Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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 determining a voltage distribution parameter of each battery pack based on its maximum voltage, its minimum voltage for discharge, and a present voltage, assign ranks to the plurality of battery packs based on the voltage distribution parameters, and determine a respective power discharge or charge based on the rank of each battery pack and a total power demand. 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.

Abstract: 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 minimize a corresponding voltage gradient versus charge of each battery pack to be below a predetermined threshold, and calculate a respective discharging share of each battery pack based on the charge and the voltage in an updated curve of voltage versus charge of each battery pack and the total power demand. 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 of each battery pack and/or keeping a certain battery pack idle.

Abstract: 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 determining a voltage distribution parameter of each battery pack based on its maximum voltage, its minimum voltage for discharge, and a present voltage, and calculate a respective discharging share of each battery pack based on the voltage distribution parameter and the maximum total rated power 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 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.

Abstract: 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.

Abstract: 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.

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: 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 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: 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.

Abstract: 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.

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 photovoltaic (PV) output forecasting are provided. The methods include determining whether a weather condition that indicates a first forecasting model to have a greater accuracy than a deep learning-based forecasting model is detected in weather data for a predetermined time span. The method also includes forecasting PV output, by a processing device, using the first forecasting model in response to a determination that the weather condition is detected in the weather data for the predetermined time span. The method further includes predicting PV output using the deep learning-based forecasting model in response to a determination that the weather condition is not detected in the weather data for the predetermined time span.

Abstract: A battery management system is provided. The battery management system includes a memory for storing program code. The battery management system further includes a processor for running the program code to extract features from battery operation data. The processor further runs the program code to train a deep learning model to model a battery degradation process of a battery using the extracted features. The processor also runs the program code to generate, using the deep learning model, a prediction of a battery capacity degradation based on the battery operation data and a current battery capacity of the battery. The processor additionally runs the program code to control an operation of the battery responsive to the prediction of the battery capacity degradation.

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 demand charge minimized operations while extending battery life, including determining a demand charge threshold based on received historical data. The systems and methods further including generating a charging pattern for a battery array from the historical. The systems and methods further including calculating a charging schedule for the battery array based on the demand charge threshold, a short term load profile, and the charging pattern. The charging schedule being calculated to follow the charging pattern without exceeding the demand charge threshold and transmitting commands to a battery controller in accordance with to the charging schedule.