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  • Open Access

    ARTICLE

    Optimal Scheduling Strategy of Source-Load-Storage Based on Wind Power Absorption Benefit

    Jie Ma1, Pengcheng Yue2, Haozheng Yu1, Yuqing Zhang3, Youwen Zhang1, Cuiping Li3, Junhui Li3,*, Wenwen Qin3, Yong Guo1

    Energy Engineering, Vol.121, No.7, pp. 1823-1846, 2024, DOI:10.32604/ee.2024.048225

    Abstract In recent years, the proportion of installed wind power in the three north regions where wind power bases are concentrated is increasing, but the peak regulation capacity of the power grid in the three north regions of China is limited, resulting in insufficient local wind power consumption capacity. Therefore, this paper proposes a two-layer optimal scheduling strategy based on wind power consumption benefits to improve the power grid's wind power consumption capacity. The objective of the upper model is to minimize the peak-valley difference of the system load, which is mainly to optimize the system… More >

  • Open Access

    ARTICLE

    Dynamic Economic Scheduling with Self-Adaptive Uncertainty in Distribution Network Based on Deep Reinforcement Learning

    Guanfu Wang1, Yudie Sun1, Jinling Li2,3,*, Yu Jiang1, Chunhui Li1, Huanan Yu2,3, He Wang2,3, Shiqiang Li2,3

    Energy Engineering, Vol.121, No.6, pp. 1671-1695, 2024, DOI:10.32604/ee.2024.047794

    Abstract Traditional optimal scheduling methods are limited to accurate physical models and parameter settings, which are difficult to adapt to the uncertainty of source and load, and there are problems such as the inability to make dynamic decisions continuously. This paper proposed a dynamic economic scheduling method for distribution networks based on deep reinforcement learning. Firstly, the economic scheduling model of the new energy distribution network is established considering the action characteristics of micro-gas turbines, and the dynamic scheduling model based on deep reinforcement learning is constructed for the new energy distribution network system with a More >

  • Open Access

    REVIEW

    Market Operation of Energy Storage System in Smart Grid: A Review

    Li Deng1, Jiafei Huan1, Wei Wang1, Weitao Zhang1, Liangbin Xie2, Lun Dong2, Jingrong Guo2, Zhongping Li2, Yuan Huang2,*, Yue Xiang2

    Energy Engineering, Vol.121, No.6, pp. 1403-1437, 2024, DOI:10.32604/ee.2024.046393

    Abstract As a flexible resource, energy storage plays an increasingly significant role in stabilizing and supporting the power system, while providing auxiliary services. Still, the current high demand for energy storage contrasts with the fuzzy lack of market-oriented mechanisms for energy storage, the principle of market-oriented operation has not been embodied, and there is no unified and systematic analytical framework for the business model. However, the dispatch management model of energy storage in actual power system operation is not clear. Still, the specific scheduling process and energy storage strategy on the source-load-network side could be more… More >

  • Open Access

    ARTICLE

    Energy-Saving Distributed Flexible Job Shop Scheduling Optimization with Dual Resource Constraints Based on Integrated Q-Learning Multi-Objective Grey Wolf Optimizer

    Hongliang Zhang1,2, Yi Chen1, Yuteng Zhang1, Gongjie Xu3,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.2, pp. 1459-1483, 2024, DOI:10.32604/cmes.2024.049756

    Abstract The distributed flexible job shop scheduling problem (DFJSP) has attracted great attention with the growth of the global manufacturing industry. General DFJSP research only considers machine constraints and ignores worker constraints. As one critical factor of production, effective utilization of worker resources can increase productivity. Meanwhile, energy consumption is a growing concern due to the increasingly serious environmental issues. Therefore, the distributed flexible job shop scheduling problem with dual resource constraints (DFJSP-DRC) for minimizing makespan and total energy consumption is studied in this paper. To solve the problem, we present a multi-objective mathematical model for… More >

  • Open Access

    ARTICLE

    A Distributionally Robust Optimization Scheduling Model for Regional Integrated Energy Systems Considering Hot Dry Rock Co-Generation

    Hao Qi1, Mohamed Sharaf2, Andres Annuk3, Adrian Ilinca4, Mohamed A. Mohamed5,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.2, pp. 1387-1404, 2024, DOI:10.32604/cmes.2024.048672

    Abstract Hot dry rock (HDR) is rich in reserve, widely distributed, green, low-carbon, and has broad development potential and prospects. In this paper, a distributionally robust optimization (DRO) scheduling model for a regionally integrated energy system (RIES) considering HDR co-generation is proposed. First, the HDR-enhanced geothermal system (HDR-EGS) is introduced into the RIES. HDR-EGS realizes the thermoelectric decoupling of combined heat and power (CHP) through coordinated operation with the regional power grid and the regional heat grid, which enhances the system wind power (WP) feed-in space. Secondly, peak-hour loads are shifted using price demand response guidance More >

  • Open Access

    ARTICLE

    Enhanced Hybrid Equilibrium Strategy in Fog-Cloud Computing Networks with Optimal Task Scheduling

    Muchang Rao, Hang Qin*

    CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 2647-2672, 2024, DOI:10.32604/cmc.2024.050380

    Abstract More devices in the Intelligent Internet of Things (AIoT) result in an increased number of tasks that require low latency and real-time responsiveness, leading to an increased demand for computational resources. Cloud computing’s low-latency performance issues in AIoT scenarios have led researchers to explore fog computing as a complementary extension. However, the effective allocation of resources for task execution within fog environments, characterized by limitations and heterogeneity in computational resources, remains a formidable challenge. To tackle this challenge, in this study, we integrate fog computing and cloud computing. We begin by establishing a fog-cloud environment… More >

  • Open Access

    ARTICLE

    A Cooperated Imperialist Competitive Algorithm for Unrelated Parallel Batch Machine Scheduling Problem

    Deming Lei*, Heen Li

    CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 1855-1874, 2024, DOI:10.32604/cmc.2024.049480

    Abstract This study focuses on the scheduling problem of unrelated parallel batch processing machines (BPM) with release times, a scenario derived from the moulding process in a foundry. In this process, a batch is initially formed, placed in a sandbox, and then the sandbox is positioned on a BPM for moulding. The complexity of the scheduling problem increases due to the consideration of BPM capacity and sandbox volume. To minimize the makespan, a new cooperated imperialist competitive algorithm (CICA) is introduced. In CICA, the number of empires is not a parameter, and four empires are maintained More >

  • Open Access

    ARTICLE

    A Novel Scheduling Framework for Multi-Programming Quantum Computing in Cloud Environment

    Danyang Zheng, Jinchen Xv, Feng Yue, Qiming Du, Zhiheng Wang, Zheng Shan*

    CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 1957-1974, 2024, DOI:10.32604/cmc.2024.048956

    Abstract As cloud quantum computing gains broader acceptance, a growing quantity of researchers are directing their focus towards this domain. Nevertheless, the rapid surge in demand for cloud-based quantum computing resources has led to a scarcity, which in turn hampers users from achieving optimal satisfaction. Therefore, cloud quantum computing service providers require a unified analysis and scheduling framework for their quantum resources and user jobs to meet the ever-growing usage demands. This paper introduces a new multi-programming scheduling framework for quantum computing in a cloud environment. The framework addresses the issue of limited quantum computing resources More >

  • Open Access

    ARTICLE

    Research on Scheduling Strategy of Flexible Interconnection Distribution Network Considering Distributed Photovoltaic and Hydrogen Energy Storage

    Yang Li1,2, Jianjun Zhao2, Xiaolong Yang2, He Wang1,*, Yuyan Wang1

    Energy Engineering, Vol.121, No.5, pp. 1263-1289, 2024, DOI:10.32604/ee.2024.046784

    Abstract Distributed photovoltaic (PV) is one of the important power sources for building a new power system with new energy as the main body. The rapid development of distributed PV has brought new challenges to the operation of distribution networks. In order to improve the absorption ability of large-scale distributed PV access to the distribution network, the AC/DC hybrid distribution network is constructed based on flexible interconnection technology, and a coordinated scheduling strategy model of hydrogen energy storage (HS) and distributed PV is established. Firstly, the mathematical model of distributed PV and HS system is established,… More >

  • Open Access

    ARTICLE

    An Elite-Class Teaching-Learning-Based Optimization for Reentrant Hybrid Flow Shop Scheduling with Bottleneck Stage

    Deming Lei, Surui Duan, Mingbo Li*, Jing Wang

    CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 47-63, 2024, DOI:10.32604/cmc.2024.049481

    Abstract Bottleneck stage and reentrance often exist in real-life manufacturing processes; however, the previous research rarely addresses these two processing conditions in a scheduling problem. In this study, a reentrant hybrid flow shop scheduling problem (RHFSP) with a bottleneck stage is considered, and an elite-class teaching-learning-based optimization (ETLBO) algorithm is proposed to minimize maximum completion time. To produce high-quality solutions, teachers are divided into formal ones and substitute ones, and multiple classes are formed. The teacher phase is composed of teacher competition and teacher teaching. The learner phase is replaced with a reinforcement search of the More >

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