Nec Corporation

Ali Hooshmand

Ratnesh Kumar Sharma

Babak Asghari

Ramin Moslemi

Kiyoshi Nakayama

Shankar Narayan Mohan

Seyyed Ali Pourmousavi Kani

Mehdi Assefi

Ceyhun Eksin

Korosh Vatanparver

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

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

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

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 forecast model from 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 monthly DCT value for the facility using the forecast model. 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.

Machine learning based demand charge

A system to manage a power grid includes one or more storage and generator devices coupled to the power grid; and a decentralized management module to control the devices including: a module to perform decentralized local forecasts; and a module to perform decentralized device reconfiguration.

Decentralized energy management platform

Systems and methods for optimal sizing of one or more grid-scale batteries for frequency regulation service, including determining a desired battery output power for the one or more batteries for a particular period of time. A battery size is optimized for the one or more batteries for the particular period of time, and the optimizing is repeated using different time periods to generate a set of optimal battery sizes based on at least one of generated operational constraints or quality criteria constraints for the one or more batteries. A most optimal battery is selected from the set of optimal battery sizes.

Optimizing sizing of grid-scale batteries for frequency regulation services

A method for power management includes applying a decentralized control to manage a large-scale community-level energy system; obtaining a global optimal solution satisfying constraints between the agents representing the energy system's devices as a state-based potential game with a multi-agent framework; independently optimizing each agent's output power while considering operational constraints and assuring a pure Nash equilibrium (NE), wherein a state space helps coordinating the agents' behavior in energy system to deal with system-wide constraints including supply demand balance, battery charging power constraint and satisfy system-wide and device-level (local) operational constraints; and controlling distributed generations (DGs) and storage devices using the agent's output.

Systems and methods are disclosed for multi-objective energy management of micro-grids. A two-layer control method is used. In the first layer which is the advisory layer, a Model Predictive Control (MPC) method is used as a long term scheduler. The result of this layer will be used as optimality constraints in the second layer. In the second layer, a real-time controller guarantees a second-by-second balance between supply and demand subject to the constraints provided by the advisory layer.

Multi-objective energy management methods for micro-grids

The invention is directed to a method or management system which 1) dispatches high efficiency generators first, 2) charges/discharges energy storage units in a way to enhance efficiency of generators in the system or avoid the necessity of dispatching generators during their low efficiency operations at all. In this way the method or management system utilizes its knowledge about the efficiency characteristics of generators in the system and its ability to change the net demand seen by the generators through charge and discharge of energy storage units to increase the overall efficiency of the energy system.

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San Jose, CA, United States of America

Ali Hooshmand