Optimal Scheduling Method Research Articles

Research on optimal cutting scheme based on three-stage guillotine cut

Smart manufacturing has become a major part of the core competitiveness of enterprises, and better scheduling optimization methods are obtained in order to reduce the material loss in the manufacturing process. In this paper, we construct a mixed integer programming model, iterate from local optimum to global optimum by genetic algorithm, and finally solve the question B of the 2022 China Graduate Student Mathematical Modeling Competition. We get the results that the utilization rate of each scheduling solution is higher than 90%, which shows the effectiveness of the model and algorithm, and can greatly improve the utilization rate of plates.

Intelligent scheduling and optimization of microenergy grid: the application and development of computer technology

Abstract For the intelligent management of micro-energy grid, the limitations of traditional scheduling optimization methods have begun to be highlighted, and computer technology has become a new generation of power system support means. This paper proposes a state estimation method based on smart grid measurement technology, and for the characteristics of micro-energy grid anomaly data, adopts the sampling value detection anti-anomaly data method based on amplitude comparison to estimate the grid dynamic process. Based on the grid state estimation dataset, a smart grid scheduling strategy based on cloud computing is constructed. The conditional value-at-risk of the penalty function of generation cost, grid cost, and motion cost is used as the objective function of the dispatch modeling, and the solution method for the optimization of the value-at-risk model is provided. Finally, an example has been built for this dispatch model to access a simulated micro-energy grid system that contains multiple nodes. The results show that the error between the voltage amplitude obtained from the dynamic state estimation of the micro-energy grid using the method of sampled-value detection against anomalous data and the real value is no more than 0.5%, and the data scheduling error rate of the cloud computing does not fluctuate significantly when dealing with a large number of scheduling data tasks, all of which are controlled to be below 0.0015. The scheduling strategy based on cloud computing has a certain degree of generalization when dealing with random and variable source and load scenarios, which can effectively improve the level of power grid business applications and complete the corresponding intelligent scheduling.

Scheduling optimization of underground mine trackless transportation based on improved estimation of distribution algorithm

The trend in underground mine development is trackless transportation, and the scheduling optimization of underground mine trackless transportation is a current research hotspot. This paper proposes a truck scheduling optimization method for underground mine trackless transportation based on an improved estimation of distribution algorithm to address the truck scheduling problem in the underground mine trackless transportation process. The transportation process of transport trucks in underground mines is analyzed. The dispatching model of transport trucks in underground mines is constructed based on the requirements of reducing transportation costs and increasing transportation efficiencies, taking into account the truck meeting situation in the ramp section and minimizing the total shift transportation distance and the total waiting time of transport trucks as the objective functions. The improved estimation of distribution algorithm is used to solve the truck scheduling model, resulting in the optimal ore blending and scheduling schemes. The comparative analysis employs a genetic algorithm, particle swarm optimization algorithm, and immune algorithm. The results demonstrate that, compared to other algorithms, the improved estimation of distribution algorithm proposed in this paper has superior performance in terms of convergence speed and the search for the optimal solution. The total number of transportation tasks associated with the optimal ore allocation scheme is at least 82, and the waiting time associated with the optimal scheduling scheme is reduced to 7.5 min. The operation time chart of transport trucks calculated by the optimal dispatching scheme can clearly depict the location of each transport truck at any time during a shift's working time, which has significant guiding significance for the actual truck transportation in the mine.

Dynamic Scheduling Optimization Method for Multi-AGV-Based Intelligent Warehouse Considering Bidirectional Channel

With the implementation of AGV technology and automated scheduling, storage and retrieval systems have become widely utilized in warehouse management. However, due to the use of unidirectional channels, AGV movement is restricted, and detours may occur frequently. Additionally, as the number of AGVs increases, deadlocks can arise, which lead to delays in order packaging and a decrease in overall warehouse performance. Hence, this paper proposes a dynamic scheduling method for task assignment and route optimization of AGVs to prevent collisions. The routing optimization method is based on an improved A* algorithm, which takes into account the dynamic map as input. Moreover, this paper investigates highly complex collision scenarios in bidirectional channels. Through simulation experiments, it is evident that scheduling methods based on bidirectional channels offer a clear advantage in terms of efficiency compared to those based on unidirectional channels.

Research on optimal allocation of flow and head in cascade pumping stations based on Harris hawks optimization

Abstract To address the problems of massive energy consumption and low operating efficiency in cascade pumping stations (CPSs), an optimized scheduling model for CPSs with water flow and head constraints was constructed in this study. The Harris hawks optimization (HHO) algorithm was employed to solve this model owing to its excellent performance in the field of engineering majorization. Based on this model, an optimal scheduling method for CPSs was proposed and applied to the three-stage pumping station system. The results demonstrate that the optimization schemes based on the HHO algorithm can improve the operational efficiency and annual cost savings under three different pumping flow conditions by 0.16, 0.55, and 0.56%, reducing the annual operating cost by ¥22,703, ¥74,581, and ¥75,356, respectively, relative to the currently used schemes. These results are better than those obtained by the particle swarm optimization (PSO) algorithm and genetic algorithm (GA). Furthermore, in terms of computational time, the optimization method with the HHO algorithm can show an improvement of 8.94–29.74% compared with those of PSO and GA, verifying the feasibility and efficiency of the HHO algorithm in the optimal scheduling for CPSs. Therefore, the proposed method is effective at solving the scheduling problem of CPSs.

Research on design and multi-frequency scheduling optimization method for flexible green ammonia system

As an important chemical product and energy carrier, green ammonia is a way to consume large quantities of green hydrogen produced from renewable electricity. For the optimization of the green ammonia production systems, a primary problem is to solve the electricity-hydrogen-ammonia matching problem under multi-frequency and multi-amplitude load fluctuations. In this work, a method for capacity designing and scheduling optimization of a hybrid multi-frequency flexible green ammonia system is constructed. The method can be applied to design the whole green ammonia system in grid-connected and isolated scenarios. The established model can accurately describe the electricity-hydrogen-ammonia matching characteristics of each module. Furthermore, many important practical engineering constraints are taken into account, such as the operating time of the chemical device, and the flexibility of the ammonia device. This work verifies the effectiveness of the proposed method based on a real case, and the impact of load range, adjustment speed, and adjustment frequency of the ammonia synthesis process on the product cost and module capacity.

Simultaneous capacity configuration and scheduling optimization of an integrated electrical vehicle charging station with photovoltaic and battery energy storage system

The implementation of an optimal power scheduling strategy is vital for the optimal design of the integrated electric vehicle (EV) charging station with photovoltaic (PV) and battery energy storage system (BESS). However, traditional design methods always neglect accurate PV power modeling and adopt overly simplistic EV charging strategies, which might result in suboptimal and infeasible designs. This study proposes a novel simultaneous capacity configuration and scheduling optimization model for PV/BESS integrated EV charging stations, which combines hybrid modeling for PV power prediction and optimal scheduling method for charging piles. The original model is then converted to a mixed integer linear programming problem by the Big-M method to greatly reduce the computational complexity. A typical scenario of commercial region is employed to demonstrate the effectiveness of the proposed approach. The results indicate that the approach could improve economic benefits by 15.67 % and reduce carbon emissions by 37.14 % compared with the entire grid-based mode. Besides, integrating the PV hybrid modeling method and optimal charging scheduling strategy could achieve superior capacity configurations and greatly improve the comprehensive performance of the system. It is thus illustrated that the approach could provide a promising solution for the optimal design and scheduling of PV/BESS integrated EV charging stations.

Optimal Economic Scheduling Method for Power Systems Based on Whole-System-Cost Electricity Price

At present, the traditional scheduling mode of power grids generally dispatches according to the power generation cost within the safe range. Transmission costs are evenly distributed to customers according to their load ratios. There are no methods for the rational distribution of transmission costs according to the utilization degree of generation and load to transmission network resources. This traditional scheduling mode will render transmission cost distribution unfair, and it is difficult to guide reasonable load distribution in time and space. Therefore, an optimal economic scheduling method for power systems based on the whole-system-cost electricity price is proposed in this paper. For the power generation and the transmission sides, the whole-system-cost electricity price model was constructed according to the power flow tracking method. For the load side, a demand-side response model of users’ responses to electricity price changes was established. Finally, the IEEE 57 node standard model was used to simulate optimal economic scheduling. The results show that the proposed method can guide the rational distribution of power flow. The power flow is shifted moderately from far away to near the power generation center, allowing for the load demand to be guided to meet nearby customers’ demands and preventing the line from blocking, the latter of which is conducive to ensuring the safety of the power grid.

A convex multi-objective distributionally robust optimization for embedded electricity and natural gas distribution networks under smart electric vehicle fleets

The indisputable environmental concerns have forced the imminent proliferation of renewable energy sources (RES) and electric vehicles (EV). However, the high penetration of such uncertain and variable sources, can pose significant challenges for maintaining supply–demand balance in electrical distribution networks (EDNs). To address these challenges, this paper presents a distributionally robust optimization (DRO) method for multi-objective scheduling in integrated electricity and natural gas distribution networks (IENGDNs). The proposed approach aims to minimize environmental-economic objectives while taking into account the high penetration of EVs and RESs. The impact of a smart EV charging strategy is evaluated to reduce operating costs and maximize the use of RESs. Additionally, demand response programs (DRPs) are used in the EDN to prevent overlapping of peak load hours between the EDN and natural gas distribution network (NGDN). Linepack technology is also used to store natural gas in NGDN pipelines, which increases the short-term flexibility of the entire IENGDNs. The proposed problem is mathematically structured as a second-order conical programming (SOCP) model to benefit from the reliable and efficient convex optimization solution. The simulations were conducted on a 123-EDN and a 40-NGDN systems. Different simulation cases show that the proposed economic-environmental framework can bring down the total emissions by 10.02%.

Layered power scheduling optimization of PV hydrogen production system considering performance attenuation of PEMEL

To analyze the additional cost caused by the performance attenuation of a proton exchange membrane electrolyzer (PEMEL) under the fluctuating input of renewable energy, this study proposes an optimization method for power scheduling in hydrogen production systems under the scenario of photovoltaic (PV) electrolysis of water. First, voltage and performance attenuation models of the PEMEL are proposed, and the degradation cost of the electrolyzer under a fluctuating input is considered. Then, the calculation of the investment and operating costs of the hydrogen production system for a typical day is based on the life cycle cost. Finally, a layered power scheduling optimization method is proposed to reasonably distribute the power of the electrolyzer and energy storage system in a hydrogen production system. In the up-layer optimization, the PV power absorbed by the hydrogen production system was optimized using MALTAB+Gurobi. In low-layer optimization, the power allocation between the PEMEL and battery energy storage system (BESS) is optimized using a non-dominated sorting genetic algorithm (NSGA-II) combined with the firefly algorithm (FA). A better optimization result, characterized by lower degradation and total costs, was obtained using the method proposed in this study. The improved algorithm can search for a better population and obtain optimization results in fewer iterations. As a calculation example, data from a PV power station in northwest China were used for optimization, and the effectiveness and rationality of the proposed optimization method were verified.

Two-stage robust optimal scheduling method for virtual power plants considering the controllability of electric vehicles

Two-stage robust optimal scheduling method for virtual power plants considering the controllability of electric vehicles

Research on optimal scheduling method of active distribution network considering multi-type flexible resources

With the increase in the utilization rate of renewable energy, the volatility and uncertainty of active distribution networks have become more significant. Comprehensive consideration of the coordination and optimization of multiple resources can improve the flexibility of the system and promote energy consumption. Effective substitution Quantitative modeling is carried out considering a large number of flexible resources based on the flexibility of nodes and networks, combined with the physical characteristics of node flexible resources. Through the analysis of the implementation principle, control mode, and operation characteristics of the soft open point of network flexibility resource, the operation model of the soft open point with combined energy storage is established. On this basis, there are two elasticity evaluation indicators: elasticity adequacy and transmission adequacy. And we incorporate flexible benefits into optimization goals. Multiple optimization scheduling models have been created, including economy node flexibility, and network flexibility. Finally, multiple target particle swarm optimization algorithms will use the proposed scheduling model, combined with trend calculation and validation, using an improved IEEE33 node system.

Research on the day-ahead scheduling optimization method of medium-depth geothermal cascade heating system

The medium-depth geothermal cascade utilization technology plays a crucial role in achieving low-carbon and low-cost heat supply. However, the traditional rigid regulation method faces challenges in ensuring real-time supply-demand balance due to the district heating demand being influenced by outdoor weather and the complexity of the operating parameters of the medium-depth geothermal cascade heating system. Consequently, problems such as low utilization of geothermal water and high operating costs arise. To address these issues, this study proposes a day-ahead scheduling optimization method for the medium-depth geothermal cascade heating system, integrating the adaptive moment estimation (Adam) optimized long short-term memory (LSTM) prediction algorithm, interior point method, and particle swarm optimization (PSO) algorithm. Based on enhanced precision prediction, this study aims to optimize variables including partial load rate (PLR), flow rate, cooling water return temperature, and chilled water return temperature. The effectiveness of the proposed optimization method is verified through the establishment of a TRNSYS simulation platform in conjunction with MATLAB, focusing on a geothermal cascade heating project in Tianjin. The results demonstrate that the proposed day-ahead scheduling optimization method effectively promotes the coordinated operation among energy systems, achieving significant energy savings for the energy station (10.13 %). The novelty of this research lies in the utilization of scientific algorithms to optimize multiple variables within the system, which holds the potential to enhance the matching of supply and demand and improve the operational effectiveness of comparable systems. In summary, this study provides valuable insights and practical implications for the field of strategic optimization. However, it is essential to acknowledge that the applicability of the findings to other geographic locations, different scale projects, or varying system configurations necessitates additional investigation. The generalizability is subject to further research and exploration.

Optimized Dispatch of Regional Integrated Energy System Considering Wind Power Consumption in Low-Temperature Environment

The wind abandonment phenomenon of cogeneration units in regional integrated energy systems (RIES) under the operation mode of “heat for electricity” and the improvement in the operation efficiency of the energy storage system under a low-temperature environment are problems that need to be solved urgently. To this end, a regional integrated energy system optimization scheduling method based on fine energy storage and wind power consumption is proposed in the paper. First, a fine energy storage model more adapted to a low-temperature environment is established on the power side to accurately simulate the actual working state of the energy storage components and quantify the uncertainty of the wind power output using the conditional value-at-risk (CVaR) theory. Then, a combined heat and power demand response mechanism is introduced on the load side to reduce the peak-to-valley difference in the heat and power loads, it is realized to promote the system’s consumption of wind power without increasing the transmission power of the contact line. Finally, the example is solved on the MATLAB platform with the objective of minimizing the total cost of the RIES optimal dispatch. The simulation results show that the proposed model is not only more adaptable to a low-temperature environment compared with the traditional model but also reduces the overall cost of the system by 2.58% while realizing the complete consumption of wind power. This innovative study provides a feasible and efficient solution to improve the performance of integrated energy systems, especially the operation capability in extreme environments.

A distributed multi-objective optimization method for scheduling of integrated electricity and hydrogen systems

The growing diversity in energy demand has led to an increasingly intertwined relationship between the electric power system (EPS) and hydrogen energy system (HES). However, these systems are presently managed by entities with distinct interests, resulting in competition and privacy concerns during the scheduling of integrated electricity and hydrogen systems (IEHSs). To address this issue, this paper proposes a multi-objective IEHS scheduling model with the aim of minimizing costs for electricity and hydrogen suppliers, taking into account bi-directional energy transactions between EPS and HES. Furthermore, we present an innovative approach based on alternating direction multiplier method (ADMM) for distributed ξ-constraint optimization. This approach efficiently captures the optimal Pareto frontier while maintaining the privacy of EPS and HES. The proposed methodology is validated on an IEHS composed of an IEEE 33-bus EPS and a 20-node HES. The results demonstrate that no matter how many solution sets, a more uniform pareto front can be obtained than the traditional method. Furthermore, it simplifies the multi-objective optimization problem and effectively protects the privacy of participants at the expense of acceptable solution time.

Optimal Dispatch Strategy for Electric Vehicles in V2G Applications

The overutilization of electric vehicles (EVs) has the potential to result in significant challenges regarding the reliability, contingency, and standby capabilities of traditional power systems. The utilization of renewable energy distributed generator (REDG) presents a potential solution to address these issues. By incorporating REDG, the reliance of EV charging power on conventional energy sources can be diminished, resulting in significant reductions in transmission losses and enhanced capacity within the traditional power system. The effective management of the REDG necessitates intelligent coordination between the available generation capacity of the REDG and the charging and discharging power of EVs. Furthermore, the utilization of EVs as a means of energy storage is facilitated through the integration of vehicle-to-grid (V2G) technology. Despite the importance of the V2G technology for EV owners and electric utility, it still has a slow progress due to the distrust of the revenue model that can encourage the EV owners and the electric utility as well to participate in V2G programs. This study presents a new wear model that aims to precisely assess the wear cost of EV batteries, resulting from their involvement in V2G activities. The proposed model seeks to provide EV owners with a precise understanding of the potential revenue they might obtain from participating in V2G programs, hence encouraging their active engagement in such initiatives. Various EV battery wear models are employed and compared. Additionally, this study introduces a novel method for optimal charging scheduling, which aims to effectively manage the charging and discharging patterns of EVs by utilizing a day-ahead pricing technique. This study presents a novel approach, namely, the gradual reduction of swarm size with the grey wolf optimization (GRSS-GWO) algorithm, for determining the optimal hourly charging/discharging power with short convergence time and the highest accuracy based on maximizing the profit of EV owners.

A two-stage distributed optimization method for home energy management systems via multi-modal data-driven algorithm

The home energy management system (HEMS), which utilizes multi-modal data from multiple sensors to generate the knowledge about decision making, is essential to the optimization of home energy management efficiency. Load scheduling based on HEMS can improve the utilization efficiency of multi-modal data and derived knowledge, achieve power supply-demand balance, and reduce users’ electricity costs. This paper proposes a distributed load optimization scheduling method for the load scheduling problem in HEMS based on multi-modal data-driven algorithm. Additionally, a two-stage data-driven optimization method is proposed, including a first-stage optimization model based on minimizing electricity costs and a second-stage optimization model based on minimizing system load fluctuations. In the first stage, cost self-optimization is performed based on energy storage devices. In the second stage, a load optimization instruction is issued by the control center, and each user optimizes the load fluctuations based on the system load data. Compared to centralized control methods, this approach reduces the computational overhead of the control center. Finally, simulation experiments based on load scheduling in the HEMS are conducted. The results of the first optimization stage show that when the battery capacity integrated into the system increases from 3.68 kWh to 6.68 kWh, user costs can be reduced from 57.572 cents to 42.064 cents. It is not only evident that the proposed method can effectively save users on electricity costs, but the introduction of larger capacity batteries also lowers these costs. The second stage of load fluctuation optimization results show that the proposed method can effectively optimize the usage data of a group of users and decrease the absolute peak-valley difference by 8.8%.

SSFSCE: Design of a Sleep Scheduling based Fan Shaped Clustering Model to enhance working Energy Efficiency of WSN

To enhance energy level in WSN is a research requirement, which assists in improving their lifetime over a series of communications. To achieve this target, a various variety of clustering & sleep scheduling models are discussed by researchers. Most of these models deploy static clustering & sleep scheduling operations, which limits their applicability & scalability levels. Moreover, dynamic clustering & scheduling models are highly complex, which reduces temporal QoS performance under real-time use cases. In order to reduce the probability of these issues, this text discusses design of the proposed Sleep Scheduling based Fan Shaped Clustering Model to enhance working Energy Efficiency of WSN. The proposed model initially deploys a Grey Wolf Optimization (GWO) Method for dynamic sleep scheduling via temporal performance analysis. The GWO Method models a fitness function that combines temporal usage levels, temporal Quality of Service (QoS), and temporal energy levels. Based on this modelling process, nodes were categorized into wake & sleep nodes, which were further clustered via destination-aware Fan Shaped Clustering (FSC), that assisted in improving QoS performance under multiple scenarios. The FSC Model was combined with a QoS-aware routing model, that assisted in selection of routing paths that can achieve low delay, high throughput, and high packet delivery with higher energy efficiency levels. Efficiency of the model was tested on node & network conditions, and its QoS performance was checked in terms of communication delay, consumption of energy, level of throughput, and Packet Delivery Ratio (PDR) levels. On the basis of these comparative evaluations, it is observed that the new proposed model is able to enhance end-to-end delay by 8.5%, reduce level of energy by 15.5%, while increasing throughput by 8.3%, and PDR by 1.5%, thus making it useful for a different real-time conditions.

A two-layer optimal scheduling method for multi-energy virtual power plant with source-load synergy

With the increasing penetration of clean energy sources such as wind power and photovoltaic in the grid, the volatility, intermittency, and randomness of their power output have impacted the safe and stable operation of the grid. To address the challenges posed by scheduling and the potential wastage of renewable energy due to these factors, a two-layer optimal scheduling model for a virtual power plant that takes into account source-load synergy is proposed in this paper. In the upper model, emphasis is placed on demand response strategies to optimize load-side dispatch. This includes encouraging customers to adjust their electricity consumption patterns through time-of-use pricing and effectively managing controllable loads for peak shaving and valley filling. These actions collectively aim to maximize the virtual power plant's overall performance. The upper-tier model then communicates the power output to the lower-tier model. In the lower model, we consider the costs associated with wind, photovoltaic, thermal, and energy storage power generation to optimize power-side scheduling. This approach ensures a comprehensive optimization process, addressing both demand and power generation aspects of the virtual power plant's operations. Compared to existing research, this paper takes into account the impact of various clean energy sources, meticulously categorizes controllable loads, and conducts in-depth modeling and analysis of demand response. Finally, the results of optimal scheduling of the virtual power plant under different scenes are compared and analyzed using examples, to validate the effectiveness of the proposed method.

Optimal Dispatch of Concentrating Solar Thermal Power (CSP)-Wind Combined Power Generation System

The random nature of renewable energy leads to the instability of the balance state of the power grid, and the frequency and voltage of the grid also fluctuate. Considering that wind energy and solar thermal power generation can complement each other in terms of temporal output power, the heat storage system of the solar thermal power station is used to regulate the joint operation of the solar thermal power station and wind power generation, and an optimal scheduling method for promoting new energy consumption of solar thermal power station is proposed. Based on the solar thermal-wind combined power generation system, the method considers the operating characteristics and constraints of each unit and uses the MATLAB optimization toolbox to simulate and verify the IEEE-14 node system. The results show that the participation of wind power grid-connected time thermal power stations can alleviate the pressure of rapid output adjustment of traditional units and can ensure the economy of the system, and the operating cost is reduced by 51.5%. The curtailment air volume was reduced from the original 2.5015 MW to 0.83015 MW, maximizing new energy consumption.