Nec Corporation

Other

State Grid Jiangsu Electric Power Company

Global Energy Interconnection Research Institute Co. Ltd

State Grid Corporation of China

State Grid Corporation of China Co., Ltd

Global Energy Interconnection Research Institute North America

State Grid Shanxi Electric Power Company

State Grid Beijing Electric Power Company

State Grid Jiangxi Electric Power Co.

Global Energy Interconnection Research Institute

State Grid of China, State Grid Jiangsu Electric Power Co Ltd

State Grid Jiangxi Electric Power Co, Geirina

Global Energy Interconnection Research Institute North America (geirina)

Geiri Co Ltd, State Grid Jiangxi Electric Power Co, State Grid Corp of China, Geirina Inc.

Di Shi

Zhiwei Wang

Ratnesh Kumar Sharma

Chunlei Xu

Haifeng Li

Xiao Lu

Zhe Yu

Ruisheng Diao

Zhehan Yi

Yishen Wang

Xiaohu Zhang

Qibing Zhang

Yanzhu Ye

Xueming Li

Siqi Wang

Shiming Xu

Jiangpeng Dai

Yingmeng Xiang

Guanyu Tian

Yingzhong Gu

Haiwei Wu

Jiajun Duan

Desong Bian

Xiaoying Zhao

Feng Guo

Wei Li

Bei Zhang

Babak Asghari

Jianyu Luo

Rakesh Patil

Feng Xue

Tingting Hou

Zheming Liang

Huimin Peng

Yusheng Luo

Junhui Zhao

Dawei He

Jiangchao Qin

Xinan Wang

Chaoyang Jing

Bibin Huang

Chang Fu

Haiyun Han

A system with deep reinforcement learning based control determines optimal actions for major components in a commercial building to minimize operation costs while maximizing comprehensive comfort levels of occupants. An unsupervised deep Q-network method is introduced to handle the energy management problem by evaluating the influence of operation costs on comfort levels considering the environment factors at each time slot. An optimum control decision can be derived that targets both immediate and long-term goals, where exploration and exploitation are considered simultaneously.

Deep reinforcement learning based real-time scheduling of Energy Storage System (ESS) in commercial campus

Systems and methods for processing measurement data in an electric power system include acquiring the measurement data by a phasor measurement unit (PMU) coupled to a line of the electric power system, and inputting a plurality of the measurement data within a predetermined time window into a K-nearest neighbor (KNN) for identifying bad data among the plurality of the measurement data, wherein when one of the plurality of measurement data contains a bad datum, the machine learning module sends the bad datum to a denoising autoencoder module for correcting the bad datum, wherein the denoising autoencoder module outputs a corrected part corresponding to the bad datum, and when one of the plurality of measurement data contains no bad datum, the machine learning module bypasses the denoising autoencoder module and outputs the one of the plurality of measurement data as an untouched part.

Systems and methods of bad data identification and recovery for electric power systems

A method to simulate a circuit includes receiving at least one circuit requirement and at least one Modular Multilevel Converter (MMC) parameter; determining an operating mode and switching states of an arm circuit; determining each capacitor current based on the switching states and an arm current; determining capacitor voltage and arm voltage; and generating an equivalent circuit model to simulate MMC-based HVDC systems and DC grids in a hybrid AC and DC power system.

Generalized equivalent circuit model of MMC-HVDC for power system simulation

Systems and methods are disclosed for control voltage profiles, line flows and transmission losses of a power grid by forming an autonomous multi-objective control model with one or more neural networks as a Deep Reinforcement Learning (DRL) agent; training the DRL agent to provide data-driven, real-time and autonomous grid control strategies; and coordinating and optimizing power controllers to regulate voltage profiles, line flows and transmission losses in the power grid with a Markov decision process (MDP) operating with reinforcement learning to control problems in dynamic and stochastic environments.

Multi-objective real-time power flow control method using soft actor-critic

State estimation in an electric power system includes acquiring electrical measurements from the electric power system at a reporting rate of the electrical measurements, processing the electrical measurements into sequence data including positive sequence data, processing positive sequence data by an observable state estimator to generate a plurality of estimated states including a plurality of estimated observable states, parameters of the observable state estimator being updated by a first training module in a first time thread, processing the plurality of estimated states by an unobservable state estimator to generate a plurality of estimated unobservable states, parameters of the unobservable state estimator being updated by a second training module in a second time thread independent of the first time thread, and outputting a plurality of final estimated states generated by concatenating the plurality of estimated observable states and the plurality of estimated unobservable states.

Systems and methods of power system state estimation

Systems and methods manage electrical loads in a grid by applying Robust principal component analysis (R-PCA) to decompose annual load profiles into low-rank components and sparse components; extracting one or more predetermined features; constructing a similarity graph; selecting submodular cluster centers through the constructed similarity graph; determining a cluster assignment based on selected centers; and applying the clustering assignment for load analysis.

Submodular load clustering with robust principal component analysis

A method for managing a power load of a grid includes performing a statistic-based distribution estimation of a composite load model using static and dynamic models with Gibbs sampling; deriving a distribution estimation of load model coefficients; and controlling grid power based on a simulation, a prediction, a stability analysis or a reliability analysis with the load model coefficients.

Bayesian estimation based parameter estimation for composite load model

Systems and methods are disclosed for power management by estimating power contingency of a grid at a cloud control center; performing decentralized real-time measurement and making local decisions at one more computer controlled outlets connected to the grid; and aggregating distributed loads to provide emergency frequency support to the grid.

Adaptive method for aggregation of distributed loads to provide emergency frequency support

Systems and methods are disclosed to control the power grid frequency by capturing the frequency change using an extended Kalman filter method with the distributed smart outlet devices at the low-voltage distribution level; and locally control the relay that provides power to the appliance by comparing the captured frequency with the threshold sent from the cloud control center.

Smart outlet system with fast frequency tracking for power system frequency control using distributed appliances

Systems and method are disclosed for monitor long-term voltage stability in a power system by estimating post-contingency operating point(s) considering PV-PQ transitions based on the current operating point; predicting a sensitivity based Thevenin index (STI) for each contingency using the estimated operating condition; and determining a voltage stability assessment (VSA) considering N&#x2212;1 contingency.

Sensitivity based Thevenin index for voltage stability assessment considering N-1 contingency

Systems and methods are disclosed to manage a microgrid with a hybrid energy storage system (HESS) includes deriving a dynamic model of a bidirectional-power-converter (BPC)-interfaced HESS; applying a first neural network (NN) to estimate a system dynamic; and applying a second NN to calculate an optimal control input for the HESS through online learning based on the estimated system dynamics.

Optimal charging and discharging control for hybrid energy storage system based on reinforcement learning

Systems and methods are disclosed to control a power inverter by extracting photovoltaic (PV) maximum power under a plurality of operating conditions as one or more reference variables; predicting the future behaviors of a PV output voltage as one or more controlled variables based on a current operational condition; generating one or more control signals for the H5 inverter that minimizes an error between the reference variable and the controlled variables; and controlling the power inverter with the one or more control signals.

MPC-based PV maximum power point tracker for transformerless H5 inverter with leakage current reduction

A control system is disclosed with a control strategy for autonomous multi-bus hybrid microgrids based on Finite-Control-Set Model Predictive Control (FCS-MPC). The control loops are expedited by predicting the future states and determining the optimal control action before switching signals are sent to converters/inverters. The method eliminates PI and PWM components, and offers 1) accurate PV maximum power point tracking (MPPT) and battery charging/discharging control, 2) DC and AC bus voltage/frequency regulation, and 3) precise and flexible power sharing control among multiple DERs.

Model predictive controller for autonomous hybrid microgrids

Data quality of Phasor Measurement Unit (PMU) is receiving increasing attention as it has been identified as one of the limiting factors that affect many wide-area measurement system (WAMS) based applications. In general, existing PMU calibration methods include offline testing and model based approaches. However, in practice, the effectiveness of both is limited due to the very strong assumptions employed. This invention presents a novel framework for online error detection and calibration of PMU measurement using density-based spatial clustering of applications with noise (DBSCAN) based on much relaxed assumptions. With a new problem formulation, the proposed data mining based methodology is applicable across a wide spectrum of practical conditions and one side-product of it is more accurate transmission line parameters for the energy management system (EMS) database and protective relay settings. Case studies are presented to demonstrate the effectiveness of the proposed method.

Data mining based approach for online calibration of phasor measurement unit (PMU)

Systems and methods for energy distribution for one or more grid-scale Energy Storage Systems (ESSs), including generating one or more time series models to provide forecasted pricing data for one or more markets, determining battery life and degradation costs for one or more batteries in or more ESSs to provide battery life and degradation costs, optimizing bids for the one or more markets to generate optimal bids based on at least one of the forecasted pricing data or the battery life and degradation costs, and distributing energy to or from the one or more ESSs based on the optimal bids generated.

Management of grid-scale energy storage systems for multiple services

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San Jose, CA, United States of America

Di Shi