Hewlett-packard Development Company, L.p.

Nec Corporation

Nec Laboratories America, Inc.

Hewlett Packard Enterprise Development LP

National Institute of Clean-and-low-carbon Energy

China Energy Investment Corporation Limited

Hewlett-packard Company

Ratnesh Kumar Sharma

Cullen Edwin Bash

Chandrakant D Patel

Abdlmonem H Beitelmal

Amip J Shah

Babak Asghari

Ali Hooshmand

Manish Marwah

Di Shi

Feng Guo

Yanzhu Ye

Thomas W Christian

Umeshwar Dayal

Ming C Hao

Surinder Singh

Chih C Shih

Brandon Ohara

Mohammad Chehreghani Bozchalui

Salil Pradhan

Geoff M Lyon

Cyril Brignone

Kiyoshi Nakayama

Sergio Escobar-Vargas

Seyyed Ali Pourmousavi Kani

Parthasarathy Ranganathan

Martin Arlitt

Gopalakrishnan Janakiraman

Ramin Moslemi

Halldór Janetzko

Justin Moore

Wayne R Mack

Rakesh Patil

Abdimonem H Beitelmal

Yanyi He

Paulo Maciel

Peng Yu

Xianjun Zhang

Christopher Gregory Malone

Tahir Cader

George H Forman

David Allen Moore

Glenn Cochran Simon

Sven Graupner

Jerome Alexander Rolia

Zhikui Wang

Daniel Keim

Alex Xin Zhang

Yuan Chen

Janet Lynn Wiener

Daniel Juergen Gmach

Kevin Dooley

Shankar Narayan Mohan

Malena Rosa Mesarina

Korosh Vatanparvar

Richard J Friedrich

Robert Allen Pereira

You Zhou

Debprakash Patnaik

Naren Ramakrishnan

Nikhil Muralidhar

Wilfredo E Lugo

Lynn Sheehan

Abdimonem Beitelal

Siqi Wang

Anna Fenelon

Ceyhun Eksin

Chandrakat Patel

Chandrakani Patel

Amirsaman Arabali

Lawton J Kainer

Chris G Malone

Ajit Renjit

Dawei He

Lulu Zhao

Motoko Furukawa

Roger Keyes

Junhui Zhao

Korosh Vatanparver

Yusheng Luo

Anupama Keeli

Mohammad Ehsan Raoufat

Zeyu Wang

Syyed Ali Pourmousavi Kani

Chenrui Jin

Sudipta Dutta

Mehdi Assefi

Lola Xiomara Bautista

Abdlmonem Beitelal

A controller, a system including such a controller, and a method for controlling or managing discharge or charge of a plurality of battery packs are provided. The controller includes one or more processor and at least one tangible, non-transitory machine readable medium encoded with one or more programs configured to perform steps to determine a respective power discharge or charge for each battery packs based on characteristic data of each battery pack, a power demand, and the first weighting factor (a) and the second weighting factor (b) for power assignment based on voltage and state of charge of each battery pack. The controller provides signals with instructions to the plurality of battery packs and/or the one or more power converters for discharging power from or charging power to the plurality of battery packs.

Controller and method for balancing discharge or charge of battery packs in energy storage system

A battery power management unit (BPMU), an electrical energy storage system comprising one or more such BPMUs, and method of using the same are provided. Such a BPMU includes a microcontroller and one or more processors having at least one tangible, non-transitory machine readable medium encoded with one or more programs. The BPMU is configured to perform steps of: reading data from the internal BMU of a respective battery pack to establish capacity, an energy baseline, state of health (SOH), and an initial value of state of charge (SOC) of the respective battery pack, checking voltage and current at a time interval, calculating power of the respective battery pack, determining and updating battery date such as SOH and SOC, and transmitting updated battery data to a system controller for controlling discharging power from or charging power to the respective battery pack.

Battery management unit to determine updated SOC and energy reading, energy storage system comprising, the same, and method of using the same

An electrical energy storage system, a controller, and methods of using the same are provided. The system includes battery packs connected in parallel, one or more battery power management unit, one or more power converters, and a controller. The controller includes one or more processor and at least one tangible, non-transitory machine readable medium encoded with one or more programs configured to perform steps for discharging or charging. The steps include: reading data including state of health (SOH) and state of charge (SOC) from each battery pack, connecting a respective battery pack with a respective power converter; receiving a power command from an energy management system, calculating a respective power rate of each battery pack based on the data of SOH, SOC, and the power command, and discharging power from battery packs to a grid or charging power to battery packs based on the power rate of each battery pack.

Controller for energy storage, system comprising the same, and methods of using the same

A controller, a system including such a controller, and a method for controlling discharging of a plurality of battery packs are provided. The controller includes one or more processor and at least one tangible, non-transitory machine readable medium encoded with one or more programs configured to perform steps to determine a respective discharge power or discharge share for each battery pack for maximizing objective function (J) of the plurality of battery packs defined in Equation (1). The controller provides signals with instructions to the plurality of battery packs and/or the one or more power converters for discharging power from the plurality of battery packs based on the respective discharging share and power of each battery pack and/or keeping a certain battery pack idle.

Controller, system and method for controlling discharge of heterogeneous battery packs

A computer-implemented method predicting a life span of a battery storage unit by employing a deep neural network is presented. The method includes collecting energy consumption data from one or more electricity meters installed in a structure, analyzing, via a data processing component, the energy consumption data, removing one or more features extracted from the energy consumption data via a feature engineering component, partitioning the energy consumption data via a data partitioning component, and predicting battery capacity of the battery storage unit via a neural network component sequentially executing three machine learning techniques.

Deep learning approach for battery aging model

Systems and methods for controlling behind-the meter energy storage/management systems (EMSs) for battery-optimized demand charge minimized operations, including determining an optimal monthly demand charge threshold based on a received customer load profile and a customer load profile and savings. The determining of the monthly demand charge threshold includes iteratively performing daily optimizations to determine a daily optimal demand threshold for each day of a month, selecting a monthly demand threshold by clustering the daily optimal demand thresholds for each day of the month into groups, and determining a dominant group representative of a load pattern for a next month. A mean demand threshold for the dominant group is selected as the monthly demand threshold, and continuous battery-optimized demand charge minimized EMS operations are provided based on the monthly demand threshold using a real-time controller configured for overriding the optimal charging/discharging profiles when a monthly demand threshold violation is detected.

Demand charge minimization in behind-the-meter energy management systems

Systems and methods for controlling battery charge levels to maximize savings in a behind the meter energy management system include predicting a demand charge threshold with a power demand management controller based on historical load. A net energy demand is predicted for a current day with a short-term forecaster. A demand threshold maximizes financial savings using the net energy demand using a rolling time horizon optimizer by concurrently optimizing the demand charge savings and demand response rewards. A load reduction capability factor of batteries is determined with a real-time controller corresponding to an amount of energy to fulfill the demand response rewards. The net energy demand is compared with the demand threshold to determine a demand difference. Battery charge levels of the one or more batteries are controlled with the real time controller according to the demand difference and the load reduction capability factor.

Demand charge and response management using energy storage

Systems and methods for minimizing demand charges includes generating historical demand profiles having a plurality of demand charge threshold (DCT) values over a period of time, filtering the DCT values using weighting factors to provide filtered DCT values such that DCT underestimations and overestimations are removed from optimization, determining an optimal DCT output value from the filtered DCT values such that the optimal DCT output value is based on the historical demand profiles and the weighting factors, and distributing energy in one or more batteries in accordance with the optimal DCT output value and current energy demand to minimize energy and demand charges.

System and method for minimizing storage demand charges by weight assignments to different demand categories

Systems and methods for controlling Battery Energy Storage Systems (BESSs), including determining historical minimum state of charge (SOC) for peak shaving of a previous day based on historical photovoltaic (PV)/load profiles, historical demand charge thresholds (DCT), and battery capacity of the BESSs. A minimum SOC for successful peak shaving of a next day is estimated by generating a weighted average based on the historical minimum SOC, and optimal charging/discharging profiles for predetermined intervals are generated based on estimated PV/load profiles for a next selected time period and grid feed-in limitations. Continuous optimal charging/discharging functions are provided for the one or more BESSs using a real-time controller configured for overriding the optimal charging/discharging profiles when at least one of a high excess PV generation, a peak shaving event, or a feed-in limit violation is detected.

System and method for model predictive energy storage system control

A computer-implemented method, system, and computer program product are provided for anomaly detection in a power system. The method includes receiving, by a processor-device, a plurality of measurements from a plurality of meters throughout the power system. The method also includes generating, by the processor-device, temporal causal networks based on pair-wise relationships between the plurality of measurements from the plurality of meters over time. The method additionally includes determining, by the processor-device, invariant relationships for the plurality of meters between the temporal causal networks. The method further includes predicting, by the processor-device, an anomaly from the invariant relationships for the plurality of meters with a residual anomaly threshold. The method also includes disabling one of the plurality of meters that originated the anomaly.

Detection of false data injection attacks in power systems using multiplex invariant networks and domain knowledge

Systems and methods for power management include determining a demand threshold by solving an optimization problem that minimizes peak demand charges and maximizes a usable lifetime for a power storage system. Power is provided to a load from an electrical grid when the load is below the demand threshold and from a combination of the electrical grid and the power storage system when the load is above the demand threshold.

Energy storage-aware demand charge minimization

A system and method are provided. The system includes a processor. The processor is configured to receive power related data relating to power usage of power consuming devices at a customer site from a plurality of sources. The processor is further configured to generate object function inputs from the power related data. The processor is additionally configured to apply the generated object function inputs to an objective function to determine an optimal capacity for a battery storage system powering the power consuming devices at the customer site while minimizing a daily operational power cost for the power consuming devices at the customer site. The processor is also configured to initiate an act to control use of one or more batteries of the battery storage system in accordance with the optimal capacity for the battery storage system.

Optimal battery sizing for behind-the-meter applications considering participation in demand response programs and demand charge reduction

Aspects of the present disclosure describe a single battery degradation model and methods that considers both CYCLING and CALENDAR aging and useful in both energy management and battery management systems that may employ any of a variety of known battery technologies.

Innovative framework combining cycling and calendar aging models

A computer-implemented method, system, and computer program product are provided for demand charge management. The method includes receiving an active power demand for a facility, a current load demand charge threshold (DCT) profile for the facility, and a plurality of previously observed load DCT profiles. The method also includes generating a data set of DCT values based on the current load DCT profile for the facility and the plurality of previously observed load DCT profiles. The method additionally includes forecasting a next month DCT value for the facility using the data set of DCT values. The method further includes preventing actual power used from a utility from exceeding the next month DCT value by discharging a battery storage system into a behind the meter power infrastructure for the facility.

Data-driven demand charge management solution

Growing community of inventors

Union City, CA, United States of America

Ratnesh Kumar Sharma