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Optimal power Flow with Stochastic Solar power Using Clustering-Based Multi-Objective Differential Evolution

ENERGIES 2022年第24期15卷 9489页

作者： Lv, Derong Xiong, Guojiang Fu, Xiaofan Wu, Yang Xu, Sheng Chen, Hao Guizhou Univ Coll Elect Engn Guiyang 550025 Peoples R China Guizhou Univ Inst Engn Invest & Design Co Ltd Guiyang 550025 Peoples R China Guizhou Elect Power Grid Dispatching & Control Ctr Guiyang 550002 Peoples R China Fujian Prov Key Lab Intelligent Identificat & Cont Quanzhou 362216 Peoples R China

Optimal power flow is one of the fundamental optimal operation problems for power systems. With the increasing scale of solar energy integrated into power systems, the uncertainty of solar power brings intractable challenges to the power system operation. The multi-objective optimal power flow (MOOPF) considering the solar energy becomes a hotspot issue. In this study, a MOOPF model considering the uncertainty of solar power is proposed. Both scenarios of overestimation and underestimation of solar power are modeled and penalized in the form of operating cost. In order to solve this multi-objective optimization model effectively, this study proposes a clustering-based multi-objective differential evolution (CMODE) which is based on the main features: (1) extending DE into multi-objective algorithm, (2) introducing the feasible solution priority technique to deal with different constraints, and (3) combining the feasible solution priority technique and the merged hierarchical clustering method to determine the optimal Pareto frontier. The simulation outcomes on two cases based on the IEEE 57-bus system verify the reliability and superiority of CMODE over other peer methods in addressing the MOOPF.

关键词： optimal power flow uncertainty differential evolution hierarchical clustering Pareto frontier

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Federated Reinforcement Learning for Electric Vehicles Charging Control on Distribution Networks

arXiv

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arXiv 2023年

作者： Qian, Junkai Jiang, Yuning Liu, Xin Wang, Qiong Wang, Ting Shi, Yuanming Chen, Wei The MoE Engineering Research Center of Software/Hardware Co-design Technology and Application The Shanghai Key Lab. of Trustworthy Computing East China Normal University China The Automatic Control Laboratory EPFL Switzerland The School of Information Science and Technology ShanghaiTech University China The State Grid Beijing Electric Power Company China The Department of Electronic Engineering Tsinghua University China

With the growing popularity of electric vehicles (EVs), maintaining power grid stability has become a significant challenge. To address this issue, EV charging control strategies have been developed to manage the switch between vehicle-to-grid (V2G) and grid-to-vehicle (G2V) modes for EVs. In this context, multi-agent deep reinforcement learning (MADRL) has proven its effectiveness in EV charging control. However, existing MADRL-based approaches fail to consider the natural power flow of EV charging/discharging in the distribution network and ignore driver privacy. To deal with these problems, this paper proposes a novel approach that combines multi-EV charging/discharging with a radial distribution network (RDN) operating under optimal power flow (OPF) to distribute power flow in real time. A mathematical model is developed to describe the RDN load. The EV charging control problem is formulated as a Markov Decision Process (MDP) to find an optimal charging control strategy that balances V2G profits, RDN load, and driver anxiety. To effectively learn the optimal EV charging control strategy, a federated deep reinforcement learning algorithm named FedSAC is further proposed. Comprehensive simulation results demonstrate the effectiveness and superiority of our proposed algorithm in terms of the diversity of the charging control strategy, the power fluctuations on RDN, the convergence efficiency, and the generalization ability. © 2023, CC BY-NC-SA.

关键词： Reinforcement learning

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Modeling and Control of Centralized Electric Vehicles for Regulation Service

Modeling and Control of Centralized Electric Vehicles for Re...

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IEEE-power-and-Energy-Society General Meeting (PESGM)

作者： Qi, Wenjin An, Haiyun Wang, Mingshen Dong, Xiaohong Jiang, Qian Zhan, Qiwei Mu, Yunfei Jia, Hongjie Jiangsu Power Design Inst Nanjing 211102 Jiangsu Peoples R China State Grid Jiangsu Elect Power Co Ltd Elect Power Res Inst Nanjing 211103 Jiangsu Peoples R China Tianjin Univ Key Lab Smart Grid Minist Educ Tianjin 300072 Peoples R China Univ Tennessee Dept Elect Engn & Comp Sci Knoxville TN 37996 USA

ISBN: (纸本)9781728155081

The high penetration of renewable energy incurs serious power fluctuations in the power system. Large scale electric vehicles (EVs) under the centralized control are able to provide considerable regulation capacity for the power system. In existing modeling methods for centralized EVs, the accurate control results for the regulation services were achieved by modeling each EV individually. However, the computational complexity was a serious problem for modeling large scale EVs. In this paper, a reduced modeling method for centralized EVs is developed by describing a population of EVs with a partial differential equation (PDE). This PDE modeling method uses a finite number of state-of-charge (SOC) intervals to describe the flows of charging EVs, idle EVs and discharging EVs. Considering the three connecting states (CNS) of EVs, this PDE modeling method realizes the power regulation for centralized EVs by changing the CNS of EVs located in different SOC intervals. Comparative simulations validate that the PDE modeling method for large scale centralized EVs achieves high control accuracy and high computational efficiency.

关键词： Electric vehicle (EV) centralized control regulation service partial differential equation (PDE)

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A novel hybrid multi-objective bacterial colony chemotaxis algorithm

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SOFT COMPUTING 2020年第3期24卷 2013-2032页

作者： Lu, Zhigang Geng, Lijun Huo, Guanghao Zhao, Hao Yao, Weitao Li, Guoqiang Guo, Xiaoqiang Zhang, Jiangfeng Yanshan Univ Key Lab Power Elect Energy Conservat & Motor Driv Qinhuangdao 066004 Hebei Peoples R China Univ Technol Sydney Sch Elect & Data Engn Sydney NSW Australia Tianjin Elect Power Design Inst Co Ltd China Energy Engn Grp Tianjin 300400 Peoples R China State Grid Tianjin Maintenance Co Tianjin 300000 Peoples R China

In this article, a novel hybrid multi-objective bacterial colony chemotaxis (HMOBCC) algorithm is proposed to solve multi-objective optimization problems. A mechanism of particle swarm optimization is introduced to multi-objective bacterial colony chemotaxis (MOBCC) algorithm to improve the performance of MOBCC algorithm. Also, three other techniques, including dynamic reverse learning operator, external archive multiplying operator and adaptive diversity maintenance operator, are further applied to improve the diversity and convergence of the algorithm. The proposed algorithm is validated using 12 benchmark problems, and three performance measures are implemented for 5 benchmark problems to compare its performance with existing popular algorithms such as MOBCC, multi-objective bacterial colony chemotaxis based on grid algorithm, non-dominated sorting genetic algorithm (NSGA-II) and multi-objective evolutionary algorithm based on decomposition. The results show that the proposed HMOBCC is very effective against existing algorithms.

关键词： Global optimization MOBCC Hybrid algorithm Intelligence computation

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Predicting the performance of solar dish Stirling power plant using a hybrid random vector functional link/chimp optimization model

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SOLAR ENERGY 2021年 222卷 1-17页

作者： Zayed, Mohamed E. Zhao, Jun Li, Wenjia Elsheikh, Ammar H. Abd Elaziz, Mohamed Yousri, Dalia Zhong, Shengyuan Mingxi, Zhu Tianjin Univ Key Lab Efficient Utilizat Low & Medium Grade Ene MOE Tianjin 300350 Peoples R China Tanta Univ Dept Power Mech Engn Fac Engn Tanta 31521 Egypt Tanta Univ Dept Prod Engn & Mech Design Tanta 31527 Egypt Zagazig Univ Dept Math Fac Sci Zagazig Egypt Fayoum Univ Dept Elect Engn Fac Engn Al Fayyum Egypt STATE GRID Int Dev Co Ltd West Changan St Beijing 100031 Peoples R China

Hybrid artificial intelligence models have become promising tools for soft computing and computational intelligence, as they can deal with complicated sustainable systems such as the prediction modeling of concentrated power systems. In these models, one or two artificial intelligence techniques are integrated with an optimization algorithm to develop a fine-tuned prediction modeling. In this paper, we develop a novel hybrid prediction model using an improved version of the Random Vector Functional Link (RVFL) network to predict the instantaneous output power and the monthly power production of a solar dish/Stirling power plant (SDSPP). A new metaheuristic algorithm called Chimp Optimization Algorithm (CHOA) has been combined with the RVFL network to effectively determine the optimal values of RVFL parameters. More so, the proposed RVFL-CHOA model is compared with four artificial-based models include the original RVFL, and three hybrid modified versions of the RVFL model using the Particle Swarm Optimization (PSO), Spherical Search Optimization (SSO), and Whale Optimization Algorithm (WOA). The prediction performance of the five models was compared using various statistical evaluation metrics. The statistical results prove the superiority and effectiveness of the proposed RFVL-CHOA method among the other investigated optimized models for performance prediction of the SDSPP. Based on the test data, the REVL-CHOA predicts the instantaneous output power and the monthly power production of the SDSPP with determination coefficient values of 0.9992, and 0.9108, and root mean square error values of about 0.00047, and 0.05995, respectively.

关键词： Solar dish Stirling power plant Random Vector Functional Link Chimp optimization algorithm Spherical search optimization Whale optimization algorithm power production prediction

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Multi-level data fusion algorithm towards privacy protection in wireless sensor networks

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INTERNATIONAL JOURNAL OF COMMUNICATION NETWORKS AND DISTRIBUTED SYSTEMS 2020年第3期25卷 265-283页

作者： Wang, Zheng Li, Liang Ao, Chen Wu, Dapeng Zhou, Wenjun Yu, Xiaojun Beijing Smart Chip Microelect Technol Co Ltd State Grid Key Lab Power Ind Chip Design & Anal T Beijing 100192 Peoples R China Chongqing Univ Posts & Telecommun 2 Chongwen Rd Chongqing Peoples R China Wuhan Univ Luojia Hill Wuchang Dist Hubei Peoples R China NingXia Elect Power Co Ltd Maintenance Co State Grid 288 Changcheng East Rd Yinchuan Ningxia Peoples R China

Data fusion is one of the key technologies in wireless sensor networks. To promote secure and efficient data fusion for wireless sensor networks, a privacy protection-based multi-level data fusion algorithm is proposed. In order to minimise the network energy consumption, the optimal number of cluster heads is selected and the adaptive clustering is performed according to the node energy and the positional relationship between nodes. Then, the cluster members collect and encrypt data, whereas the cluster head cleans and integrates the encrypted data. Furthermore, by analysing the correlation between data and constructing the BP neural network, cluster heads and the sink node can fuse the data in a cluster and the data between clusters to achieve the optimal data fusion. Results show that the mechanism proposed in this paper can significantly reduce resource overheads, effectively guarantee data security and fusion efficiency.

关键词： wireless sensor network data fusion privacy protection data compression BP neural network

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Radiation Hardening design of Nonvolatile Hybrid Flip-Flop Based on Spin Orbit Torque MTJ and SRAM

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SPIN 2022年第3期12卷

作者： Zhao, Dongyan Wang, Yubo Chen, Yanning Shao, Jin Fu, Zhen Wang, Guangyao Pan, Biao Zhang, Peng Pan, Cheng Beijing Smart Chip Microelect Technol Co Ltd State Grid Key Lab Power Ind Chip Design & Anal T Beijing 100191 Peoples R China Beijing Chip Identificat Technol Co Ltd Beijing 100191 Peoples R China Beihang Univ Sch Integrated Circuit Sci & Engn Beijing 100191 Peoples R China

Spin orbit torque magnetic tunnel junction (SOT-MTJ) has been widely used for designing nonvolatile flip-flop (NVFF), thanks to its ultrafast switching speed, zero static power consumption, nearly unlimited endurance, etc. However, in the space environment, the write pulse width of the SOT-MTJ is comparable to radiation-induced current duration. Thus, the SOT-MTJ-based NVFF may suffer from soft error induced by the nonvolatile single event upset (NVSEU) in the backup mode. In this paper, we analyze the sensitive nodes in different work modes and propose the radiation hardening circuits for master-slave NVFF (MS-NVFF) to solve the NVSEUs. Based on a physics-based SOT-MTJ compact model and a 40nm CMOS design kit, the reliability of MS-NVFF is verified by simulation with 2pC charge, which could be reliably integrated into aerospace and avionics electronics in hostile environments.

关键词： SOT-MTJ MS-NVFF NVSEU radiation hardening design

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A BJT-based temperature sensor using two sub-OPAMPs Chopper ripple reduction technique with inaccuracy of ±0.25 °C (3σ) from-45 °C to 85 °C

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MICROELECTRONICS JOURNAL 2022年 123卷 105429-105429页

作者： Guo, Qiansong Ma, Xia Xiao, Zhiming Hu, Yi Feng, Jingbin Wang, Meiyu Hu, Weibo Nankai Univ Shenzhen Grad Sch Lab Integrated Circuits & Smart Syst Shenzhen Peoples R China Nankai Univ Inst Photo Elect Thin Film Devices & Technol Tianjin Peoples R China Hitrend Technol Shanghai Co Ltd Shanghai Peoples R China Beijing Smart Chip Microelect Technol Co Ltd State Grid Key Lab Power Ind Chip Design & Anal Te Beijing Peoples R China

This paper presents a BJT-based temperature sensor inaccuracy of +/- 0.25 degrees C (3 sigma). To handle noises in AFE and ADCs, two modified techniques are put forward, respectively. One technique utilized in the analog front end is modified choppers with two sub-OPAMPs. Two sub-OPAMPs chopped under two non-overlapped clocks with 90-degree phase difference are used to suppress the chopper ripple while removing low frequency noises. To achieve an accurate digitization, a zoom ADC which combines SAR coarse conversion and Sigma-Delta fine conversion is applied for its high resolution and energy-efficiency. One-time offset calibration is used in the OPAMP of the 1st integrator to ensure the good integrator performance. After one-point trimming, the proposed design achieves inaccuracy of +/- 0.25 degrees C (3 sigma) from -45 degrees C to +85 degrees C. It occupies 0.18 mm(2) in a 110 nm CMOS, and draws 12.3 mu A from a 3 V supply, with a conversion time of either 4 ms or 8 ms.

关键词： BJT Calibration Chopping Sigma-delta Temperature sensor

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SOT-MRAM-based Binary Neural Networks Demonstration for Single Character Recognition

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SPIN 2022年第1期12卷

作者： Zhao, Dongyan Wang, Yubo Chen, Yanning Shao, Jin Fu, Zhen Liu, Fang Bai, Yue Zhao, Faqiang Zhong, Mingchen Pan, Cheng Dong, Yi Cao, Kaihua Beijing Smart Chip Microelect Technol Co Ltd State Grid Key Lab Power Ind Chip Design & Anal T Beijing 100192 Peoples R China Beijing Chip Identificat Technol Co Ltd Beijing 102200 Peoples R China Beihang Univ Sch Integrated Circuit Sci & Engn Beijing 100191 Peoples R China

With the substantial increase in the amount of data, the mismatch between the processing speed of the hardware and the software results in the 'Memory Wall' problem. Processing-in-memory (PIM), in which the compute and memory units are integrated, can avoid frequent data transmission. Binary neural network (BNN) uses binary weights and activations instead of full-precision weights and activations in the convolutional neural network, which reduces computational complexity with minor influence on accuracy. In this paper, we used a one-step operation to write a pair of SOT-MRAM cells and verified the two basic operations in BNN: XNOR and bitcount. Then, we employed an external control circuit with FPGA and accomplished 'I' single-character recognition based on vector-matrix multiplication in the SOT-MRAM array.

关键词： Processing-in-memory SOT-MRAM BNN XNOR

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The Implementation of Electric Pile Sharing Operation System based on Identity-Based Cryptograph 3

The Implementation of Electric Pile Sharing Operation System...

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3rd IEEE International Conference on Information Systems and Computer Aided Education, ICISCAE 2020

作者： Peiyi, Wang Zhenjiang, Pang Jiayi, Liu Yanfang, Yuan Yanjie, Zhang State Grid Key Laboratory of Power Industrial Chip Design and Analysis Technology Beijing Smart-Chip Microelectronics Technology Co. Ltd. Beijing China Beijing Engineering Research Center of High-reliability Ic with Power Industrial Grade Beijing Smart-Chip Microelectronics Technology Co. Ltd. Beijing China State Grid Shanxi Marketing Service Center Taiyuan China

ISBN: (纸本)9781728183039

Identity-Based Cryptograph (IBC) takes the user's identity (such as email address, mobile phone number, personal name, etc.) as the public key, which reduces the process of exchanging digital certificates and public keys, effectively reduces the complexity of key and certificate management in the public-key system, and makes the security system easy to deploy and manage. It is very suitable for end-to-end offline secure communication, cloud data encryption, attribute-based encryption, policy-based encryption of various occasions. In order to improve the safety level of end-to-end off-line safety communication in electric vehicle pile sharing application and reduce the system complexity, this paper proposes an identity authentication system solution based on identification algorithm. © 2020 IEEE.

关键词： Piles

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