Search Results (4,517)

Low-carbon microgrids are complex energy systems in which photovoltaic (PV) uncertainty, time-varying operating costs, distributed coordination, and information privacy must be addressed simultaneously. This paper couples TCN–BiLSTM probabilistic PV forecasting with privacy-preserving distributed online economic dispatch. Quantile forecasts are converted into a risk-aware net demand by combining the median PV forecast with a lower-side uncertainty reserve. The resulting dispatch problem also includes a step-type carbon-trading cost. To solve the problem when cost gradients are unavailable, we propose a probabilistic PV forecasting-driven differentially private distributed online one-point bandit optimization algorithm (PPF-DP-DOBO). Each generator uses one function-value query per iteration and perturbs its communicated state with Laplace noise. The analysis establishes a per-release differential privacy guarantee, its sequential composition over the dispatch horizon, and an individual dynamic regret bound that explicitly depends on PV forecasting uncertainty. Under bounded weighted path variation and cumulative forecasting uncertainty, the regret is sublinear with order $\mathcal{O}\left(T^{3/4}\right)$. Simulations on a modified IEEE 162-bus system show that the method tracks the risk-aware net demand, preserves the expected privacy–performance trade-off, and yields lower average regret and carbon cost in the evaluated probabilistic-PV setting than in the no-PV case. Full article

Current limiting turns the closed loop of a grid-forming converter into a mode-switching system. For projection-based (feedback-optimization) limiting, this paper shows that conventional linearization fails at the constraint boundary in two distinct ways, and that dynamic mode decomposition (DMD) of trajectories of a nonlinear averaged-dq model recovers the physically consistent spectra. At unconstrained operating points, the Jacobian of the smooth primal–dual field is evaluated where that field is not stationary, and it contains a spurious lightly damped mode belonging to neither the projected nor the unprojected formulation. At constrained operating points, the dual flow pins the constraint whenever the multiplier is active and, because the current predictor is exact in steady state, a backstop limiter sharing the projection limit places the raw current reference on the limiter kink; the constrained equilibria are then non-isolated and path-dependent, and the two well-conditioned one-sided Jacobians differ by an order-one amount. Audited against branch-consistent and one-sided linearizations and certified by out-of-sample reconstruction, DMD recovers the correct spectra over a two-parameter operating map and flags the non-linearizable points. A matched comparison with saturation and virtual-impedance baselines attributes the loss of the network resonance to hard clipping of the bridge current, which instead exposes a lightly damped filter resonance; a backstop above the projection limit restores unique, linearizable equilibria at the price of retaining the resonance. Sensitivities to identification settings, grid strength, controller gains, control delay, and measurement noise are quantified, and a three-converter microgrid study shows a sustained oscillation where a shared threshold admits no equilibrium. Full article

Peer-to-peer (P2P) energy trading and shared energy storage (SES) are effective means for multi-microgrid systems to improve operational flexibility, but the cooperative surplus they create must be allocated among participants with heterogeneous carbon responsibilities. However, existing allocation mechanisms determine bargaining power mainly by trading volumes or costs, and most low-carbon dispatch studies internalize carbon costs into the dispatch objective, conflating physical efficiency with fairness of allocation. To address these issues, this paper proposes a three-stage P2P coordination framework for multi-microgrids, comprising day-ahead planning (Stage I), intraday rolling dispatch (Stage II), and ex-post settlement (Stage III). In Stages I–II, a unit-commitment-economic-dispatch decomposition keeps the commitment decisions centralized while the continuous dispatch is optimized in a fully distributed manner, which preserves both optimality and peer privacy. Within the rolling alternating direction method of multipliers (ADMM) dispatch, dimension-grouped consensus penalties and a dual warm-start chain keep each hourly window tractable under forecast errors. In Stage III, an environmental contribution metric capturing both the scale and cleanliness of self-generation is defined, and an asymmetric Nash bargaining solution weighted by the metric is derived in closed form. Case studies on a four-microgrid system with SES over twelve operating days across four seasons show that the cooperative surplus reaches 1012.37–2668.37 RMB/day, i.e., 6.2% to 39.7% of standalone costs. The distributed rolling dispatch reproduces the centralized optimum of every hourly window within a relative deviation of $5.4\times {10}^{-3}$, and under 10% day-ahead and intraday forecast errors, the executed costs stay within 0.84% of the perfect-foresight bound, with budget balance and individual rationality holding on all twelve days. The framework thus provides carbon-responsibility fairness as an auditable settlement layer without distorting the physically efficient dispatch. Full article

Industrial microgrids integrating distributed photovoltaic (PV) systems require accurate grid exchange forecasting for effective energy management. However, grid exchange exhibits substantial fluctuations due to physical events such as cloud shading, PV curtailment, and surplus export. Most existing deep learning methods rely primarily on raw historical measurements and overlook the physical causes of these variations. This study proposes an event-driven CNN-LSTM framework that augments raw PV and grid signals with a PV ramp rate feature to capture abrupt transitions in solar output. Seven operational indicators are extracted from measured microgrid data; however, an empirically selected three-feature input set comprising PV generation, grid exchange, and PV ramp rate is used for aggregate forecasting, while the remaining event flags are assessed separately through feature ablation and event period analysis. The framework is evaluated using one year of hourly day-ahead data and one month of 15 min very-short-term data from a 998.58 kWp PV installation. Compared with baseline models, the proposed model achieves the best DA forecasting performance, with an RMSE of 118.26 kW and R2 of 0.8463. Ablation analysis confirms that raw PV generation provides the dominant predictive information, whereas the ramp rate feature yields targeted improvements during PV disturbances. Event period analysis further shows that binary event flags provide useful information during specific physical events but may reduce aggregate forecasting accuracy when permanently incorporated into the model. Full article

Integrating battery energy storage systems is crucial for increasing the flexibility of power systems with a high proportion of variable renewable energy sources. Although lithium-ion batteries dominate applications due to their high energy density and falling costs, their use in large-scale stationary settings is limited by rapid capacity fade, performance degradation, safety concerns and reliance on critical raw materials. Vanadium redox flow batteries (VRFBs) offer a promising alternative due to their scalability, long lifetime, inherent safety, and the recoverability of capacity fade associated with electrolyte imbalance through periodic rebalancing. The optimization problem determines the energy capacity and installed power that minimize the total annualized cost within a predefined storage design range, including investment, operational, and market interaction costs. This is achieved using 2024 real power data at a resolution of 15 min from photovoltaic and wind plants, combined heat and power, and grid exchanges. The annual optimization assumes perfect foresight of electricity demand, generation, and prices over the investigated horizon. Within the investigated design range, the model selects the upper-bound energy capacity of $2000\mathrm{kWh}$ together with an interior power solution of $625\mathrm{kW}$; this configuration reduces annualized system costs by about 8% compared to the no-storage scenario, achieving a levelized cost of electricity supply (LCES) of 0.0847 €/kWh. However, when degradation and end-of-life replacement are accounted for, the lithium-ion LCES increases to 0.09217 €/kWh under the baseline zero-residual-value convention. Full article

Energy-burdened rural communities require hybrid renewable energy storage solutions that are not only economically viable but also resilient to extended generation shortfalls. This study develops a spatially informed techno-economic and resilience assessment framework for hydrogen–biogas renewable microgrids and applies it to Robeson County, North Carolina. A Hydrogen Priority Index (HPI) is used to identify locations where high household energy burden coincides with favorable renewable energy suitability. Four community-scale microgrid configurations are then evaluated in HOMER Pro under standard economic criteria and an embedded seven-day winter solar shortfall stress scenario. Results show that biogas integration reduces net present cost by 28.4% by restructuring the optimal system architecture and reducing PV and battery oversizing. PV–battery configurations cannot achieve near-complete resilience under the imposed stress scenario regardless of component scaling, while hydrogen-inclusive configurations reduce stress-period unmet load by 97.2%. The full hydrogen–biogas hybrid delivers this resilience-constrained performance at 26% lower net present cost than the hydrogen-only configuration. These findings demonstrate that combining spatial prioritization with resilience-constrained techno-economic assessment supports more equitable and deployment-ready planning of renewable microgrids for underserved rural communities. Full article

The increase in global digitization increases the demand for data centers, and to provide a flexible and reliable service to end users, their continuous operation is of utmost priority. The continuous operation of data centers presents significant challenges, including rising electricity bills and carbon emissions, and data center microgrids (DCMGs) are an effective choice. However, their optimized operation, considering both operational costs and carbon emissions, needs to be addressed. Therefore, in this regard, this paper proposes an emission-aware operational-cost-optimized energy management framework for a data center microgrid, utilizing a newly developed alpha-refined memetic grey wolf optimizer ($\alpha$-MGWO) algorithm. The framework incorporates detailed data center load modeling, emission-aware cost-minimization modeling, and the formulation of an objective function aimed at reducing both the operational costs and carbon emissions of the DCMG. The results obtained using $\alpha$-MGWO are compared with other well-established metaheuristic algorithms. Furthermore, a numerical analysis, balanced scheduling decisions, and statistical and convergence analyses demonstrate the effectiveness of $\alpha$-MGWO for DCMG operation. Moreover, the effectiveness of $\alpha$-MGWO in achieving optimal energy management is evaluated through four different scenarios along with a sensitivity analysis based on a fixed market price and DCMG operation without renewable energy integration. The results indicate that the first scenario offers the most favorable conditions for DCMG operation, achieving an operational cost of 37,626.35 ($), carbon emissions of 294,392.08 (kg), and the highest renewable energy contribution of 50.71%. By jointly optimizing economic and environmental objectives and increasing renewable energy utilization, the proposed framework provides a quantitative approach to improving the sustainability of energy-intensive data center infrastructure. Full article

To address the strong load rigidity and high energy consumption of large-scale broiler breeding, as well as the lack of precise load-characteristic support and efficient scheduling methods for breeding-oriented energy supply and consumption optimization, this paper conducts load-characteristic analysis for large-scale broiler houses and proposes an energy management optimization method based on the improved dream optimization algorithm (IDOA). First, year-round field monitoring was performed on a large-scale broiler farm in Laiyuan, Hebei, to analyze the load characteristics across seasons and breeding cycles and reveal the load evolution rules. Second, a comprehensive operational cost objective is established, considering the renewable operation and maintenance cost, the time-of-use power trading cost, and the carbon emission cost. Third, adaptive weight, dynamic mutation, and opposition-based learning strategies are embedded into the IDOA to better balance global exploration and local exploitation, and the improved algorithm outperforms other meta-heuristic algorithms on the CEC2017 benchmark functions. Simulation tests covering the four-season typical days and key breeding stages demonstrate that, compared with the rule-based dispatch strategy, the proposed method lowers the daily operating cost and effectively smooths the grid power profile, while the ESS state of charge is always maintained within the preset limits. Sensitivity analyses on the ESS capacity and the load and renewable forecasting errors further verify the robustness of the dispatch results, and the single-dispatch runtime of about 23 ms amply satisfies the real-time requirement of field implementation. This study offers theoretical support and practical reference for energy saving and carbon reduction in large-scale livestock breeding and breeding-park energy system scheduling. Full article

Buildings in climate-vulnerable regions face critical challenges in reducing energy demand while supporting environmental and social resilience. In the Peruvian Amazon, off-grid riverine communities experience energy insecurity and seasonal flooding, threatening both the built environment and cultural continuity. This research presents a non-built architectural case study for an Indigenous community-based ecotourism lodge in Loreto, Peru. The methodology combines culturally informed passive bioclimatic strategies with design-stage quantitative calculations based on secondary sources, architectural programming assumptions, and a ten-year SENAMHI meteorological dataset (2014–2024). The proposed photovoltaic microgrid comprises 52 bifacial N-Type TOPCon modules (31.72 kWp) and a 286.3 kWh LiFePO4 battery system. Under the base demand scenario—defined by lighting loads, 180 standard outlets, an outlet simultaneity factor of 0.8, and an effective evening-use period of 5 h/day—the system is projected to cover modeled demand under average-month solar conditions. However, rainy-season performance remains conditional when bifacial gain is excluded, so the system is interpreted as preliminary off-grid sizing rather than evidence of continuous off-grid operation. Compared with a 30 kW diesel-generator baseline, the photovoltaic-battery configuration projects an 84.3% annual OPEX reduction. The design also estimates 570 L/day of biogas from a 2.0 m³ biodigester, 233,600 L/year of potable water savings, and proposes 152 Calycophyllum spruceanum and 15 Ceiba pentandra trees for NbS-based stabilization. These design-stage estimates suggest a reproducible research-by-design workflow for preliminary environmental, energetic, and socio-cultural evaluation of off-grid Amazonian ecotourism infrastructure. Full article

The large-scale integration of distributed photovoltaic (PV) generation and electric vehicles (EVs) increases uncertainty in distribution-network source and load conditions and complicates system operation. Conventional optimization methods based on iterative solutions and day-ahead forecasts may not meet the real-time operating requirements of active distribution networks. Therefore, a multi-time-scale Graph Attention Network (GAT)-based online optimization and control method is proposed for distribution networks with emerging source-load resources. First, stochastic and time-varying source-load scenarios are generated using a PV output model and an EV charging-load model. Second, the reactive power optimization model is solved offline to construct a control-decision dataset that accounts for the different dynamic responses of the regulation devices. The GAT is then trained to learn the nonlinear mapping from distribution-network operating states to control decisions, forming a multi-time-scale online decision-making model. The long-time-scale model regulates discrete devices, whereas the short-time-scale model regulates continuous devices. For the IEEE 33-bus case study, the average GAT inference time is 0.0703 s, compared with 83.7667 s for PSO. The hour-level model achieves MAE/RMSE values of 0.0398/0.0407, while the 15 min model achieves 0.0374/0.0397; all bus voltages are maintained within 0.95–1.05 p.u. after optimization. The model is validated under the tested operating distribution; broader robustness and field applicability require further validation. Full article

The Gulf of Thailand presents a mild but consistent wave energy resource that remains largely untapped. This study investigates the hydrodynamic and aerodynamic performance of a breakwater-integrated Oscillating Water Column (OWC) coupled with a Wells turbine and a 10 kW direct-drive Permanent Magnet Synchronous Generator (PMSG). A comprehensive 3D Computational Fluid Dynamics (CFD) wave-to-generator model was developed using Ansys Fluent. The Volume of Fluid (VOF) method tracked the air-water interface, while a 6-Degrees of Freedom (6-DOF) dynamic mesh technique resolved the true transient acceleration of the turbine rotor. Regular 5th-order Stokes waves ($H=0.5$ to $2.5\mathrm{m}$, $T=4$ and $6\mathrm{s}$) representing shallow water conditions (depth 10 m) were simulated. The coupled electro-mechanical results demonstrated that under the damping of the PMSG, the turbine operated stably at loaded rotational speeds of 254–340 RPM, safely mitigating free-wheeling overspeed. Notably, the system delivered an average electrical power of 4.37 kW under mild sea states ($H=0.5\mathrm{m}$) due to a hydrodynamic funneling effect between the detached breakwaters and the OWC structure. Conversely, under extreme monsoon conditions ($H=2.5\mathrm{m}$), the average power saturated at 5.08 kW (with a 7.71 kW peak) due to wave breaking and destructive interference from strong backwash. These findings validate the techno-economic feasibility of integrating OWC systems into existing coastal infrastructure to maximize energy extraction in low-wave-energy climates, offering a sustainable power solution for coastal microgrids in Southeast Asia. Full article

Structural bottlenecks including congestion and high costs are increasingly encountered by traditional urban ground logistics as last-mile delivery demand surges. Physical spatial limitations can potentially be bypassed by low-altitude logistics as a novel three-dimensional transportation mode. However, optimal system performance can hardly be achieved through simple technological substitution alone. A framework for integrating low-altitude logistics into smart cities is proposed in this paper. This framework is systematically discussed across four dimensions including spatial environments and energy systems. The aspects of smart governance and social adaptation are also evaluated. The implementation effectiveness of this system is shown to be highly dependent on scientific hub siting and multi-layer network construction. Furthermore, the system-level carbon reduction potential is constrained by the decarbonization progress of regional power grids. Deep coupling with building microgrids is therefore required. A cross-departmental digital governance system must also be established for large-scale application. The equity of public services and labor structure transformations must be accommodated alongside this expansion. It is emphasized that low-altitude logistics is not the ultimate solution for urban logistics. Instead, it is considered an effective supplement under specific high-density scenarios and clean energy conditions. Support is thus provided for urban logistics infrastructure planning and policy formulation. Full article

When droop control of virtual synchronous generators (VSGs) with rotational inertia is adopted in islanded alternating current (AC) microgrids, the coupling mechanism among reactive power loads, control parameters and system virtual inertia remains unclear, and there is a lack of quantitative evaluation schemes. To address this issue, this paper proposes a quantitative evaluation method for virtual inertia based on a reduced-order equivalent model. First, the control system of grid-forming converters is simplified to construct a reduced-order model for microgrid voltage support, and the analytical expression of voltage dynamics and the calculation formula of the system-level inertia time constant are derived. Subsequently, a three-dimensional response surface is utilized to multi-dimensionally analyze the influence laws of filter cutoff frequency, reactive power load, rotational inertia and damping coefficient on system inertia and steady-state voltage drop. Finally, a reduced-order model of the AC microgrid is established based on the RT-Lab hardware-in-the-loop (HIL) experimental platform, and multiple sets of results validate the correctness of the theoretical analysis. Full article

The decarbonization of port operations is becoming increasingly important due to tightening environmental regulations and the growing adoption of shore power solutions. However, the large-scale deployment of conventional onshore power supply (OPS) systems often is constrained by limited grid capacity, high infrastructure costs, and variability in the environmental performance of grid electricity. Against this backdrop, this study investigates whether an integrated port microgrid can provide a viable alternative for enabling cold ironing in grid-constrained ports. A case study is developed for a commercial port characterized by a maximum grid import capacity of 3 MW and a peak shore power demand of approximately 20 MW. Two alternative configurations are evaluated: conventional onboard auxiliary engine generation and an integrated microgrid incorporating combined heat and power (CHP) units, photovoltaic generation, battery energy storage, and thermal integration through heat recovery. Results indicate that the integrated microgrid can satisfy the required shore power demand while significantly reducing both costs and emissions compared with onboard generation under the assumptions of the case study. The proposed configuration achieves a levelized cost of energy (LCOE) of 0.157 €/kWh, corresponding to a reduction of approximately 22% compared to the conventional onboard auxiliary engine generation, and a reduction in annual CO₂ emissions of about 36%. The system also maintains operational continuity during a simulated 24 h grid outage at the hourly simulation resolution, while an N + 1 criterion is adopted separately for sizing the on-site generation architecture. Beyond the quantitative benefits, the findings highlight the role of cogeneration as an enabling technology for integrating multiple energy vectors within port infrastructures. By coupling electricity generation, thermal recovery, renewable energy, and storage within a coordinated microgrid architecture, the proposed solution transforms grid-capacity limitations into opportunities for energy system optimization. The study contributes to the growing literature on ports as multi-energy hubs and provides evidence that integrated CHP-based microgrids can represent a technically feasible and economically competitive pathway for supporting cold ironing in ports where grid reinforcement is constrained, delayed, or costly under the conditions examined in this study. Full article