Abstract: 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.

Abstract: 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.

Abstract: A system and methods are provided for a decentralized transactive energy management. The method includes calculating, by a processor-device, power balancing at one of a plurality of nodes responsive to current statistics at the one of a plurality of nodes. The method also includes estimating, by the processor-device, a present energy demand for the one of a plurality of nodes responsive to the current statistics. The method additionally includes obtaining, by the processor-device, an amount of excess energy available another of the plurality of nodes. The method further includes optimizing, by the processor-device, a power flow between the one of the plurality of nodes and the another of the plurality of nodes to satisfy the present energy demand for the one of the plurality of nodes. The method also includes transferring the excess energy from the another of the plurality of nodes to the one of the plurality of nodes.

Abstract: 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.

Abstract: 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.

Abstract: A computer-implemented method for controlling voltage fluctuations of a microgrid including a plurality of distributed generators (DGs) is presented. The computer-implemented method includes collecting, by a resiliency controller including at least a voltage control module, measurement data from the microgrid, using, by a reactive power estimator, reactive power estimations to calculate an amount of reactive power for each of the DGs, and using a dynamic droop control unit to distribute the reactive power to each of the DGs of the microgrid.

Abstract: 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.

Abstract: A computer-implemented method for controlling voltage fluctuations of a microgrid including a plurality of distributed generators (DGs) is presented. The computer-implemented method includes collecting, by a resiliency controller, measurement data from the microgrid, using a model predictive control (MPC) module to distribute reactive power to each of the DGs of the microgrid, and using a droop based controller to guide operation of each of the DGs of the microgrid.

Abstract: Computer-implemented methods and, a system are provided. A method includes constructing by an Energy Management System (EMS), one or more optimization-based techniques for resilient battery charging based on an optimization problem having an EMS cost-based objective function. The one or more optimization-based techniques are constructed to include a battery degradation metric in the optimization problem. The method further includes charging, by the EMS, one or more batteries in a power system in accordance with the one or more optimization-based techniques.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: Systems and methods for controlling behind-the meter energy storage/management systems (EMSs) to maximize battery lifetime, including determining optimal monthly demand charge thresholds based on a received customer load profile, battery manufacturer specifications, and battery operating conditions and parameters. The determining of the monthly demand charge threshold includes iteratively performing daily optimizations to determine battery utilization, and minimize demand charge for each day for the load profile. A battery lifetime is predicted based on manufacturer specifications and utilization determined by the daily optimizations. A battery capacity retention value and battery capacity loss are determined based on an annual discharged energy (AADE) and an average battery state-of-charge (SoC). An optimal monthly demand threshold is selected based on the predicted battery lifetime and demand charge utilization.

Abstract: A computer-implemented method is provided for controlling a Battery Energy Storage System (BESS) having a battery set and connected to a Photovoltaic (PV) panel set. The method includes enforcing, by a processor device, a multi-objective Model Predictive Control (MPC) optimization on the BESS. The multi-objective MPC optimization includes a first objective of reducing a possibility of Demand Charge Threshold violations by minimal DCT increments which provide a higher demand charge savings, a second objective of improving a robustness of the BESS against energy forecast errors by increasing a State Of Charge (SOC) of the battery set, and a third objective of maximizing PV-utilization. The method further includes controlling, by the processor device, charging and discharging of the BESS in accordance with the multi-objective MPC optimization to meet the first, second, and third objectives.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: A system and method for management of one or more grid-scale Energy Storage Systems (GSSs), including generating an optimal GSS schedule in the presence of frequency regulation uncertainties. The GSS scheduling includes determining optimal capacity deployment factors to minimize penalties for failing to provide scheduled energy and frequency regulation up/down services subject to risk constraints; generating a schedule for a GSS unit by performing co-optimization using the optimal capacity deployment factors, the co-optimization including tracking upper and/or lower bounds on a state of charge (SoC) and including the bounds as a hard constraints; and calculating risk indices based on the optimal scheduling for the GSS unit, and outputting an optimal GSS schedule if risk constraints are satisfied. A controller charges and/or discharges energy from GSS units based on the generated optimal GSS schedule.

Abstract: 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.