Optimal Scheduling Strategy Research Articles

Research on comprehensive optimisation of AGVs scheduling at intelligent express distribution centres based on improved GA

This study addresses optimisation challenges in scheduling automatic guided vehicles (AGVs) for express distribution centres. A comprehensive model is developed that simultaneously considers makespan, AGV usage, and AGV recharging frequency to identify the optimal scheduling strategy and determine an effective recharging threshold. To address this, an enhanced adaptive genetic algorithm called LS-AGA (L-value and S-value based Adaptive Genetic Algorithm) is proposed. The LS-AGA employs a Logistic chaotic map to create an initial population. Fitness value factors and fitness entropy are incorporated to calculate individual L-values for selection, crossover, and mutation, maintaining a balance between fitness value and population diversity. The SoftMax function is introduced to map the L-values and fitness values into the corresponding probabilities, subsequently calculating individual S-values to optimise crossover and mutation rates. The addition of a catastrophe operator further enhances optimisation. Numerical and validation experiments demonstrate that LS-AGA outperforms existing improved genetic algorithms in solving the proposed model.

Dynamic scheduling for job shop with machine failure based on data mining technologies

PurposeInfluenced by the constantly changing manufacturing environment, no single dispatching rule (SDR) can consistently obtain better scheduling results than other rules for the dynamic job-shop scheduling problem (DJSP). Although the dynamic SDR selection classifier (DSSC) mined by traditional data-mining-based scheduling method has shown some improvement in comparison to an SDR, the enhancement is not significant since the rule selected by DSSC is still an SDR.Design/methodology/approachThis paper presents a novel data-mining-based scheduling method for the DJSP with machine failure aiming at minimizing the makespan. Firstly, a scheduling priority relation model (SPRM) is constructed to determine the appropriate priority relation between two operations based on the production system state and the difference between their priority values calculated using multiple SDRs. Subsequently, a training sample acquisition mechanism based on the optimal scheduling schemes is proposed to acquire training samples for the SPRM. Furthermore, feature selection and machine learning are conducted using the genetic algorithm and extreme learning machine to mine the SPRM.FindingsResults from numerical experiments demonstrate that the SPRM, mined by the proposed method, not only achieves better scheduling results in most manufacturing environments but also maintains a higher level of stability in diverse manufacturing environments than an SDR and the DSSC.Originality/valueThis paper constructs a SPRM and mines it based on data mining technologies to obtain better results than an SDR and the DSSC in various manufacturing environments.

Optimal Scheduling Strategy of Microgrid Based on Reactive Power Compensation of Electric Vehicles

This paper proposes a microgrid optimal scheduling strategy based on the reactive power compensation of electric vehicles to address the issue of interactive fluctuation of voltage and power resulting from a high proportion of new energy integration into the grid. Firstly, for accurate prediction of electric vehicle charging and discharging behavior, the Monte Carlo simulation method was employed for day-ahead prediction. Secondly, an optimization method for reactive power compensation was developed, considering distributed sources such as wind turbines, photovoltaics, and electric vehicles, with the objective of minimizing network loss and power generation costs. Additionally, user-side prices and electric vehicle charging prices were taken into consideration. Finally, utilizing the IEEE14 bus simulation, a comparison was made between the microgrid with and without electric vehicle reactive support capacity. Results demonstrated that, while maintaining a power factor greater than 0.95, the proposed method reduced the average daily generation cost by 0.35%, average daily network loss by 7.0%, average daily bus voltage fluctuation by 48.60%, average electricity price by 2.88%, and EV user charging cost by 21.29%. These findings illustrate that the proposed reactive power compensation optimization method effectively mitigates voltage and power fluctuations resulting from new energy integration, ensuring system safety, reliability, and economic efficiency.

Energy-efficient flexible job shop scheduling problem considering discrete operation sequence flexibility

The classical flexible job shop scheduling problem (FJSP) normally assumes that operations of each job have strict sequence constraints, i.e., each operation can be processed only after its previous operation is completed. However, in the actual production, the phenomenon that some operations of a job don't have any sequence constraints is very common. With regard to this, we firstly propose a FJSP with discrete operation sequence flexibility (FJSPDS) aiming at minimizing the makespan and total energy consumption, simultaneously. An effective mathematical model is established for the FJSPDS and its validity is proved by the CPLEX; and then an improved memetic algorithm (IMA) is designed to solve the FJSPDS. In the IMA, a new flexible sequencing method for determining process plan of each job and a right-leaning decoding method are proposed. And some effective crossover and mutation operators and an effective local search operator are designed to accelerate the convergence speed and expand the solution space of the algorithm. A total of 110 FJSPDS benchmark instances are constructed to conduct numerical simulation experiments. Experimental results show that our proposed IMA has superior performance in almost all of the instances compared with three well-known evolutionary algorithms. Our proposed model and algorithm can help the production managers who work with flexible manufacturing systems to obtain optimal scheduling schemes considering operations which have or don't have sequence constraints.

Research on short-term joint optimization scheduling strategy for hydro-wind-solar hybrid systems considering uncertainty in renewable energy generation

Due to its randomness, intermittence, and volatility, the high-proportional integration of wind and solar power poses challenges to the safe and stable operation of power systems. Cascade hydropower stations have a high response speed, high adjustability, and stable output. This study proposed a hydro-wind-solar hybrid system and investigated its short-term optimal coordinated operation based on deep learning and a double-layer nesting algorithm. A stochastic complementary scheduling model was constructed to maximize the cascade energy storage. The particle swarm optimization algorithm-dynamic programming (PSO-DP) coupled with the inner and outer nesting optimization algorithm reduces the problem-solving complexity. The hybrid system was applied to a national comprehensive development base of renewable energy with integrated wind, solar, and hydropower in China. Studies have shown the following: The hydro-wind-solar hybrid system has a certain degree of scalability. The utilization of deep learning methods can fully consider the uncertainty of wind and solar. The internal and external nested optimization algorithm, which realizes the reasonable and efficient distribution of water and electricity, and improves the future power generation capacity of cascade hydropower stations, was used to solve the problem. This study provided a valuable reference for the large-scale utilization of other renewable energy sources worldwide.

A Multi-timescale Scheduling Strategy for Residential Integrated Energy System

In recent years, multi-energy complementation has been deepening, and the application rate of new energy sources has been increasing. A multi-timescale optimal scheduling strategy is proposed to mobilize the energy flexibility of residential integrated energy systems (RIES) and to reduce the negative impact of source-load forecast uncertainty on the scheduling plan. Firstly, for the energy supply structure of RIES, the operation constraint model of each energy hub equipment is established. Then, the optimal scheduling is divided into three stages: day-ahead planning, intra-day planning, and real-time planning, each of which will consider the volatility of source-load prediction and determine the operation status of the equipment according to its importance level. The experimental results show that the proposed multi-timescale scheduling strategy can ensure the economy while minimizing the deviation of the main grid power purchase in the real-time and day-ahead phases, providing a basis for the development of new energy capacity in the grid to grow to higher penetration rates.

Optimized scheduling study of user side energy storage in cloud energy storage model.

With the new round of power system reform, energy storage, as a part of power system frequency regulation and peaking, is an indispensable part of the reform. Among them, user-side small energy storage devices have the advantages of small size, flexible use and convenient application, but present decentralized characteristics in space. Therefore, the optimal allocation of small energy storage resources and the reduction of operating costs are urgent problems to be solved. In this study, the author introduced the concept of cloud energy storage and proposed a system architecture and operational model based on the deployment characteristics of user-side energy storage devices. Additionally, a cluster scheduling matching strategy was designed for small energy storage devices in cloud energy storage mode, utilizing dynamic information of power demand, real-time quotations, and supply at the load side. Subsequently, numerical analysis was conducted to verify that the proposed operational mode and optimal scheduling scheme ensured the maximum absorption of renewable energy, improved the utilization rate of energy storage resources at the user side, and contributed to peak shaving and load leveling in the power grid. The model put forward in this study represents a valuable exploration for new scenarios in energy storage application.

Research on wind-solar-storage system optimal scheduling based on multi-objective particle swarm algorithm

For the current problems that economic benefit and energy saving can not be considered in the existing optimal scheduling schemes of wind-solar-diesel-storage system, this paper proposes an optimal scheduling scheme of this system based on multi-objective particle swarm optimization. The mathematical models of active power output of wind turbines, solar turbines and energy storage units of wind-solar-diesel-storage system are established respectively. According to the historical data of wind power generation and photovoltaic power generation, the power prediction data of corresponding prediction models are adopted respectively On this basis, the objective function is constructed by minimizing the operating cost of the system and maximizing the wind-solar consumption ratio, and the system active power, energy storage battery capacity and diesel generator power are taken as constraints Using multi-objective particle swarm optimization algorithm to solve the output of each unit in the system. Simulation results illustrate the optimization scheme can well achieve the target of taking into account both economic benefits and energy saving in the optimal scheduling of wind-solar-diesel-storage system.

Optimal scheduling for charging and discharging of electric vehicles based on deep reinforcement learning

The growing scale of electric vehicles (EVs) brings continuous challenges to the energy trading market. In the process of grid-connected charging of EVs, disorderly charging behavior of a large number of EVs will have a substantial impact on the grid load. Aiming to solve the problem of optimal scheduling for charging and discharging of EVs, this paper first establishes a model for the charging and discharging scheduling of EVs involving the grid, charging equipment, and EVs. Then, the established scheduling model is described as a partially observable Markov decision process (POMDP) in the multi-agent environment. This paper proposes an optimization objective that comprehensively considers various factors such as the cost of charging and discharging EVs, grid load stability, and user usage requirements. Finally, this paper introduces the long short-term memory enhanced multi-agent deep deterministic policy gra dient (LEMADDPG) algorithm to obtain the optimal scheduling strategy of EVs. Simulation results prove that the proposed LEMADDPG algorithm can obtain the fastest convergence speed, the smallest fluctuation and the highest cumulative reward compared with traditional deep deterministic policy gradient and DQN algorithms.

Research on Optimal Scheduling Strategy of Microgrid Considering Electric Vehicle Access

The random output of renewable energy and the disorderly grid connection of electric vehicles (EV) will pose challenges to the safe and stable operation of the power system. In order to ensure the reliability and symmetry of the microgrid operation, this paper proposes a microgrid optimization scheduling strategy considering the access of EVs. Firstly, in order to reduce the impact of random access to EVs on power system operation, a schedulable model of an EV cluster is constructed based on the Minkowski sum. Then, based on the wavelet neural network (WNN), the renewable energy output is predicted to reduce the influence of its output fluctuation on the operation of the power system. Considering the operation constraints of each unit in the microgrid, the network active power loss and node voltage deviation are taken as the optimization objectives, and the established microgrid model is equivalently transformed via second-order cone relaxation to improve its solution efficiency. Based on network reconfiguration and flexible load participation in demand response, the economy and reliability of system operation are improved. Finally, the feasibility and effectiveness of the proposed method are verified based on the simulation examples.

Source-load uncertainty-based multi-objective multi-energy complementary optimal scheduling

The uncertainty of the source-load data, accompanied by the contradiction between different goal orientations, poses a challenge to the decision-making of the scheduling scheme. To solve the issue of multi-objective optimal scheduling under the condition of source-load uncertainty, this paper proposes a multi-objective multi-energy complementary optimal scheduling scheme based on source-load uncertainty. In the proposed method, four main steps: uncertainty analysis of the source-load data, design of multi-energy complementary scheduling scheme, optimal calculation of the scheduling scheme, and multi-scenario analysis, are involved. In addition, the effect of the source-load prediction on the optimal scheduling scheme is further analyzed for management implications. In the empirical analysis, the source-load data with 15-min intervals is introduced as the sample data, and different optimization algorithms and compromise solution determination methods are selected for comparative analysis. Compared with other optimization algorithms, the proposed method has an average decrease of 22.699%, 7.587% and 22.149% in the total cost of generation (TCG), the spinning reserve cost (CSR) and the carbon emission (CE), respectively, and the average increase in the rate of new energy generation (RNE) is 11.969%. The empirical analysis shows that the proposed method outperforms all benchmark methods, which can provide valuable insights for intraday rolling scheduling under the condition of source-load uncertainty and multi-objective optimization.

Risk-limiting dispatching strategy considering demand response in multi-energy microgrids

Multi-energy microgrids deploy distributed power sources with flexible generation characteristics to meet the changing needs of users. Because they comprise renewable as well as traditional energy sources, they can play an important role in the transition to clean and efficient low-carbon power generation. However, both the complexity of coordinating different power sources and random fluctuations of renewable-energy generation capacity pose significant challenges. To solve these problems, a risk-limiting dispatching strategy for multi-energy microgrids is proposed that considers net load-demand response. First, a bi-directional demand-response mechanism is introduced, and a flexible load is used as a dispatchable resource to optimize the net load curve of the system. Second, entropy-based reliability modeling is conducted to address the multiple uncertainties in system operation. Third, a risk-limiting dispatching model of the multi-energy microgrid is established that considers three optimization objectives: economy, environmental protection, and reliability. Finally, the NSGA-III algorithm is used to obtain the set of optimal solutions for the model, and a compromise solution that meets the system operation requirements is selected based on the technique for order preference by similarity to an ideal solution (TOPSIS) with a criteria importance though intercriteria correlation (CRITIC) and analytic hierarchy process (AHP, CRITIC–AHP) hybrid assignment. The simulation results show that the proposed optimal scheduling strategy improves the operational reliability index while ensuring system economy and environmental protection. This study provides a new approach for controlling system risk and ensuring continuous energy-supply capability under uncertain conditions.

Tariff-Based Optimal Scheduling Strategy of Photovoltaic-Storage for Industrial and Commercial Customers

Photovoltaic (PV) power generation exhibits stochastic and uncertain characteristics. In order to improve the economy and reliability of a photovoltaic-energy storage system (PV-ESS), it is crucial to optimize both the energy storage capacity size and the charging and discharging strategies of the ESS. An optimal scheduling model for PV-ESS is proposed in this paper, comprehensively considering factors in terms of energy cost and charging/discharging constraints of the PV-ESS. Moreover, the model employs a particle swarm optimization-backpropagation (PSO-BP) neural network to predict the PV power using historical generation data from a factory in Xiamen. The proposed two PV-ESS scheduling strategies are compared under three weather conditions. In the demand management strategy, the ESS can flexibly respond to different weather conditions and load demand changes, and effectively reduce the electricity cost for users.

Medium- and long-term interval optimal scheduling of cascade hydropower-photovoltaic complementary systems considering multiple uncertainties

An effective way to promote the consumption of renewable energy resource generation is by forming a joint power supply system between photovoltaic (PV) power plants widely distributed along a river basin and large-scale cascade hydropower plants, using shared transmission lines to transmit power to the power grid system. However, renewable energy resource generation has significant prediction uncertainty and can flood into the power grid, which is detrimental to its safe and stable operation. To solve the above issues, this paper proposes an improved interval optimization method to model the uncertainties of the inflow runoff of a cascade hydropower plant and the photovoltaic power output. On this basis, a medium- and long-term interval optimization scheduling model is established for a cascade hydro-PV complementary system based on extreme scenarios. This model does not need to obtain an accurate probability density distribution for renewable energy, and can realize the overall optimization of runoff-PV uncertainty scenarios within a certain range, taking into account the economy and robustness of system operation. In order to ensure the solution efficiency, the original model is transformed into a mixed integer linear programming (MILP) model by a variety of deterministic transformation methods and linearization techniques. The case study shows that: 1) Under the premise that the system power output is relatively stable, the power curtailment of the interval optimized scheduling scheme proposed in this paper is reduced by 47.5% compared with the robust scheduling scheme. The ability to cope with extreme scenarios is also significantly improved compared with the deterministic scheduling scheme. 2) There is a game relationship between the robustness and economy of the obtained scheduling scheme. When the fluctuation interval width of random variables increases from 20% to 30% and 40%, the corresponding total power generation decreases by 2.4% and 4.7%, respectively. 3) A large penalty factor is conducive to reducing the amount of abandoned hydropower, but in general, the penalty factor and ecological flow level have no obvious influence on the dispatching results, and the values of both can be reasonably determined according to the actual operation requirements.

A coarse- and fine-grained niching-based differential evolution for multimodal optimization problems and its application in multirobot task allocation

Multimodal optimization problems (MOPs) need to locate the global optima as many as possible and refine the accuracy of the identified optima as high as possible. How to divide niches reasonably and search solutions effectively are two difficult problems facing MOPs. In this paper, a coarse- and fine-grained niching-based differential evolution (CFNDE) algorithm with micro-search strategy is proposed to divide population reasonably and search the optima effectively, which mainly includes four contributions. Firstly, a framework of coarse- and fine-grained niching (CFN) strategy is proposed, where the coarse-grained clustering method provides the information of clusters to the fine-grained clustering method to accurately divide the population into niches. Secondly, a micro-search-based mutation (MSM) strategy is proposed to accelerate the convergence of population, which searches for the optimal solution in the range between the current individual and the nearest individual. Thirdly, a multi-level local search (MLLS) strategy that incorporates the cluster-level and population-level search mechanisms is proposed to refine the accuracy of solutions. Finally, a stagnation detection and re-search (SDR) strategy is proposed to re-search the local optima in evolution. We compare CFNDE with the-state-of-art multimodal optimization algorithms on the widely used benchmark CEC’2013. The experimental results show that CFNDE performs better than or similar to some recently proposed algorithms in 17 out of 20 tested functions. Besides, the proposed CFNDE is also applied to the multirobot task allocation problems and CFNDE provides multiple completely different optimal scheduling schemes to deal with emergencies.

Research on optimal configuration of park-level multi-energy complementary system with multiple evaluation indexes

At present, energy shortages are becoming increasingly severe, and the concept of park level multi energy complementary systems (MECS) has provided direction for sustainable energy development. In recent years, how to improve the economy and reliability of multi energy complementary systems has become a research hotspot in this field. In this paper, a two-layer optimal scheduling strategy is proposed to allocate the capacity of various energy equipment in the park, considering the comprehensive energy self-sufficiency rate, comprehensive energy utilization rate and energy shortage expectation. The proposed capacity allocation scheme can effectively improve the economy of MECS in the park. Finally, the effectiveness and practicability of the algorithm are verified by simulation analysis.

An energy dispatch optimization for hybrid power ship system based on improved genetic algorithm

Due to the energy crisis and environmental deterioration, the emerging hybrid energy ship power system gradually replaced the traditional ship power system to keep environmental friendliness by employing the clean energy. However, the increase of energy storage and photovoltaic generation system brings enormous challenge to the optimization scheduling of hybrid energy ship power system. For this reason, an improved genetic algorithm-based optimal scheduling strategy for the hybrid energy ship power system is developed in this paper. Firstly, a novel hybrid energy ship power system model including the diesel generator, energy storage system, propulsion system, dynamic load and photovoltaic power generation device is constructed under the constraint of energy efficiency and greenhouse gases emissions. Considering the various navigation situations that the ship may encounter, such as photovoltaic power generation limit in extreme weather and diesel generator power change in load shedding, the corresponding scheduling optimization problems for the hybrid energy ship power system are established. Under the cost and gas emission constraints, an improved genetic algorithm-based scheduling optimization algorithm is proposed. By introducing the nonlinear parameter change model in crossover and mutation operator, the performance of improved genetic algorithm can be enhanced, such as convergence speed and global optimization ability. Compared with current works, the proposed scheduling optimization strategy can achieve the lowest cost while reducing environmental impacts. Finally, simulation results under the given navigation cases demonstrate the superiority of the proposed improved genetic algorithm-based scheduling optimization strategy.

Dynamic scheduling of hydrogen-containing integrated energy systems considering multi-energy transactions

With the continuous development of hydrogen energy production and storage technology, hydrogen energy has gradually become one of the clean energy sources with acceptable cost. The hydrogen decarbonization process of natural gas pipeline transportation and its end energy units such as fuel cells is gradually accelerated. For this reason, considering a variety of energy markets, this paper establishes the operation scheduling model of the integrated energy system (IES) of combined cooling, heating, and power (CCHP) supply with the solid oxide fuel cell (SOFC) as the core which is compared with the traditional gas turbine cogeneration system in the economy and carbon emission reduction and introduces the model predictive control (MPC) algorithm to solve the rolling optimal scheduling scheme. The scheduling results show that the addition of hydrogen conversion units can effectively solve the phenomenon of light abandonment. Under the current energy price, when the proportion of photovoltaic (PV) installed capacity is greater than 0.33, the energy storage potential of hydrogen conversion units can be fully utilized and the economic benefits of SOFC co-production are better than gas turbines. After taking into account the high carbon emission factor of the power grid, there is no big difference in the carbon emission costs of the two cogeneration methods. When the carbon emission factor of the power grid is lower than 0.3 and the hydrogen production efficiency is higher than 75%, the carbon emission reduction benefits of SOFC have obvious advantages.

Optimal operation of smart energy hub considering high-temperature heat and power storage

Due to the impacts of high intermittent renewable resources, increasing their penetration in the generation sector of energy systems is still challenging. In this regard, the integration of different energy networks can play a key role in dealing with this challenge. This paper focuses on the analysis and optimization of multi-generation energy storage (MGES) system’s performance and investigates the role of this type of energy storage in the operation of future smart energy systems by considering 100% green energy goals. Hence, this paper first introduces high-temperature heat and power storage (HTHPS) system, as a novel MGES unit, for a local integrated energy system (IES) with different energy carriers. Then, a novel optimal energy scheduling scheme for this system is presented by considering the energy hub concept. The proposed IES is introduced as a Smart Energy Hub (SEH) which supplies local energy demands through local renewable resources generation, as well as upstream energy networks, by considering a competitive energy market. Finally, the performance of the proposed SEH is investigated by simulating different case studies and its effectiveness is proved in increasing the renewable generation penetration.

Optimization model for home energy management system of rural dwellings

In the context of the increasing popularity of distributed energy, this paper proposes a home energy management system method considering the working state of flexible load to deal with the problem of mismatch between home energy supply and demand. Specifically, in this paper, the flexible load in the system is studied, and the model of flexible load and its working state are established. On this basis, combined with the real-time electricity price of the grid, the model of home energy management system is constructed to minimize the electricity purchase cost and improve the utilization rate of renewable energy, the genetic algorithm is used to solve it, and the influence of time step, flexible load ratio and whether to use energy storage equipment on the optimization degree of the model are analyzed. The research results show that, while meeting the requirements of human comfort and electricity demand, the daily electricity purchase cost of the system with flexible load scheduling is 22.5% lower than that of the system without flexible load scheduling, and the utilization rate of renewable energy is increased by 16.5%. In terms of model optimization, shortening the prediction time step, increasing the proportion of flexible load and using energy storage equipment can further reduce the electricity purchase cost. The case study confirms the effectiveness of the proposed home energy management system model, which can provide an efficient optimal scheduling scheme for household energy saving.