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The Tahuna 1 System which is located on Sangihe Island, North Sulawesi, Indonesia consists of 4 power plants, namely the Tahuna Diesel Power Plant, Tamako Diesel Power Plant, Sangihe Solar Power Plant, and Ulung Peliang Micro Hydro Power Plant are synchronized through the 20 kV medium voltage network with a total net power capacity of 15,358 kW. The cumulative value of the Levelized Cost of Electricity (LCOE) of the Tahuna 1 System in 2023 reached Rp4,210.86/kWh, because there is no normal operation scheduling for the generating units per hour as a reference in operating the system. Until now, the dispatcher of the Tahuna Command Center must coordinate with the power plant operator to operate the generating units without any priority order of operation, but only the availability and readiness of each unit. In this research, a scheme for hourly generating unit operation scheduling was designed on the Tahuna 1 System to minimize the LCOE by considering the system load profile, generating unit capacity, and spinning reserve with a modification of the Merit Order and Priority Listing method to increase the selectivity of determining the priority list of generating unit operations based on the cheapest LCOE ranking (Rp/kWh) according to the range of generating zones created from the intersection of the cost function equation between all diesel generating units. The design of the hourly operation scheduling scheme of generating units was carried out on 12 units of Tahuna Diesel Power Plant, and 6 units of Tamako Diesel Power Plant as independent variables. Based on the results of the operation scheduling scheme with consideration of two spinning reserve units, there is a decrease in the LCOE value of the Tahuna 1 System from the initial Rp4,210.86/kWh to Rp2,913.66/kWh or 30.81% more economical compared to the current conditions. This scheduling scheme is also a better choice to implement when viewed from the operational reliability of the 20 kV distribution system.

To efficiently utilize the power generated by a photovoltaic (PV) system, integrating it with an energy storage system (ESS) is essential. Furthermore, maximizing the economic benefits of such PV‐ESS integrated systems requires selecting the optimal capacity and performing optimal energy operation scheduling. Although many studies rely on rule‐based energy operation scheduling, these methods prove inadequate for complex real‐world scenarios. Moreover, they often focus solely on determining the ESS capacity to integrate into existing PV systems, thereby limiting the possibility of achieving optimal economic benefits. To address this issue, we propose an optimal energy operation scheduling and system sizing scheme for a PV‐ESS integrated system based on metaheuristic algorithms. The proposed scheme employs a zero‐shot PV power forecasting model to estimate the potential power generation from a planned PV system. A systematic analysis of the installation, operation, and maintenance costs is then incorporated into the economic analysis. We conducted extensive experiments for comparing economic benefits of various scheduling methods and capacities using real electrical load data collected from a private university in South Korea and estimated PV power data. According to the results, the most effective metaheuristic algorithm for scheduling is simulated annealing (SA). Additionally, the optimal PV system, battery, and power conversion system capacities for the university are 13,000 kW each, 10% of the PV system capacity, and 60% of the battery capacity, respectively. The estimated annual electricity tariff calculated from the data used in the experiment is$3,315,484. In contrast, SA‐based scheduling in the optimal PV‐ESS integrated system achieved annual economic benefits of $ 875,000, an improvement of approximately 7% over rule‐based scheduling of $817,730.

To address efficient operation scheduling of shipboard helicopter groups under multi-mission demands and limited deck space, a novel Flexible Operation Mode (FOM) was proposed. Mission grouping, deck operation processes, and mission time were flexibilized to construct a mission planning method. From the perspective of the deck operation lifecycle, the scheduling problem was modeled as a six-stage mixed-integer program. A bi-level optimization framework was introduced, prioritizing maximization of mission time window satisfaction and secondarily minimizing mean deck operation time. Spatial evolution during the transportation phase was managed via an offline trajectory library that converted high-dimensional constraints into low-dimensional parameter mappings, significantly reducing real-time solution complexity. A Leader-Follower Particle Swarm Optimization (LFPSO) algorithm was developed, featuring a three-stage stochastic priority encoding and a mission-chain-driven launch-recovery decoupling strategy to reduce decision coupling. A hierarchical population structure enhanced co-evolution of global search and local refinement. The case simulation results show that the proposed model and algorithm can effectively solve the deck operation scheduling problem in complex mission scenarios, and are significantly superior to the Continuous Operation Mode (COM) and the Fixed-process FOM (FFOM) in key performance indicators such as mission time window satisfaction, average deck operation time, and average mission flight time. Its effectiveness in enhancing system scheduling capability and performance stability has been verified. This research provides systematic support for the flexible construction and intelligent decision-making of ship aviation operation systems.

The compound energy system is an important component of zero-carbon buildings. Due to the complex form of the system and the difficult-to-capture characteristics of thermo-electric coupling interactions, the operation control of the zero-carbon building’s energy system is difficult in practical engineering. Therefore, it is necessary to carry out relevant optimization methods. This paper investigated the current research status of the control and scheduling of compound energy systems in zero-carbon buildings at home and abroad, selected a typical zero-carbon building as the research object, analyzed its energy system’s operational data, and proposed an operation scheduling algorithm based on day-ahead flexible programming and intraday rolling optimization. The multi-energy flow control algorithm model was developed to optimize the operation strategy of heat pump, photovoltaic, and energy storage systems. Then, the paper applied the algorithm model to a typical zero-carbon building project, and verified the actual effect of the method through the actual operational data. After applying the method in this paper, the self-absorption rate of photovoltaic power generation in the building increased by 7.13%. The research results provide a theoretical model and data support for the operation control of the zero-carbon building’s compound energy system, and could promote the market application of the compound energy system.

Coaxial closed-loop geothermal systems, increasingly recognized as scalable and low-impact geothermal solutions, remain limited by conductive heat transfer between the reservoir and wellbore. This study investigates three strategies to enhance thermal output: (i) dynamic operation scheduling, (ii) substitution of conventional fluids with Organic Rankine Cycle (ORC) working fluids, and (iii) targeted conductive enhancements near the well. Using a CMG STARS simulation framework, system performance was evaluated over 1- to 20-year horizons, introducing a characteristic thermal recovery curve as a tool for analyzing long-term behavior. Results show that extended recovery durations raise outlet temperatures but with diminishing returns, identifying approximately 80% recovery as a practical optimization point. Fluids such as n-pentane and R245fa deliver substantially greater ORC-compatible heat than water, with thermo-siphoning observed under low-flow conditions. Conductive enhancement geometries, namely ring and fishbone configurations, exhibit distinct performance profiles, with rings outperforming fishbones due to larger injected volumes and greater advantage due to reservoir reach. One-year gains range from 4.5–9.4% for rings and 0.65–1.37% for fishbones, stabilizing at 3.7–7.8% and 0.55–1.18% after 20 years. These findings provide design and operational guidance for advancing coaxial closed-loop systems in low-carbon energy deployment.

Designing a cellular manufacturing (CM) system involves three major decisions: cell formation (CF), cellular layout (CL), and cellular scheduling (CS). The integrated design of CM systems is investigated in this paper by proposing two mathematical models. The first model integrates cellular layout problem with cell formation problem to determine optimal cell configuration and the layout of machines and cells in order to minimize the total movement costs. The second model takes also the cellular scheduling into consideration with the objective of minimizing the total completion time of parts. Two genetic algorithms are developed to solve the real-sized problems. The proposed models are formulated as mixed integer linear programming, and two numerical examples are solved in order to investigate the effects of integration in the CM systems design. The results show that considering CF, CL, and CS decisions in a simultaneous manner can significantly improve the performance of the CM systems.

This document presents an economic optimization model which identifies the location, the nominal plant capacity and the operation scheduling for set of biorefineries of second-generation ethanol using the biomass obtained as waste in the sugarcane industry. The model also determines the gasoline volumes that will be mixed with ethanol in order to produce a mixed fuel. Given a planning horizon of the operation of the system, the model obtains its optimal parameters at fixed time intervals (annual) so the global optimum is obtained by minimizing the mathematical expectation of the stochastic process generated when the product demand is assumed random with known density. Partial optimization of the process is achieved using a mixed integer linear programming model. Real information obtained from the Secretariat of Energy for the management of biorefineries in the state of Veracruz of the Mexican Republic is included and numerical results are reported.

Uncertainties of renewable energy sources (RESs) such as wind turbine (WT) and photovoltaic (PV) units are one of the considerable challenges of prosumer microgrids (PMGs) for the optimal day‐ahead operation. In this study, a new probabilistic scenario‐based method of optimal scheduling and operation of PMGs is developed. In this regard, different scenarios are generated using Monte Carlo Simulations (MCS). Furthermore, k‐means, k‐medoids, and differential evolution algorithms (DEA) are deployed to cluster the scenarios in the proposed method. A realistic commercial PMG in Iran is selected to apply the introduced method. The validity of the developed probabilistic optimization method for PMG operation is examined by comparing the results under various scenario reduction algorithms and MCS ones. The comparison of the obtained results and those of other existing deterministic methods highlights the advantages of the presented method. Furthermore, the sensitivity analyses are carried out to investigate the robustness of the developed method against the increase in the system uncertainty level. According to the test results, it is concluded that the k‐medoids algorithm has the best performance in comparison with the k‐means and the DEA‐based clustering under various conditions. Proposing a novel scenario‐based O.F to optimize the operation costs of prosumers. Comparison of the proposed method and other available deterministic ones. Comparison of different scenario reduction methods. Validation of the scenario reduction‐based method by using MCS. Investigation of the proposed method robustness against the uncertainty increment.

For the flexibility of Park load, a degree of positive and negative coupling between different loads is considered. And an active aggregation optimization method of park load is proposed based on flexibility and correlation degree. According to the scenario data of historical demand response, an optimization model is build, and a certain number of aggregates meeting the demand response scenarios are formed by active aggregation to guide the scheduling of daily operation periods. Through the optimization results, not only the optimal polymer is obtained, but also a certain number of standby polymers is obtained. While completing the response indicators issued by the power grid, the flexibility and relevance of the load in each park are fully applied to reduce the calculation scale of daily operation scheduling.

We study a problem arising from surgical operations scheduling and model it as a resource-constrained machine scheduling problem with machine eligibility restriction to minimize the makespan. By decomposing the problem into two sub-problems, we develop effective heuristic algorithms to solve the problem. We test the proposed algorithms on randomly generated instances as well as real data set from a large hospital. The numerical results show the effectiveness and potential practical value of the models and the algorithms.