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Data-Driven Risk-Adjusted robust Energy Management for Microgrids Integrating Demand Response Aggregator and Renewable Energies

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IEEE TRANSACTIONS ON SMART GRID 2023年第1期14卷 365-377页

作者： Yuan, Zhi-Peng Li, Peng Li, Zhen-Long Xia, Jing Beijing Jiaotong Univ Sch Elect & Informat Engn Beijing 100044 Peoples R China North China Elect Power Univ State Key Lab Alternate Elect Power Syst Renewabl Beijing 102206 Peoples R China Beijing Goldwind Sci & Creat Wind Power Equipment R&D Ctr Beijing 100176 Peoples R China

Microgrids, integrating user-side demand response and zero marginal cost renewable energies, are potential components for future smart grids to reduce carbon emissions and improve power system resilience. In this paper, a day-ahead microgrid energy management framework with demand response aggregator as an intermediate coordinator is developed. The corresponding scheduling strategy is obtained to maximize the social welfare of the microgrid system, with considering the privacy of end-users and the uncertainty of renewable energies. To this end, firstly, a accelerated distributed optimization method based on Alternating Direction Method of Multipliers, named as FAST-PP-ADMM, is developed to protect the end-users privacy and improve the scalability of the microgrid system. Secondly, a data-driven risk-adjusted uncertain set is constructed with a distributionally robust chance-constraints model to characterize the forecast error of renewable energies. Based on the constructed uncertain set, a two-stage robust microgrid-side energy management model is solved by using the column-and-constraint generation (C&CG) method. Finally, the effectiveness of the proposed energy management framework and scheduling strategy is verified by simulations.

关键词： Demand response aggregator data-driven uncertain set distributed optimization microgrid energy management two-stage robust optimization

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robust Hierarchical Scheduling Approach to Coordinate Wind Power Penetrated Transmission System and LNG Railway Transportation

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IEEE TRANSACTIONS ON SMART GRID 2023年第2期14卷 996-1010页

作者： Long, Tao Bie, Zhaohong Jiang, Lizhou Chen, Chen Li, Gengfeng Xie, Haipeng Xi An Jiao Tong Univ State Key Lab Elect Insulat & Power Equipment Shaanxi Prov Key Lab Smart Grid Xian 710049 Peoples R China

Rail transport of liquefied natural gas (LNG), as an essential means of transmitting energy across regions, has been widely implemented and promoted. The significant growth of gas-fired power and power-to-gas units has intensified the interaction and interdependency between wind power penetrated transmission system and LNG transported railway network. Hence, a robust and hierarchical scheduling approach to coordinate the transmission system and LNG railway transportation is innovatively proposed. First, an advanced time-space network (TSN) model is established with additional security constraints to exactly simulate the railway traffic. Then, a two-stage robust scheduling model is formulated with an uncertainty set incorporating random components failures of the transmission system and the railway network as well as variability of wind power and loads. Subsequently, the two-stage robust scheduling problem is decomposed into two interactive levels according to the analytical target cascading (ATC) technique, thus ensuring the information privacy and decision independency of the power and railway systems. Finally, an ATC-based hierarchical solution framework embedded with the column-and-constraint generation (C & CG) algorithm is presented to solve the proposed model. Case studies demonstrate the effectiveness and benefit of collaboratively managing the power and railway systems.

关键词： Rail transportation Liquefied natural gas Transportation Wind power generation Uncertainty Rails Generators Analytical target cascading (ATC) liquefied natural gas (LNG) time-space network (TSN) two-stage robust optimization wind power accommodation

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Resilient day-ahead microgrid energy management with uncertain demand, EVs, storage, and renewables

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CLEANER ENGINEERING AND TECHNOLOGY 2024年 20卷

作者： Niknami, Ahmad Askari, Mohammad Tolou Ahmadi, Meysam Amir Nik, Majid Babaei Moghaddam, Mahmoud Samiei Islamic Azad Univ Semnan Branch Dept Elect Engn Semnan Iran Islamic Azad Univ Damghan Branch Dept Elect Engn Damghan Iran

Managing microgrid energy presents a complex challenge due to unpredictable renewable sources, fluctuating demand, and diverse equipment like batteries, distributed generators, and electric vehicles. This paper introduces a novel two-step optimization model, the robust Day-Ahead Scheduling for Enhanced Resilience, tailored for microgrid operations. The model addresses the integration of electronic generation, uncertain demand patterns, and small-scale renewable resources. Detailed formulations optimize microgrid energy use, including strategic battery usage, efficient electric vehicle charging, balancing device utilization, and distributed generation dispatch. This multi-faceted approach aims to minimize costs over 24 h, including energy loss, power purchases, reduced power usage, generator operation, and battery/EV expenses. Employing a column-and-constraint generation (C&CG) algorithm ensures efficient problem solving. The proposed model achieved a significant reduction in operational costs, outperforming existing methods by at least 8%. Notably, it minimized energy purchases, energy losses, and load shedding while improving voltage stability, showcasing its effectiveness in enhancing microgrid performance and resilience.

关键词： Resiliency Microgrid two-stage robust optimization Demand response Storage Electric vehicle Uncertainty

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Graph-based robust Unit Commitment For Wind Power Considering Frequency Nadir Constraint 3

Graph-based Robust Unit Commitment For Wind Power Considerin...

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3rd International Conference on Energy and Electrical Power Systems (ICEEPS)

作者： Zhang, Wei Xie, Xinglang Song, Xueqing Hu, Xu Qin, Chao Dai, Zhen Han, Jian Liu, Yi CSG AI Technol Co Ltd Guangzhou Peoples R China Tianjin Univ Minist Educ Key Lab Smart Grid Tianjin Peoples R China

ISBN: (纸本)9798350375145;9798350375138

To enhance the data processing efficiency of large-scale power system Unit Commitment (UC) model, this paper constructs a UC graph model based on graph database. Then, in response to the challenges posed by the uncertainties in high-penetration wind power systems and low system inertia, a robust UC model for wind power considering linearized frequency nadir constraint (FNC) is established. Firstly, a nonlinear four-variables FNC is derived based on the System Frequency Response (SFR) model, and a heuristic least-squares piecewise linearization method is employed to linearize it. Subsequently, a day-ahead UC model that considers the four-variables FNC is formulated. Building upon this foundation, wind power output uncertainties are considered, leading to a two-stage robust optimization model for wind power that accounts for FNC. Finally, the IEEE RTS 24-bus system is utilized to analyze this two-stage UC model. The results demonstrate that the model proposed in this study can effectively reduce the maximum frequency drop when power disturbances occur. Furthermore, the dispatch strategy operates at the lowest cost under the most adverse conditions, thereby enhancing wind power integration capacity.

关键词： UC graph model frequency nadir constraint highpenetration wind power two-stage robust optimization

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Day-ahead robust optimal dispatch of integrated energy station considering battery exchange service

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JOURNAL OF ENERGY STORAGE 2022年 50卷

作者： Zhao, Xianqiu Yang, Yongbiao Xu, Qingshan Southeast Univ Sch Elect Engn Nanjing 210096 Peoples R China Nanjing Ctr Appl Math Nanjing 210018 Peoples R China

In order to improve the energy utilization efficiency and decrease the environmental pollution, multiple energy production, conversion and storage devices are integrated as a whole, called an integrated energy station (IES), which can meet various energy demands. In this paper, a day-ahead robust optimal scheduling strategy of IES for electricity, cold energy and heat energy supply is proposed, where the battery exchange service is incorporated by introducing the electric vehicle exchange station (EVES). Distinguished from the prevalent detailed modeling of each battery, an integrated model of EVES based on the state of charge (SOC) interval of batteries is established to reduce the number of variables, by which the charging priority of batteries is embedded into the optimal dispatch of IES. Considering the uncertainties of renewable energy resources and load demands, a two-stage robust optimization model of day-ahead dispatch of IES is formulated to minimize the total operation cost under the worst case, which can be solved efficiently by the column-and-constraint generation (C&CG) algorithm. The effectiveness of the proposed method is demonstrated in the case study. The results show that the total operation cost of IES decreases by 9.2%, indicating the role played by the flexibility of EVES. Moreover, the worst-case cost is reduced compared with the deterministic approach.

关键词： Integrated energy station Electric vehicle exchange station Battery exchange service two-stage robust optimization

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Economic Dispatch of Integrated Energy Systems Considering Renewable Energy and Load Uncertainty 8

Economic Dispatch of Integrated Energy Systems Considering R...

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8th International Conference on Power Energy Systems and Applications, ICoPESA 2024

作者： Xu, Bin Wang, Ao Wang, XiaoMing Zhao, WenGuang Bi, Rui Yuan, HuaKai State Grid Anhui Electric Power Research Institute Hefei China Hefei University of Technology Anhui Province Key Laboratory of Renewable Energy Utilization and Energy Saving Hefei China

ISBN: (纸本)9798350351668

In recent years, severe weather events occur frequently, which have a great impact on the integrated energy system. In order to reduce the risk, this paper proposes an economic scheduling model for the integrated energy system considering the uncertainty of renewable energy and load. Firstly, a typical electrothermal integrated energy system is established, and its deterministic operation model is established with the goal of economic optimization. Secondly, considering the factors of bad weather, a regulation strategy based on energy storage-grid joint is proposed to deal with the uncertainty of source and load under bad weather, and a two-stage robust optimal scheduling model is constructed, which is solved by C&CG algorithm. Finally, the effectiveness of the proposed model is verified by analyzing the scheduling results under different parameters and the response ability under bad weather through a case simulation. © 2024 IEEE.

关键词： integrated energy systems renewable energy two-stage robust optimization uncertainty

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Energy Storage Configuration Method of Virtual Power Plant Considering Source-Load Uncertainty 5

Energy Storage Configuration Method of Virtual Power Plant C...

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5th International Conference on Clean Energy and Electric Power Engineering, ICCEPE 2024

作者： Bai, Yang Song, Haiyan Qian, Tianneng Xu, Xinyu Qin, Wei Gu, Weihua Zhang, Zihao Economic and Technological Research Institute State Grid Nantong Power Supply Company Nantong China College of Electrical Engineering and Control Science Nanjing Tech University Nanjing China

ISBN: (纸本)9798350390315

With the rapid development of virtual power plants, how to eliminate the negative impact of uncertainty on both sides of source and load on virtual power plants has become an urgent problem to be solved. In this paper, a virtual power plant energy storage configuration method based on two-stage robust optimization is proposed to deal with the uncertainty of photovoltaic output and load demand. By constructing an uncertain set, the scheduling scheme of the system in the 'worst' scenario is optimized. The effectiveness and superiority of the method under different uncertainty conditions are verified by simulation analysis, which provides a reference for the configuration of energy storage capacity in the construction of virtual power plant. © 2024 IEEE.

关键词： Energy Storage Configuration two-stage robust optimization Uncertainty Conditions VPP

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robust Optimal Economic Dispatch of Microgrid with Stepwise Demand Response Mechanism

Robust Optimal Economic Dispatch of Microgrid with Stepwise ...

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IEEE/IAS Industrial and Commercial Power System Asia (IEEE I and CPS Asia)

作者： He, Yi Sun, Yonghui Zhou, Wei Wu, Pengpeng Chen, Li Meng, Yunfan Hohai Univ Coll Energy & Elect Engn Nanjing Peoples R China

ISBN: (纸本)9798350339345

In order to solve the influence of uncertainty on scheduling in the microgrid and reduce the conservativeness of the robust optimization algorithm, a robust optimized economic scheduling strategy for microgrid with stepwise demand response mechanism is proposed in this paper. Firstly, a stepwise demand response mechanism is constructed to enhance the motivation of customer response and optimize the load curve on demand side. Then, considering the uncertainty of PV output, a two-stage robust optimal dispatching model of the microgrid is established. Based on the optimized load curve, the optimization model could be solved by column and constraint generation method. Finally, the effectiveness of the proposed strategy is verified by simulation cases. The results show that the proposed model can effectively enhance the enthusiasm of customers to participate in demand response and reduce the conservativeness of the robust optimization algorithm.

关键词： microgrids uncertainty stepwise demand response mechanism two-stage robust optimization economic dispatch

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robust Energy Management of High-Speed Railway Co-Phase Traction Substation With Uncertain PV Generation and Traction Load

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2022年第6期23卷 5079-5091页

作者： Liu, Yuanli Chen, Minwu Cheng, Zhe Chen, Yinyu Li, Qunzhan Southwest Jiaotong Univ Sch Elect Engn Chengdu 611756 Peoples R China

Co-phase traction power supply system provides the insights for solving the existing power quality and electrical sectioning issues in high-speed railways, and the flexible control of co-phase traction substation (CTSS) with the integration of photovoltaic (PV) and hybrid energy storage system (HESS) attracts widespread attention. However, the strong volatilities and uncertainties of PV and traction load introduce challenges to the reliable and economic operation of CTSS. In this study, the energy management of CTSS aims at minimizing the operating cost of CTSS by coordinating PV, HESS, power flow controller (PFC) and the energy transactions with the power grid, where the three-phase voltage unbalance constraint is taken into account in particular. Furthermore, to handle the uncertainties of PV and traction load, a two-stage robust optimization model is proposed. The first stage determines the operating status of HESS and energy transaction status with grid, and the second stage aims at finding the optimal scheduling power of CTSS and the worst-case scenario with maximized operating cost of CTSS. A column-and-constraint generation algorithm is employed to solve this robust optimization model, along with efficient linearization approaches for the formulation of mixed-integer linear programming. The proposed methodology is tested with a real high-speed railway line case in China.

关键词： Rail transportation Uncertainty optimization Batteries Load modeling Substations Power quality Co-phase traction substation two-stage robust optimization energy management uncertainty

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A new optimal scheduling method for electricity-hydrogen shared energy storage in data center clusters 6

A new optimal scheduling method for electricity-hydrogen sha...

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6th International Conference on Energy Systems and Electrical Power, ICESEP 2024

作者： Bian, Yifan Xie, Lirong Ye, Jiahao Ma, Lan School of Electrical Engineering Xinjiang University Urumqi China

ISBN: (纸本)9798350353563

In recent years, the energy consumption of data centers (DCs) has been on a sharp upward trend, and how to achieve joint optimal scheduling and energy management of multiple DCs is the key to low-cost operation of DCs. The shared electric-hydrogen storage (SEHES) is introduced to DC clusters (DCC), and a novel SEHES business model is proposed to enhance system's economy and fully utilize the complementary energy consumption characteristics of different DCs. First, the refined models of each component in DCC are constructed. Then, a two-stage robust optimization model of SEHES is constructed with the objective of economy, considering the uncertainty of renewable energy and data load (DL), and the DL's demand response strategy is formulated. Finally, the model is solved using the column-and-constraint generation algorithm. The simulation results show that the introduction of SEHES can effectively improve the economy and robustness of DCC, which verifies the validity of the proposed model. © 2024 IEEE.

关键词： data center economy electricity-hydrogen shared energy storage two-stage robust optimization uncertainty

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