Search Results (913)

Computing capacity has become a metered factor of production for digitally intensive enterprises, yet its consumption exhibits strong temporal heterogeneity—tidal intraday cycles, weekly contrasts, seasonal surges, and project-driven regime shifts—so that individually provisioned capacity is structurally underutilized. This paper proposes a park-level computing power sharing center (CPSC) as an institutional mechanism that converts the temporal complementarity of co-located enterprises into measurable cost savings. We develop a general mode-design framework that separates CPU core-hours from GPU card-hours, characterizes demand via deterministic tides and stochastic modulations, and derives optimal pooled capacity commitments through a newsvendor-type quantile condition. A parametric calibration protocol maps observable temporal features—peak-to-trough ratios, inter-tenant phase spreads, and residual volatility—into closed-form diversity-factor expressions with Monte Carlo confidence intervals. The procurement model covers a multi-option contract menu (on-demand, one–three-year reserved instances, savings plans, and spot), region-specific pricing, hardware class tariffs, and ancillary costs, including network egress, migration, and data sovereignty compliance; benefits are measured relative to each tenant’s individually optimal reserved portfolio, not naive retail procurement. A mechanism design analysis incorporating Shapley value allocation, Bayesian incentive compatibility, and penalty structures ensures individual rationality and robustness to misreporting and strategic load shifting. We further develop an energy model—with utilization-dependent power draw, facility PUE, embodied carbon, and marginal grid emission factors—showing that financial savings translate into genuine emission reductions only when pooling enables physical capacity retirement rather than mere billing reallocation. The framework is applied to twenty representative Chinese parks spanning seven functional categories; all park-level data are reconstructed from public sources using the calibration methodology, and the reported figures are model-derived projections, not empirical measurements. The model yields procurement saving estimates of 4.6–20.2% relative to individually optimal reserved-procurement portfolios, with high-diversity parks at the upper end. Sensitivity analyses across regional tariffs, hardware mixes, and cross-country utilization benchmarks (Uptime Institute, US DOE, EU Commission) confirm robustness and delineate boundary conditions. This paper concludes with a data provenance taxonomy and a phased implementation roadmap. Full article

The frequent occurrence of extreme high-temperature events has significantly affected the operating characteristics and distributed photovoltaic (PV) hosting capacity of distribution networks. However, existing hosting capacity assessment methods rarely consider the accumulated heat effect caused by sustained high temperatures. To address this issue, this paper proposes a coordinated planning method for enhancing distributed PV hosting capacity under extreme high-temperature scenarios. First, an accumulated heat load model is developed to characterize the temporal cumulative influence of sustained high temperatures on temperature-sensitive loads. Meanwhile, the uncertainties associated with PV output fluctuations and load demand variations are considered to represent the stochastic characteristics of source-side generation and load-side consumption. Subsequently, a multi-objective source–network–load coordinated planning model is established to maximize distributed PV hosting capacity while minimizing the hosting capacity enhancement cost. A multi-objective artificial bee colony (MO-ABC) algorithm incorporating Sobol sequence-based quasi-Monte Carlo sampling (Sobol-MC) and a constraint domination-based constraint handling strategy are further developed to solve the proposed model efficiently. Simulation results on the modified IEEE 33-bus distribution system show that the proposed method increases distributed PV hosting capacity by 69.52% under extreme high-temperature scenarios through coordinated optimization of PV inverter reactive power control, VAR compensation, and Incentive-based Demand Response (IDR). Full article

Renewable energy scenarios are widely used for power system stochastic optimization and risk evaluation, yet massive redundant scenarios boost computational complexity, waste resources and destabilize results, necessitating scenario reduction. This paper proposes an improved two-stage clustering method to overcome the manual parameter tuning defects of conventional clustering-based reduction. Specifically, the Snow Ablation Optimizer is embedded into DBSCAN to auto-adjust core hyperparameters, realizing efficient initial scenario reduction and suppressing the interference of abnormal data. Afterwards, K-means optimized via the Calinski–Harabasz index is adopted for secondary reduction to adaptively identify optimal cluster numbers and enhance the representativeness of reserved scenarios. Basic comparative simulations validate its technical superiority: handling 5000 raw scenarios only takes around 2 min, with the Wasserstein distance decreased by 7.73% and 17.79% versus backward reduction and forward selection, while the silhouette coefficient rises by 12.73% and Davies–Bouldin index drops by 7.95% compared with classic K-means. Further stochastic unit commitment tests quantify tangible economic and low-carbon gains in day-ahead scheduling, and empirical coefficient-based scaling analysis extends these benefits to long-term grid operation and policy deployment for TSOs/DSOs. The integrated results confirm the method’s technical, economic and sustainable merits, delivering actionable quantitative support for high-renewable power system low-carbon planning and energy policy formulation. Full article

Hosting capacity (HC) has become a key concept in planning and operating modern distribution networks to sustainably integrate distributed energy resources (DERs), including photovoltaic systems, wind generation, battery energy storage, and electric vehicles. However, the literature shows variation in HC definitions, assessment assumptions, limiting criteria, and reporting practices, complicating cross-study comparison and utility implementation. This paper examines HC from four interconnected perspectives: limiting factors and performance indices, assessment methods, enhancement strategies, and standardization efforts. The review examines the influence of voltage constraints, thermal loading, power quality, protection coordination, network topology, system inertia, regulatory requirements, and load diversity on HC. It compares major assessment approaches, including deterministic, stochastic, time-series, optimization-based, iterative, hybrid, data-driven, and artificial intelligence-based methods, highlighting their strengths, limitations, and suitable applications. The paper also reviews HC enhancement techniques for sustainable network capacity utilization, including network reinforcement, smart inverter control, demand-side flexibility, energy storage, and coordinated multi-layer control. Particular attention is given to emerging HC standardization and the gaps between formal standards and the research frontier, especially in probabilistic, dynamic, and real-time assessment. Overall, HC depends on binding network constraints, operating conditions, assumptions, and controls, while methodological and reporting gaps limit comparability and sustainable DER integration. Full article

Inter-hub feeder service is a promising application for electric vertical takeoff and landing aircraft (eVTOL), especially for passengers connecting to subsequent flights under tight time constraints. We develop an optimization framework that accounts for heterogeneous passenger behavior. Using stated-preference survey data, we incorporate delay-risk perception under remaining connection time constraints, identify heterogeneous preference classes, and formulate a bilevel optimization model. The upper level selects eVTOL schedules under given resource and fare configurations. The lower level captures the stochastic user equilibrium of heterogeneous passengers choosing among eVTOL and external transport alternatives. To solve the resulting mixed-integer nonlinear bilevel problem, we propose a Neural Bilevel Optimization and generalized Benders decomposition (Neur2BiLO-GBD) hybrid algorithm. Numerical experiments on the Shanghai Hongqiao–Pudong corridor show that the baseline profit-maximizing plan also generates positive social net utility for the feeder system. Fleet size, charging infrastructure, and fare affect operator profit and social net utility differently, so their high-value regions do not fully coincide. When external transport has larger potential delays and remaining connection time is short, eVTOL is more likely to achieve both high operator profit and high social net utility. Full article

The accelerated deployment of electric vehicles requires planning tools able to quantify how much charging infrastructure can be integrated into distribution systems without violating operational constraints. This paper proposes a multi-scenario optimization framework for the siting and sizing of electric vehicle charging stations in radial distribution networks. The problem is formulated as a mixed-integer nonlinear programming model in which candidate-station slots, binary siting decisions, integer EV assignments, hourly power-flow constraints, voltage limits, thermal limits, charger power factor, and charging strategy are coordinated. The objective function combines hosting capacity maximization with active energy losses and voltage deviation terms through a scalarized formulation. Unmanaged and managed charging strategies are evaluated under weekday and weekend operating scenarios. Four adaptive population-based optimizers are analyzed under identical computational conditions: particle swarm optimization, a population-based genetic algorithm, JAYA, and the multi-verse optimizer. Monte Carlo random sampling is included separately as a non-adaptive baseline without memory or learning. The methodology is tested on a modified 33-bus distribution system using Colombian demand profiles and line-current limits. The campaign includes 720 cases and 7200 independent runs. In the 720-case stochastic campaign, the largest feasible solution serves 765 EVs, equivalent to 5.508 MW, with a minimum voltage of 0.9084 p.u. and a maximum loading of 99.83%. Statistical validation shows no significant Holm-adjusted pairwise differences among the adaptive algorithms in hosting capacity, while PSO provides the most robust feasibility behavior. Supplementary robustness analyses quantify the influence of candidate-site definition, objective scaling, voltage limits, base charging-power scale, and native-load growth. A complementary deterministic 69-bus assessment under a normalized branch-current envelope preserves the qualitative managed-versus-unmanaged trend, with feasible sequential allocations of 779 and 225 equivalent EV charging units, respectively. The proposed framework provides a reproducible basis for identifying robust EVCS locations, estimating hosting capacity, and quantifying tradeoffs among charging capacity, network losses, voltage performance, and computational effort. Full article

Fixed full-boundary headlands create redundant non-working space in irregular fields, while working direction and swath offset jointly affect coverage quality and inter-swath turning cost. This study proposes a dynamic-headland-aware coverage path planning framework that allocates headland space according to swath endpoints and turning demand and generates parallel working swaths and Bézier U-turns for each direction–offset candidate. Field-specific empirical cumulative distribution function (ECDF)-midrank normalization converts turning distance, coverage error, and headland ratio into relative quality scores, and DQN-SHADE searches the resulting non-smooth discrete evaluation landscape. In geometric simulations on 24 actual field boundaries, DQN-SHADE achieved the lowest mean $\mathrm{gap}$ to the discrete reference optimum (0.0034) and the highest threshold success rate, ${\mathrm{SR}}_{5\times {10}^{-3}}$, of 80.83% among six stochastic optimizers under a common candidate-evaluation budget. Relative to fixed full-boundary headlands, dynamic headland allocation reduced the mean headland ratio from 13.30% to 7.52%, increased retained working area by 16,378.23 m² per field, and maintained 98.16% mean coverage. The proposed framework improves field-space utilization while maintaining coverage quality and geometric feasibility under the evaluated simulation conditions. Full article

Planning network reinforcement and flexibility as separate tasks obscures when they substitute for, or complement, one another. We formulate a scenario-hour mixed-integer linear program (MILP) that selects photovoltaic and wind capacity, battery power and energy, energy-neutral demand shifting, photovoltaic-inverter reactive power, and branch-rating reinforcement under one planning budget. Every operating constraint is enforced for every scenario and hour; scenario probabilities enter only the secondary tie-breaking terms. The formulation is therefore a deterministic multi-scenario model with an all-scenario feasibility requirement rather than a conventional stochastic program. Electrical-distance zones tie flexible-demand potential to local load, root-path screening limits reinforcement candidates to electrically relevant branches, and inner polygonal limits keep the model linear. A nonlinear backward–forward-sweep alternating-current (AC) power flow then checks all 72 optimized dispatch points. On the MATPOWER case141 141-bus radial distribution system, the integrated plan accommodates 40.899 MW of renewable capacity, 5.28% more than the generation–network baseline. A capacity-matched sequential control, with the same BESS and demand-response totals, candidate set, and inverter support, accommodates 40.826 MW; the two results are effectively equivalent at the 1% MIP tolerance, showing that the larger gain over the originally prescribed sequential package includes resource-quantity and siting effects. The integrated plan adds 21.267 MVA of branch rating, 23.03% less than the generation–network baseline. All nonlinear AC operating points satisfy the 0.95–1.05-p.u. voltage band and post-investment ratings. Changing the branch-rating weight to 25% below and above its nominal value still selects reinforcement, and disabling reinforcement reduces hosting capacity by 49.35–53.19% across the tested weights. Operational flexibility improves the use of transfer capacity but does not supply sustained branch headroom. Full article

As energy systems transition toward low-carbon configurations, strategic energy investments are increasingly important for decarbonization, energy security and system resilience. Evaluating these investments has become complex because their expected outcomes depend on technical performance, anticipated returns, uncertainty, competition, and policy design. This review covers recent advances in literature employing mathematical modelling approaches and optimization frameworks to evaluate and quantify strategic energy investments. In particular, it considers stochastic, robust, bilevel and equilibrium-based approaches used to represent uncertainty, strategic interactions and market responses in investment planning. While previous reviews largely examine investment valuation, system planning, uncertainty and strategic market modeling separately, the present review examines how long-term investment decisions interact with electricity-market operation, strategic competition and sustainability policies. The findings show that these interactions can affect investment profitability and technology choices, while operational flexibility and risk exposure further influence long-term investment value. The review therefore provides a structured synthesis of how technical, market and policy factors are incorporated into optimization models for strategic energy investment decisions. It also identifies limitations in current modeling approaches and highlights the need for more realistic, scalable and policy-relevant models. Full article

This paper presents a novel master–slave stochastic optimization framework for the optimal siting and sizing of fixed-step capacitor banks in medium-voltage distribution networks, explicitly addressing the inherent variability of load demand that is typically neglected in conventional deterministic approaches. The proposed methodology integrates a scenario-based stochastic optimization model with a Chu and Beasley genetic algorithm (CBGA) as the master stage, which handles discrete placement decisions, and a successive-approximation power flow method (SAPF) as the slave stage, which evaluates the technical and economic performance of each candidate solution under multiple load scenarios. To capture demand uncertainties, 365 daily load realizations are generated using independent Gaussian noise with a relative standard deviation of 10% applied to each load point. These are subsequently reduced to ten representative scenarios via k-means clustering, reducing the number of power-flow evaluations per candidate solution from 365 to 10 (a 36.5-fold reduction); the reduced scenarios exhibit a low mean absolute error (MAE: <2%) with respect to the original mean, indicating faithful representation of the average load behavior, while the silhouette score is modest (approximately 0.25), consistent with the unimodal nature of the generated data and implying that the clusters are not well separated. Extensive simulations on a 33-bus test feeder considering three energy-cost-escalation scenarios (0%, 10%, and 20%) over a 20-year planning horizon demonstrate that both the deterministic and stochastic approaches reduce the total net present cost by 16.52% to 17.34% compared to the uncompensated network; the stochastic approach consistently delivers solutions that are either superior or comparable to deterministic planning (yielding up to approximately 0.16% additional cost reduction) while offering enhanced robustness against load variability. The stochastic framework offers distinct advantages, including robust solutions across a wide range of operating conditions, an inherent ability to adjust investment levels in response to probabilistic load distributions, and the ability to quantify uncertainty in decision making, with the most significant benefits observed when energy costs are low and load variability is high. The convergence of both approaches at a 20% escalation level further validates the reliability of high-resolution deterministic modeling when economic factors strongly dominate the optimization objective. This study underscores the importance of probabilistic modeling for modern distribution network planning, providing a practical and computationally efficient decision-support tool for utility planners to enhance grid resilience and operational efficiency in the context of increasing demand variability and renewable energy integration. Full article

The rapid transition toward electric vehicles (EVs) in Saudi Arabia requires reliable and sustainable charging infrastructure capable of supporting long-distance highway transportation. However, the deployment of emergency charging systems is challenged by sparse charging infrastructure, stochastic emergency charging demand, battery degradation under harsh climatic conditions, and the need for cost-effective integration of renewable energy resources. This study presents a three-stage unified techno-economic planning framework for renewable-assisted emergency EV charging networks that integrates strategically located charging hubs with a coordinated fleet of solar-assisted Mobile Emergency Charging Vehicles (MECVs). The proposed framework jointly optimizes charging hub locations, photovoltaic (PV) generation capacity, battery energy storage system (BESS) sizing, and MECV allocation while explicitly accounting for stochastic emergency charging demand, renewable-energy utilization, and temperature-dependent battery degradation. Emergency charging demand is modeled using Monte Carlo simulation based on EV penetration scenarios, and battery aging is incorporated into the optimization through a temperature-dependent degradation model. The planning problem is formulated as a Mixed Integer Nonlinear Programming (MINLP) model and comparatively solved using three independent metaheuristic algorithms, namely the Musical Chairs Algorithm (MCA), Particle Swarm Optimization (PSO), and Grey Wolf Optimization (GWO). The proposed framework is evaluated using two representative highway corridors in Saudi Arabia. The results indicate that renewable-assisted charging can reduce annual grid-related CO₂ emissions by approximately 25,360 and 57,641 t CO₂/year for the Riyadh–Dammam and Riyadh–Makkah corridors, respectively. Battery degradation contributes approximately 12.7–14.2% of the total annualized system cost, highlighting the importance of incorporating lifecycle degradation into infrastructure planning. Temperature sensitivity analysis further indicates the significant influence of harsh climatic conditions on battery lifetime, renewable-energy utilization, and overall system economics. The proposed framework provides a practical planning methodology for developing reliable, sustainable, and economically viable emergency EV charging infrastructure in regions with similar geographical and climatic characteristics. Full article

Rapid, reliable, and energy-efficient data collection is essential for disaster response, where terrestrial communication networks may be disrupted or unavailable. Unmanned Aerial Vehicles (UAVs) provide a flexible means of collecting critical sensing data, but their operation is constrained by limited onboard energy, stochastic wireless conditions, complex three-dimensional environments, and stringent latency requirements. This paper presents a structured multi-UAV framework that separates mission optimisation into spatial, temporal, and safety layers. In the spatial layer, a three-dimensional Travelling Salesman Problem with Neighbourhoods (3D-TSPN) formulation enables UAVs to collect data by entering valid sensing regions rather than visiting exact sensor coordinates. An Age of Information (AoI)-aware Genetic Algorithm (GA) optimises the sensor-visitation sequence, while Rapidly Exploring Random Tree Connect (RRT-Connect) generates obstacle-aware feasible paths in the three-dimensional environment. In the temporal layer, a Lyapunov-based controller selects between local processing and binary offloading to a single Mobile Edge Computing (MEC) node according to queue backlog, processing delay, energy consumption, information freshness, wireless-link feasibility, and task deadlines. In the safety layer, continuous-time conflict detection and bounded temporal or spatial adjustments are used to monitor and mitigate inter-UAV and obstacle-related risks. The framework is evaluated under stochastic wireless, mobility, computation, and obstacle conditions using 20 independent random seeds. Across the corresponding 20 proposed-policy runs, it achieves a 100% mission-validity rate, complete sensor coverage, no dropped tasks, and zero final collision or near-miss events. Compared with planning-oriented and MEC-oriented baselines, the proposed framework achieves lower information age, average delay, processing delay, energy consumption, and system cost under the evaluated conditions, while maintaining reliable multi-UAV coordination. The layered design also clarifies the contribution of each component: 3D-TSPN provides spatial flexibility, the AoI-aware GA improves route sequencing, RRT-Connect supports obstacle-aware path feasibility, Lyapunov control enables queue-aware processing decisions, and safety monitoring supports coordinated multi-UAV operation. These results indicate that integrating spatial planning, computation control, and safety coordination within a clearly separated layered architecture can provide an effective solution for multi-UAV disaster-response data collection in complex three-dimensional environments. Full article

Capacity planning for air-cargo hub terminals is still performed stage by stage against deterministic mean-value forecasts, which conceals both the coupling of the six-stage handling chain and the pronounced upper-tail variability of cargo demand. This study proposes a risk-averse configuration framework that jointly decides line deployment, cargo-class assignment, and capacity tier across receiving, screening, sortation, storage, ULD build-up, and loading. The model is a two-stage stochastic program optimizing four criteria simultaneously—time-weighted service level, life-cycle cost, a Pollaczek–Khinchine congestion penalty, and a CVaR-augmented unmet-demand measure—with chance constraints handled by sample-average approximation and 5000 Monte Carlo scenarios compressed to 30 by forward Kantorovich reduction. An improved NSGA-II with hierarchical function–class–tier encoding, sequence-aware repair, time-priority mutation, and scenario-recursive fitness evaluation solves the resulting mixed-integer problem. On a calibrated super-hub instance (2800 t outbound per day), the knee-point design attains 96.4% of the service-maximal service level at 88.0% of its cost and cuts the conditional value-at-risk of unmet demand at the 0.90 confidence level by about 42% versus a deterministic-equivalent plan; the value of the stochastic solution reaches 9.9%. The algorithm outperforms NSGA-II, NSGA-III, MOEA/D-DE, SPEA2, and MOPSO on hypervolume and inverted generational distance. Compositional volatility, not aggregate volume, emerges as the dominant driver of service collapse. Full article

In remote regions with abundant hydropower resources, upstream and downstream river basin virtual power plants (RBVPPs) operated by different entities are hydraulically coupled. Independent scheduling may therefore lead to inefficient water allocation and profit losses because downstream inflow depends on upstream reservoir releases. This study proposes a multi-RBVPP scheduling strategy based on a bidirectional water compensation contract. The independent-operation outflow schedule serves as the contractual baseline, and both increases and decreases in upstream releases are compensated according to their time-dependent effects on the downstream inflow. These effects are quantified using matrices for hydraulic connectivity, flow distribution coefficients, and water travel time. In the Stackelberg framework, the downstream RBVPP determines the time-varying compensation prices and electricity sales plan, whereas the upstream RBVPP adjusts reservoir releases and generation schedules subject to its own operational constraints. Only boundary outflow information is exchanged, preserving the privacy of internal operations. Conditional value-at-risk (CVaR) and scenario-based stochastic optimization are used to address uncertainties in renewable generation and hydrological conditions. The results for wet, normal, and dry conditions show that the proposed framework increases the profits of both RBVPPs and improves the coordinated use of basin water resources. Full article

Against the backdrop of the “dual-carbon” goals, the penetration of distributed photovoltaics (PV) in distribution networks continues to increase. Under the combined effects of stochastic PV generation and load variability, assessing PV hosting capacity in distribution networks under fault conditions has become increasingly challenging. To address the limited consideration of uncertainty and fault conditions in existing studies, this paper proposes a stochastic optimization framework for evaluating PV hosting capacity under fault conditions that considers load uncertainty and mobile shared energy storage (MSES). First, historical data are used to generate representative uncertainty scenarios through Latin hypercube sampling. An optimization model is then formulated subject to secure distribution network operation constraints to determine the optimal capacities and locations of distributed PV installations. Meanwhile, MSES is incorporated to further enhance PV hosting capacity through flexible spatial and temporal energy transfer. Finally, simulations on the modified IEEE 33-bus system demonstrate that MSES can effectively increase PV hosting capacity and renewable energy accommodation. However, under fault conditions, changes in voltage profiles and the emergence of reverse power flows further reduce PV hosting capacity. Although incorporating load uncertainty increases the system cost, the resulting PV hosting capacity is more representative of practical operating conditions, thereby providing quantitative decision-making support for the planning and operation of distribution networks with high PV penetration. Full article