce synthetic natural gas, thereby closing the carbon cycle. Specifically, a dynamic model for hydrogen-blending gas turbines is developed, characterizing the thermodynamic performance under variable hydrogen blending ratios (0–20%), which enables the system to adaptively adjust fuel composition in response to real-time fluctuations in wind and solar power. Furthermore, a tiered carbon trading mechanism is introduced to internalize environmental costs and constrain emissions. Simulation results demonstrate that the proposed variable blending strategy effectively mitigates wind curtailment, reducing curtailment costs to 0.31 million ¥, and creates a “double-peak, double-valley” carbon emission profile, reducing the net load peak-to-valley difference by 18.5%. The proposed framework achieves a balance between economic efficiency and deep decarbonization, attaining an optimal unit carbon reduction cost of 0.142 ¥/kWh, demonstrating improved economic and environmental performance of dynamic electro-carbon-hydrogen coupling under variable operating conditions. Full article

Technology upgrades are a central lever for sustainability, yet many optimization models primarily account for use-phase emissions and treat embodied impacts and technological change exogenously. We propose a multi-period mixed-integer optimization framework that couples upgrade timing, technology choice, and operations with a life-cycle assessment (LCA) structure. The model (i) separates use-phase and embodied impacts at the transition level, (ii) supports time-weighted valuation of impacts through a flexible weighting sequence (time value of carbon), and (iii) incorporates endogenous learning-by-doing that can reduce both investment costs and embodied impacts of future upgrades. We derive an exact Benders (L-shaped) decomposition that separates discrete upgrade dynamics from a linear operating subproblem. Computational experiments illustrate model behavior and report runtimes under an outer-loop implementation with open-source solvers, highlighting that decomposition becomes most beneficial when extensions substantially enlarge the dispatch layer (e.g., scenario expansion). Experiments also show that ignoring embodied impacts can mis-rank upgrade schedules and even violate life-cycle caps, that stronger time-weighting pushes upgrades earlier, and that learning can make staged upgrades economically preferable. Full article

High-dimensional global optimization and microgrid economic scheduling problems are often dominated by nonlinear search landscapes, strong coupling among decision variables, and stringent operational constraints, which severely limit the effectiveness of conventional metaheuristic approaches. In response to these challenges, this study presents a multi-strategy cooperative optimization framework derived from stock exchange trading principles, referred to as MESETO. The proposed method departs from the single-path evolutionary process of the standard SETO algorithm by introducing a diversified strategy collaboration mechanism that enables the dynamic adjustment of search behaviors throughout the optimization process. Multiple complementary update strategies are jointly employed to balance global exploration and local exploitation, while an adaptive probability regulation scheme continuously reallocates computational effort toward strategies that demonstrate superior performance. In addition, a solution validation mechanism is incorporated to prevent population degradation by rejecting ineffective evolutionary moves, thereby enhancing convergence stability. Extensive numerical experiments conducted on the CEC2017 and CEC2022 benchmark suites across different dimensional configurations demonstrate that MESETO consistently achieves improved solution accuracy, faster convergence, and stronger robustness compared with several representative state-of-the-art metaheuristic algorithms. Furthermore, the applicability of the proposed optimizer is verified through a 24 h microgrid economic scheduling case that integrates renewable energy sources, energy storage systems, dispatchable generators, and grid interaction. Simulation results confirm that MESETO effectively reduces operational costs while maintaining stable and efficient scheduling performance. Overall, the results indicate that MESETO constitutes a reliable and efficient optimization framework for solving complex global optimization problems and practical energy management applications. Full article

As renewable energy penetration accelerates, battery energy storage systems have become essential for enhancing flexibility, reliability, and economic efficiency in power system operations. For the daily operations of grids, the unit commitment (UC) problem plays a central role in determining the optimized scheduling of generation resources, but current formulations rarely incorporate battery degradation dynamics. The accurate representation of battery aging is crucial, as degradation costs may influence dispatch. This review provides a synthesis of existing approaches for integrating battery degradation into UC formulations. We survey and compare major classes of degradation models and then examine how these models have been embedded into UC frameworks, highlighting trade-offs between modeling accuracy and tractability. This paper concludes with identified research gaps and recommendations for future UC formulations that more faithfully capture battery degradation while maintaining computational efficiency. This review aims to serve as a foundation for researchers and system operators seeking to incorporate realistic battery aging mechanisms into operational decision-making for the evolving low-carbon grid. Full article