Operation Optimization Research Articles

Nonlinear Relationship of Multi-Source Land Use Features with Temporal Travel Distances at Subway Station Level: Empirical Study from Xi’an City

The operation of the subway system necessitates a comprehensive understanding of passenger flow characteristics at station locations, as well as a keen awareness of the average travel distances at these stations. Moreover, the travel distances at the station level bear a direct relationship with the built environment composed of land use characteristics within the station’s catchment area. To this end, we selected the land use features within an 800 m radius of the station (land use area, distribution of points of interest, and the surrounding living environment) as the influencing factors, with the travel distances at peak hours on the subway network in Xi’an as the research subject. An improved SSA-XGBOOST-SHAP interpretable machine learning framework was established. The research findings demonstrate that the proposed enhanced model outperforms traditional machine learning or linear regression methods in terms of R-squared, MAE, and RMSE. Furthermore, the distance from the city center, road network density, the number of public transit routes, and the land use mix have a pronounced influence on travel distances, reflecting the significant impact that mature built environments can have on passenger attraction. Additionally, the analysis reveals a notable nonlinear relationship and threshold effect between the built environment variables comprising land use and the travel distances during peak hours. The research results provide data-driven support for operational strategy management and line capacity optimization, as well as theoretical underpinnings for enhancing the efficiency and sustainability of the entire subway system.

Collaborative Pricing Strategy of Distribution Network and Electric Vehicle Aggregator Based on Integral System

Scaled EVs as a new type of load have great potential to participate in the standby market. However, the uncertainty of EV users' participation in aggregator regulation affects the optimal strategy for aggregators to participate in the market. Aiming at the uncertainty of users' willingness and the conflict of interest between aggregators and the distribution network, this paper proposes an optimal scheduling strategy of master-slave game for EV aggregators to improve users' willingness with a point system. First, the second-order cone optimal current model of the distribution network is established, and then the distribution network node marginal tariff is obtained. Second, the aggregator revenue model with energy storage is established, and the price-guided demand response model is proposed, while the aggregator point system is established to guide the charging behavior of EV users, so that the cooperative operation optimization problem between the distribution grid and the aggregator can be solved, and the economic interests among EV users, EV aggregators and distribution grid operators can be effectively balanced.

Optimal design of greenfield energy hubs in the context of carbon neutral energy supply

This paper proposes a sequential co-optimization (SCO) framework for the design of greenfield energy hubs, aiming to achieve net zero emissions. The SCO framework offers an evolutionary approach to long-term planning and operation, taking into account various evolving factors such as carbon emission policies, technological and economic advancements in energy supply components, changes in land-use, and developments at different stages of the design horizon. The framework ensures that the design of greenfield developments aligns with their evolving characteristics, determining the optimal dispatch flow for firming renewable energy through co-generation and demand flexibility options like smart loads, electric vehicles, and hydrogen-based systems. A sensitivity analysis is conducted to study the impact of emissions, evolving characteristics of greenfield developments, and techno-economic aspects of supply units on the energy hub design. Case studies demonstrate that the proposed design enables large-scale distributed energy systems to meet local energy demand and participate in the electricity market. The SCO framework provides a robust and flexible platform for the correlated optimization of planning and operation of greenfield energy hubs, for advancing towards net zero emissions.

Reliability Analysis of Modular Multilevel Converters in MVDC Applications

In modular multilevel converters (MMCs) designed for medium-voltage DC (MVDC) systems, since the required sub-modules (SMs) in one arm are fewer, the converters cannot afford the same level of redundancy as observed in high-voltage DC (HVDC) applications. In order to ensure high availability of converters in MVDC systems, reliability becomes a challenge and requires different design approaches. Considering the redundancy design, operating modes, and correlation across failures of SMs in one arm, this paper analyses and compares reliability in MMCs for MVDC systems with multiple SM topologies. Under different scenarios in MVDC systems, SM topologies with better performance of reliability have been identified in this research. It is demonstrated that, for a given output voltage in converters, three-level SMs generally provide higher reliability than two-level SMs. By implementing passive and load-sharing operation schemes in converters, the MMC reliability can be improved compared with the conventional active mode. In terms of the correlation between lifetime of SMs in the same arm, the smaller disturbance introduced by one faulty SM promotes the improvement of converter reliability. Also, the correlation between failures of SMs in one arm has greater effects on the reliability of an MMC with higher level of redundancy. The outcomes of this work contribute to the optimisation of converter design and operation with high reliability and low investment for an MMC in MVDC systems.

The control unit of the combined photovoltaic/thermal system

During the last years the design and implementation of energy-saving solutions remains a relevant and integral task for implementation the route of decreasing carbon emissions and reduce the negative impact on the environment. Using combined photovoltaic/thermal systems is limited by their high price connected mainly with the complexity of their control systems, even taking into account availability of government programs for financial support for the use of alternative energy. The novelty of the proposed work consists in the development of a mathematical model and the analytical study of the control system of combined photovoltaic/thermal system for research the operation of control and logic elements and their response to transient and emergency modes during such systems operation. Aim of the work - operational analysis, analytical and optimization studies of the control system of the combined photovoltaic/thermal system to ensure reliable operation in transient and emergency modes and significantly simplify the design of such a system. In this article was implemented mathematical modeling methods and using specialized software. As a result of the work the mathematical model of the control system of combined photovoltaic/thermal system which operates with variable set of control and logic elements was developed. A structural solution of the control system is proposed for further implementation and field testing. The response criteria of the control system to transient and emergency operation modes of the combined photovoltaic/thermal system have been developed. Practical value. It was established the permissible and critical values of a number of parameters characterizing the operation of combined photovoltaic/thermal system in normal mode and deviations from which indicate the presence of an emergency situation are determined. The proposed algorithms of the control system will make it possible to increase the reliability of combined photovoltaic/thermal system and reduce the risk of emergency situations during field tests and further operation.

Development of a conceptual model for management of technological losses of electricity in distribution networks 150-0.38 kV

The work developed a conceptual model of the process of managing the technological costs of electrical energy in 150-0.38kV distribution networks. The relevance and necessity of developing a conceptual model of the process of managing the technological costs of electricity, which is carried out in relation to the system of distribution of electrical energy (or the electrical distribution system) as a whole, including distribution electrical networks of 150-0.38 kV, is analyzed. The paper proposes a conceptual decomposition, considers the description and formalization of the power distribution system management process based on the criterion of reducing the technological costs of electricity. The proposed conceptual model describes the process of managing the technological costs of electricity in the power distribution system, as a process of regular improvement (optimization) of the functioning of the power distribution system and allows further optimization and automation of the process of managing the technological costs of electricity in the power distribution system, in order to achieve higher levels of energy efficiency of its functioning. It is shown that this model can be used to solve the tasks of not only strategic and tactical management (in terms of years and months) when forming development plans and current operation of distribution systems, but also operational optimization of technological costs of electricity at the pace of the process. Provided that the appropriate software is developed (including the forecasting of power distribution system modes and external environmental influences), the system for managing the technological costs of electrical energy is represented by three control loops that correspond to the stages of its development. Processes and information links between them are shown for each control loop. Each process in the conceptual model of the management of technological costs of electric energy has its own formal representation through generalized functional dependencies, and the information links between the processes correspond to the arguments and values of these functions. The conceptual model describes the process of managing the technological costs of electrical energy in the power distribution system as a process of regular improvement (optimization) of its functioning through increasing its energy efficiency.

Design, analysis, and operation optimisation of a shipborne absorption refrigeration–ice thermal storage system based on waste-heat utilization

The integration of waste-driven absorption refrigeration technology into marine applications is recognized as a promising low-carbon solution. Nonetheless, its practical application does not inherently outperform electric compression technology, primarily due to the absorption system’s inherent thermal inertia and limited adjustability. This characteristic renders the system less responsive to the dynamic refrigeration requirements typically encountered on board vessels. In this study, a shipborne ammonia absorption refrigeration–ice storage system is proposed to balance the cooling load between supply and demand. To evaluate the efficiency of the integrated system for ice storage and cooling during ship sailing, key operational variables including refrigeration temperature, cooling water temperature, and generation temperature are analyzed to assess the thermal cycle performance of the system. A typical shipping route is considered for cooling operation optimisation of the absorption refrigeration–ice storage system. This paper explores four typical cooling-supply strategies and determines the operation modes for the ice storage period and the cooling period based on considerations of energy consumption, energy utilization, and economic factors. The final optimisation results indicate that the combined system has significant advantages based on energy consumption and operation cost. In comparison to compression and absorption refrigeration systems, the proposed system exhibits 52.4% and 8.31% reductions in energy consumption, respectively. Additionally, the operation costs are decreased by 12.27% and 27.41%, respectively, and the life cycle costs are lowered by 11.51% and 32.35%, respectively. The findings of this combined system can provide a technical reference for the design and operation of a ship waste-heat refrigeration system.

Unified Paradigm of Start-Up Strategy for Pumped Storage Hydropower Stations: Variable Universe Fuzzy PID Controller and Integrated Operation Optimization

Unified Paradigm of Start-Up Strategy for Pumped Storage Hydropower Stations: Variable Universe Fuzzy PID Controller and Integrated Operation Optimization

Fast retrieval of XCO2 over east Asia based on Orbiting Carbon Observatory-2 (OCO-2) spectral measurements

Abstract. The increase in greenhouse gas concentrations, particularly CO2, has significant implications for global climate patterns and various aspects of human life. Spaceborne remote sensing satellites play a crucial role in high-resolution monitoring of atmospheric CO2. However, the next generation of greenhouse gas monitoring satellites is expected to face challenges, particularly in terms of computational efficiency in atmospheric CO2 retrieval and analysis. To address these challenges, this study focuses on improving the speed of retrieving the column-averaged dry-air mole fraction of carbon dioxide (XCO2) using spectral data from the Orbiting Carbon Observatory-2 (OCO-2) satellite while still maintaining retrieval accuracy. A novel approach based on neural network (NN) models is proposed to tackle the nonlinear inversion problems associated with XCO2 retrievals. The study employs a data-driven supervised learning method and explores two distinct training strategies. Firstly, training is conducted using experimental data obtained from the inversion of the operational optimization model, which is released as the OCO-2 satellite products. Secondly, training is performed using a simulated dataset generated by an accurate forward calculation model. The inversion performance and prediction performance of the machine learning model for XCO2 are compared, analyzed, and discussed for the observed region over east Asia. The results demonstrate that the model trained on simulated data accurately predicts XCO2 in the target area. Furthermore, when compared to OCO-2 satellite product data, the developed XCO2 retrieval model not only achieves rapid predictions (<1 ms) with good accuracy (1.8 ppm or approximately 0.45 %) but also effectively captures sudden increases in XCO2 plumes near industrial emission sources. The accuracy of the machine learning model retrieval results is validated against reliable data from Total Carbon Column Observing Network (TCCON) sites, demonstrating its ability to effectively capture CO2 seasonal variations and annual growth trends.

Forecasting wastewater flows and pollutant loads: A comparison of data-driven models within the urban water system framework

The ability to forecast key features of wastewater treatment plant (WWTP) influent, is emerging as an important tool to enable advanced WWTP operational strategies. Various data driven models have been reported in the scientific literature for WWTP influent forecast however, there has been no quantitative comparison of them against each other. The present study is the first to undertake this comparison utilising a high-frequency reference dataset generated from the Urban Water System model. Specifically, the performance of autoregressive models was compared against time-delay networks, nonlinear autoregressive networks and long short-term memory networks. Time-delay networks were generally found to outperform the other tested methods, although the reliability of the generated forecasts decreases as the prediction horizon exceeds one hour. While longer prediction horizons would be desirable, there is a trade-off between model accuracy and overall optimisation of plant operation. This study also highlights the challenge of dealing with both concentration and flow variations suggesting the need for future research to analyse the separate impacts of flow and concentration variations on model performance.

SOLUCIONES BASADAS EN INTELIGENCIA ARTIFICIAL PARA EL DESARROLLO DE NEGOCIOS EN ENTORNOS PORTUARIOS

Artificial intelligence (AI) is transforming numerous sectors, including the port industry. This article addresses how AI-based solutions can revolutionize business development in port environments, providing significant competitive advantages. The state of the art reveals that ports face increasing challenges in terms of operational efficiency, resource optimization, and competitiveness. In this context, AI emerges as a powerful tool capable of analyzing large volumes of data and making informed decisions.The content of the article is structured into several key sections. First, it explores the competitive advantages offered by AI in the port sector, such as resource optimization, strategic decision-making, improved security, environmental sustainability, and customer experience. Specific applications such as predictive analytics, process automation, and route optimization are discussed. Case studies of ports that have successfully implemented AI technologies, such as the Port of Rotterdam, the Port of Hamburg, and the Port of Singapore, are presented to illustrate the benefits obtained and the challenges overcome.The development of the research focuses on the algorithms and analysis techniques used, including machine learning, neural networks, and natural language processing. Various applications in the port sector are analyzed, from facility pre-design and space optimization to Capex and Opex estimations and business generation capacity. AI is also used to improve operational efficiency and optimize investments and costs.The conclusions emphasize that, although the adoption of AI presents challenges, such as the need for advanced technological infrastructure and continuous staff training, the potential benefits are significant. AI enables ports to improve operational efficiency, reduce costs, increase security and sustainability, and offer better services to customers. Finally, recommendations for the successful implementation of AI solutions are proposed, emphasizing the importance of detailed strategic planning, collaboration with technology partners, and adequate staff training.article is to provide an extensive comparison of some of them, showing their similarities and also their regulatory differences, as well as possible gaps and inconsistencies.

Decentralized Smart Energy Management in Hybrid Microgrids: An Evaluation of Operational Modes, Resource Optimization, and Environmental Impacts in Nigeria

Escalating energy demands and climate change challenges necessitate the adaptation of renewable-based microgrid systems in the energy sector. The proposed work employs a robust Multi Agent System (MAS) technique to achieve efficient and automated control of the hybrid microgrid operation. The hybrid microgrid system incorporates Renewable Energy Sources (RES), a diesel generator, and a battery storage system. The operation of the hybrid microgrid consists of three distinct modes: islanded, transition to grid, and grid-oriented mode. The system’s performance is optimized by considering factors like climatic patterns, energy costs, connected source characteristics, and load demand. Different climatic scenarios are assessed for each mode of operation, where the best, extreme sunny, extreme cloudy, and worst climate conditions are considered for islanded mode; sunny and cloudy scenarios are considered for transition to grid mode as well as grid-feed and grid-tied modes are considered for grid-oriented operation of the microgrid. The simulation studies are performed using the MATLAB/Simulink R2021a environment. Furthermore, Particle Swarm Optimization (PSO) is implemented to optimize power allocation within the microgrid and enhance its cost-effectiveness. The optimization results demonstrate efficient utilization of available energy sources along with effective energy management facilitated by the MAS control system. The results emphasize the importance of adopting a MAS approach for achieving smart energy management through comprehensive analysis and integrating decentralized energy management techniques for optimal accommodation of distributed energy resources in hybrid microgrids

Cooperative operation optimization for rural multi-microgrid and county-integrated energy operators considering typical energy scenarios

Despite being rich in renewable energy, China's rural areas are backwards in terms of energy use. Rural multi-microgrid cooperative operation optimization can effectively promote renewable energy use in rural areas. Many different microgrid energy scenarios have been developed for rural areas of China, and they have different resource endowments and unit compositions. Moreover, frequent power-related interactions occur between counties and villages. This study analyzes four typical microgrid energy scenarios in rural areas of China and optimizes their synergistic operation based on county-integrated energy operators. First, a mathematical model of rural microgrids for four energy scenarios and a trading mechanism between rural multi-microgrid and county-integrated energy operators were constructed. Subsequently, an upper-level optimization model that minimizes operating costs was developed for the county-integrated energy operator. A low-level optimization model was developed for rural multi-microgrid usage, and it minimized the operating costs. Finally, Stackelberg game theory was utilized to resolve the optimization issue. The results showed that the cooperative optimization of rural multi-microgrid and county-integrated energy operations can reduce the operating costs of both parties compared to that when each subject is operating alone. This optimization reduced the rural multi-microgrid cost from 12 773.64 yuan to 11 508.67 yuan and county-integrated energy operator cost from 3898.37 yuan to 1581.79 yuan. Moreover, it reduced both parties' dependence on external power grids; increased the self-balancing capacity of the rural multi-microgrid and county-integrated energy operator from 0.424 to 0.715 and 0.694 to 0.852, respectively; substantially increased the capacity of renewable energy consumption through power interaction; and reduced the risk of fluctuations in system operating costs.

An Electrolyzer Model for Power System Operation Optimization Over Broad Temperature Range

An Electrolyzer Model for Power System Operation Optimization Over Broad Temperature Range

A multi-objective operation optimization model for the electro-thermal integrated energy systems considering power to gas and multi-type demand response

A multi-objective operation optimization model for the electro-thermal integrated energy systems considering power to gas and multi-type demand response

An Integrated Event-Driven Real-Time Tactical–Operational Optimization Framework for Smart Port Operations Planning

The ongoing issues in global supply chain disruptions have raised many concerns of port productivity, among which port congestion is a key issue. This article implements an integrated tactical–operational optimization framework which raises the capabilities of port information systems to deliver smarter decision-making processes in ports through a decision support system. To this end, we developed a library of multiple smart models for the optimization of port operations, independently engaged in parallel but mathematically coordinated to achieve autonomous real-time distributed optimization, using a novel event-driven structure to enable future implementations using digital twins. The framework was tested to benchmark different commercial solvers on several real instances for the port under study. The results show a strong improvement in port operational planning.

Simulation and analysis of optimal energy-saving mode in microfield of underground rail transit

Underground rail transit has become one of the most popular transportation modes because of its advantages such as fast running speed, large passenger flow and punctuality. This mode of transportation can alleviate urban traffic congestion to a certain extent, so underground rail transit has been vigorously developed, and the number of rail lines has increased exponentially. However, with the continuous development of underground rail transit, the energy consumption of train operation is also increasing, resulting in a waste of energy. To solve this problem, this paper proposes to improve the energy-saving technology of train operation by using train autopilot control strategy. Firstly, a train operation optimization model considering train position and speed limit is established by using automatic train driving strategy, and the nonlinear problem of the optimization model is solved by genetic algorithm. The micro-field theory is introduced into the control strategy of automatic train driving, and the energy-saving model of underground rail transit is established. The energy consumption is optimized from three aspects: train energy-saving technology, line planning and overall operation planning of rail transit. The simulation results show that the average impact rate and energy consumption of trains under the proposed energy-saving model are significantly reduced compared with the other two groups of trains, and the average impact rate and energy consumption are significantly reduced compared with the other two groups of trains. The running time and energy consumption of the energy-saving model are lower than those of the experimental group. To sum up, the energy-saving model of underground rail transit proposed in this paper not only reduces the operating energy consumption, but also improves the passenger comfort, which can provide low-cost energy-saving technology for underground transportation field, and has positive significance for urban low-carbon development.

A Systematic Review of Heterogeneous Catalysis Applied to the Treatment of Pharmaceutical Wastewater: Operational Conditions and Statistical Analysis

The use of heterogeneous photocatalysis has garnered significant attention, mainly due to its remarkable efficacy in degrading recalcitrant compounds. The main objective of this research was to investigate this process applied to pharmaceutical treatment. For that, an analysis of a Final Bibliographic Portfolio (FBP), using the systematic review of the PRISMA and the ProKnow-C method, and a meta-analysis study in a historical series from 2010 to 2020, were performed for scientific works published in indexed journals from the Scopus and Web of Science databases and fully available in English. The works were filtered after a careful reading of the titles, followed by the exclusion of repeated documents and those that were not aligned with the research from 3498 articles, 40 of which were chosen to compose the FBP that addressed the classes of antibiotics, antihypertensives, analgesics, and anti-inflammatory drugs after scientific recognition and exclusion due to not fitting into one of the four FBP structured stages: (1) identification, (2) triage, (3) eligibility, and (4) inclusion. The following gaps were highlighted: (i) a limited number of studies working with interactions of the interfering variables; (ii) a large number of experiments not considering the natural constituents of wastewater; (iii) the use of drug concentrations high above the values found in aquatic matrices; (iv) little applicability of the process at the real scale. In this meta-analysis study, operational parameter optimization was fundamental to guarantee degradation efficiencies above 80% with a variety of pharmaceutical pollutants, the main representatives studied of which were tetracycline, nimesulide, diclofenac, ibuprofen, and atenolol. However, there is still a need to determine the best conditions for this technique when using real effluents, which have the utmost importance for the process on a large scale.

Degradation performance rapid prediction and multi-objective operation optimization of gas turbine blades

The performance degradation of high-temperature blades will affect the safety and efficiency of operation. In response to issues such as ignoring strength characteristics, slow degradation performance prediction, insufficient applicability and accuracy of traditional proxy models, and long operation optimization cycle, a degradation performance prediction model of turbine blades based on the back propagation neural network is established in this research. Furthermore, the efficiency-stress multi-objective operation optimization method based on genetic algorithm is proposed. The results show that the R-square value of the degradation performance neural network prediction model is greater than 0.9 for predicting characteristic parameters. The prediction speed is improved by 104 orders of magnitude compared to the traditional computational fluid dynamics method. The influence mechanisms of turbine outlet flow rate, cooling gas inlet total temperature, and cooling gas flow rate on the degradation performance are revealed. Under fouling extreme conditions and corrosion/wear extreme conditions, the operating parameters of turbine blades are optimized. The maximum values of the efficiency improvement, power increase and maximum von Mises stress reduction are 0.74 %, 2.13 MW and 43 MPa, respectively. This study can provide an effective and novel method for real-time degradation performance prediction and rapid operating condition optimization of turbine blades.

Study on the Operation Optimization of Medium-Depth U-Type Ground Source Heat Pump Systems

Deep geothermal energy is a sustainable and renewable spacing heating source. Although many studies have discussed the design optimization of deep borehole systems, few have accomplished optimization and in-depth analysis of system operation control. In this study, an analytical model of the U-type deep borehole heat exchanger is proposed, and the average relative error between the simulated outlet temperatures and experimental data is −3.2%. Then, this paper presents an integrated model for the operation optimization study of the U-type deep-borehole ground source heat pump system. The optimal control of flow rate is adopted to match the variation in heating load. Compared with the constant-flow rate (110 m3/h) operation mode, the variable flow rate method reduces the power consumption of the heat pump and circulating pump by 22.1%, from 288,423 kW·h to 224,592 kW·h, during 2112 h of operation. In addition, the system has a larger RHS and COP when the thermal conductivity of the backfill material increases. When the borehole depth increases by 200 m from 2300 m, the energy consumption of the circulating pump will drop from 85,844 kW·h to 56,548 kW·h. The COP of the heat pump unit will decrease approximately linearly as the heating load increases, and the total power consumption will increase accordingly. This work can provide guidance for the design and optimization of U-shaped GSHP systems.