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The utilization of the district heating system (DHS) thermal inertia is greatly influenced by heating regulation modes and time steps, however, which has been rarely analysed in almost all integrated heat and power dispatch system (IHPD) models, for the purpose of wind power integration. In this study, an IHPD model considering the thermal inertia of the DHS was established, in which a data interaction problem amongst the power sources, heat sources and district heating networks was solved. Thereafter, four most commonly used heating operation modes and different heating regulation time steps were applied to the IHPD model, respectively, to analyse and compare their effects on the IHPD system under different operation conditions, mainly considering the operation cost and the wind power integration. Results of the case studies showed that the variable flow and variable supply temperature mode produced the lowest operation cost of the IHPD system during a whole heating season. Consequently, this mode is recommended to be applied in the optimization operation of the IHPD system with the wind power integration. Meanwhile, a heating regulation time step within 3 h could make full use of the DHS thermal inertia, thus to improve the operational flexibility of the IHPD system. [ABSTRACT FROM AUTHOR]

In company operations, of course, records in the form of stock data and transaction data are needed to make reports. Likewise, KOMBAS has still used manual recording so there are still many shortcomings that cause cooperative operations to be not optimal. The method used is a waterfall methodology consisting of needs analysis, system design, and testing. The type of research used is qualitative research by conducting an interview process with a resource person, namely the chairman of the Bekasi Batik Cooperative (KOMBAS). The results of the project are an analysis of business flows, system flows, and business models, and then the final result is a website-based application design that has various features according to the company's needs. The features available in the application include goods data, supplier data, customer data, employee data, and transaction data (sales, purchases, and returns). These features, in addition to being able to enter data, also have access to update and delete. This project concludes that the design of a website-based inventory application will certainly provide efficiency and productivity for cooperatives that provide automation processes, smoother workflows, and faster access to information so that it can save time, costs, and resources, and improve service quality. [ABSTRACT FROM AUTHOR]

In the context of the global energy crisis and environmental shifts, solar energy, recognized for its cleanliness and renewability, has increasingly captured attention in the application within the architectural sector. As a significant portion of energy consumption, the electrification of buildings and their transformation towards green systems are crucial for energy saving and emission reduction. With technological advancements, solar-powered building electrical systems have emerged as a focal point of research. The core issues affecting their widespread application are the system's thermodynamic efficiency and integration strategies. Despite advancements in solar cell efficiency and device-level optimization, there remains a deficiency in system-level thermodynamic analysis and integrated optimization, limiting the efficiency and practicality of solar energy in construction. This study initiates with an energy and exergy analysis to delve into the thermodynamic efficiency of solar-powered building electrical systems, clarifying the efficiency of energy conversion and the irreversibility losses. Building on these findings, strategies for system integration and operational optimization are proposed, aiming for a comprehensive optimization of solar systems through the establishment of targeted design and operational standards. This research not only provides a theoretical foundation for enhancing the energy efficiency of solar systems but also offers practical guidance for designing and managing efficient, sustainable building electrical systems. [ABSTRACT FROM AUTHOR]

This article presents an inventory management model for profit maximization in a Peruvian retail company (FIDEL) using linear programming techniques and the Branch and Bound algorithm. To introduce this paper we refute, through a review of previous studies in the same field, the use of this approach for its effectiveness in business decision-making. Gathering data on costs, demand and the available capital for the company's different product lines was done through the use of interviews. The proposed model aims to maximize revenue based on the quantities of goods purchased, while considering minimum demand and capital constraints. Validation was performed with Lingo software to confirm the consistency and feasibility of the model. The resulting values revealed a significant increase in projected profits compared to actual data. [ABSTRACT FROM AUTHOR]

Integrating photovoltaic (PV) systems plays a pivotal role in the global shift toward renewable energy, offering significant environmental benefits. However, the PV installation should provide financial benefits for the utilities. Considering that the utility companies often incur costs for both energy and peak demand, PV installations should aim to reduce both energy and peak demand charges. Although PV systems can reduce energy needs during the day, their effectiveness in reducing peak demand, particularly in the early morning and late evening, is limited, as PV generation is zero or negligible at those times. To address this limitation, battery storage systems are utilized for storing energy during off-peak hours and releasing it during peak times. However, finding the optimal size of PV and the accompanying battery remains a challenge. While valuable optimization models have been developed to determine the optimal size of PV–battery systems, a certain gap remains where peak demand reduction has not been sufficiently addressed in the optimization process. Recognizing this gap, this study proposes a novel statistical model to optimize PV–battery system size for peak demand reduction. The model aims to flatten 95% of daily peak demands up to a certain demand threshold, ensuring consistent energy supply and financial benefit for utility companies. A straightforward and effective search methodology is employed to determine the optimal system sizes. Additionally, the model’s effectiveness is rigorously tested through a modified Monte Carlo simulation coupled with time series clustering to generate various scenarios to assess performance under different conditions. The results indicate that the optimal PV–battery system successfully flattens 95% of daily peak demand with a selected threshold of 2000 kW, yielding a financial benefit of USD 812,648 over 20 years.

This paper introduces a novel hybrid parking system that integrates stationary charging piles (SCPs) and mobile charging robots (MCRs) to optimize urban parking utility. The model categorizes parking spaces based on the presence of SCPs, considering customer behavior including improper parking. It also introduces an operational algorithm—Earliest Available Device First (EADF)—to manage real-time scheduling of MCRs efficiently. Through strategic planning and operational management, the system aims to enhance social welfare by balancing cost-efficiency with flexible charging solutions. We evaluate our approach based on real-world data, demonstrating how MCRs significantly improve both the strategic and accumulated operational aspects of urban parking facilities. The results showcase the potential of hybrid systems in urban environments, promoting higher utility and cost-effective management.

Power generation from fossil fuels has long had a negative impact on the environment. Nowadays, a paradigm shift in power generation is being witnessed, with increasing investment in renewable energy sources. Despite this progress, efficient energy storage is still limited. Given this challenge, pumped storage technology can be one of the viable solutions. This involves storing gravitational energy by pumping water into a reservoir at a higher altitude, which is later converted into electrical energy using a turbine. This paper studies a pump hydro storage system (PHS) operation in water supply systems (WSSs), with the aim of minimizing operating costs and evaluating its effectiveness. Replacing conventional pumps with pump-as-turbines (PATs) provides a flexible and cost-effective approach. The proposed methodology aims to optimize the operation of these PATs considering dynamic energy prices. The developed computational model was applied to different operational scenarios and analyzed in terms of cost-effectiveness. The results show that the lower the average ratio between time-differentiated purchase and fixed sell energy tariffs, the greater the optimization potential of using PAT. In the WSS case study analyzed, energy cost reductions of 43.4–68.1% were achieved, demonstrating the effectiveness of PHS in WSS particularly for energy tariffs with large variations. HIGHLIGHTS The management of a pumped storage system integrated into water supply systems is still little explored.; The integration of dynamic energy pricing with PAT operation represents an opportunity for water utilities to reduce their costs.; The computational model developed using optimization algorithms allows for efficient operation of PAT in WSSs.;

Integrating photovoltaic (PV) systems plays a pivotal role in the global shift toward renewable energy, offering significant environmental benefits. However, the PV installation should provide financial benefits for the utilities. Considering that the utility companies often incur costs for both energy and peak demand, PV installations should aim to reduce both energy and peak demand charges. Although PV systems can reduce energy needs during the day, their effectiveness in reducing peak demand, particularly in the early morning and late evening, is limited, as PV generation is zero or negligible at those times. To address this limitation, battery storage systems are utilized for storing energy during off-peak hours and releasing it during peak times. However, finding the optimal size of PV and the accompanying battery remains a challenge. While valuable optimization models have been developed to determine the optimal size of PV–battery systems, a certain gap remains where peak demand reduction has not been sufficiently addressed in the optimization process. Recognizing this gap, this study proposes a novel statistical model to optimize PV–battery system size for peak demand reduction. The model aims to flatten 95% of daily peak demands up to a certain demand threshold, ensuring consistent energy supply and financial benefit for utility companies. A straightforward and effective search methodology is employed to determine the optimal system sizes. Additionally, the model's effectiveness is rigorously tested through a modified Monte Carlo simulation coupled with time series clustering to generate various scenarios to assess performance under different conditions. The results indicate that the optimal PV–battery system successfully flattens 95% of daily peak demand with a selected threshold of 2000 kW, yielding a financial benefit of USD 812,648 over 20 years. [ABSTRACT FROM AUTHOR]

Against the backdrop of increasingly prominent environmental issues, new energy consumption issues, and energy supply and demand balance issues, the optimization of multi time scale operation of distributed electro hydrogen coupling systems has become a research focus. Based on this, this article optimizes the multi time scale operation of a distributed electric hydrogen coupling system that takes into account grid interaction. By designing a system framework for distributed electro hydrogen coupling systems, operational strategies for each system were proposed. Analyzed the uncertainty and response characteristics of wind and solar power generation units and load demand, and constructed a multiple uncertainty model for distributed electric hydrogen coupling system. At the same time, a three stage, multi time scale operation optimization model of the electric hydrogen coupling system was constructed based on the response characteristics of the distributed electric hydrogen coupling system. The construction of these models reduced scheduling costs by 12.55% and increased clean energy consumption rate by 13.50%. [ABSTRACT FROM AUTHOR]

Copyright of Revista Producao Online is the property of Associacao Brasileira de Engenharia de Producao and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This study focuses on the reduced-order modeling and the operational optimization of the heating network of Université Libre de Bruxelles (ULB) to decrease its energy consumption and CO2 emissions. A reduced-order model of the network was developed, and alternatives to the current operation were investigated to minimize its operational cost. As a very first improvement, replacing the current fixed-speed pumps with optimally operated variable-speed pumps, the pumping energy can be reduced by up to 96 %. Further optimizing the network operating temperatures can lead to a reduction of the pumping energy, the heat losses and the CO2 emissions by 99 %, 19 %, and 7 %, respectively, compared to the current operation.

Abstract Many power plants have been transformed and operated using low‐pressure cylinder near‐zero output (LP‐ZO) technology in China to make way for or provide necessary guarantees for renewable energy power generation. This research focuses on utilizing numerical simulation tools to improve the operational economy of units with LP‐ZO. The LP‐ZO thermodynamic and energy‐consumption model of a reference plant is established, and the momentum particle swarm optimization method is used to optimize the unit operations. Moreover, a model to determine the economic operational range of the combined heat and power plant with LP‐ZO is proposed. The results show that the LP‐ZO mode operations of the unit can reduce the minimum peak shaving rate and improve the flexibility of operations. The energy efficiency of the LP‐ZO mode is increased due to the recovery of a significant portion of the cold source losses. However, the irreversible exergy loss caused by large temperature difference heat exchange reduces the exergy efficiency of the unit. After operational optimization, the reference plant can save approximately 68.15 t d−1 of coal and reduce carbon dioxide emissions by about 27 t tce−1 d−1. This study provides an operating approach to the CHP plant with LP‐ZO unit in enhancing flexibility and economy.

At present, the national power system is gradually achieving separation of government and enterprise, and corporate restructuring; Implement the separation of network factories and bidding for online access, and gradually establish a modern enterprise system. Effectively carrying out energy conservation and consumption reduction work, reducing the cost of power generation and supply, is an inevitable requirement for the survival and development of power enterprises themselves.To effectively carry out energy-saving and consumption reduction work, as a group power generation company and power plant, it is necessary to solve the following problems: how to evaluate the actual and expected level of unit performance, how to quantitatively determine the impact of various factors on unit performance, how to improve operational level to reduce energy consumption, how to assess and evaluate the implementation of energy-saving work from a management perspective, and so on. Therefore, it is necessary to have a complete and scientific expert analysis system for optimizing power plant operation.

Against the backdrop of increasingly prominent environmental issues, new energy consumption issues, and energy supply and demand balance issues, the optimization of multi time scale operation of distributed electro hydrogen coupling systems has become a research focus. Based on this, this article optimizes the multi time scale operation of a distributed electric hydrogen coupling system that takes into account grid interaction. By designing a system framework for distributed electro hydrogen coupling systems, operational strategies for each system were proposed. Analyzed the uncertainty and response characteristics of wind and solar power generation units and load demand, and constructed a multiple uncertainty model for distributed electric hydrogen coupling system. At the same time, a three stage, multi time scale operation optimization model of the electric hydrogen coupling system was constructed based on the response characteristics of the distributed electric hydrogen coupling system. The construction of these models reduced scheduling costs by 12.55% and increased clean energy consumption rate by 13.50%.

Many power plants have been transformed and operated using low‐pressure cylinder near‐zero output (LP‐ZO) technology in China to make way for or provide necessary guarantees for renewable energy power generation. This research focuses on utilizing numerical simulation tools to improve the operational economy of units with LP‐ZO. The LP‐ZO thermodynamic and energy‐consumption model of a reference plant is established, and the momentum particle swarm optimization method is used to optimize the unit operations. Moreover, a model to determine the economic operational range of the combined heat and power plant with LP‐ZO is proposed. The results show that the LP‐ZO mode operations of the unit can reduce the minimum peak shaving rate and improve the flexibility of operations. The energy efficiency of the LP‐ZO mode is increased due to the recovery of a significant portion of the cold source losses. However, the irreversible exergy loss caused by large temperature difference heat exchange reduces the exergy efficiency of the unit. After operational optimization, the reference plant can save approximately 68.15 t d−1 of coal and reduce carbon dioxide emissions by about 27 t tce−1 d−1. This study provides an operating approach to the CHP plant with LP‐ZO unit in enhancing flexibility and economy. [ABSTRACT FROM AUTHOR]

The vanadium flow batteries that employ the vanadium element as active couples for both half-cells, thus avoiding cross-contamination, are promising large-scale energy storage devices. In this work, the flow rate is optimized by incorporating the temperature effects, attempting to realize a more accurate flow control and subsequently enhance the performance of vanadium flow batteries. This work starts with the development of a comprehensive dynamic model on the basis of mass conservation, followed by a modeling validation and a thorough investigation of the temperature effects on electrolyte viscosity and internal resistance. After that, the flow rate is optimized to incorporate such effects. It is found that the flow rate strategy needs to be regulated with the variation of temperature due to the variations of electrolyte viscosity and internal resistance. Moreover, a relatively low flow rate is preferable for low-temperature applications, while for the high-temperature use, a relatively high flow rate is encouraged. Such in-depth investigation can not only provide a cost-effective method to optimize the flow rate and predict the behaviors of vanadium flow batteries, but can also be of great benefit to the management, application, and promotion of vanadium flow batteries. [ABSTRACT FROM AUTHOR]

The main task of the impurity removal process is to control the oxidation reduction potential (ORP) within the range of the optimized set value. The impurity removal process is essentially an oxidation-reduction process. Oxidation reduction potential (ORP) is an external reflection of reaction state inside the impurity removal reactor. However, actual industry is time-varying, nonlinear and multimode. It is difficult to determine the appropriate dosage of impurity remover in practice. This will lead to large fluctuations in the operation mode, affecting the safety and stability of the process and the final product quality. To solve these problems, an intelligent dynamic setting control framework (IDSCF) for the multimode impurity removal process is proposed in this paper. It includes a preset module of the dosage of impurity remover based on impurity remover utilization (IRU) estimation, an operation mode detection module based on autoencoder, a normal mode adjustment module based on fuzzy logic, and an unsteady mode adjustment module based on case-based reasoning (CBR). The framework can determine the reasonable preset dosage of impurity remover and adjust the dosage according to the current operation mode of the impurity removal process. Because the operation mode is related to the residual dosage of impurity remover added over a period of time, that is, the accumulative effect of the large-scale metallurgical reactor. When calculating the preset dosage of impurity remover, the reactant accumulation ratio (RAR) is calculated, which makes the calculation of the preset value more reasonable. In addition, it can detect the unsteady modes causing large fluctuations in the process and adjust them in time. Experiments are carried out in accordance with the data of an actual cobalt removal process. The results show that this method can effectively improve the stability of the impurity removal process, control the ORP within the set range and cope with complex mode changes. [ABSTRACT FROM AUTHOR]

Copyright of Electric Power Automation Equipment / Dianli Zidonghua Shebei is the property of Electric Power Automation Equipment Press and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Acidic tin–iron flow batteries (TIFBs) employing Sn/Sn2+ and Fe2+/Fe3+ as active materials are regarded as promising energy storage devices due to their superior low capital cost, long lifecycle, and high system reliability. In this paper, the performance of TIFBs is thoroughly investigated via a proposed dynamic model. Moreover, their design and operational parameters are comprehensively analyzed. The simulation results show that (i) a flow factor of two is favorable for practical TIFBs; (ii) about 20% of the system's efficiency is decreased as the current density increases from 40 mA cm−2 to 200 mA cm−2; (iii) the optimal electrode thickness and electrode aspect ratio are 6 mm and 1:1, respectively; and (iv) reducing the compression ratio and increasing porosity are effective ways of lowering pump loss. Such in-depth analysis can not only provide a cost-effective method for optimizing and predicting the behaviors and performance of TIFBs but can also be of great benefit to the design, management, and manufacture of tin–iron flow batteries. [ABSTRACT FROM AUTHOR]

As one of the most important and advanced technology for carbon-mitigation in the building sector, building performance simulation (BPS) has played an increasingly important role with the powerful support of building energy modelling (BEM) technology for energy-efficient designs, operations, and retrofitting of buildings. Owing to its deep integration of multi-disciplinary approaches, the researchers, as well as tool developers and practitioners, are facing opportunities and challenges during the application of BEM at multiple scales and stages, e.g., building/system/community levels and planning/design/operation stages. By reviewing recent studies, this paper aims to provide a clear picture of how BEM performs in solving different research questions on varied scales of building phase and spatial resolution, with a focus on the objectives and frameworks, modelling methods and tools, applicability and transferability. To guide future applications of BEM for performance-driven building energy management, we classified the current research trends and future research opportunities into five topics that span through different stages and levels: (1) Simulation for performance-driven design for new building and retrofit design, (2) Model-based operational performance optimization, (3) Integrated simulation using data measurements for digital twin, (4) Building simulation supporting urban energy planning, and (5) Modelling of building-to-grid interaction for demand response. Additionally, future research recommendations are discussed, covering potential applications of BEM through integration with occupancy and behaviour modelling, integration with machine learning, quantification of model uncertainties, and linking to building monitoring systems.

Copyright of e & i Elektrotechnik und Informationstechnik is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

• Scheduling and routing of autonomous mobile vehicles in robotic mobile fulfillment systems. • Integrated assignment and sequencing of inventory pods and orders to picking stations. • Development of MIP models for both the sequential and integrated solution approaches. • Investigation of impact of the chosen performance metric in the optimization process. • An integrated approach leads to more efficient fulfillment systems. In robotic mobile fulfillment systems (RMFS), mobile robots carry inventory shelves autonomously from the storage area to picking stations and back. The scheduling of these robots and the order picking activities can be modeled as a collection of interrelated optimization problems. In this paper, we focus on the following optimization problems: the order allocation to picking stations, order sequencing, the inventory pod selection, and the robot scheduling. We present new mixed-integer programming (MIP) models for these decision problems and extended on existing models from the literature. To improve the models further, we include interstation travel which means that inventory racks can be transported from one picking station straight to another station without returning it to the storage area in between, hence reducing the overall travel distance. Moreover, In previous research on RMFS, only some of these decision problems are integrated. Therefore, we developed an integrated model to study the interdependencies between the decision problems. The models are validated through simulations and different performance metrics are analyzed such as the number of pod visits, the total distance travelled, and the system makespan. Moreover, we introduce a cost metric to facilitate the objective evaluation of the system performance. From the computational experiments we conclude that the integration of the decision problems results in significantly better performing systems compared to sequentially optimizing them. However, this comes at the cost of more computational effort. Furthermore, including interstation visits can further improve the overall system performance. [ABSTRACT FROM AUTHOR]

Networked microgrids (MGs) with inverter-based and droop-controlled distributed energy resources (DERs) require operational optimization with guaranteed stability performance to ensure the stable energy supply with minimum cost, yet it remains an open challenge. Additionally, the discrete nature of MGs leads to convergence issues to existing optimization methods thereby leading to difficulties obtaining feasible solutions for large-scale networks. This article develops a paradigm for discrete droop control to improve microgrids’ controllability in managing voltage and frequency fluctuations. With the emergence of Internet of Things, the computational tasks are distributed among local resources. The utilized Distributed and Asynchronous Surrogate Lagrangian Relaxation (DA-SLR) method distributes the optimization tasks among the MGs and efficiently coordinates the distributed subsystems. A small-signal model of the operational optimization is then developed to verify the system’s stability. Numerous case studies have proven the DA-SLR’s efficacy in comparison to various variations of the alternating direction of multipliers method (ADMM).

The computational analysis of real-world engineering problems often relies on time-consuming simulation calculations. This presents challenges in balancing computational burden and precision when applying traditional metaheuristic algorithms to large-scale complex problems. While there have been numerous surrogate-assisted metaheuristic optimization algorithms proposed, most of them are designed for solving expensive problems within 30–50 dimensions. In this paper, a novel approach called Parallel-multi-swarm Gaussian Process Regression surrogate-based particle swarm optimization (Parallel-MS-GPRS-PSO) is proposed for high-dimensional problems. This approach combines a standard particle swarm optimization (PSO) algorithm, multi-swarm cooperative PSO (MSCPSO), and a GPR surrogate-based PSO (PSO-GPR). By integrating PSO-GPR and MSCPSO, the proposed method aims to effectively explore and exploit the search space while enhancing the global and local performance of the surrogate model. To validate the effectiveness of the proposed algorithm, it is compared with several existing PSO algorithms on seven benchmark functions and an engineering problem. The results illustrate that the Parallel-MS-GPRS-PSO approach outperforms the existing algorithms. Moreover, the method shows promising performance in computationally expensive engineering optimization problems with 288 variables. • Parallel-MS-GPRS-PSO approach is designed to address high-dimensional problems. • The method combines PSO, MSCPSO, and PSO-GPR to enhance surrogate model performance. • The proposed algorithm outperforms existing PSO approaches on benchmark functions. • The method shows promising performance in complex engineering optimization problem. [ABSTRACT FROM AUTHOR]

In order to reduce energy consumption and CO2 emissions in the building sector, more and more renewable energy sources are integrated into energy systems. Especially geothermal fields combined with heat pumps are able to supply buildings with heat and cold at low carbon emissions. However, using geothermal fields as heat and cold source influences the ground temperature. Consequently, the ground temperature can change dramatically over a building's lifetime, leading to less efficient operation of the energy system. Therefore, a sustainable operation is required to ensure the long-term efficiency of geothermal fields. In this paper, we develop an optimization model to derive operating strategies for an efficient long-term operation of a building energy system coupled to a geothermal field. The investigated energy system is the main building of the E.ON Energy Research Center in Aachen, Germany, which includes a heat pump, two boilers, a combined heat, and power unit, a glycol cooler, and a geothermal field with 41 probes. For each component, we develop energy-based sub-models, which are connected to form the overall system. The geothermal field is modeled by using a g-functions approach as well as a simplified resistance-capacitance approach. To achieve short computing times and realize an optimization horizon of several years, the optimization problem is formulated as mixed-integer linear programming (MILP). The developed model is optimized regarding two different objectives: the minimization of energy costs and the minimization of long-term temperature changes in the ground. Conclusions for an efficient and sustainable operation of the field, especially for the cooling supply, can be derived from the optimization results. It is shown that a state of equilibrium should be aimed to achieve an energy-efficient operation, in which the temperature of the field is close to the initial ground temperature. [ABSTRACT FROM AUTHOR]

This paper provides a study of the optimal scheduling of building operation to minimize its energy cost under building operation uncertainties. Opposed to the usual way that describes thermal comfort using a static range of air temperature, the optimization of a tradeoff between energy cost and thermal comfort predicted mean vote (PMV) index is addressed in this paper. In order to integrate the calculation of the PMV index with the optimization procedure, we develop a sufficiently accurate approximation of the original PMV model which is computationally efficient. We develop a model-based periodic event-triggered mechanism (ETM) to handle the uncertainties in the building operation. Upon the triggering of predefined events, the ETM determines whether the optimal strategy should be recalculated. In this way, the communication and computational resources required can be significantly reduced. Numerical results show that the ETM method is robust with respect to the uncertainties in prediction errors and results in a reduction of more than 60% in computation without perceivable degradation in system performance as compared to a typical closed-loop model predictive control.

The vanadium flow batteries that employ the vanadium element as active couples for both half-cells, thus avoiding cross-contamination, are promising large-scale energy storage devices. In this work, the flow rate is optimized by incorporating the temperature effects, attempting to realize a more accurate flow control and subsequently enhance the performance of vanadium flow batteries. This work starts with the development of a comprehensive dynamic model on the basis of mass conservation, followed by a modeling validation and a thorough investigation of the temperature effects on electrolyte viscosity and internal resistance. After that, the flow rate is optimized to incorporate such effects. It is found that the flow rate strategy needs to be regulated with the variation of temperature due to the variations of electrolyte viscosity and internal resistance. Moreover, a relatively low flow rate is preferable for low-temperature applications, while for the high-temperature use, a relatively high flow rate is encouraged. Such in-depth investigation can not only provide a cost-effective method to optimize the flow rate and predict the behaviors of vanadium flow batteries, but can also be of great benefit to the management, application, and promotion of vanadium flow batteries.

Acidic tin–iron flow batteries (TIFBs) employing Sn/Sn2+ and Fe2+/Fe3+ as active materials are regarded as promising energy storage devices due to their superior low capital cost, long lifecycle, and high system reliability. In this paper, the performance of TIFBs is thoroughly investigated via a proposed dynamic model. Moreover, their design and operational parameters are comprehensively analyzed. The simulation results show that (i) a flow factor of two is favorable for practical TIFBs; (ii) about 20% of the system’s efficiency is decreased as the current density increases from 40 mA cm−2 to 200 mA cm−2; (iii) the optimal electrode thickness and electrode aspect ratio are 6 mm and 1:1, respectively; and (iv) reducing the compression ratio and increasing porosity are effective ways of lowering pump loss. Such in-depth analysis can not only provide a cost-effective method for optimizing and predicting the behaviors and performance of TIFBs but can also be of great benefit to the design, management, and manufacture of tin–iron flow batteries.

Operational optimization is essential in modern industry and unsuitable operations will deteriorate the performance of industrial processes. Since measuring error and multiple working conditions are inevitable in practice, it is necessary to reduce their negative impacts on operational optimization under the case-based reasoning (CBR) framework. In this paper, a local density-based abnormal case removal method is proposed to remove the abnormal cases in a case retrieval step, so as to prevent performance deterioration in industrial operational optimization. More specifically, the reasons as to why classic CBR would retrieve abnormal cases are analyzed from the perspective of case retrieval in industry. Then, a local density-based abnormal case removal algorithm is designed based on the Local Outlier Factor (LOF), and properly integrated into the traditional case retrieval step. Finally, the effectiveness and the superiority of the local density-based abnormal case removal method was tested by a numerical simulation and an industrial case study of the cut-made process of cigarette production. The results show that the proposed method improved the operational optimization performance of an industrial cut-made process by 23.5% compared with classic CBR, and by 13.3% compared with case-based fuzzy reasoning. [ABSTRACT FROM AUTHOR]

The nonferrous metallurgical (NFM) industry is a cornerstone industry for a nation's economy. With the development of artificial technologies and high requirements on environment protection, product quality, and production efficiency, the importance of applying smart manufacturing technologies to comprehensively percept production states and intelligently optimize process operations is becoming widely recognized by the industry. As a brief summary of the smart and optimal manufacturing of the NFM industry, this paper first reviews the research progress on some key facets of the operational optimization of NFM processes, including production and management, blending optimization, modeling, process monitoring, optimization, and control. Then, it illustrates the perspectives of smart and optimal manufacturing of the NFM industry. Finally, it discusses the major research directions and challenges of smart and optimal manufacturing for the NFM industry. This paper will lay a foundation for the realization of smart and optimal manufacturing in nonferrous metallurgy in the future. [ABSTRACT FROM AUTHOR]

In Autonomous Train (AT) operations, predicting future movements of trains to ensure the safety and efficiency of this public transport, is a critical task performed in the yard and terminal stations or between the source station/terminal and destination. Artificial Intelligence (AI) enabled Autonomous Vehicles (AVs) analyse their surroundings and make informed decisions based on the collected information. AVs learn their surroundings through perceptual motion, and predict the movements of various agents, such as humans, bicycles, and animals, through the prediction phase. If the predictions are accurate, AVs take proactive measures, making travel safer for everyone. However, the uncertainty in predicting agent/object's behaviour remains substantially difficult, despite significant research being conducted in this area. The objective of this study is to investigate the optimisation of AT Operations during Transmission (ATOT), with the goal of reducing wait times and ensuring safety even in the presence of obstacles, particularly animals. Quantum Lévy flight optimisation (QLFO) addresses these concerns on operational scalability of ATs. It generates real-time spatial data to make these operations efficient and to reduce the average waiting times. The YoLoV5 object recognition identifies the potential risks, such as individuals traversing rails or animals lying along the way and either rerouting or halting ATs to prevent crashes. The QLFO detects problems either before or after the train arrives at a junction or terminus and takes proactive measures using YoLov5 evaluation on the satellite image captured over the rail track. Evaluation experiments show that the proposed ATOT achieves a 9.3% reduction in the average waiting time spent. [ABSTRACT FROM AUTHOR]

A method of extracting exhaust waste heat and heating inlet wellhead by glycol wall heat exchange technology is proposed. Calculation and system design of exhaust air waste heat based on heat balance theory, combined with the actual case of Ningxia Yindonggou Coal Mine project, the Threlkeld enthalpy difference method was used to develop an analytical model for single tube heat transfer under wet conditions and to optimize the operating conditions. The results show that the glycol inter-wall heat transfer technology can extract the heat from the mine return air and meet the heat load required to prevent freezing at the wellhead. The system operates stably when the inlet temperature of the heat transfer medium in the tube is -2 ℃ and the flow rate is 1.49 m/s to 2.76 m/s. When the outdoor temperature is below -15 ℃, the wellhead temperature can still reach above 5 ℃.

With liquefied natural gas becoming increasingly prevalent as a flexible source of energy, the design and optimization of industrial refrigeration cycles becomes even more important. In this article, we propose an integrated surrogate modeling and optimization framework to model and optimize the complex CryoMan Cascade refrigeration cycle. Dimensionality reduction techniques are used to reduce the large number of process decision variables which are subsequently supplied to an array of Gaussian processes, modeling both the process objective as well as feasibility constraints. Through iterative resampling of the rigorous model, this data‐driven surrogate is continually refined and subsequently optimized. This approach was not only able to improve on the results of directly optimizing the process flow sheet but also located the set of optimal operating conditions in only 2 h as opposed to the original 3 weeks, facilitating its use in the operational optimization and enhanced process design of large‐scale industrial chemical systems. [ABSTRACT FROM AUTHOR]

• Review of traditional allowable flux density concept for tubular receiver systems. • Outline of novel bottom-up concept for enhanced consideration of operating limits. • Systematic, comprehensive review of thermomechanical and damage modeling methods. • Assessment as to implementation in bottom-up concept reveals considerable potential. Concentrating solar power plays a vital role in the transformation of global energy landscape towards sustainable and environmentally sound energy supply. Currently, tower systems with molten salt tubular receivers are most common in commercial scale applications. Operational optimization of such systems necessitates detailed knowledge of operating limits of receiver components exposed to inhomogeneous solar flux densities of up to 1 MW / m 2 and local salt temperatures of in part more than 600 ° C , fluctuating at various time scales. Traditionally, the operating limits aforementioned are captured in a simplified manner via the top-down concept of allowable flux density. To the authors' view, there is considerable room for improvement over this approach as far as optimization of inherent thermomechanical and damage modeling are concerned. What is more, an alternative bottom-up concept, though implying more stringent requirements on model and processing performance, promises notably increased economic viability essentially due to reduced safety margins in operation and condition-based maintenance strategies. In this paper, essential approaches and assumptions of thermomechanical and damage modeling methods in topical literature are comprehensively discussed and assessed in terms of their potential for the approach outlined to be demonstrated at a pilot scale test facility. As a result, it is concluded that modeling can be substantially improved applying extended analytical methods from the literature. In addition, depending on model complexity and available computational resources, a few heuristic-numerical models are potentially applicable in favor of more detailed thermomechanical modeling regarding i.a. actual receiver geometry and local boundary conditions. [ABSTRACT FROM AUTHOR]

Building an efficient, safe, and sustainable energy system has been listed as one of the national energy development strategies in China. Through unified management and optimization for the processes of energy generation, transmission, conversion, and distribution, the integrated energy system (IES) can meet the diversified demands on energy with high efficiency and effectiveness, providing the basis to form a low-carbon sustainable social development mode. This research reviews the studies and issues of system modeling, assessment, and operational optimization on the IES. The ongoing problems that need further investigation are also presented. Besides, research of data-driven approaches on the IES will be discussed, based on which the future research directions are suggested here. [ABSTRACT FROM AUTHOR]

The industry is responsible for about one third of the global energy consumption, and thus, reducing energy and emissions in this sector is crucial to reach global climate goals. Methods like operational optimization can improve industrial processes’ efficiency, leading to a decrease of excess heat or total energy. By combining operational optimization with flexibility measures like thermal energy storage, intermittent heat can be optimally utilized.This work analyzes the optimal utilization of excess heat for district heating in a real-world industrial use case, a chipboard production plant. The process includes a combined heat and power unit and units for production. Electricity can be sold to the grid, and two district heating demands need to be met while satisfying the production schedule of the plant. To exploit the utilization of excess heat, four adaptations to the process are considered: Increased district heating feed-in capacities, the integration of two different thermal energy storages, and the activation of an additional heat exchanger for district heating.To optimize the process's operation with and without the adaptations, an optimization framework is developed. Its modular structure allows for straight-forward design adaptations. The outcomes show that the process's efficiency can be significantly improved by applying operational optimization and considering the process adaptations. In all scenarios, the currently contracted district heating feed-in capacity can be significantly increased, leading to profits up to 108,000 € per month and reduction of energy losses up to 87 %. Although the heat exchanger's activation yields the highest profits of all scenarios, thermal energy storages can increase the process's flexibility. In the end, the decision, which modification is most viable depends on the operator's objectives.

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This article focuses on addressing the chiller sequencing problem of chiller plants by establishing a comprehensive energy management framework. The contribution of this work is threefold. Firstly, a distributive modeling architecture is presented that establishes five concurrent models as input to the framework. A synergy among these models is exploited to formulate a chance-constrained stochastic chiller sequencing problem. Secondly, a quantified life expectancy model of the chiller plant is introduced and a case is made for why it is influential in delivering industrially applicable solutions. The model attempts to strike a balance between economic optimality and improved reliability. Thirdly, a chiller data pre-processing protocol incubating two heuristic algorithms is proposed to address measurement uncertainties of the chiller plant state variables. Furthermore, this work develops a robust ensemble model to accurately forecast the cooling load and embeds a chain of reformulations to improve the global solution's optimality. The developed framework is realized with the plant at the Indian Institute of Technology Gandhinagar. The results confirm that the proposed framework leads to a significant amount of power savings. In comparison to conventional scheduling, the chiller plant power consumption can be reduced by up to 6.2 %, thereby illustrating its efficacy. • Energy management of a chiller plant by adopting a distributive modeling architecture. • Improved reliability and operational stability using a quantified life expectancy model. • Proposes a robust data pre-processing protocol to address measurement uncertainties. • Practically reproducible strategies when validated yield up to 6.2 % power savings. [ABSTRACT FROM AUTHOR]

This document is a corrigendum for an article titled "Research on multi-timescale operation optimization of a distributed electro-hydrogen coupling system considering grid interaction." The correction addresses an error in the affiliation of one of the authors. The authors apologize for the mistake and clarify that it does not affect the scientific conclusions of the article. The corrigendum also includes information about the publication date and citation details. The document concludes with a note stating that the views expressed in the article are solely those of the authors and do not necessarily represent the views of their affiliated organizations or the publisher. [Extracted from the article]

Operational optimization is essential in modern industry and unsuitable operations will deteriorate the performance of industrial processes. Since measuring error and multiple working conditions are inevitable in practice, it is necessary to reduce their negative impacts on operational optimization under the case-based reasoning (CBR) framework. In this paper, a local density-based abnormal case removal method is proposed to remove the abnormal cases in a case retrieval step, so as to prevent performance deterioration in industrial operational optimization. More specifically, the reasons as to why classic CBR would retrieve abnormal cases are analyzed from the perspective of case retrieval in industry. Then, a local density-based abnormal case removal algorithm is designed based on the Local Outlier Factor (LOF), and properly integrated into the traditional case retrieval step. Finally, the effectiveness and the superiority of the local density-based abnormal case removal method was tested by a numerical simulation and an industrial case study of the cut-made process of cigarette production. The results show that the proposed method improved the operational optimization performance of an industrial cut-made process by 23.5% compared with classic CBR, and by 13.3% compared with case-based fuzzy reasoning.

Residential battery energy storage system (BESS) adoption is hindered with its expensive price in current market. Optimally sized BESS can excel the fiscal benefits and thus can be economically sensible. An optimization problem, which targets to minimize the total annual cost including both energy and battery degradation-based costs, is formulated to investigate the added benefits of operational optimization of a BESS over the baseline operation of BESS based on self-consumption maximization (SCM). A case study is conducted considering a Victorian electricity customer in Australia. Results revealed that installation of residential BESSs are fiscally beneficial at current market prices only if adequate rebates are available. A fiscally beneficial upper limit of BESS capacity that can be installed with the prevailing government incentives depending on the PV system capacity is also found. For a given installed cost of BESS (AU$/kWh), optimal BESS capacity is larger when operational optimization of BESS is considered. However, with a time-of-use (TOU) tariff scheme, BESSs based on operational optimization outperforms BESSs based on SCM in terms of the minimum total annual cost, return on investment and PV curtailment avoidance which excels the fiscal benefits of a BESS and results in indirect greenhouse gas emission reduction. • Operational optimization of energy storage increases the return on investment. • It also reduces PV curtailment and greenhouse gas emission. • Results are compared with an energy storage based on self-consumption maximization. • A Victorian electricity customer in Australia is taken as the case study. • Battery energy storages are economically sensible with prevailing government rebates. [ABSTRACT FROM AUTHOR]

Environmental sustainability of railway superstructure and substructure refurbishments with heavy construction equipment This study analyzes the environmental potentials of railway superstructure and substructure refurbishments with heavy construction equipment (excavating machine, track renewal train and track tamping machine), wherefore a comparative LCA to a refurbishment with conventional earthworks is carried out. The study object is a refurbishment section of a project of the ÖBB. Thereby, relevant processes until the completion of the refurbishment are examined. Differences regarding fuel consumption of the construction equipment, the shares on equipment production (ratio of construction duration to equipment lifetime), material recycling for unbound base courses, material transport, transport logistics and replacement bus services depending on necessary route closures are therefore examined in detail. The comparison of the LCA results shows advantages of up to 46 % for the heavy equipment variant, although these machines require higher fuel consumptions for construction processes due to higher engine power. However, rail‐bound transports, avoiding the need of a site road, the possibility of material recycling in the excavating machine and the shortening of the route closure period cause positive environmental effects, which compensate the additional fuel consumption for the construction processes. [ABSTRACT FROM AUTHOR]