Search Results (1,271)

The increasing integration of distributed energy resources and flexible loads has transformed university campuses into complex energy systems that require coordinated operational strategies capable of managing renewable uncertainty while maintaining economic and environmental performance. This paper proposes a two-stage stochastic mixed-integer linear programming (MILP) framework for the optimal day-ahead energy management of a smart university campus. The proposed model jointly coordinates photovoltaic generation, battery energy storage systems, electric vehicle charging, HVAC operation, and demand response under uncertainties associated with solar generation, electricity demand, energy prices, and ambient temperature. Unlike previous campus energy management approaches, the proposed framework explicitly distinguishes first-stage scheduling decisions from second-stage recourse actions, enabling adaptive operation while preserving decision consistency across uncertainty scenarios. A realistic case study based on the operational characteristics of the Universidad Politécnica Salesiana campus in Ecuador is used to evaluate the proposed methodology. The results demonstrate that the coordinated stochastic scheduling strategy reduces daily operating costs by 36.37%, decreases CO₂ emissions by 42.81%, and lowers peak grid demand by 37.99% compared with conventional operation. In addition, photovoltaic self-consumption reaches 91.7%, while renewable energy utilization increases to 93.4% without compromising occupant thermal comfort. The proposed framework provides a scalable pathway toward low-carbon, resilient, and energy-efficient smart campus operation. Full article

In grid-connected smart integrated energy systems with high shares of renewable generation, source-side variability and inadequate coordination among battery storage, other energy carriers, and the external grid limit local renewable-electricity utilization and impede deep decarbonization. This study proposes a machine-learning-assisted, renewable-driven framework for multi-energy coupling and scenario-based multi-objective optimization of electricity–heat–hydrogen–storage systems. Historical meteorological and load data are processed using K-means clustering and Latin hypercube sampling to construct representative operating scenarios across multiple volatility regimes and characterize source–load uncertainty. The equipment model includes photovoltaic arrays, wind turbines, heat pumps, electrolyzers, fuel cells, grid-interactive battery energy storage, thermal storage, and hydrogen storage; cross-carrier conversion dynamics and emissions from purchased electricity and natural gas are embedded in the energy-balance constraints. A mixed-integer linear programming formulation then co-optimizes battery charging and discharging, grid exchange, and other multi-energy flows with respect to operating cost, carbon emissions, and renewable-energy curtailment. At 95% renewable-energy penetration, the proposed method achieves a renewable-energy absorption rate of 91.6% and a curtailment rate of 8.4%. Across the carbon-price cases, annualized operating cost ranges from 126.5 × 10⁴ to 141.2 × 10⁴ USD yr⁻¹, while carbon-emission intensity ranges from 26.4 to 38.5 gCO₂/kWh_eq. Under the specified high-risk grid disturbances, the coordinated strategy limits load shedding to 1.8%—73% below deterministic scheduling and 79% below the heuristic benchmark—and maintains 92.6% hydrogen self-sufficiency. These results provide a data-driven modeling and decision framework for battery–grid coordination and deep decarbonization in smart integrated energy systems. Full article

The World Bank’s Lighthouse Strategy identifies Morocco as a first mover exporter of green hydrogen and its derivatives to Europe; however, the engineering feasibility of the associated transport and storage network has not been quantitatively demonstrated. This study addresses that gap through an integrated spatial and multiperiod optimization framework that couples a spatially explicit Mixed Integer Linear Programming (MILP) model with a FlexSim/FloWorks digital twin for discrete event and hydraulic simulation. The MILP simultaneously optimizes electrolysis deployment, hydrogen storage technologies, and multimodal transport across a four node Moroccan export corridor (TanTan, Mohammedia, Jorf Lasfar, and Tanger Med) for the 2030, 2040, and 2050 planning horizons under a net present value objective. The optimal configuration combines a dedicated hydrogen backbone pipeline for the high volume production corridor with shortsea cabotage for the distribution branches, achieving a full chain levelized cost of ammonia (LCOA) of 1176 USD/t, consistent with the World Bank benchmark and reducing costs by 57 USD/t compared with an all cabotage configuration. The optimal network remains robust over a wide range of capital cost and financing assumptions, while the digital twin confirms the hydraulic and operational feasibility of the integrated pipeline–shipping system without critical port congestion. These findings demonstrate that combining optimization with digital twin validation provides a robust engineering basis for planning Morocco’s green hydrogen export infrastructure and supports investment decisions aligned with future CBAM compliant hydrogen and ammonia supply chains. Full article

The U-shaped Disassembly Line Balancing Problem (UDLBP) is a challenging combinatorial optimization problem for which efficient solution approaches remain limited. This study proposes an efficient branch-bound-and-remember (BBR) algorithm that integrates a memory-based mechanism and U-shaped dominance rules to effectively reduce the search space. Specifically, a new branching method, an additional lower bounding method, and new dominance rules are developed to suit the UDLBP, and different search strategies are developed and explored. Extensive computational experiments are conducted on a comprehensive set of benchmark instances to evaluate the performance of the proposed approach. The results demonstrate that the proposed BBR algorithm can consistently identify the best-known solutions for the evaluated benchmark instances. Compared with constraint programming, mixed-integer linear programming, and several state-of-the-art metaheuristic algorithms, the proposed approach achieves competitive solution quality and computational efficiency, consistently matching the best-known solutions with an average recorded CPU time of 0.0199 s under the 500 s computational setting. These findings indicate that the proposed algorithm provides an efficient optimization framework for solving UDLBP, achieving high-quality solutions with substantially low computational cost. Full article

Battery-powered propulsion offers a pathway for reducing inland shipping emissions. However, low battery energy density limits sailing range and may require energy replenishment during a voyage, thereby complicating energy and voyage planning for inland electric vessels. Time-of-use (TOU) pricing creates cost-saving opportunities but further complicates planning because energy replenishment and sailing speed are closely coupled. This study develops a joint optimization framework for an inland electric vessel under TOU pricing that determines replenishment ports, technologies, amounts, and leg-specific sailing speeds to minimize total replenishment costs. The problem is formulated as a mixed-integer nonlinear programming model and reformulated as a mixed-integer linear programming approximation through equivalent linearization and speed discretization. A Yangtze River case study with five operating conditions evaluates the proposed framework. The results show that, under the proposed framework, TOU pricing reduces total replenishment costs by 42.4–43.4% compared to fixed pricing. Relative to a sailing-speed optimization benchmark, joint optimization under TOU pricing reduces replenishment costs by 9.8–12.5% and energy consumption by 4.2–5.6%. The cost-saving potential also varies with the voyage time limit, voyage start time, relative charging and battery swapping rates, and the availability of opportunity charging. Full article

This study develops a probabilistic carbon-aware optimization framework for residential 24/7 carbon-free electricity (CFE) under uncertain load, PV generation and dynamic grid carbon intensity. Historical half-hourly monitoring data are represented through KDE-Copula scenario generation, while grid carbon factors are described by time-series decomposition and ARMA-based residual scenarios. A two-stage mixed-integer linear program jointly sizes and dispatches battery energy storage (BESS) and thermal energy storage (TES) by minimizing annualized lifecycle CO₂ emissions. The results show that coordinated BESS–TES operation improves PV utilization and avoids carbon-intensive grid imports, especially in winter. In the examined capacity range, increasing BESS capacity from 0 to 100 kWh reduces annual lifecycle emissions from approximately 8600 to 6000 kg CO₂ yr⁻¹, corresponding to about a 30% reduction, whereas the marginal benefit of additional TES is constrained by DHW demand and its embodied emissions. Electrical storage is therefore the principal carbon-shifting resource, while a moderately sized TES complements it by moving heat-pump operation toward low-carbon and PV-rich periods. The framework provides a practical basis for carbon-aware design of residential electrification systems. Full article

This paper studies candidate intermediary node deployment for influence diffusion under the linear threshold model (LTM). Given fixed diffusion sources, target nodes, and a budget, the decision maker selects candidate intermediary nodes to maximize the expected total weight of activated targets. Once deployed, a candidate node enables its associated potential arcs whose other endpoints belong to the effective network. Using the LTM live-arc representation, we establish distributional equivalence between sampling on the potential graph and then restricting each scenario to the deployed induced network, and sampling directly on the deployed network. This leads to a finite-scenario sample-average approximation (SAA) mixed-integer formulation based on canonical live paths; the resulting deployment objective is monotone and supermodular but is generally not submodular, so the classical greedy-approximation guarantee for monotone submodular maximization does not apply in general. Since the compact SAA formulation contains many scenario–target variables and covering constraints, solving the formulation directly can be computationally demanding. We therefore propose a scenario-decomposed branch-and-Benders-cut algorithm that solves the finite-scenario SAA model to optimality. Each scenario subproblem is separable by target and has a closed-form dual optimum, so Benders cuts are separated by scanning required-node sets rather than solving linear programs inside callbacks. On five real networks and 225 SAA instances, the algorithm solves all instances within one hour, averaging 27.46 s; the compact SAA formulation solves 172 instances, with an average capped time of 1444.11 s. 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

The increasing penetration of photovoltaic (PV) generation in industrial parks creates challenges associated with renewable-energy accommodation due to temporal mismatches between PV output and electricity demand. Electric vehicle (EV) fleets with coordinated charging and discharging capabilities provide potential flexibility for mitigating PV curtailment and improving local energy utilization. This paper proposes a hierarchical multiobjective rolling-horizon vehicle-to-grid (V2G) control framework for EV fleets in industrial parks. First, an individual EV model is developed by considering charging/discharging modes, travel requirements, V2G willingness, and acceptable departure state-of-charge (SOC) shortfall. Based on the uncontrolled-charging baseline, pointwise fleet flexibility limits are evaluated by considering SOC constraints and departure-energy recoverability. Second, a lexicographic three-level mixed-integer linear programming (MILP) model is established to prioritize PV accommodation, followed by purchased-energy cost and user-related operating costs. The proposed optimization is implemented through a causal rolling-horizon strategy, where updated measurements and forecast information are used to revise future schedules without accessing future realized PV outputs. Case studies on a representative industrial-park system show that the causal rolling-horizon strategy increases PV utilization from 90.50% to 93.89% and reduces total operating cost by approximately 13.0% compared with the static day-ahead schedule under the tested PV deviation scenario. Furthermore, 20 paired trials with a fixed fleet composition and regenerated EV-state and PV-error realizations show consistent system-level gains, accompanied by higher user compensation and battery use. The multi-scenario comparison further shows that the proposed hierarchical rolling-horizon V2G configuration achieves 99.26% PV utilization and lower total operating cost than uncontrolled charging and unidirectional smart-charging (V1G) operation. The results indicate that the proposed framework can effectively coordinate EV-fleet flexibility and hierarchical objectives for PV accommodation in industrial parks. Full article

As Renewable Energy Communities (RECs) drive a shift toward decentralized power systems, innovative solutions to manage the inherent intermittency of distributed energy resources are essential. This paper investigates the potential of electric vehicles (EVs) as dynamic flexibility providers within the REC framework. By leveraging smart charging and Vehicle-to-Everything (V2X) technologies, EV fleets can act as key assets to facilitate the transition toward active distribution networks by providing upward and downward power reserves within local flexibility markets. This study presents a Mixed-Integer Linear Programming (MILP)-based Energy Management System (EMS) to optimally manage a case study REC in Northern Italy, characterized by renewable power plants and V2X-enabled EV charging stations for both electric cars and electric trucks. The proposed EMS model aims to simultaneously maximize the energy virtually shared within the REC and the provision of upward and downward reserves by the EV fleet over the considered time horizon. The EMS optimal results are analyzed for two distinct periods of the year, namely one week in spring and one in autumn, demonstrating that the flexibility guaranteed by the EVs can significantly impact the energy-sharing mechanism of the REC while at the same time providing additional revenues to the REC members. Full article

High photovoltaic (PV) penetration can create hourly mismatches among office-building demand, renewable generation, electricity prices, and grid carbon intensity. This study develops a reproducible, deterministic, time-coupled day-ahead mixed-integer linear programming model for a predetermined PV–battery energy storage system (BESS), simplified heating, ventilation, and air-conditioning (HVAC) load response, and electric-vehicle (EV) charging/vehicle-to-building operation. Separate charging and discharging variables, binary operating states, a cyclic 50% BESS terminal state of charge, and PV- and grid-origin battery accounts eliminate simultaneous operation, initial-energy depletion, and grid-to-BESS-to-grid arbitrage. Five scenarios and five one-factor-at-a-time sensitivity families are independently re-optimized for a Suzhou office-building case. For the selected summer day, Scenario 2 costs 1974.04 CNY. Scenario 3 costs 1974.73 CNY before DR revenue, earns 323.85 CNY, and has a net cost of 1650.88 CNY. Its grid import and grid-side emissions are 1.27% and 1.28% below Scenario 2, respectively, whereas its pre-incentive cost is 0.04% higher. The corrected DR formulation leaves the reported optima unchanged within 2.6 × 10⁻⁹. The maximum hourly balance residual across all runs is 1.3 × 10⁻⁹ kW. Full article

The hybrid flow shop scheduling problem (HFSP) with unrelated parallel machines (UPMs), sequence-dependent setup times (SDSTs), and inter-stage transportation times has recently emerged as a prominent research topic. To address this scheduling problem with the objective of minimizing the maximum completion time (makespan), this paper first formulates a mixed-integer linear programming (MILP) model based on the machine-position modeling idea. Exact solution analyses on small-scale instances reveal that the strong coupling effect of these triple constraints concentrates the computational bottleneck on the time-consuming proof of optimality, thereby underscoring the strongly NP-hard nature of the investigated HFSP-SDST-T problem. To efficiently solve large-scale instances, a novel adaptive co-evolutionary memetic algorithm (ACMA) is proposed. ACMA adopts a dual-population co-evolutionary framework, where a customized genetic algorithm (GA) is designed for global exploration and a Lévy flight-enhanced particle swarm optimization (PSO) improves local search capability. To dynamically balance exploration and exploitation, a Dynamic Role Allocation (DRA) mechanism is developed to adaptively reassign individuals between the two populations according to their evolutionary states. Moreover, a progressive two-stage memetic enhancement strategy is proposed to overcome premature convergence by sequentially activating deep variable neighborhood search (VNS) and a catastrophe-based diversification strategy, enabling adaptive responses to different stagnation levels. Extensive experiments, including ablation studies, comparisons with benchmark algorithms, and computational complexity analysis, are conducted on small- and large-scale instances. The results show that ACMA consistently obtains the exact optimal solutions obtained from the MILP model for small-scale instances and achieves competitive performance on large-scale complex instances. Furthermore, Wilcoxon signed-rank tests confirm the statistical significance of the performance differences, supporting the reliability of the experimental results. Full article

Carbon pricing can change manufacturers’ material choices, while the costs of managing the residual waste remain geographically uneven. We formulate a manufacturer–regulator bilevel model for a household plastic-waste closed-loop supply chain with quantity-dependent recycled-bale prices, activity-specific carbon accounts, physical interregional waste routing, and a proportional regional cost-burden standard. The lower level is explicitly a single coordinating-regulator linear program rather than a game among independent regions. Its primal constraints, dual constraints, and strong-duality equality are embedded in the manufacturer problem; binary-continuous products are exactly linearized using the manufacturer’s SOS1 price-grid variables. Thus, every reported policy point is obtained from the same 12-region mixed-integer equilibrium formulation. Across 36 central policy combinations, HiGHS reports a zero mixed-integer programming gap, and the largest feasibility and optimality residual is $5.24\times {10}^{-8}$. Raising the carbon tax from 0 to 10 USD/tCO₂ increases the real recycling rate (RRR) from 15.33% to the bale-capacity limit of 29.85% and reduces physical emissions by 11.64%. Tightening the allowed regional burden deviation from 25% to 5% reduces the standard deviation of normalized residual-waste cost burden by 77.89% and interregional residual-waste transfers by 77.05%, but does not change the RRR. This zero-recycling effect overturns the earlier assumption-driven result: a pure routing-based cost-balancing rule cannot mechanically stimulate the manufacturer’s recycled-input demand. A global analysis of 300 parameter sets and five independent regional samples re-solves 1800 equilibrium models; all have a zero solver gap and pass the residual audit. Carbon-induced RRR increases have a median of 17.03 percentage points, while strict-versus-loose burden-threshold changes in RRR are zero in every set. The results distinguish carbon efficiency, regional cost incidence, and fiscal incidence and show that policy complementarity must be demonstrated through endogenous decision links rather than imposed response functions. Full article

Off-grid photovoltaic (PV) hydrogen production systems must coordinate rapidly varying PV power, battery energy, and the operating states of multiple alkaline water electrolyzers. Inappropriate coordination may lead to PV curtailment, frequent unit switching, and persistent workload concentration on a small number of electrolyzers. This paper develops an efficiency- and load-balanced operation (ELBO) scheme as an improved rule-based supervisory strategy rather than an online optimization method. ELBO adopts a two-level decision structure. A planned number of online electrolyzers is first determined from the moving-average PV power and the reference power associated with high single-unit efficiency. This planned count is then corrected using real-time PV power, battery state of charge, and the previous electrolyzer states. The controller adjusts the powers of the online units before changing their number, uses the battery to bridge temporary power deficits, and distributes the remaining adjustable power under the operating and ramp-rate constraints. Five representative PV profiles selected from one year of measured data were used to compare ELBO with PV-following operation (PFO), multi-electrolyzer coordinated operation (MECO), and an offline mixed-integer linear programming (MILP) benchmark. ELBO produced 1328 kg of hydrogen, which was 8.85% and 6.07% higher than PFO and MECO, respectively. Its overall PV-to-hydrogen efficiency and PV utilization reached 65.2% and 94.9%, respectively, with 36 start–stop events. MILP produced 1345 kg of hydrogen, only 1.28% more than ELBO, but required the complete future PV sequence. Ablation analysis further shows that the planned-count layer, moving-average filtering, battery-supported retention, and load-balancing allocation contribute to different and complementary aspects of capacity matching, operating continuity, and workload distribution. The results indicate that the benefit of ELBO arises from the ordered coordination of these supervisory functions and that it provides a practical compromise between operating performance, workload distribution, information requirements, and computational complexity under the representative conditions considered. Full article

The coordinated operation of interconnected multi-carrier energy hubs is a key enabler of cost-efficient and flexible energy management in modern smart cities. This paper develops a comprehensive optimization framework for the day-ahead scheduling of interconnected energy hubs in residential and commercial sectors. The problem is formulated as a mixed-integer linear programming (MILP) model that jointly manages electricity, natural gas, and thermal energy flows. To enhance operational flexibility, the proposed model incorporates demand response programs for both electrical and thermal loads, multiple energy storage technologies, and electric vehicles with vehicle-to-grid (V2G) capability. Six operating scenarios are defined to assess the impact of different resources and coordination levels, ranging from independent hub operation to fully integrated interconnected scheduling. Simulation results show that coordinated operation of the energy hubs, supported by flexible loads, storage systems, and electric vehicles, can significantly reduce total daily operating costs compared with conventional standalone configurations. The findings confirm that energy exchange among hubs, combined with demand-side flexibility and EV participation, improves both economic performance and system efficiency. The proposed framework offers a scalable scheduling approach for future integrated multi-energy systems. Full article