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Optimal dispatch for cross-regional integrated energy system with renewable energy uncertainties: A unified spatial-temporal cooperative framework

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ENERGY 2024年 292卷

作者： Zhang, Zhenwei Wang, Chengfu Wu, Qiuwei Dong, Xiaoming Shandong Univ Key Lab Power Syst Intelligent Dispatch & Control Jinan 250061 Shandong Peoples R China Tsinghua Univ Tsinghua Berkeley Shenzhen Inst Tsinghua Shenzhen Int Grad Sch Shenzhen 518055 Peoples R China

Cross -regional energy sharing is a promising way to alleviate the imbalance in energy distribution. However, there is a risk that the uncertain fluctuations of intermittent renewable energy sources (RES) could be transmitted across regional transmissions, resulting in large-scale system economic inefficiency and security concerns. To this end, a unified spatial -temporal cooperative framework for the integrated energy system, which considers the coordination between intra-regional multi -energy coupling and cross -regional multi -agent -system energy sharing, is proposed in this paper. First, in the temporal dimension, a multi -time -scale stochastic decision -making model is established based on stochastic model predictive control (SMPC). Thus, slow -response units are arranged to gain economic goals in day -ahead stage and the closed -loop rolling adjustment is implemented on fastresponse units to achieve accuracy supply -demand balance in real-time stage. Second, in the spatial dimension, a fully distributed dispatch approach is adopted to address the information sharing barriers, which only requires neighboring information and considers consistency of boundary control variables on border lines. Finally, to improve the computational efficiency, the established complex time sequential model is decomposed into a set of time -discrete parallel sub -problems, using the optimal condition decomposition (OCD) technique. Simulation results on a cross -regional test system demonstrate that proposed framework can increase the wind power consumption capacity and reduce operation cost through cross -regional cooperation, while the parallel decomposition can save 67.25 % of the computation time relative to the serial computation.

关键词： Cross-regional energy sharing Multi-energy coupling Integrated energy system Optimal condition decomposition Stochastic model predictive control

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Multi-stage power Source and Grid Coordination Planning Method Considering Grid Load Uniformity

Multi-stage Power Source and Grid Coordination Planning Meth...

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2020 IEEE/IAS Industrial and Commercial power system Asia, I and CPS Asia 2020

作者： Li, Zhiqi Han, Xueshan Yang, Ming Ma, Yiran Shandong University Key Laboratory of Power System Intelligent Dispatch and Control Jinan China

ISBN: (数字)9781728143033

ISBN: (纸本)9781728143033

In view of the increasing uncertainty of power supply and load, the concept of power source and grid coordination uniformity planning is proposed, and the standard deviation of transmission line load rate is taken as the uniformity evaluation index of power source and grid planning. A multi-stage and multi-objective optimization model of power source and grid expansion planning is established to minimize the comprehensive cost of the whole planning cycle. The improved PSO-GA hybrid algorithm is used to solve the model, which improves the efficiency of solution. The analysis of a simple IEEE garver-6 node system shows that the model and the solution method are effective and feasible, and are suitable for the coordinated planning of the power source and grid under the diversified development of power supply and load. © 2020 IEEE.

关键词： Multiobjective optimization

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A Voltage Adaptive Dynamic Harmonic Model of Nonlinear Home Appliances

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IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS 2020年第5期67卷 3607-3617页

作者： Sun, Yuanyuan Xie, Xiangmin Zhang, Linghan Li, Shurong Shandong Univ Minist Educ Key Lab Power Syst Intelligent Dispatch & Control Sch Elect Engn Jinan 250061 Peoples R China

The widespread application of nonlinear power electronic devices in residential loads has resulted in significant harmonic distortions. Harmonic source modeling is of great importance since it is a prerequisite to estimate the harmonic pollution. In this paper, a dynamic harmonic coupled matrix model (HCMM) is proposed for the nonlinear home appliances. This model is adaptive to the load supply voltage conditions;therefore, the dynamic harmonic generating characteristics of nonlinear home appliances can be demonstrated by the proposed HCMM. Moreover, the HCMM derivation process is based on measurement, independent of the load equivalent circuit, which makes the proposed method most suitable for nonlinear home appliances with unknown circuit topologies. The widely linear complex partial least squares method, which has a fast speed and high robustness, is used to estimate the HCMM. A procedure to establish the HCMM for multistate home appliances is also proposed in this paper. lab experiments were performed to verify the accuracy and validity of the proposed methodology. The aggregate harmonics caused by multiple nonlinear residential loads have been analyzed based on the proposed HCMM.

关键词： Harmonic analysis Home appliances Load modeling power system harmonics Integrated circuit modeling Adaptation models Equivalent circuits Aggregate harmonics dynamic harmonic characteristics harmonic modeling home appliances

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SolarSAM: Building-scale photovoltaic potential assessment based on Segment Anything Model (SAM) and remote sensing for emerging city

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RENEWABLE ENERGY 2024年 237卷

作者： Li, Guanglei Wang, Guohao Luo, Tengqi Hu, Yuxiao Wu, Shouyuan Gong, Guanghui Song, Chenchen Guo, Zhiling Liu, Zhengguang Shandong Univ Key Lab Power Syst Intelligent Dispatch & Control Minist Educ Jinan 250061 Peoples R China Northwest A&F Univ Dept Power & Elect Engn Yangling 712100 Peoples R China Hong Kong Polytech Univ Dept Bldg Environm & Energy Engn Hung Hom Kowloon Hong Kong Peoples R China Ningbo Inst Digital Twin & Eastern Inst Technol Ningbo 315200 Peoples R China Beijing Informat Sci & Technol Univ Higher Informat Ind Technol Res Inst Beijing 100192 Peoples R China Univ Manchester Dept Chem Engn Manchester M13 9PL England

Driven by advancements in photovoltaic (PV) technology, solar energy has emerged as a promising renewable energy source due to its ease of integration onto building rooftops, facades, and windows. For emerging cities, the lack of detailed street-level data presents a challenge for effectively assessing the potential of buildingintegrated photovoltaic (BIPV). To address this, this study introduces SolarSAM, a novel BIPV evaluation method that leverages satellite imagery and deep learning techniques, and an emerging city in northern China is utilized to validate the model performance. SolarSAM segmented various building rooftops using text promptguided semantic segmentation during the process. Separate PV models were then developed for Rooftop PV, Facade-integrated PV, and PV windows, using this segmented data and local climate information. The potential for BIPV installation, solar power generation, and city-wide power self-sufficiency were assessed, revealing that the annual BIPV power generation potential surpassed the city's total electricity consumption by a factor of 2.5. Economic and environmental analysis were also conducted for the BIPVs on different buildings;the levelized cost of electricity is 0.18-0.41 CNY/kWh, and the annual total carbon reduction is 7.08 x 107 T CO2. These findings demonstrated the model's performance and revealed the potential for BIPV power generation.

关键词： Potential assessment Building-integrated photovoltaic Semantic segmentation Satellite imagery

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Two-Mode Balancing control Scheme for Modular DC/DC Converter Interfaced Battery Swapping Stations

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IEEE TRANSACTIONS ON TRANSPORTATION ELECTRIFICATION 2025年第1期11卷 1640-1651页

作者： Ma, Zhan Gao, Feng Li, Wei Jia, Ming Wasylowski, David Lv, Qi Gao, Xiongying Zhou, Xin Uwe Sauer, Dirk Shandong Univ Key Lab Power Syst Intelligent Dispatch & Control Minist Educ Jinan 250061 Peoples R China Rhein Westfal TH Aachen Inst Power Elect & Elect Drives ISEA Chair Electrochem Energy Convers & Storage Syst D-52074 Aachen Germany Shandong Univ Sch Control Sci & Engn Jinan 250061 Peoples R China Southchip Semicond Technol Co Ltd Shanghai 201210 Peoples R China Rhein Westfal TH Aachen Inst Power Elect & Elect Drives ISEA Flexible Elekt Netze FEN GmbH Aachen 52074 Germany Rhein Westfal TH Aachen Inst Power Generat & Storage Syst PGS EON ERC Aachen 52074 Germany Rhein Westfal TH Aachen Inst Power Elect & Elect Drives ISEA Chair Electrochem Energy Convers & Storage Syst Aachen 52074 Germany Tsinghua Univ Dept Elect Engn Beijing 100084 Peoples R China Yangzhou Huading Elect Co Ltd Yangzhou 225127 Peoples R China

In comparison to the traditional structure, the utilization of the modular dc/dc converter (MDDC) for integrating power battery packs in electric vehicle (EV) battery swapping stations (BSSs) presents notable advancements in cost-effectiveness, conversion efficiency, and stability. This article introduces a two-mode balancing control scheme tailored for the state-of-charge (SoC) differences due to the distinctive plug-and-play operation of BSS. First, the proposed scheme efficiently addresses high swapping demands by expediting the generation of full battery packs using a sorting selection control during the on-grid mode. Subsequently, during the off-grid mode, a flexible energy transfer loop, incorporating the dc inductor, capacitor, and submodules (SMs), enhances balancing capabilities through an internal circulation control. It can effectively address scenarios with low swapping demands where there is a shortage of battery packs that are not fully charged. Moreover, the proposed method ensures equal switching loss distribution and accelerates balancing speed, adding to its appeal. The practical validation of the proposed scheme is achieved through a 1-kW/1-kWh MDDC-BSS prototype equipped with 16 SMs.

关键词： Battery balancing battery swapping modular dc/dc converter (MDDC) state-of-charge (SoC) Battery balancing battery swapping modular dc/dc converter (MDDC) state-of-charge (SoC)

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A hybrid machine learning and meta-heuristic algorithm based service restoration scheme for radial power distribution system

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INTERNATIONAL TRANSACTIONS ON ELECTRICAL ENERGY systemS 2021年第6期31卷 p1-25页

作者： Srivastava, Ishan Bhat, Sunil Thadikemalla, Venkata Sainath Gupta Singh, Arvind R. Visvesvaraya Natl Inst Technol Elect Engn Dept Nagpur Maharashtra India VRSEC Dept Elect & Commun Engn Vijayawada India Shandong Univ Sch Elect Engn Key Lab Power Syst Intelligent Dispatch & Control Minist Educ Jinan Peoples R China

In-service Restoration (SR), the healthy section of the feeder can be re-energized by finding the optimal path for power flow. Through conventional methods which are mainly deterministic in nature, the computational burden is very high. Therefore, researchers have proposed various meta-heuristic based methods to solve the SR problem. But since, these methods are probability based;one single algorithm cannot guarantee optimal solution for all scenarios. Hence, the authors have proposed a Machine Learning (ML) based framework, which can predict the best SR scheme for a particular fault scenario among the SR solutions obtained through various meta-heuristic algorithms. The supervised ML model is developed using the fault features as input values and the best performing meta-heuristic algorithm as the target value. To check the validity of the ML framework, the authors have taken four different meta-heuristic algorithms, which are, Enhanced Integer Coded Particle Swarm Optimization (EICPSO), Shuffled Frog Leaping Algorithm (SFLA), Non-Dominated Sorting Genetic Algorithm-II (NSGA-II), and Ant Colony Optimization (ACO) algorithm. The ML model can be extended for any number of algorithms. Multi-class Support Vector Machine (SVM) algorithm is used as the estimator in the current work to train and test the developed model. The results obtained through SVM are 95.95% accuracy, 94% precision, and 94% f1-score. The performance of SVM is better when compared with other state-of-the-art ML estimators namely K-Nearest Neighbor (KNN), Random Forest (RF), and Logistic Regression (LR). The experiment is conducted for IEEE 33 bus and IEEE 69 bus test systems. A total of 320 fault instances are created using power system Computer-Aided Design (PSCAD) software. The features of these fault points are used as input for the ML model which is extracted using a discrete wavelet transform. This study is conducted for the balanced radial test system.

关键词： distribution automation expert system machine learning meta‐ heuristic techniques power distribution power system optimization service restoration support vector machine

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Probabilistic Planning of Transmission Network Considering Operational Flexibility of power system

Probabilistic Planning of Transmission Network Considering O...

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Smart power & Internet Energy systems (SPIES), International Conference on

作者： Jianjie Li Ping Li Peng Li Junshao Guo Mingqiang Wang Binzhou Electric Company Binzhou China Key Laboratory of Power System Intelligent Dispatch and Control Ministry of Education Shandong University Jinan China

The uncertainties about equipment failure will bring great challenges to the operation of transmission network. Therefore, it is important for transmission network to possess sufficient flexibility to accommodate the fluctuation of load and deal with unit failures. This paper proposes a probabilistic planning model of transmission network considering operational flexibility, which makes decision-making optimization for transmission network planning and reduces the influence of uncertainties about equipment failure. Moreover, the proposed model introduces the probability and expectation of up-flexibility-not-supplied (UFNS) and expected-energy-not-supplied (EENS), then uses penalty cost to quantify them, which enhances the reliability and economy of transmission network planning. Furthermore, the absolute value linearization method is used to linearize the expectation of UFNS and EENS, which greatly improves the calculation efficiency. The proposed model is solved using the CPLEX solver in General Algebraic Modeling system (GAMS). The practicality of model is verified by the IEEE-RTS system.

关键词： Uncertainty Fluctuations Probabilistic logic Software Planning power system reliability Software reliability

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Confidence Interval Based Distributionally Robust Real-Time Economic dispatch Approach Considering Wind power Accommodation Risk

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IEEE TRANSACTIONS ON SUSTAINABLE ENERGY 2021年第1期12卷 58-69页

作者： Li, Peng Yang, Ming Wu, Qiuwei Shandong Univ Key Lab Power Syst Intelligent Dispatch & Control Jinan 250061 Peoples R China Tech Univ Denmark DTU Dept Elect Engn Ctr Elect Power & Energy CEE DK-2800 Lyngby Denmark Shandong Univ Sch Elect Engn Jinan 260061 Peoples R China

This article proposes a confidence interval based distributionally robust real-time economic dispatch (CI-DRED) approach, which considers the risk related to accommodating wind power. In this article, only the wind power curtailment and load shedding due to wind power disturbances are evaluated in the operational risk. The proposed approach can strike a balance between the operational costs and risk even when the wind power probability distribution cannot be precisely estimated. A novel ambiguity set is developed based on the imprecise probability theory, which can be constructed based on the point-wise or family-wise confidence intervals. The worst pair of distributions in the established ambiguity set is then identified, and the original CI-DRED problem is transformed into a determined nonlinear dispatch problem accordingly. By using the sequential convex optimization method and piecewise linear approximation method, the nonlinear dispatch model is reformulated as a mixed integer linear programming problem, for which off-the-shelf solvers are available. A fast inactive constraint filtration method is also applied to further relieve the computational burden. Numerical results on the IEEE 118-bus system and a real 445-bus system verify the effectiveness and efficiency of the proposed approach.

关键词： Wind power generation Robustness Uncertainty Wind farms power systems Optimization Generators Ambiguity set confidence interval distributionally robust economic dispatch imprecise probability theory operational risk

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Coordinated operation and optimal sizing strategy of P2G-CNG trucks scheme on wind farm for participating in retail gas market

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FRONTIERS IN ENERGY RESEARCH 2024年 12卷

作者： Salimu, Athuman Zhang, Feng Zhang, Gang Yang, Libin Shandong Univ Sch Elect Engn Key Lab Power Syst Intelligent Dispatch & Control Minist Educ Jinan Peoples R China Harbin Engn Univ Yantai Res Inst Yantai Peoples R China State Grid Qinghai Elect Power Co Clean Energy Dev Inst Xining Qinghai Peoples R China

The increase in wind penetration and the stochastic nature of wind can cause some wind power to be curtailed, leading to a loss of revenue. Wind farm owners can invest in power to gas (P2G)-compressed natural gas (CNG) truck systems to utilize the curtailed wind power that would otherwise have been wasted. This paper proposes a coordinated operation and optimal sizing strategy of P2G and CNG trucks. P2G derives its power from curtailed wind power and also purchases electricity at low prices to facilitate methane (natural gas) production. The methane is directly stored in CNG trucks, which are used as temporary storage and as a means of transporting the gas, thereby reducing costs by eliminating storage tank. Firstly, a coordinated operation strategy for P2G and CNG trucks considering curtailed wind power and electricity purchased from the grid is presented. Secondly, an optimal sizing optimization model to determine the capacity of P2G and the number of CNG trucks using particle swarm optimization is presented, considering the coordinated operation strategy and operation constraints to maximize profit when the proposed system participates in the retail gas market. Finally, case studies were conducted to verify the viability of the proposed strategy using curtailed wind power data, electricity price data, and gas price data. Result show that optimal capacity of the P2G plant is 9.9 MW, and the optimal number of CNG trucks is 2. There is a 19.8% cost reduction with a payback period of 5.728 years compared to the conventional strategy with a payback period of 8.52 years. 89.74% of curtailed wind power is utilized, while only 10.26% of curtailed wind power is unutilized. Higher utilization of curtailed wind power that would otherwise have been wasted is achieved. The system earns a profit by selling the produced natural gas to a retail gas market.

关键词： curtailed wind power power to gas natural gas coordinated operation optimal sizing gas transportation retail natural gas market

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power system transient voltage vulnerability assessment based on knowledge visualization of CNN

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INTERNATIONAL JOURNAL OF ELECTRICAL power & ENERGY systemS 2024年第PartB期155卷

作者： Li, Zhendong Yan, Jiongcheng Liu, Yutian Liu, Weipeng Li, Li Qu, Hanbing Shandong Univ Key Lab Power Grid Intelligent Dispatch & Control Minist Educ 17923 Jingshi Rd Jinan 250061 Peoples R China Stevens Inst Technol Dept Elect & Comp Engn Hoboken NJ 07030 USA State Grid Shandong Elect Power Co Jinan Elect Power Supply Co Jinan 250011 Peoples R China

Because of the increasing proportion of grid-connected converters, the transient voltage problem has increased the risk of the secure and stable operation of power systems. As the extension of transient voltage stability assessment (TVSA), transient voltage vulnerability assessment is the key technique to detect the crucial elements which can cause the insecurity risk of power system transient voltage. Although deep learning-based TVSA models have demonstrated the high accuracy, it is more important to display the knowledge learnt by the models and utilize it to recognize the weakness of transient voltage. This paper presents a transient voltage vulnerability assessment method by knowledge visualization of convolutional neural network (CNN). First, the image-form sample construction method is proposed to characterize the power system operating condition, which prepares for training CNN-based TVSA model. Then, the knowledge in TVSA model is visualized based on Gradientweighted Class Activation Mapping (Grad-CAM) and Activation Maximization (AM) algorithms, which is analyzed from the perspective of power system stability. Finally, a comprehensive vulnerability indicator is proposed for identify the weakness of transient voltage, which combines the knowledge inside the TVSA model and the indicators with clear physical meaning. Simulation results of the New England 39-bus test system and the real Shandong provincial power grid in China demonstrate that the visualization makes the model more reliable and the proposed method can accurately identify the vulnerable branches.

关键词： power system Transient voltage vulnerability assessment Knowledge visualization Grad-CAM Convolutional neural network

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