Optimal Scheduling Strategy Research Articles

Day-Ahead Scheduling Strategy Optimization of Electric–Thermal Integrated Energy System to Improve the Proportion of New Energy

The coordinated use of electricity and a heat energy system can effectively improve the energy structure during winter heating in the northern part of China and improve the environmental pollution problem. In this paper, an economic scheduling model of an electric–thermal integrated energy system, including a wind turbine, regenerative electric boiler, solar heat collection system, biomass boiler, ground source heat pump and battery is proposed, and a biomass boiler was selected as the auxiliary heat source of the solar heat collection system. A mixed integer linear programming model was established to take the operating cost of the whole system as the target. A day-ahead optimization scheduling strategy considering the demand side response and improving new energy consumption is proposed. In order to verify the influence of the coordinated utilization of the flexible load and energy storage equipment on the optimal scheduling in the model built, three scenarios were set up. Scenario 3 contains energy storage and a flexible load. Compared with scenario 1, the total cost of scenario 3 was reduced by 51.5%, and the abandonment cost of wind energy was reduced by 43.3%. The use of a flexible load and energy storage can effectively reduce the cost and improve new energy consumption. By increasing the capacity of the energy-storage device, the wind power is completely absorbed, but the operation and maintenance cost is increased, so the capacity of energy storage equipment is allocated reasonably according to the actual situation.

An optimized scheduling strategy combining robust optimization and rolling optimization to solve the uncertainty of RES-CCHP MG

The popularization of combined cooling, heating and power microgrid containing renewable energy sources (RES-CCHP MG) can effectively alleviate the energy crisis and reduce the emission of air pollutants. However, uncertainties in renewable energy generation and load negatively affect the operation of microgrid. To solve this problem, a multi-time scale optimal dispatch strategy combining robust optimization and rolling optimization is proposed. First, the price-based demand response is introduced to establish the RES-CCHP MG model that is more in line with practical application requirements. In the day-ahead dispatching stage, robust adjustment coefficients are introduced to improve the defects of traditional robust optimization which is conservative, and a two-layer robust optimization model is established to improve the ability to resist risks. In the intra-day dispatching stage, in order to reduce the impact of day-ahead prediction error and real-time power fluctuation, the hierarchical rolling optimization model based on model prediction control is established to face the situation that different loads have different response speeds. The simulation results show that compared with the traditional optimal dispatching strategy, the operating cost and the peak-to-valley difference of electric load are reduced by 5.4% and 14.7%, respectively, and the utilization of renewable energy is increased by 4.8%.

Research on Demand-Based Scheduling Scheme of Urban Low-Altitude Logistics UAVs

Aiming at the problem of the scheduling scheme of urban low-altitude logistics unmanned aerial vehicles (UAVs), this paper establishes a demand-based UAV scheduling scheme model using an improved simulated annealing algorithm, taking minimizing the cost of distribution as the objective function and considering restrictions such as UAV performance constraints, airspace constraints, and distribution constraints, among others. For verification, actual express data and airspace constraints in Shanghai are taken as examples. Two urban air traffic networks are constructed using road and building data. The analysis results show that the planning scheme of this model is superior to other forecasting models in terms of delivery cost and delivery time. In addition, this model can flexibly calculate the optimal scheduling scheme under the constraints of multiple parameters, according to the requirements of delivery volume, delivery distance, UAV performance, etc.

Risk aware decomposition of online scheduling for large flexible consumers considering the age of information

The stability and security of the power system is significantly influenced by large electricity consumers, especially those with flexible demand, whose electricity purchase scheduling has been extensively studied. However, the high penetration of renewable energy and various widespread distributed storage systems have increased uncertainty of the power system, making it difficult to plan effective day-ahead electricity purchase for major electrical consumers. Online scheduling is therefore required so that the surplus can be sold to and the insufficient can be purchased from the real-time market at the lowest electricity cost. To this end, we formulate a multi-stage online scheduling model for large power consumers with flexible demand. To improve the efficiency of online scheduling, we decompose the problem into multiple single-stage sub-problems. Aimed at each single-stage scheduling, an online algorithm is designed to ensure the performance of optimal scheduling strategies maintains at a level comparable to the offline fixed strategies obtained from an oracle who has complete knowledge of the problem’s settings. We prove that our proposed online energy schedule algorithm is able to achieve O(T) regret bound for the single-stage online scheduling problem. To further highlight the significance of data and improve the multi-stage performance of our online scheduling algorithm, we also involve the notion of age of information combined with regret bound to help determine the optimal interval length.

Optimal scheduling for electricity–gas integrated energy system under the carbon-neutrality goal

In light of China’s carbon-neutral policy and the Energy Internet, it is crucial to reduce the carbon emissions resulting from the operation of the electricity–gas integrated energy system (EGIES). To address this issue, this paper proposes an optimal scheduling strategy for EGIES in line with the carbon-neutrality goal. Firstly, the oxygen-enriched combustion power plant, equipped with a power to gas unit, is modeled to maximize its carbon capture ability. Next, an optimal scheduling model for EGIES is proposed, which considers the bonus-penalty ladder carbon price and the oxygen-enriched combustion power plant, with the aim of minimizing the total operation costs and carbon emissions of the EGIES. Finally, the proposed model is applied to an EGIES, consisting of a 33-bus electric distribution network and a 20-bus natural gas network. The simulation results demonstrate that the proposed method can reduce the operation costs of the EGIES by up to 22.5% and the carbon emissions by up to 75.1t when compared to existing models.

Distributed robust operation of integrated energy system considering gas inertia and biogas–wind renewables

Multiple uncertainties caused by renewable energy and loads bring challenges to the secure operation of integrated energy system (IES). Concurrently, the centralized control methods based on global information of IES cannot preserve the data privacy of each individual subsystem. To deal with these issues, this paper proposes a distributed two-stage robust optimization (TRO) scheduling strategy for IES considering gas inertia and biogas–wind renewables. To address the uncertainties of wind turbines output and electricity loads in IES, a TRO model is first established, and the column and constraint generation (C&CG) algorithm is employed to handle the min–max–min problem of TRO model. On this basis, a distributed cooperative scheduling framework based on the consensus-based alternating direction method of multipliers (C-ADMM) for IES is developed, which requires no overall system information but only local information. Additionally, natural gas pipelines that consider gas inertia are used as gas storage facilities, which provides IES with additional scheduling resources and flexibility. Furthermore, an adaptive step-size strategy is proposed to adjust the step-size of ADMM in real time, which reduces the dependence of ADMM performance on the initial parameters selection and improves its convergence performance. The simulation results in a three-subsystem joint IES are presented to demonstrate the effectiveness of the proposed distributed TRO scheduling strategy.

Optimal production scheduling with multi-round information interaction for demander-dominated decentralized scheduling problem

Demander-dominated market scenarios are becoming increasingly common owing to the emergence of alternative service providers and competitive market environment. However, these scenarios have not been considered in previous studies pertaining to decentralized scheduling problems. Thus, service providers cannot formulate optimal production scheduling schemes for such scenarios. In this study, we investigate a demander-dominated decentralized scheduling problem in which the demander can adopt the private-strategic behavior of transferring partial orders towards its alternative service providers. The aim of this study is to provide guidance to service providers for developing high-quality production scheduling solutions under asymmetric information. First, we design a multi-round information interaction mechanism with a learning strategy to realize information interaction. Subsequently, a metaheuristic algorithm termed MAM is developed based on the multi-round information interaction mechanism to solve the proposed problem. A problem-dependent initialization method and a solution generation method integrating the learning strategy are developed to improve the search efficiency. Experimental results indicate the usefulness of the initialization method and learning strategy. Based on a comparison with two well-established adapted algorithms, the effectiveness of the proposed algorithm is confirmed, particularly for instances with loose due dates. Furthermore, we analyze the effect of MAM on both the service provider and the demander by comparing it with traditional centralized approaches. Statistical results show that the proposed algorithm yields high-quality solutions for the service provider and that maintaining the confidentiality of private information is conducive to the demander, particularly when the due dates are tight.

Optimal Scheduling of Multi-energy System Considering Economy and Energy Conservation

In recent years, the optimal scheduling of multienergy has become the focus of the research. Against this background, this paper builds a model of a multienergy flow system of cooling, heating, and power, and advanced adiabatic compressed air energy storage (AA-CAES) is introduced to smooth wind power generation (WPG) and supply heating/cooling energy. Simulated annealing algorithm (SAA) is employed to energy-saving scheduling of the system with “exergy assessment” method. The energy-saving index and the exergy efficiency are compared in different cases. SAA is compared with the particle swarm optimization (PSO) algorithm in solving the optimal scheduling strategy. The cooling, heating, and power demands of an industrial park and WPG in typical days are employed to the simulation. The scheduling results of exergy input of SAA are far less than those of PSO in typical days of different seasons. The multienergy system without AA-CAES is also modeled, and energy-saving economic scheduling is carried out. The exergy efficiency of the system with AA-CAES is between 38% and 58% while the exergy efficiency of the system without AA-CAES is merely between 27% and 48%.

Multi-agent reinforcement learning enabled link scheduling for next generation Internet of Things

In next generation Internet of Things (NG-IoT) networks, numerous pieces of information are aggregated from the user devices and sensor nodes to the local computing units for further computing to support high-level applications. Those multitudinous transmission demands have raised new challenges for current link scheduling protocols. The centralized link scheduling protocols are inappropriate in some large-scale NG-IoT scenarios. The previously distributed link scheduling uses the randomized transmission scheme to avoid interference, making it hard to utilize the bandwidth resources fully. The multi-agent machine learning (MAML) technique is a potential approach to finding the most optimal link scheduling strategy. At the same time, the over-large state space will take a long time for them to approach the optimal solution, which reduces the practicality of the MAML. To fully utilize the bandwidth resource and improve the efficiency of link scheduling, this paper studies a multi-agent reinforcement learning enabled link scheduling problem. Different from the conventional MAML techniques that randomly select a state to do their exploration, in our multi-agent reinforcement learning algorithm, a good state is firstly obtained within polynomial time steps by executing a distributed and randomized sub-algorithm. We say a state is good if it is not far from the optimal state. Then, our multi-agent reinforcement learning scheme starts from the good state and does its exploration with an ɛ greedy scheme, which significantly reduces the time steps to get close to the optimal link scheduling strategy. Extensive simulations are conducted to investigate the performance of our work.

Hybrid JAYA algorithm for workflow scheduling in cloud

Workflow scheduling and resource provisioning are two of the most critical issues in cloud computing. Developing an optimal workflow scheduling strategy in the heterogeneous cloud environment is extremely difficult due to its NP-complete nature. Various optimization algorithms have been used to schedule the workflow so that users can receive Quality of Service (QoS) from cloud service providers as well as service providers can achieve maximum gain but there is no such model that can simultaneously minimize execution time and cost while balancing the load among virtual machines in a heterogeneous environment using JAYA approach. In this article, we employed the hybrid JAYA algorithm to minimize the computation cost and completion time during workflow scheduling. We considered the heterogeneous cloud computing environment and made an effort to evenly distribute the load among the virtual machines. To achieve our goals, we used the Task Duplication Heterogeneous Earliest Finish Time (HEFT-TD) and Predict Earliest Finish Time (PEFT). The makespan is greatly shortened by HEFT-TD which is based on the Optimistic Cost Table. We used a greedy technique to distribute the workload among Virtual Machines (VMs) in a heterogeneous environment. Greedy approach assigns the upcoming task to a VM which have lowest load. In addition, we also considered performance variation, termination delay, and booting time of virtual machines to achieve our objectives in our proposed model. We used Montage, LIGO, Cybershake, and Epigenomics datasets to experimentally analyze the suggested model in order to validate the concept. Our meticulous experiments show that our hybrid approach outperforms other recent algorithms in minimizing the execution cost and makespan, such as the Cost Effective Genetic Algorithm (CEGA), Cost-effective Load-balanced Genetic Algorithm (CLGA), Cost effective Hybrid Genetic Algorithm (CHGA), and Artificial Bee Colony Algorithm (ABC).

Day-ahead scheduling of the electrothermal integrated system considering multiple thermal inertia

For the problem of optimal scheduling of integrated energy systems involving wind power consumption, by mining multiple thermal inertia, a stochastic optimal scheduling strategy with consideration of mixed uncertainties is proposed. Firstly, the network thermal inertia is explored by considering the dynamic characteristics and thermal storage of heat network. Then the heat load flexibility constraint is introduced to model the thermal inertia of buildings. Finally heat pumps and heat storage are used as time-shiftable loads or sources, which are jointly modelled with CHP units to solve the peak-valley mismatch of electric and heat loads, and on this basis, the model is established. In this paper, the Barry Island’s case which is composed of 33-node thermal network coupled with 9-node grid system is used as an example for analysis, and results show that by exploring the multiple thermal inertia, both the complementarity and the flexible dispatch between multiple heterogeneous energy sources in integrated energy system can be realized.

Optimal scheduling strategy of the hydrogen-electric coupled system considering source-load uncertainty

Hybrid energy storage systems are increasingly envisaged to be used in the construction of microgrids to alleviate the intermittency of renewable energy output and achieve large-scale penetration into the power grid. The hydrogen-electric coupled system (HECS) combines long-term hydrogen storage with short-term battery storage and realizes the conversion of multiple energy flows through power to gas and gas to power. However, the optimal scheduling of such a hybrid energy storage system is more complex than a single one. Faced with this issue, this paper proposed a HECS scheduling method based on the storage degradation cost to solve the power distribution problem. Furthermore, to reduce the impact of source-load uncertainty, the improved k-medoids method is used to extract representatively typical and extreme scenarios from the annual data to evaluate the economy and reliability of the system operation. According to the simulation result, we can conclude that the proposed method achieves cost-optimal scheduling by maximizing hydrogen production in the on-grid mode and reducing the battery cycle cost in the off-grid mode. Compared with the state machine control strategy, the proposed method has lowered the operation cost in all simulation scenarios and shows its effectiveness. Finally, the results show that the operation cost can be reduced by applying a demand response program, especially in the off-grid mode.

Escope: An Energy Efficiency Simulator for Internet Data Centers

Contemporary megawatt-scale data centers have emerged to meet the increasing demand for online cloud services and big data analytics. However, in such large-scale data centers, servers of different generations are installed gradually year by year, making the data center heterogeneous in computing capability and energy efficiency. Furthermore, due to different processor architectures, complex and diverse load dynamic changing, business coupling, and other reasons, operators pay great attention to processor hardware power consumption and server aggregation energy efficiency. Therefore, the simulation and analysis of the energy efficiency characteristics of data center servers under different processor architectures can help operators understand the energy efficiency characteristics of data centers and make the optimal task scheduling strategy. This is very beneficial for improving the energy efficiency of the production system and the entire data center. The Escope simulator designed in this study can simulate the online quantity (placement strategy) of different types of servers in the data center and the optimal operating range of the servers. The purpose of this is to analyze the energy efficiency characteristics of all servers in the data center and provide data center operators with the energy efficiency and energy proportionality characteristics of different servers, improve server utilization, and perform reasonable scheduling. Through the simulation experiment of Escope, it can be proved that running the server at the highest energy efficiency point or running the server under full load cannot improve the energy efficiency of the entire data center. The simulation algorithm provided by Escope can select the optimal set of servers and their corresponding utilization. Escope can set up a variety of simulation strategies, and data center operators can simulate data center energy efficiency according to their own needs. Escope can also calculate the power cost savings of introducing new servers in the data center, which provides an essential reference for operators to purchase servers and design data centers.

Closed-loop scheduling optimization strategy based on particle swarm optimization with niche technology and soft sensor method of attributes-applied to gasoline blending process

Gasoline blending scheduling optimization can bring significant economic and efficient benefits to refineries. However, the optimization model is complex and difficult to build, which is a typical mixed integer nonlinear programming (MINLP) problem. Considering the large scale of the MINLP model, in order to improve the efficiency of the solution, the mixed integer linear programming - nonlinear programming (MILP-NLP) strategy is used to solve the problem. This paper uses the linear blending rules plus the blending effect correction to build the gasoline blending model, and a relaxed MILP model is constructed on this basis. The particle swarm optimization algorithm with niche technology (NPSO) is proposed to optimize the solution, and the high-precision soft-sensor method is used to calculate the deviation of gasoline attributes, the blending effect is dynamically corrected to ensure the accuracy of the blending effect and optimization results, thus forming a prediction-verification-reprediction closed-loop scheduling optimization strategy suitable for engineering applications. The optimization result of the MILP model provides a good initial point. By fixing the integer variables to the MILP-optimal value, the approximate MINLP optimal solution can be obtained through a NLP solution. The above solution strategy has been successfully applied to the actual gasoline production case of a refinery (3.5 million tons per year), and the results show that the strategy is effective and feasible. The optimization results based on the closed-loop scheduling optimization strategy have higher reliability. Compared with the standard particle swarm optimization algorithm, NPSO algorithm improves the optimization ability and efficiency to a certain extent, effectively reduces the blending cost while ensuring the convergence speed.

Integrated scheduling optimization of production and transportation for precast component with delivery time window

PurposeProduction and transportation of precast components, as two continuous service stages of a precast plant, play an important role in meeting customer needs and controlling costs. However, there is still a lack of production and transportation scheduling methods that comprehensively consider delivery timeliness and transportation economy. This article aims to study the integrated scheduling optimization problem of in-plant flowshop production and off-plant transportation under the consideration of practical constraints of customer order delivery time window, and seek an optimal scheduling method that balances delivery timeliness and transportation economy.Design/methodology/approachIn this study, an integrated scheduling optimization model of flowshop production and transportation for precast components with delivery time windows is established, which describes the relationship between production and transportation and handles transportation constraints under the premise of balancing delivery timeliness and transportation economy. Then a genetic algorithm is designed to solve this model. It realizes the integrated scheduling of production and transportation through double-layer chromosome coding. A program is designed to realize the solution process. Finally, the validity of the model is proved by the calculation of actual enterprise data.FindingsThe optimized scheduling scheme can not only meet the on-time delivery, but also improve the truck loading rate and reduce the total cost, composed of early cost in plant, delivery penalty cost and transportation cost. In the model validation, the optimal scheduling scheme uses one less truck than the traditional EDD scheme (saving 20% of the transportation cost), and the total cost can be saved by 17.22%.Originality/valueThis study clarifies the relationship between the production and transportation of precast components and establishes the integrated scheduling optimization model and its solution algorithm. Different from previous studies, the proposed optimization model can balance the timeliness and economy of production and transportation for precast components.

Energy saving and carbon reduction schemes for families with the household PV-BES-EV system

The household PV-BES-EV system (photovoltaic - battery energy storage - electric vehicle system) connected to the grid can guarantee the electricity demand of the household and facilitate the flexible trade with the grid to achieve the goals of cutting energy bills and reducing carbon emissions. In related issues, past studies often assume that renewable energy is both economical and environmentally friendly, and thus regarded it as the primary power source. While in fact, in some regions where the feed-in tariff is higher than the sales price at a certain time, households may be driven by arbitrage opportunities to frequently purchase electricity from the grid at the certain time to gain benefits, resulting in far greater carbon emissions than expected within the household measurement boundary. To avoid the environmental burden caused by pure economic incentives and to provide practical and sustainable solutions consistent with various energy price policies, the optimal scheduling schemes of the household PV-BES-EV system that considers both economic and environmental incentives for different scenarios solved by the genetic algorithm is proposed in this paper. Household data in Shanghai, China and Hamburg, Germany were selected for modelling, representing the dual-objective optimization scenario with arbitrage opportunities and the single-objective optimization scenario with sales price higher than feed-in tariff, respectively. Results show that for urban and rural residents in Shanghai, the energy bill and carbon emission would decline about 105%, 48% and 88%, 62%, respectively. As for Hamburg, the accordingly results would be 69% and 64%. In addition, based on the cost-effectiveness of the system in long-term, this paper puts forward policy recommendations that align with local conditions to further improve the economy of the system.

Improved Hungarian algorithm–based task scheduling optimization strategy for remote sensing big data processing

ABSTRACT With the development of remote sensing technology and computing science, remote sensing data present typical big data characteristics. The rapid development of remote sensing big data has brought a large number of data processing tasks, which bring huge challenges to computing. Distributed computing is the primary means to process remote sensing big data, and task scheduling plays a key role in this process. This study analyzes the characteristics of batch processing of remote sensing big data. This paper uses the Hungarian algorithm as a basis for proposing a novel strategy for task assignment optimization of remote sensing big data batch workflow, called optimal sequence dynamic assignment algorithm, which is applicable to heterogeneously distributed computing environments. This strategy has two core contents: the improved Hungarian algorithm model and the multi-level optimal assignment task queue mechanism. Moreover, the strategy solves the dependency, mismatch, and computational resource idleness problems in the optimal scheduling of remote sensing batch processing tasks. The proposed strategy likewise effectively improves data processing efficiency without increasing computer hardware resources and without optimizing the computational algorithm. We experimented with the aerosol optical depth retrieval algorithm workflow using this strategy. Compared with the processing before optimization, the makespan of the proposed method was shortened by at least 20%. Compared with popular scheduling algorithm, the proposed method has evident competitiveness in acceleration effect and large-scale task scheduling.

Optimal Scheduling Strategy of Regional Power System Dominated by Renewable Energy Considering Physical and Virtual Shared Energy Storage

In view of the current situation of the global energy crisis and environmental pollution, the energy industry transition and environmental governance are urgently needed. To deal with the problem above, the construction of a power system dominated by renewable energy (PSDRE) with wind turbine (WT), photovoltaic (PV), biomass power (BP), and other clean, low-carbon, renewable energy sources as the principal part has become a consensus all over the world. However, the random and uncertain power output of renewable energy will not only put pressure on the power system but also lead to the unreasonable and insufficient usage of renewable energy. In this context, the energy storage (ES) effects of flexible resources, such as physical energy storage of batteries and demand response (DR), are analyzed first. Next, a modeling method for the operational characteristics of physical and virtual shared energy storage (PVSES) in regional PSDRE (RPSDRE) is proposed. Finally, an optimal scheduling strategy for RPSDRE that considers PVSES is proposed to achieve coordination of WT, PV, PVSES, and other flexible resources. The case study on RPSDRE in Lankao county, Kaifeng city, Henan province of China verifies the effectiveness and practicability of the proposed strategy.

An online service provisioning strategy for container-based cloud brokers

Container-based cloud brokers sit between multiple cloud providers and users, renting virtual machines (VMs) from multiple cloud providers, provisioning services to users, and executing jobs in the form of containers. Current cloud providers offer two billing models for Infrastructure-as-a-Service (IaaS), including on-demand and reserved. Given the huge uncertainty of user requests, cloud brokers can combine two billing options to rent VMs to reduce costs while gaining the flexibility to handle short-term fluctuations in requests. Optimal scheduling and auto-scaling strategies depend on information about user requests over a long period of time in the future, which is often difficult to predict accurately. To solve this problem, this work proposes an online service provisioning strategy to optimize the cost of cloud brokers to satisfy user requests without requiring any future information. Our proposed strategy consists of an online container scheduling algorithm and an online reserved VM pool auto-scaling algorithm. It is shown theoretically that the two proposed online algorithms can achieve guaranteed competitive ratios of n+1n−1TT−3(μ+1) and 2−1T respectively. Eventually, the effectiveness of our proposed algorithms is validated with both simulated and multiple real user request datasets. Our proposed strategy can significantly reduce the cost of cloud brokers compared to several benchmarks.

Bi-layer game method for scheduling of virtual power plant with multiple regional integrated energy systems

Regional integrated energy system (RIES) can be connected with the virtual power plant (VPP) through power tie line, and participate in the VPP demand response dispatch. In order to improve the benefit of VPP and RIES, a bi-layer game method for scheduling of VPP with multiple RIES is proposed. The upper layer is the master-slave game between VPP and RIES-coalition. VPP guides RIES-coalition to formulate purchase and sale power quantity strategy, in response to the power demand scheduling of VPP by the power purchase and sale price incentive policy. The lower layer is cooperative game among RIES-coalition members. Within the RIES-coalition, a multiple energy optimization and coordination scheduling strategy was established to meet the “electrical/ heating/ gas” load demand of each RIES, and cooperation benefits are shared among members based on Shapley value method. CIQPSO algorithm is used to solve the Nash equilibrium point of the game model, namely, the optimal power price strategy of VPP and the optimal purchase and sale power quantity strategy of each RIES. The results of example show that the proposed strategy can improve the peak shaving and valley filling effect of VPP, maximize the benefit of RIES-coalition, and ensure the reliable operation of VPP.