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11th Frontier Academic Forum of Electrical Engineering, FAFEE 2024

作者： Jin, Xurong Zhou, Xuenian Li, Wei Wang, Guobin Wang, Lele Yang, Guohua Yang, Na Yin, Jiang Marketing Service Center of State Grid Ningxia Electric Power Co. Ltd. Yinchuan750001 China School of Electrical and Electronic Engineering Ningxia University Yinchuan750021 China

ISBN: (纸本)9789819788279

In order to solve the challenges brought by the access of distributed renewable energy sources to the dual-side operation of virtual power plants, an optimal scheduling model of virtual power plant alliance based on cooperative game is proposed. Firstly, an optimization model of demand-side user-VPP-supply-side equipment alliance cooperative game is established with Virtual Power Plant (VPP) as the intermediary;secondly, an improved Shapley value allocation method is used to reasonably allocate the alliance revenue;finally, an example simulation is carried out to verify the validity and economy of the VPP alliance cooperative game model. © Beijing Paike Culture Commu. Co., Ltd. 2025.

关键词： Benefit Allocation Cooperative Game optimization scheduling Virtual Power Plants

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The Multi-Objective Distributed Robust optimization scheduling of Integrated Energy Systems Considering Green Hydrogen Certificates and Low-Carbon Demand Response

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PROCESSES 2025年第3期13卷

作者： Yang, Yulong Yan, Han Wang, Jiaqi Northeast Elect Power Univ Sch Elect Engn Jilin 132000 Peoples R China

To address the issues of energy wastage and uncertainty impacts associated with high levels of renewable energy integration, a multi-objective distributed robust low-carbon optimization scheduling strategy for hydrogen-integrated Integrated Energy Systems (IES) is proposed. This strategy incorporates a green hydrogen trading mechanism and low-carbon demand response. Firstly, to leverage the low-carbon and clean characteristics of hydrogen energy, an efficient hydrogen utilization model was constructed, consisting of electricity-based hydrogen production, waste heat recovery, multi-stage hydrogen use, hydrogen blending in gas, and hydrogen storage. This significantly enhanced the system's renewable energy consumption and carbon reduction. Secondly, to improve the consumption of green hydrogen, a novel reward-punishment green hydrogen certificate trading mechanism was proposed. The impact of green hydrogen trading prices on system operation was discussed, promoting the synergistic operation of green hydrogen and green electricity. Based on the traditional demand-response model, a novel low-carbon demand-response strategy is proposed, with carbon emission factors serving as guiding signals. Finally, considering the uncertainty of renewable energy, an innovative optimal trade-off multi-objective distributed robust model was proposed, which simultaneously considered low-carbon, economic, and robustness aspects. The model was solved using an improved adaptive particle swarm optimization algorithm. Case study results show that, after introducing the reward-punishment green hydrogen trading mechanism and low-carbon demand response, the system's total cost was reduced by approximately 5.16% and 4.37%, and carbon emissions were reduced by approximately 7.84% and 6.72%, respectively. Moreover, the proposed multi-objective distributed robust model not only considers the system's economy, low-carbon, and robustness but also offers higher solving efficiency and optimization perfo

关键词： green hydrogen certificate trading low carbon demand response distributed robustness integrated energy system optimization scheduling

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Multi-objective optimization scheduling for extensive plain lake water resources incorporating flood resource utilization

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JOURNAL OF HYDROLOGY 2025年 651卷

作者： Han, Yalei Dong, Zengchuan Cui, Can Zhang, Tianyan Luo, Yun Hohai Univ Coll Hydrol & Water Resource Nanjing 210098 Peoples R China Natl Key Lab Water Disaster Prevent Nanjing 210098 Peoples R China Yellow River Inst Hydraul Res Zhengzhou 450003 Peoples R China

Plain lakes are crucial water sources for regional development, but increasing hydrological changes and intensified human activities have exacerbated conflicts among their functions. This study focuses on Hongze Lake, significantly impacted by the South-to-North Water Diversion East Line Project, which elevated water levels and substantially altered regional water resource utilization, while flood management regulations have remained unchanged. To investigate the impact of adjusting flood limit water level on various functions of Hongze Lake, a multi-objective optimal scheduling model for water resources was developed, encompassing water supply, water diversion, navigation, and ecological conservation. This model was combined with a projection pursuit optimization model to solve the scheduling scheme set. Results reveal changes in competitive relationships within the non-dominated solution set following adjustments in flood limit water levels. Notably, water diversion volume increased by 1.591 billion m 3 and the ecological assurance rate improved by 5.92 %. However, there was a 0.46 rise in water supply deficit and a 3.84 % decline in guaranteed navigation rate. Multi-objective evaluation indicates a decreased importance of the ecological objective, while the importance of water supply and water diversion increased significantly. Optimal scheduling schemes resulted in an average water level rise of 0.18 m, increasing cumulative water storage by 266 million m 3 while ensuring flood control. It is demonstrated that the lake's regulation and storage capacity are effectively improved by adjusting flood limit water level. This research provides a methodology for the utilization of flood resources and the management of water resources in lake-type reservoirs, with implications for balancing competing water demands.

关键词： Flood resource utilization optimization scheduling Multi-objective NSGA-III algorithm Projection pursuit method Hongze Lake

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Highway Service Area Multi-Timescale optimization scheduling Considering the Spatiotemporal Dynamic Evolution of Electric Vehicles

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IEEE TRANSACTIONS ON SMART GRID 2025年第1期16卷 678-690页

作者： Chen, Fangjian Xia, Mingchao Chen, Qifang Song, Yuguang Xian, Yiming Xiu, Sanmu Su, Su Beijing Jiaotong Univ Sch Elect Engn Beijing 100044 Peoples R China

With the increasing demand for long-distance travel of electric vehicles (EVs), the uncertain fast charging behavior of EVs poses great pressure on the energy system of highway service areas (HSAs). The development of information and communication technology provides new insights for promoting advance perception and coordinated optimization among various entities. In this context, this paper proposes an HSA multi-timescale optimization scheduling strategy considering the spatiotemporal dynamic evolution of EVs. Firstly, the information exchange structure among various functional departments of the highway is formulated, and the highway topology model and spatiotemporal extended EV model are established as the basis for EV charging selection and EV load prediction. Then, a multi-timescale scheduling strategy suitable for multi-energy systems in HSAs is proposed to support the economic and self-sustained operation of the system. The chance-constrained method in the day-ahead stage and the two-layer model predictive control (MPC) method in the intraday and real-time stages are employed to mitigate fluctuations in power generation and demand. The effectiveness of the proposed solution is widely validated through simulations, the results indicate that the proposed EV evolution method can effectively predict the EV load, and the scheduling strategy can ensure the economy and reliability for the operation of HSA energy system.

关键词： Road transportation Spatiotemporal phenomena Load modeling Vehicle dynamics Roads Transportation Dynamic scheduling Electric vehicle spatiotemporal dynamic evolution highway service area multi-timescale optimization scheduling

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optimization scheduling for multi-source water distribution systems in mountainous region based on seagull optimization algorithm

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MODERN PHYSICS LETTERS B 2025年第13期39卷

作者： Wang, Dongrui Chen, Hongxun Ma, Zheng Yi, Bobo Shanghai Univ Shanghai Inst Appl Math & Mech Sch Mech & Engn Sci 149 Yanchang Rd Shanghai 200072 Peoples R China China Ship Sci Res Ctr 185 Gaoxiong Rd Shanghai 200011 Peoples R China

Multi-source water distribution systems (WDSs) are critical to solving the increasing demand for urban water supply. Appropriate management of limited resources necessitates optimization of water scheduling in order to reduce energy consumption. However, certain complexities of applying such systems bring severe challenges to optimal scheduling methods, exemplified in mountain regions, where larger elevation gradients make distribution more complicated than in plain regions. Therefore, this study attempts to present best practices in how to reduce the energy consumption of water supply, especially in complex mountainous regions, through innovation of optimal scheduling methods. Based on the seagull optimization algorithm (SOA), a systematic optimization scheduling method for multi-source WDSs is proposed. The optimization results are compared with those obtained from the genetic algorithm. A case study of such optimization in the mountainous region of C-County, China is presented. Power consumption prior and post optimization is compared. The results show that this optimization scheduling method is both effective and feasible. Annual power consumption can be reduced by significant amounts, savings of 23.3% in this case study, and the optimal solution can be deployed with 40 iteration steps.

关键词： Water distribution system optimization scheduling multi-source seagull optimization algorithm

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optimization scheduling of community integrated energy system considering integrated demand response

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JOURNAL OF BUILDING ENGINEERING 2024年 98卷

作者： Zhang, Liting Li, Qifen Fang, Yue Yang, Yongwen Ren, Hongbo Fan, Longfei Tai, Nengling Shanghai Univ Elect Power Sch Energy & Mech Engn Shanghai 201306 Peoples R China Shanghai Jiao Tong Univ Sch Elect Informat & Elect Engn Shanghai 200240 Peoples R China

Amid surging global energy demands and worsening environmental decay, Community Integrated Energy Systems offer a viable solution through the coordination of diverse energy sources and technologies. With the goal of minimizing operating costs and carbon emissions while maintaining the power balance of electricity, cooling, heating, and natural gas, this study proposes an optimization scheduling strategy of the community integrated energy system combined with integrated demand response. Through a case study, the community integrated energy system involvement in integrated demand response was rigorously assessed and the impact of altering PV penetration rates was examined. The results indicate that application integration demand response can effectively reduce 20.26 % of the total system operating costs by shifting both peak load of electricity and heating or cooling. And the transferable and reduced loads in integrated demand response perform better than the replaceable load. The empirical analysis also confirms a 31.39 % reduction in total operational costs and a 25.51 % decrease in carbon emissions upon increasing the PV penetration rate from 20 % to 50 %, demonstrating the dual benefits of economic efficiency and environmental sustainability. This work serves as a pivotal reference for future policy formulation in sustainable community energy systems.

关键词： Community integrated energy system Integrated demand response PV penetration optimization scheduling

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optimization scheduling of virtual power plant with carbon capture and waste incineration considering P2G coordination

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ENERGY REPORTS 2022年 8卷 7200-7218页

作者： Zhang, Zongnan Du, Jun Zhu, Kongge Guo, Jing Li, Menghan Xu, Tao Jiangsu Univ Sci & Technol Sch Energy & Power Zhenjiang 212100 Jiangsu Peoples R China State Grid Jiangsu Elect Power Co Ltd Lianyungang Power Supply Branch 1 Xingfu Rd Lianyungang 222000 Jiangsu Peoples R China Jiaxing Power Supply Co State Grid Zhejiang Elect Power Co Ltd Jiaxing 314000 Zhejiang Peoples R China

In order to promote multi-energy complementation and low carbonization, an optimal scheduling model of virtual power plant (VPP) with carbon capture and waste incineration considering powerto-gas coordination is proposed. By introducing the collaborative utilization framework with a carbon capture plant-power to gas (P2G)-gas unit system, the CO2 in the carbon capture can be used as the raw material of P2G, which produces natural gas to be supplied to the gas unit. In addition, the power consumption of the carbon capture and the flue gas treatment can be transferred through joint dispatching to smooth the fluctuation of renewable energy output, so that the wind power and photovoltaic can be indirectly dispatchable and flexibly utilized. In view of the high dimensional nonlinearity of the proposed optimization model and the difficulty in solving it, a new Gaussian compound differential evolution algorithm is designed to solve the model. The simulation results show that the proposed model and method can provide peak load shifting capacity and improve renewable energy consumption, effectively reducing the cost and carbon emission of VPP. (c) 2022 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by-nc-nd/4.0/).

关键词： Power-to-gas Carbon capture Waste incineration optimization scheduling Compound differential evolution algorithm

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optimization scheduling Policy for Computed Tomography (CT)

Optimization Scheduling Policy for Computed Tomography (CT)

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11th International Conference on Service Systems and Service Management (ICSSSM)

作者： Cheng, Yuanjun Guo, Dongyao Zheng, Xiaohe Luo, Li Sichuan Univ Dept Ind Engn & Engn Management Sch Business Chengdu 610064 Peoples R China

ISBN: (纸本)9781479931347

In order to improve the utilization of CT in hospital, this paper build a MIP based scheduling model and solve it with CPLEX to get the optimization policy. Numeric example with real data from a large general hospital in West China are employed to do comparison between optimization policy and manual policy to prove the effectiveness of the model.

关键词： CT Mixed Integer Programming optimization scheduling Comparison

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optimization scheduling for Flexible Manufacturing System using Petri net 4

Optimization Scheduling for Flexible Manufacturing System us...

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4th International Conference on Green Technology and Sustainable Development (GTSD)

作者： Tung Pham Truong Phuoc Pham Dang Binh Luu Duc Pham Van Dong Univ Engn Technol Dept Quang Ngai Vietnam Da Nang Univ Technolgy Dept Mech Da Nang Vietnam

ISBN: (纸本)9781538651261

This paper presents a new optimization scheduling method for manufacturing system based on the Timed Petri Net model and combined a heuristic search method A Petri net model for flexible manufacturing system is proposed FMS optimization scheduling is determined by searching on Petri net's Teachability graph. A heuristic search algorithm is proposed to reach fast optimization results. Some experiments are carried out to demonstrate usefulness of our algorithm.

关键词： Petri net heuristic search optimization scheduling reachablility graph

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Reliability and performance of resource efficiency in dynamic optimization scheduling using multi-agent microservice cloud-fog on IoT applications

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COMPUTING 2024年第12期106卷 3837-3878页

作者： Krishnan, Ragi Durairaj, Selvam PSG Coll Technol Dept Math Coimbatore 641004 Tamil Nadu India VIT Chennai Sch Comp Sci & Engn Chennai 600127 Tamil Nadu India

In the ever-evolving landscape of technology, both in the Internet of Things (IoT) and of microservice-based cloud-fog with IoT applications, reliability has emerged as a pivotal challenge. This research explores the intricate integration of the IoT with cloud computing through fog computing, addressing critical challenges and presenting an advanced strategy for dynamic task scheduling, reliability, and performance enhancement. It highlights the limitations of current cloud-only architectures in meeting the dynamic requirements of IoT with fog, which demand more efficient data processing and enhanced communication management. Fog and cloud computing are the best options, and an optimal solution is leveraging fog computing's proximity to IoT devices for reduced latency and cloud computing's extensive storage and processing capabilities. The paper introduces a novel Dynamic Mayfly optimization scheduling (DMOS) algorithm based on and designed to minimize energy usage while meeting task deadlines and maintaining system reliability and performance. This algorithm's effectiveness is validated through simulations on an enhanced CloudSim platform, demonstrating its superiority in energy efficiency and performance reliability over traditional methods. This work contributes significantly to IoT and cloud computing, proposing a dynamic and intelligent approach to task scheduling crucial for IoT systems' sustainable, reliable, and efficient operation. Through comprehensive experimentation and comparative analysis, we demonstrate the efficacy of our approach in improving reliability, reducing latency, and optimizing resource utilization in Cloud-Fog-based IoT applications. Our findings highlight the potential of dynamic optimization scheduling with multi-agent microservices to significantly enhance the performance and scalability of IoT systems, paving the way for more efficient and reliable IoT deployments in diverse domains.

关键词： Reliability optimization scheduling Cloud-fog microservice IoT application Markov processes

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