Search Results (697)

Waste haulage represents one of the most critical and cost-intensive operations in surface mining, accounting for up to 50% of the total operating costs. Under such operating conditions, the implementation of continuous systems such as In-Pit Crushing and Conveying (IPCC) is an alternative to truck haulage, as it demonstrates a higher degree of economic efficiency. In a theoretical and practical sense, due to its direct impact on the extraction plan, defining the optimal position of the crusher and consequently the system of conveyors is often the most challenging problem of this methodology. This paper introduces an innovative approach to determining the optimum waste haulage configuration by comparing conventional truck-based transport with IPCC systems. The model is formulated as a Mixed-Integer Linear Programming (MILP) problem, explicitly incorporating spatial dimensions and the relocation costs of semi-mobile crushers. The model situates the crusher in a way that reduces transfer costs throughout production periods and it has been tested on a hypothetical open pit. Full article

This paper develops an optimal dispatch model for an integrated wind–solar hydrogen-to-ammonia system to address the mismatch between renewable-energy fluctuations and chemical production loads. The model incorporates renewable variability, electrolyzer dynamics, hydrogen-storage regulation, and ammonia-synthesis load constraints, and is solved using a multi-time-scale MILP framework. An efficiency-priority power allocation strategy is further introduced to account for performance differences among electrolyzers. Using real wind–solar output data, a 72-h case study compares three operational schemes: the Balanced Scheme, the Steady-State Scheme, and the Following Scheme. The proposed Balanced Scheme reduces renewable curtailment to 2.4%, lowers ammonia load fluctuations relative to the Following Scheme, and decreases electricity consumption per ton of ammonia by 19.4% compared with the Steady-State Scheme. These results demonstrate that the integrated dispatch model and electrolyzer-cluster control strategy enhance system flexibility, energy efficiency, and overall economic performance in renewable-powered ammonia production. Full article

Efficient berth allocation remains a critical challenge in bulk port operations due to the stochastic nature of vessel arrivals and the complex interaction among loading resources. This study proposes an integrated optimisation–simulation framework to minimise total demurrage costs under uncertainty. The mathematical model was formulated as a mixed-integer linear program (MILP) to determine the optimal assignment and sequencing of vessels to berths and shiploaders, subject to time-window and capacity constraints. The MILP was solved using a Simulated Annealing (SA) metaheuristic to improve computational efficiency for large-scale instances. Subsequently, the optimised berth plans were evaluated in FlexSim, a discrete-event simulation environment, to assess the operational variability arising from stochastic factors, including vessel arrival times, service durations, and loader availability. System performance was measured through vessel waiting time, berth utilisation rate, and demurrage cost variability across multiple replications. Results indicate that the proposed SA–FlexSim framework reduced average demurrage costs by 28.7% compared to the deterministic MILP and by 21.3% relative to standalone SA, confirming its effectiveness and robustness under uncertain operating conditions. The hybrid methodology provides a practical decision-support tool for terminal operators seeking to enhance scheduling reliability and cost efficiency in bulk port environments. Full article

The increasing penetration of microgrids (MGs) in modern power distribution systems requires advanced operational strategies to ensure both economic efficiency and technical reliability. This study developed an optimal economic framework for battery energy storage in MG connected to distribution systems in order to minimize operational costs while considering renewable integration and battery charging and discharging cost and degradation cost as well, and their impact on grid technical constraint. An MG is interconnected to the IEEE-33 radial distribution feeder through an additional bus, where the BESS operates to minimize the total operating cost over a 24 h horizon. The formulation captures the charging and discharging dynamics of the BESS, BESS degradation, state-of-charge constraints, electricity price signals, and the network’s operational limits. The optimization problem is solved using Mixed Integer Linear Program (MILP) to obtain the optimal scheduling of BESS charging and discharging which minimizes the total operating cost and maintains grid constraint within the allowable limit by optimizing the power exchange between the MG and the distribution grid. Simulation results showed that the proposed approach reduces operational costs and optimize grid power exchange, while maintaining technical reliability of the distribution system by enhancing its voltage profiles, improving its feeder loading capability, and reducing the system losses. This study provides a practical tool for enhancing both economic and technical performance in MG-connected distribution systems. Full article

A multistage coordinated expansion-planning framework for distribution systems is developed to jointly optimize investments in the network, distributed generation (DG), and energy storage systems (ESS). Network reinforcements select from multiple feeder and transformer candidates, while DG installations consider conventional and photovoltaic (PV) options. In this study, a set of candidate buses are considered for the installation of PVs and energy storage systems. Therefore, the expansion plan can determine the optimal installation locations and timing of these candidate assets. The objective minimizes total cost in net-present-value terms, covering investment, maintenance, generation, and operating components. Representative hourly load profiles are incorporated to capture ESS dispatch behavior and PV output variability; operating costs are modeled via piecewise linearization. To preserve connectivity and preclude islanding in the presence of DG and ESS, modified radiality constraints are imposed. The formulation is a mixed-integer linear program solvable efficiently by commercial optimizers, and numerical studies confirm the method’s effectiveness. Full article

Guaranteeing the effective coordination of directional overcurrent relays (DOCRs) within microgrids (MGs) is a complex nonlinear problem due to bidirectional power flows, varying fault current levels, and the need for adaptive operation across multiple grid configurations. To address this challenge, this paper proposes a hybrid matheuristic approach combining a Biased Random-Key Genetic Algorithm (BRKGA) with Mixed-Integer Linear Programming (MILP). This formulation treats the selection of relay characteristic curves as a decision variable, allowing for simultaneous optimization of time multiplier settings (TMS), plug setting multipliers (PSM), and curve types. The BRKGA handles the global search, while the embedded MILP decoder performs exact optimization under fixed conditions. The proposed BRKGA–MILP method was tested on the IEC benchmark microgrid under multiple operating modes. Compared with conventional MILP-based coordination, it achieved up to 18.31% reduction in total relay operating times (11.81% on average) while maintaining proper coordination time intervals (CTI). Relative to previous heuristic and hybrid approaches, the method improved protection speed by up to 14.87%. These results indicate that the proposed framework effectively enhances coordination performance in adaptive microgrid protection, particularly under bidirectional power flows and varying fault current levels. Full article

With the rapid advancement of battery technology, new energy hybrid tugboats have been progressively adopted. In order to align with the trend of electrifying tugboat fleets, a mixed-integer linear programming (MILP) model for the joint scheduling of new energy hybrid tugboats and berths has been established. The model incorporates the constraint imposed by the limited number of tugboat charging connectors. The objective is to minimize the total cost over the scheduling horizon, including ship waiting, delayed-departure costs, and the operating costs of both conventional diesel and hybrid tugboats. In light of the characteristics inherent to the problem, a hybrid solution approach combining CPLEX with a heuristic-enhanced whale optimization algorithm (WOA) is employed to solve the model. A case study was conducted using data on the energy consumption of tugboats at Xiamen Port. The effectiveness of the model and algorithm was then verified through a series of small-scale instance experiments. Finally, a comprehensive sensitivity analysis of key parameters is finally conducted, including the number of tugboat charging connectors, battery capacity, and charging rate. This analysis provides valuable guidance for port tugboat operations. Full article

Inference now dominates the lifecycle footprint of large language models, yet published estimates often use inconsistent boundaries and optimize carbon while ignoring water. We present a provider-agnostic framework that unifies scope-transparent measurement with time-resolved, SLO-aware orchestration and jointly optimizes carbon and consumptive water. Measurement reports daily medians at a comprehensive serving boundary that includes accelerators, host CPU/DRAM, provisioned idle, and PUE uplift, and provides accelerator-only whiskers for reconciliation. Optimization uses a mixed-integer linear program solved over five-minute windows; it selects region, batch size, and phase-aware hardware for prefill and decode while enforcing $p95$ TTFT and TPOT as well as capacity constraints. Applied to four representative models, a single SLO-aware policy reduces comprehensive-boundary medians by 57 to 59 percent for energy, 59 to 60 percent for water, and 78 to 80 percent for location-based CO₂, with SLOs met in every window. For a day with 500 million queries on GPT-4o, totals fall from 0.344 to 0.145 GWh, 1.196 to 0.490 ML, and 121 to 25 t CO₂ (location-based). The framework offers a deployable template for carbon- and water-aware LLM serving with auditable and scope-transparent reporting. Full article

This work presents a two-level Energy Management System (EMS) for Renewable Energy Communities (RECs) combining rule-based local control with Particle Swarm Optimization (PSO) coordination. A central Energy Management Hub (CEMH) uses digital twins of each Home EMS to optimize community performance through price-signal adjustments rather than direct control. The method achieves near-optimal self-consumption and incentive gains, largely within 10% of an MILP benchmark, while reducing computational time by about threefold. The approach ensures scalability, resilience, and fairness through a transparent incentive redistribution mechanism, enabling real-time and socially accepted REC coordination. Full article

To address the challenges of climate change mitigation and operational flexibility in active distribution networks (ADNs) amid high renewable energy penetration, this paper proposes a low-carbon economic dispatch framework integrating demand-side carbon regulation and cyber–physical system (CPS)-enabled shared energy storage. First, a consumer-side emission penalty mechanism is developed by fusing a carbon emission flow (CEF) model with price elasticity coefficients. This mechanism embeds carbon costs into end-user electricity pricing, guiding users to adjust consumption patterns (e.g., reducing usage during high-carbon-intensity periods) and shifting partial carbon responsibility to the demand side. Second, a CPS-based shared energy storage mechanism is constructed, featuring a three-layer architecture (physical layer, control decision layer, security layer) that aggregates distributed energy storage (DES) resources into a unified, schedulable pool. A cooperative, game-based profit-sharing strategy using Shapley values is adopted to allocate benefits based on each DES participant’s marginal contribution, ensuring fairness and motivating resource pooling. Finally, a unified mixed-integer linear programming (MILP) optimization model is formulated for ADNs, co-optimizing locational marginal prices, DES state-of-charge trajectories, and demand curtailment to minimize operational costs and carbon emissions simultaneously. Simulations on a modified IEEE 33-bus system demonstrate that the proposed framework reduces carbon emissions by 4.5–4.7% and renewable energy curtailment by 71.1–71.3% compared to traditional dispatch methods, while lowering system operational costs by 6.6–6.8%. The results confirm its effectiveness in enhancing ADN’s low-carbon performance, renewable energy integration, and economic efficiency. Full article

Large-scale Aircraft Routing Problems (ARPs) remain challenging for standard Branch-and-Bound (B&B) and modern Mixed-Integer Linear Programming (MILP) solvers due to vast search spaces and instance-agnostic heuristics. Methods: We develop a learning-to-accelerate framework centered on a Two-Stage Route Selection Graph Convolutional Network (TRS-GCN) that predicts the importance of flight string variables using structural, LP relaxation, and operational features. Predictions are injected into the solver via three mechanisms: an ML-guided feasibility pump for warm starts, static problem reduction through predictive pruning, and a dynamic hybrid branching rule that blends ML scores with pseudo-costs. A synthetic generator produces realistic ARP instances with seed solutions for robust training. Results: On large instances derived from Bureau of Transportation Statistics data, TRS-GCN-guided static reduction safely pruned up to 49.2% of variables and reduced the time to reach the baseline solver’s 12-h target objective by 52.4%. The dynamic search strategy also yielded more incumbents within fixed time budgets compared with baselines. Conclusion: Integrating TRS-GCN into MILP workflows improves search efficiency for ARPs, offering complementary gains from warm-starting, pruning, and branching without changing the underlying optimality guarantees. Full article

The transition from conventional fossil-fueled buses to electric buses (EBs) is accelerating in the global public transportation sector. However, owing to the limitations of battery lifespan and capacity, EBs have a shorter driving range than conventional buses, and their power consumption is highly variable depending on the ambient temperature. In addition, battery lifespans are affected by charging and discharging cycles and battery age over time in all situations, which requires a method of operation that considers these factors. In this study, we estimated the driving, heating, and cooling energy consumptions based on the dispatch schedule and actual power consumption of EBs. The estimated energy consumption was then used as an input to plan the amount of charging power by time of day to optimize the charging and battery degradation costs. The optimization methodology employed mixed-integer linear programming (MILP), which facilitates discrete charging decision-making and ensures an optimum solution for operation costs by taking cost factors into account. In this phase, the scenarios were configured according to the time-of-use (TOU) charging cost and whether or not battery degradation. Battery degradation can be divided into cycle and calendar aging. The scenarios that considered both TOU and battery degradation reduced the average operating costs by approximately 1.43, 12.3, and 5.69% in spring/fall, summer, and winter, respectively, compared with scenarios that did not consider either. Full article

Uncontrolled charging of large electric bus fleets can strain constrained power grids, such as South Africa’s. This study develops and evaluates a demand-oriented charging strategy for Golden Arrow Bus Services using a Mixed-Integer Linear Programming (MILP) model calibrated with real operating data. The model schedules fleet charging over an off-peak window to minimise the highest total demand charge (Notified Maximum Demand, NMD) while respecting arrival state of charge (SOC), Time-of-Use (ToU) tariffs, and ensuring all vehicles are fully charged before dispatch. Compared to the unmanaged baseline, the optimised schedules reduce the peak demand charge by 17%, keeping total depot demand below 1 MW and ensuring full fleet readiness. The strategy also eliminates all energy consumption during expensive peak-tariff windows in both winter and summer. Further analysis shows that raising the minimum arrival SOC reduces the required optimum per-bus demand approximately linearly (≈1.5 kW per +5% SOC), whereas widening the SOC arrival range increases demand variability. This MILP framework demonstrates that exploiting SOC diversity and modest charge capacity capping can significantly lower peak demand and operational costs, offering a validated model for depots in other capacity-constrained power systems. Full article

The growing demand for renewable energy positions biorefineries as key to enhancing biofuel competitiveness. This study proposes a novel MILP superstructure integrating resource seasonality, process selection, and heat integration to optimize biomethanol production in a sugarcane biorefinery. A multi-objective optimization balancing net present value (NPV) and avoided CO₂ emissions reveals that energy integration improves environmental performance with limited economic impact. The model estimates the production of up to 66.85 kg of biomethanol/ton sugarcane from bagasse gasification, 40.7 kg e-methanol/ton sugarcane via CO₂ hydrogenation, and 3.68 kg of biomethane/ton sugarcane from biogas upgrading. Hydrogen production through biomethane reforming and photovoltaic-powered electrolysis increases methanol output without raising emissions. The integrated system achieves energy efficiencies of up to 57.3% and enables the avoidance of up to 493 kg of CO₂/ton sugarcane over the planning horizon. When thermal integration is excluded, efficiency drops by 8% and net energy production per area falls by 11%, due to the need to divert bagasse to cogeneration. Although economic challenges remain, CO₂ remuneration ranging from USD 3.27 to USD 129.79 per ton could ensure project viability. These findings highlight the role of integrated energy systems in enabling sustainable and economically feasible sugarcane biorefineries. Full article

The transition to sustainable energy systems increasingly relies on advanced optimization methods to address the challenges of designing and operating them efficiently. Seasonal storage systems play a pivotal role in aligning renewable energy generation with fluctuating energy demand, with ice storage emerging as a promising solution for seasonal energy storage. This paper presents a novel optimization framework for the operation of seasonal ice-storage systems, leveraging Mixed-Integer Linear Programming (MILP) with time-series aggregation (TSA) techniques. The proposed model accurately captures the physical behavior of ice storage, incorporating both latent and sensible heat storage phases, discrete temperature levels, and charging/discharging efficiency curves. A key feature of this framework is its ability to address computational challenges in large-scale optimization, while maintaining high detail. Using a business park in Germany as a case study, the results demonstrate a significant reduction in computational time of up to 80% for 110 typical periods, with only a 2.5% deviation in the objective value and 9% in the Seasonal Energy Efficiency Ratio (SEER), although this efficiency gain depends on the number of typical periods used. This work addresses key gaps in seasonal ice-storage optimization models and provides a robust tool for designing and optimizing sustainable energy systems. Full article