Multi-objective Optimal Scheduling Research Articles

Multi-objective optimal scheduling model with IGDT method of integrated energy system considering ladder-type carbon trading mechanism

In order to meet the challenge of global low-carbon development, the concept of integrated energy system (IES) has been proposed and demonstrated, and its low-carbon operation mode is the research hotspot at present. In this paper, a multi-objective optimal scheduling model considering the participation of park-level IES (PIES) in the carbon market is proposed, which takes into account the multiple uncertainties on the renewable energy and load. Firstly, for the PIES operator (PIESO) participate in the carbon market, this paper introduces the ladder-type carbon trading mechanism. Then, for the uncertainties of the renewable energy and load, this paper carries out uncertainty modeling from the perspective of PIESO risk aversion and risk preference based on IGDT method. Finally, a multi-objective optimal scheduling model including economic and environmental objective is established. The actual PIES is selected for case study, and the results show that the operating profit of PIESO under the risk aversion strategy decreases by 5.91%, the operating profit of PIESO under the risk seeking strategy increases by 6.12%. Aiming at the impact of ladder-type carbon trading on system operation profits and carbon emissions, three scenarios were set, including whether to introduce a ladder-type carbon trading mechanism, changes in the initial price and the tradable carbon emission ratio. The results show that compared with the traditional carbon trading mechanism, the introduction of the ladder-type carbon trading mechanism reduces carbon emissions by 2.73%, and the setting of reasonable ladder-type carbon trading price and tradable carbon emission ratio can significantly reduce the carbon emissions, improve operating profit of PIES.

Multi-objective optimization for scientific workflow scheduling based on Performance-to-Power Ratio in fog–cloud environments

The ever-expanding processing of large-scale scientific workflow applications has increased power consumption and reliability costs. However, considering the complexity of computational data processing in fog–cloud computing, existing workflow scheduling techniques conceived for traditional distributed systems is limited and powerless. Therefore, for profit-driven cloud service providers, minimizing power consumption and maximizing scheduling reliability of scientific workflows is a pressing concern. In this paper, we model the scientific workflow scheduling as a multi-objective optimization problem to compromise the conflicting energy consumption and scheduling reliability solution in two steps. The first step places the tasks with lower computational requirements on fog resources and computationally complex tasks on cloud resources, avoiding resources with high failure rates. In the second step, we adapt the Reliability-Aware stepwise Performance-to-Power Ratio procedure to further reduce energy consumption by sampling the machine’s utilization levels with a distinguished Performance-to-Power Ratio (PPR). The PPR is defined as the calculation of the number of transactions performed at a specific time divided by the active power consumed. Simulation results under synthesized and real-world workflow applications demonstrated that dis-RMEE achieved considerably higher reliability values under the minimized energy constraint compared with some opponent workflow mapping algorithms.

Multi-Objective Optimal Source–Load Interaction Scheduling of Combined Heat and Power Microgrid Considering Stable Supply and Demand

With the development of smart grids, it has become possible to take demand-side resource utilization into account to improve the comprehensive benefits of combined heat and power microgrids (CHP-MGs). In order to improve the benign interaction between the source and the load of the system, the source side decouples the thermoelectric linkage through energy storage devices and improves the system multi-energy supply capacity by introducing various energy flow forms of energy devices. On the demand side, considering the elasticity of electric heating load and the diversity of heating mode, an integrated demand response (IDR) model is established, and a flexible IDR price compensation mechanism is introduced. On this basis, aiming at the optimal stability of supply and demand and the minimum operating cost of the system, a multi-objective optimal operation model of combined heat and power source–load interaction is constructed, taking into account the user satisfaction with energy consumption and the internal equipment load constraints of the system. Finally, an improved multi-objective optimization algorithm is used to solve the model. The analysis of the algorithm shows that the source–load interaction multi-objective optimal scheduling of the cogeneration microgrid considering the stability of supply and demand can effectively improve the stability of supply and demand and the economy of the system.

Multi-Objective Optimal Scheduling of CHP Microgrid Considering Conditional Value-at-Risk

A combined heating and power (CHP) microgrid has high flexibility and economy, but the output of renewable energy is uncertain. Meanwhile, excessive flexible load adjustment in the demand response process will increase user dissatisfaction. In order to solve the above problems, this paper quantifies uncertainty with the conditional value-at-risk (CVaR) of relative disturbance. Additionally, a multi-objective optimal scheduling model that takes into account both the operating economy and the demand-side power consumption satisfaction is established. In order to solve the multi-objective mixed-integer nonlinear programming problem well, we propose an improved sparrow search algorithm (ISSA), which solves the problem that the sparrow search algorithm (SSA) is prone to low accuracy, insufficient in population diversity and easy to be trapped in local optimum. Combined with the non-dominated solution ranking method, ISSA has the ability of multi-objective optimization. Finally, simulation on a typical CHP microgrid is performed. The optimization results under different confidence levels and risk preference coefficients are compared and analyzed. When the risk preference coefficient is 0.1, 2 and 5, the minimum rotating reserve capacity is 75.17 kW, 82.83 kW, and 105.70 kW in the electric part and 40.08 kW, 59.89 kW, and 61.94 kW in the thermal part. The effectiveness of the proposed CVaR of relative disturbance is verified.

Multi-objective optimization of micro-energy network considering exergy efficiency

Compared with energy networks that only distribute a single kind of energy, the unique feature of micro-energy networks lies in the efficient and coordinated utilization of heterogeneous energy. To measure the quality of energy and to compare the capability of different energy sources or systems, exergy is a generally accepted tool in thermodynamics. Therefore, it is of great significance to introduce exergy to analyze energy utilization in quality. First, based on exergy analysis of a micro-energy network, a multi-objective optimal scheduling strategy considering exergy efficiency and economic costs is proposed, and a multi-objective optimal scheduling model is established. Second, the specified weights cannot be adjusted flexibly during the scheduling process; the optimal model can update the weights hourly and find the multi-objective optimal solution. The hunting algorithm is used to solve the optimization problem of this scheduling model, which has multiple constraints and variables. Finally, the simulation results show that the operating cost of the multi-objective scheduling model is reduced by 3.93% in summer, 7.87% in winter, and the overall exergy efficiency of the proposed model is increased by 1.92% in summer, 2.46% in winter, compared to single-objective optimization models. The results prove that the proposed multi-objective optimal scheduling strategy is effective and feasible.

Multi-objective optimization scheduling for manufacturing process based on virtual workflow models

Currently, processing time, energy consumption and processing quality are three significant optimization objectives for manufacturing process. The variety of optimization objectives and the constraints of processes make the production scheduling an NP-hard problem. To improve processing quality, feedback processing is requisite in productive process, but the nonlinearity of feedback process causes further scheduling complexity. The purpose of this research is to realize the multi-objective optimization scheduling of manufacturing process with feedback process. To solve these problems, a virtual workflow modeling method for parallel manufacturing process of manifold varieties of jobs is proposed in this study, and based on the models, a Multi-Objective Virtual Workflow Scheduling Algorithm (MOVWSA) is contributed. In MOVWSA, the genetic evolution based on two-dimensional chromosome coding and weighted elite retention metrics is employed to select processing equipment for processes, and a sequence-selective strategy is proposed to specify processing order and starting time of each process. It is shown from the comparative test results on flexible job shop scheduling benchmark instances (MK01–MK10) that the proposed MOVWSA can provide more dominant static solutions for multi-objective optimization scheduling. The simulation test illustrates that MOVWSA with virtual workflow modeling is capable of dynamically adjusting the processing plans when reprocessing events occur to ensure the stability of processing with makespan and energy consumption. Consequently, the method contributed in this paper achieves the static and dynamic multi-objective optimization scheduling for manufacturing process with nonlinear feedback process by the two mechanisms of virtual modeling and evolutionary optimization.

Multi-objective optimal scheduling of microgrid with electric vehicles

With the increasing global attention to environmental protection, microgrids with efficient usage of renewable energy have been widely developed. Currently, the intermittent nature of renewable energy and the uncertainty of its demand affect the stable operation of a microgrid. Additionally, electric vehicles (EVs), as an impact load, could severely affect the safe dispatch of the microgrid. To solve these problems, a multi-objective optimization model was established based on the economy and the environmental protection of a microgrid including EVs. The linear weighting method based on two-person zero-sum game was used to coordinate the full consumption of renewable energy with the full bearing of load, and balance the two objectives better. Moreover, the adaptive simulated annealing particle swarm optimization algorithm (ASAPSO) was used to solve the multi-objective optimization model, and obtain the optimal solution in the unit. The simulation results showed that the multi-objective weight method could diminish the influence of uncertainty factors, promoting the full absorption of renewable energy and full load-bearing. Additionally, the orderly charging and discharging mode of EVs could reduce the operation cost and environmental protection cost of the microgrid. Therefore, the improved optimization algorithm was capable of improving the economy and environmental protection of the microgrid.

An Improved Genetic Algorithm-Based Traffic Scheduling Model for Airport Terminal Areas

This paper takes the airport terminal area as the main research content and combines genetic algorithm with airport terminal area analysis theory to analyze and study the traffic scheduling in the airport terminal area. Based on the study of traditional traffic scheduling techniques and key techniques of genetic algorithms, this paper participates in the actual project of genetic algorithm-based traffic scheduling, analyzes the requirements of the project, focuses on the design and implementation of the traffic scheduling algorithm module in the genetic algorithm-based traffic scheduling system, and conducts further research on the pathfinding by constraints submodule. In this paper, the flight approach and departure sequencing problem and runway allocation problem are the main research objects. The dynamic optimal scheduling model of flight approach and departure is established by considering the interests and demands of airlines and airports, and a new scheduling algorithm is proposed. In this paper, a brief introduction to the airport terminal area is given, and the feasibility of the approach/departure optimal scheduling is introduced from the perspective of airlines with a long-range parallel two-runway airport as the research background. Secondly, through the analysis of the flight approach and departure process and the study of the approach and departure cooperative optimization strategy, a single-runway flight approach and departure traffic scheduling model under the joint sequencing strategy is established with the optimization objective of minimizing the total flight delay time, and the model is solved by using the sliding time window algorithm. Then, based on the single-runway scheduling model, a multirunway multiobjective flight optimal scheduling model is established with the objectives of minimizing total delay time, increasing runway throughput per unit time and fairness of flight delay time allocation, and a dynamic algorithm (STW-GA) combining sliding time window algorithm and dual-structured chromosome genetic algorithm is proposed to solve the model.

Study on multi-objective optimal operation of Xiaolangdi Reservoir based on NNIA algorithm

The contradiction between the reservoir and the target is becoming more and more prominent, and the current reservoir scheduling method is difficult to well support the regional social and economic development. This paper takes the Xiaolangdi Reservoir on the mainstream of the Yellow River as the research object, establishes a dual-objective and three-objective reservoir optimal operation model, uses NNIA, MOPSO and FSS-NNIA to solve the dual-objective reservoir optimal operation model, and validates FSS-NNIA through quantitative analysis for the rationality and effectiveness of the system, FSS-NNIA is used to solve the three-objective reservoir optimal operation model, and to explore the competitive game relationship between the three objectives. The results show that: (1) Compared with NNIA and MOPSO algorithms, FSS-NNIA has a wide range of non-inferior solution distribution, it has advantages in uniformity and stability, and higher-quality Pareto-Front curves can be obtained; (2) The different performance indexes showed the superiority of the FSS-NNIA compared to the other algorithms. For example, the hypervolume (HV) of FSS-NNIA is 0.116 and 0.0016 larger than MOPSO and NNIA; the non-dominated solutions of FSS-NNIA is 71 and 31 more than MOPSO and NNIA. Also, the computational time for the FSS-NNIA was 27 s and 5 s less than MOPSO and NNIA, respectively; (3) Solving the three-objective optimization scheduling model by FSS-NNIA verifies that there is a competitive relationship between the three objectives, and uses the three-dimensional projection method to obtain the transformation efficiency distribution law of any two objectives. The research results provide a new solution idea and method for reservoir multi-objective optimization scheduling, which has important theoretical significance for supplementing and improving the theory and method of the reservoir multi-objective scheduling, and has been important guiding significance and application value for the comprehensive utilization and scheduling operation of Xiaolangdi Reservoir.

Dynamic multiobjective optimization and multivariate analysis for power generation scheduling of the diesel generators in dynamically positioned vessels

Economic and environmental topics together with the higher performance of marine vessels have been becoming more demanding in recent years. This work aims to investigate the optimal operation of diesel generators in the dynamically positioned vessels. Due to the continuous external power demand, the dynamic multiobjective optimization model for optimal scheduling of the diesel generators is generated, taking the fuel consumption and greenhouse gas emission into consideration simultaneously. The present study proposes a 2-phase analysis framework for each time step. A newly developed meta-heuristic optimization algorithm, multi-objective ant lion optimizer (MOALO) is adopted to find the Pareto set in Phase-I. A necessary decision-making procedure based on a simple additive weighting (SAW) approach is utilized to find the final compromise solution in Phase-II. Furthermore, multivariate analysis (MVA) methods are employed to mine the historical features of economic and environmental performances as well as the loadings of all generators. Self-organizing mapping (SOM) together with cluster analysis is utilized to study the relationships among data samples. Multidimensional scaling (MDS) is adopted to examine the relationships among these attributes. An oil rig platform equipped with 8 diesel generators is selected as an illustrative example. The dynamic Pareto fronts and corresponding final compromise solutions obtained from SAW are provided. The effects of various weights upon the history of fuel consumption and emission are examined. The hidden information about the performances in history is studied through SOM and MDS. Results from the numerical example demonstrate that dynamic multiobjective optimization and multivariate analysis extend the application of optimization and data mining in the field of power scheduling for diesel generators in dynamically positioned vessels. The findings of this study add to the understanding of relationships among loads of the generators and corresponding economic and environmental features.

An effective multi-objective task scheduling and resource optimization in cloud environment using hybridized metaheuristic algorithm

In today’s world, cloud computing plays a significant role in the development of an effective computing paradigm that adds more benefits to the modern Internet of Things (IoT) frameworks. However, cloud resources are considered to be dynamic and the demands necessitated for resource allocation for a certain task are different. These diverse factors may cause load and power imbalance which also affect the resource utilization and task scheduling in the cloud-based IoT environment. Recently, a bio-inspired algorithm can work effectually to solve task scheduling problems in the cloud-based IoT system. Therefore, this work focuses on efficient task scheduling and resource allocation through a novel Hybrid Bio-Inspired algorithm with the hybridized of Improvised Particle Swarm Optimization and Ant Colony Optimization. The vital objective of hybridizing these two approaches is to determine the nearest multiple sources to attain discrete and continuous solutions. Here, the task has been allocated to the virtual machine through a particle swarm and continuous resource management can be carried out by an ant colony. The performance of the proposed approach has been evaluated using the CloudSim simulator. The simulation results manifest that the proposed Hybridized algorithm efficiently scheduling the task in the cloud-based IoT environment with a lesser average response time of 2.18 sec and average waiting time of 3.6 sec as compared with existing state-of-the-art algorithms.

Multi-objective risk-based optimal power system operation with renewable energy resources and battery energy storage system: A novel Hybrid Modified Grey Wolf Optimization–Sine Cosine Algorithm approach

This research work proposes a Hybrid Modified Grey Wolf Optimization–Sine Cosine Algorithm for the multi-objective optimal scheduling of hybrid power system taking into consideration the risk factor arising due to the intermittent/uncertain nature of the renewable power generation sources. The hybrid power system is modelled considering the thermal generation units, wind energy system, solar photo voltaic system, electric vehicle and battery energy storage system. The multi-objective optimization problem is proposed based on the simultaneous minimization of the total operating cost and system risk. The conditional value at risk is introduced as the risk index to analyse the system risk due to uncertainties in power deliveries by the renewable energy resources, electric vehicle and battery energy storage system during the scheduling process. The integral contribution of this research work focuses on the establishment an optimal generation schedule based on the combined optimization of the total operating cost and system risk. The simultaneous minimization of the operating cost and the risk index is performed with the multi-objective Hybrid Modified Grey Wolf Optimization–Sine Cosine Algorithm and has been used to develop a Pareto-optimal front. The implementation of the fuzzy min–max technique is opted to fetch the best compromised solution. The standard test systems of IEEE-30 bus and Indian-75 bus system are used to validate the potency of the proposed approach. Comparative analysis has been established to highlight the results obtained with the proposed approach is appreciable than other optimization techniques.

Research on multi-objective optimal scheduling of virtual power plant based on self-supplied coal-fired power plants

With the development of renewable energy, improving the absorption capacity of power grid has become a difficult problem. It is very important to establish virtual power plants based on self-supplied coal-fired power plant to coordinate renewable energy consumption, especially in Xinjiang Province, China. This paper studied the optimal scheduling of a virtual power plant including wind turbines, photovoltaic units, and energy storage equipment based on the self-supplied power plant. First, a mathematical model of the power and power generation cost of each unit inside the virtual power plant was established, and the demand response mechanism was introduced. Secondly, a multi-objective optimization model is established by considering the maximization of net income of virtual power plants, the minimization of system coal consumption and the maximization of user interruption load benefits, and the use of analytic hierarchy process to determine the weights of three objective functions, of which user interruption load benefits are used to reflect the enthusiasm of users to interrupt the load. Finally, the particle swarm algorithm is used to solve the model. The optimization results show that when the coal price rises, the net income of the system decreases, and even a loss occurs, however, the change in coal price has little effect on the Interrupted load of user. In addition, multi-objective optimization can improve the enthusiasm of users while ensuring the net income of the system, it proves that the model has a good optimization effect.

Risk-averse day-ahead generation scheduling of hydro–wind–photovoltaic complementary systems considering the steady requirement of power delivery

Optimizing day-ahead generation schedules of hydro–wind–photovoltaic (PV) complementary systems (HWPCSs) can help to promote the accommodation of wind and solar energies. However, it is challenging to formulate appropriate generation schedules for the large HWPCS that contains cascade hydropower plants, in particular, a steady requirement of power delivery is considered in the optimization model. To further improve complementary performance of the large HWPCS, we propose a risk-averse day-ahead generation scheduling approach that considers the steady requirement of power delivery. First, a representative scenario set is used to characterize forecast uncertainties of the wind and PV power. Then, a multi-objective optimal generation scheduling model with consideration of the operational risks of electricity curtailment and power shortage is proposed. Finally, a two-layer nested optimization framework is designed to derive the system’s generation schedule. The clean energy base in the upper Yellow River basin, China was selected as a case study. The results show that: (1) forecast uncertainties of wind and PV power are more likely to induce power shortage risk in summer and autumn, but to induce electricity curtailment risk in spring and winter; (2) without using extra constraint handling strategies, the proposed approach could directly yield a stair-shaped power delivery curve, which is good for long-distance power transmission applications; and (3) compared with a traditional method without considering the operational risks, the proposed generation scheduling approach could significantly reduce the comprehensive risk rate by 65% on average, while the cascade hydropower production and peak shaving performance are satisfactory. Therefore, the proposed approach is effective in guiding the day-ahead generation scheduling of the HWPCSs that contain cascade hydropower plants.

Multi-Objective Optimization of Clustering-Based Scheduling for Multi-Workflow on Clouds Considering Fairness

Distributed computing, such as cloud computing, provides promising platforms to execute multiple workflows. Workflow scheduling plays an important role in multi-workflow execution with multi-objective requirements. Although there exist many multi-objective scheduling algorithms, they focus mainly on optimizing makespan and cost for a single workflow. There is a limited research on multi-objective optimization for multi-workflow scheduling. Considering multi-workflow scheduling, there is an additional key objective to maintain the fairness of workflows using the resources. To address such issues, this paper first defines a new multi-objective optimization model based on makespan, cost, and fairness, and then proposes a global clustering-based multi-workflow scheduling strategy for resource allocation. Experimental results show that the proposed approach performs better than the compared algorithms without significant compromise of the overall makespan and cost as well as individual fairness, which can guide the simulation workflow scheduling on clouds.

Multi-Objective Optimal Scheduling of Household Appliances for Demand Side Management Using a Hybrid Heuristic Algorithm

Multi-Objective Optimal Scheduling of Household Appliances for Demand Side Management Using a Hybrid Heuristic Algorithm

Multi-objective Optimal Operation of Centralized Battery Swap Charging System with Photovoltaic

Electric vehicles (EVs) are widely deployed throughout the world, and photovoltaic (PV) charging stations have emerged for satisfying the charging demands of EV users. This paper proposes a multi-objective optimal operation method for the centralized battery swap charging system (CBSCS), in order to enhance the economic efficiency while reducing its adverse effects on power grid. The proposed method involves a multi-objective optimization scheduling model, which minimizes the total operation cost and smoothes load fluctuations, simultaneously. Afterwards, we modify a recently proposed multi-objective optimization algorithm of non-sorting genetic algorithm III (NSGA-III) for solving this scheduling problem. Finally, simulation studies verify the effectiveness of the proposed multi-objective operation method.

Multi-Objective Optimal Scheduling of Household Appliances for Demand Side Management Using a Hybrid Heuristic Algorithm

Multi-Objective Optimal Scheduling of Household Appliances for Demand Side Management Using a Hybrid Heuristic Algorithm

Multi-Objective Comprehensive Charging/Discharging Scheduling Strategy for Electric Vehicles Based on the Improved Particle Swarm Optimization Algorithm

To solve the problems that a large number of random and uncontrolled electric vehicles (EVs) connecting to the distribution network, resulting in a decrease in the performance and stability of the grid and high user costs, in this study, a multi-objective comprehensive charging/discharging scheduling strategy for EVs based on improved particle swarm optimization (IPSO) is proposed. In the distribution network, the minimum root-mean-square error and the minimum peak valley difference of system load are first designed as objective functions; on the user side, the lowest charge and discharge cost of electric vehicle users and the lowest battery loss cost are used as objective functions, then a multi-objective optimization scheduling model for EVs is established, and finally, the optimization through IPSO is performed. The simulation results show that the proposed method is effective, which enhances the peak regulating capacity of the power grid, and it optimizes the system load and reduces the user cost compared with the conventional methods.

Multi-objective Optimal Scheduling Method for Photovoltaic Charging Station of Electric Vehicle

Multi-objective Optimal Scheduling Method for Photovoltaic Charging Station of Electric Vehicle