Currently, the focus of integrated energy system scheduling research is the multi-objective’s optimized operational strategies that take into account the economic benefits, carbon emissions, and new energy consumption rates of such systems. The integration of electric trucks with battery charging and swapping capabilities, along with their corresponding battery swapping stations, into an integrated energy system can not only optimize system operation, but also reduce investment costs associated with building energy storage equipment. This study first constructs an operational model for the electric trucks, as well as an electric truck battery swapping station, of the flexible charging and discharging; then, an optimized scheduling model of an integrated energy system is proposed, including an electric truck battery swapping station and using stepped carbon trading. On the basis of meeting the charging and battery swapping needs of electric trucks and coordinating the system’s electrica... [more]

The integration of distributed power generation mainly consisting of photovoltaic and wind power into active distribution networks can lead to safety accidents in grid operation. At the same time, climate change can also cause voltage fluctuations, direct current injection, harmonic pollution, frequency fluctuations, and other issues. To achieve economic and safe operation of the distribution network, an active distribution network-network planning model considering the dynamic configuration of energy storage system energy storage is constructed. This model focuses on energy storage batteries with high ease of use, high modularity, and strong mobility. The route location planning involving different load operating scenarios in spring, summer, autumn, and winter is constructed. The objective function includes the revenue from selling electricity in the distribution network, the expenditure on purchasing electricity in the distribution network, and the cost during the planned constructio... [more]

Consideration of upstream congestion caused by busy downstream machinery, as well as transportation time between different production stages, is critical for improving production efficiency and reducing energy consumption in process industries. A two-stage hybrid flow shop scheduling problem is studied with the objective of the makespan and the total energy consumption while taking into consideration blocking and transportation restrictions. An adaptive objective selection-based Q-learning algorithm is designed to solve the problem. Nine state characteristics are extracted from real-time information about jobs, machines, and waiting processing queues. As scheduling actions, eight heuristic rules are used, including SPT, FCFS, Johnson, and others. To address the multi-objective optimization problem, an adaptive objective selection strategy based on t-tests is designed for making action decisions. This strategy can determine the optimization objective based on the confidence of the objec... [more]

This paper aims to investigate the seru scheduling problem while considering the dual effects of worker cooperation and learning behavior to minimize the makespan and order processing time. Given the complexity of this research problem, an improved shuffled frog leaping algorithm based on a genetic algorithm is proposed. We design a double-layer encoding based on the problem, introduce a single point and uniform crossover operator, and select the crossover method in probability form to complete the evolution of the meme group. To avoid damaging grouping information, the individual encoding structure is transformed into unit form. Finally, numerical experiments were conducted using numerical examples of large and small sizes for verification. The experimental results demonstrate the feasibility of the proposed model and algorithm, as well as the necessity of considering worker dual behavior in the seru scheduling problem.

A combined approach of Latin hypercube sampling and K-means clustering is proposed in this study to address the uncertainty issue in wind and solar power output. Furthermore, the loads are categorized into three levels: primary load, secondary load, and tertiary load, each with distinct characteristics in terms of demand. Additionally, a load demand response characteristic model is developed by incorporating the dissatisfaction coefficient of electric and thermal loads, which is then integrated into the system’s operational costs. Moreover, an electricity−hydrogen−thermal power system is introduced, and a source-load coordination response mechanism is proposed based on the different levels of demand response characteristics. This mechanism enhances the interaction capability between the power sources and loads, thereby further improving the economic performance of the virtual power plant. Furthermore, the operation economy of the virtual power plant is enhanced by considering the parti... [more]

A hierarchical population task planning method is presented to enhance the test efficiency and reliability of intelligent technology test ships under various tasks and complex limitations. Firstly, a mathematical model of the vehicle path problem for multi-voyage vessel testing is developed, which aims to minimize the ship’s fixed and fuel costs, taking into account the energy and space constraints of an intelligent technology test vessel, as well as practical factors such as the dependencies and temporal relationships between test tasks. Second, to fairly minimize constraint complexity in the planning process, an offshore test task planning architecture based on the concept of hierarchical population is explored and built. This architecture separates task planning into four levels and allocates the tasks to distinct populations. Using this information, a grouping genetic algorithm is suggested based on the characteristics of the population. This algorithm uses a unique coding method t... [more]

In today’s urban hotspot regions, service traffic exhibits dynamic variations in both time and location. Traditional fixed macro base stations (FMBSs) are unable to meet these dynamic demands due to their fixed coverage and capacity. Therefore, this paper introduces a novel algorithm for the joint optimization of the placement of terrestrial vehicle-mounted mobile micro base stations (mBSs), the correlation of service clusters (SCs) with mBSs, and resource assignments. The objective is to maximize the matching degree between network capacity and service demands while adhering to constraints related to the power, coverage, and bandwidth of mBSs, as well as the data rate required for the services. Additionally, we investigate the mobility of the mBSs towards the SCs in the spatiotemporal changing service demand network and obtain optimal trajectories for the mBSs. We begin by formulating the problem of maximizing the matching degree by analyzing the capacity provided by the base stations... [more]

This work considers the incorporation of renewable ammonia manufacturing sites into existing ammonia supply chain networks while accounting for ammonia price uncertainty from existing producers. We propose a two-stage stochastic programming approach to determine the optimal investment decisions such that the ammonia demand is satisfied and the net present cost is minimized. We apply the proposed approach to a case study considering deploying in-state renewable ammonia manufacturing in Minnesota’s supply chain network. We find that accounting for price uncertainty leads to supply chains with more ammonia demand met via renewable production and thus lower costs from importing ammonia from existing producers. These results show that the in-state renewable production of ammonia can act as a hedge against the volatility of the conventional ammonia market.

Optimized production scheduling can greatly improve efficiency and reduce waste in the steel manufacturing industry. With the increasing demands on the economy, the environment, and society, more and more factors need to be considered in the production scheduling process. Currently, only a few methods are developed for the comprehensive evaluation and prioritization of scheduling schemes. This paper proposes a novel MCGDM (multi-criteria group decision making) method for the ranking and selection of production scheduling schemes. First, a novel indicator system involving both qualitative and quantitative indicators is put forward. Diverse statistical methods and evaluation functions are proposed for the evaluation of quantitative indicators. The evaluation method of qualitative indicators is proposed based on heterogeneous data, cloud model theory, and group decision-making techniques. Then, a novel Group AHP model is proposed to determine the weights of all evaluation indicators. Fina... [more]

Energy storage technology can well reduce the impact of large-scale renewable energy access to the grid, and the liquid carbon dioxide storage system has the characteristics of high energy storage density and carries out a variety of energy supply, etc. Therefore, this paper proposes an integrated energy system (IES) containing liquid carbon dioxide storage and further exploits the demand-side regulation potential on the basis of which an integrated demand response model is proposed to consider the cooling, heating, and electricity loads. On this basis, an IES optimal scheduling model with the lowest total system operating cost as the objective function is established, the Yalmip toolbox and Cplex commercial solver are used to solve the algorithms, and the optimal scheduling results are obtained for electricity, heat, and cold under four scenarios, and it is proved through comparative analyses that the model and scheduling strategy established in this paper can optimize the load profil... [more]

The problem of power grid capacity expansion focuses on adding or modernizing generation and transmission resources to respond to the rise in demand over a long-term planning period. Traditionally, the problem has been mainly viewed from technical and financial perspectives. However, with the rise in the frequency and severity of natural disasters and their dire impacts on society, it is paramount to consider the problem from a nexus of resilience, sustainability, and equity. This paper presents a novel multi-objective optimization framework to perform power grid capacity planning, while balancing the cost of operation and expansion with the life cycle impacts of various technologies. Further, to ensure equity in grid resilience, a social vulnerability metric is used to weigh the energy not served based on the capabilities (or lack thereof) of communities affected by long-duration power outages. A case study is developed for part of the bulk power system in the state of Colorado. The f... [more]

A major operation in petroleum refinery plants, blend scheduling management of stocks and their mixtures, known as blend-shops, is aimed at feeding process units (such as distillation columns and catalytic cracking reactors) and production of finished fuels (such as gasoline and diesel). Crude-oil, atmospheric residuum, gasoline, diesel, or any other stream blending and scheduling (or blend scheduling) optimization yields a non-convex mixed-integer nonlinear programming (MINLP) problem to be solved in ad hoc propositions based on decomposition strategies. Alternatively, to avoid such a complex solution, trial-and-error procedures in simulation-based approaches are commonplace. This article discusses solutions for blend scheduling (BS) in petroleum refineries, highlighting optimization against simulation, continuous (simultaneous) and batch (sequential) mixtures, continuous- and discrete-time formulations, and large-scale and complex-scope BS cases. In the latter, ordinary least squares... [more]

With the continuous expansion of grid-connected wind, photovoltaic, and other renewable energy sources, their volatility and uncertainty pose significant challenges to system peak regulation. To enhance the system’s peak-load management and the integration of wind (WD) and photovoltaic (PV) power, this paper introduces a distributionally robust optimization scheduling strategy for a WD−PV thermal storage power system incorporating deep peak shaving. Firstly, a detailed peak shaving process model is developed for thermal power units, alongside a multi-energy coupling model for WD−PV thermal storage that accounts for carbon emissions. Secondly, to address the variability and uncertainty of WD−PV outputs, a data-driven, distributionally robust optimization scheduling model is formulated utilizing 1-norm and ∞-norm constrained scenario probability distribution fuzzy sets. Lastly, the model is solved iteratively through the column and constraint generation algorithm (C&CG). The outcomes dem... [more]

Dynamic load balancing in cloud computing is crucial for efficiently distributing workloads across available resources, ensuring optimal performance. This research introduces a novel dynamic load-balancing approach that leverages a deep learning model combining Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) to calculate load values for each virtual machine (VM). The methodology aims to enhance cloud performance by optimizing task scheduling and stress distribution. The proposed model employs a dynamic clustering mechanism based on computed loads to categorize VMs into overloaded and underloaded clusters. To improve clustering efficiency, the approach integrates Reinforcement Learning (RL) with a sophisticated Hybrid Lyrebird Falcon Optimization (HLFO) algorithm. HLFO merges the Lyrebird Optimization Algorithm (LOA) and Falcon Optimization Algorithm (FOA), enhancing the effectiveness of load balancing. A Multi-Objective Hybrid Optimization model is introduced... [more]

Driven by global targets to reduce greenhouse gas emissions, energy systems are expected to undergo fundamental changes. In light of carbon neutrality policies, China is expected to significantly increase the proportion of hydrogen and electricity in its energy system in the future. Nevertheless, the future trajectory remains shrouded in uncertainty. To explore the potential ramifications of varying growth scenarios pertaining to hydrogen and electricity on the energy landscape, this study employs a meticulously designed bottom-up model. Through comprehensive scenario calculations, the research aims to unravel the implications of such expansions and provide a nuanced analysis of their effects on the energy system. Results show that with an increase in electrification rates, cumulative carbon dioxide emissions over a certain planning horizon could be reduced, at the price of increased unit reduction costs. By increasing the share of end-use electricity and hydrogen from 71% to 80% in 20... [more]

This study explores enhancing the resilience of electric and natural gas networks against extreme events like windstorms and wildfires by integrating parts of the electric power transmissions into the natural gas pipeline network, which is less vulnerable. We propose a novel integrated energy system planning strategy that can enhance the systems’ ability to respond to such events. Our strategy unfolds in two stages. Initially, we devise expansion strategies for the interdependent networks through a detailed tri-level planning model, including transmission, generation, and market dynamics within a deregulated electricity market setting, formulated as a mixed-integer linear programming (MILP) problem. Subsequently, we assess the impact of extreme events through worst-case scenarios, applying previously determined network configurations. Finally, the integrated expansion planning strategies are evaluated using real-world test systems.

New energy storage methods based on electrochemistry can not only participate in peak shaving of the power grid but also provide inertia and emergency power support. It is necessary to analyze the planning problem of energy storage from multiple application scenarios, such as peak shaving and emergency frequency regulation. This article proposes an energy storage capacity configuration planning method that considers both peak shaving and emergency frequency regulation scenarios. A frequency response model based on emergency frequency regulation combined with low-frequency load shedding is established, taking into account the frequency safety constraints of the system and the principle of idle time reuse, to establish a bi-level programming model. In the upper-level model, the optimization objective is to minimize the annual operating cost of the system during the planning period, combined with the constraints of power grid operation to plan the energy storage capacity. The lower-level... [more]

The interdependence of various energy forms and flexible cooperative operation between different units in an integrated energy system (IES) are essential for carbon emission reduction. To address the planning problem of an electric−thermal integrated energy system under low-carbon conditions and to fully consider the low carbon and construction sequence of the integrated energy system, a low-carbon-oriented capacity optimization method for the electric−thermal integrated energy system that considers construction time sequence (CTS) and uncertainty is proposed. A calculation model for the carbon transaction cost under the ladder carbon trading mechanism was constructed, and a multi-stage planning model of the integrated energy system was established with the minimum life cycle cost, considering carbon transaction cost as the objective function, to make the optimal decision on equipment configuration in each planning stage. Finally, a case study was considered to verify the advantages of... [more]

An integrated energy system (IES) breaks down barriers between different energy subsystems, enhancing energy reliability and efficiency. However, issues such as uneven equipment capacity allocation and suboptimal scheduling persist in multi-energy flow IES. To maximize economic benefits while ensuring energy balance and the operational characteristics of the equipment, a capacity matching optimization and scheduling strategy model for IES was developed. Firstly, mathematical models for the electricity, gas, and thermal networks within the IES were established. Secondly, considering the efficiency of energy conversion between different forms and constraints of energy storage in the electricity−thermal−gas interconnected energy system, optimization solutions were obtained using regional contraction algorithms and sequential quadratic programming methods. Finally, case studies conducted in a real park demonstrated that, through optimized capacity matching, unit prices for electricity, hea... [more]

Local path planning is a necessary ability for mobile robot navigation, but existing planners are not sufficiently effective at dynamic obstacle avoidance. In this article, an improved timed elastic band (TEB) planner based on the requirements of mobile robot navigation in dynamic environments is proposed. The dynamic obstacle velocities and TEB poses are fully integrated through two-dimensional (2D) lidar and multi-obstacle tracking. First, background point filtering and clustering are performed on the lidar points to obtain obstacle clusters. Then, we calculate the data association matrix of the obstacle clusters of the current and previous frame so that the clusters can be matched. Thirdly, a Kalman filter is adopted to track clusters and obtain the optimal estimates of their velocities. Finally, the TEB poses and obstacle velocities are associated: we predict the obstacle position corresponding to the TEB pose through the detected obstacle velocity and add this constraint to the co... [more]

This paper addresses the challenging scheduling of crude oil operations (SCOO) problem, characterized by the intricate sequencing of activities involving discrete events and continuous variables. Given the NP-Hard nature of scheduling problems due to their combinatorial complexity, this study employs a data-driven optimization approach. Initially, historical operational data relevant to the SCOO are scrutinized; however, due to data limitations, small-scale instances are solved using a mathematical programming model to generate data. Subsequently, operational solution data are processed using the Apriori algorithm, a renowned data mining technique. The insights gained are translated into heuristic rules, laying the groundwork for a novel data-driven heuristic algorithm tailored for the SCOO problem. This algorithm is then applied to a 45-day scheduling scenario, demonstrating the efficacy of the proposed approach.

The proposed research aims to accomplish an improved A* algorithm for mobile robots in complex environments. In this novel algorithm, the guidance of environment information is added to the evaluation function to enhance the adaptability of the algorithm in complex environments. Additionally, to solve the problem of path smoothness, the optimal selection rules for child nodes and the bidirectional optimization strategy for path smoothing are introduced to reduce redundant nodes, which effectively makes the search space smaller and the path smoother. The simulation experiments show that, compared with the colony algorithm and Dijkstra algorithms, the proposed algorithm has significantly improved performance. Compared with the A* algorithm, the average planning time is reduced by 17.2%, the average path length is reduced by 2.05%, the average turning point is reduced by 49.4%, and the average turning Angle is reduced by 75.5%. The improved A* algorithm reduces the search space by 61.5% o... [more]

Non-enzymatic glycation is a process, which can be best described as a significant posttranslational modification of various proteins. It emerges in hyperglycemic conditions and may have an impact on albumin stability as well as its activity and physical and chemical properties, essentially affecting all its physiological functions. The goal of this research was to answer the following questions: (i) how does the glycation of defatted human serum albumin by glucose−fructose syrup (GFS) alter its tertiary structure; (ii) does palmitic acid (PA), a component of palm oil, affect the in vitro glycation process and cause conformational changes of glycated albumin; and (iii) does PA inhibit the formation of Advanced Glycation End-Products (AGEs)? Therefore, in order to point out differences in the tertiary structure of macromolecules, the absorption and emission of fluorescence spectra and their second derivatives, excitation fluorescence and synchronous spectra, Red-Edge Excitation Shift (R... [more]

With increasingly stringent carbon policies, the development of traditional heavy industries with high carbon emissions has been greatly restricted. Manufacturing companies surveyed use multifunctional machining machines and variable speed cranes, as the lack of rational planning results in high energy wastage and low productivity. Reasonable scheduling optimization is an effective way to reduce carbon emissions, which motivates us to work on this research. To reduce the comprehensive energy consumption of the machining process and transportation process in an actual manufacturing environment, this paper addresses a new low-carbon scheduling problem of flexible manufacturing and crane transportation considering multi-state collaborative configuration (LSP-FM&CT-MCC). First, an integrated energy consumption model based on multi-state machining machines and cranes is established to optimize the overall energy efficiency of the production process. Then, a new hybrid differential evolution... [more]

In many flexible job shop scheduling problems, transportation scheduling problems are involved, increasing the difficulty in problem-solving. Here, a novel artificial Physarum polycephalum colony algorithm is proposed to help us address this problem. First, the flexible job shop scheduling problem with transportation constraints is modeled as a state transition diagram and a multi-objective function, where there are ten states in total for state transition, and the multi-objective function considers the makespan, average processing waiting time, and average transportation waiting time. Second, a novel artificial Physarum polycephalum colony algorithm is designed herein with two main operations: expansion and contraction. In the expansion operation, each mycelium can cross with any other mycelia and generate more offspring mycelia, of which each includes multiple pieces of parental information, so the population expands to more than twice its original size. In the contraction operation,... [more]