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: Systems and methods for minimizing demand charges, including determining one or more optimal monthly demand charge thresholds based on historical load data, time of use charges, demand charges, and energy storage unit size for one or more end users. A grid power dispatch setpoint is calculated for a particular time step based on a daily load forecast and a daily economic dispatch solution based on the determined optimal monthly demand charge thresholds. A grid power dispatch setpoint for a subsequent time step is determined by iteratively solving the daily energy dispatch for the subsequent time step to determine an optimal grid power dispatch setpoint. Energy and demand charges are minimized by controlling charging and discharging operations for the energy storage unit in real-time based on the determined optimal grid power dispatch setpoint.

Abstract: 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.

Abstract: Aspects of the present disclosure describe structures of, and methods for operating a resiliency controller in an islanded microgrid. The resiliency controller exhibits a dynamic droop algorithm for frequency control, employs multiple modules including a frequency control module for microgrid frequency regulation and a State-of-Charge (SOC) based dynamic droop control unit.

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: A method of managing power between the multiple components of a hybrid electrical energy storage system (HESS) that includes providing at least two power storage elements, and at least one renewable power source. The method further includes managing the power flow among the at least two power storage elements with a fuzzy logic controller. The fuzzy logic controller uses a hardware processor that is configured to increase or decrease current to each of the at least two power storage elements using a fuzzy rule base that is dependent upon at least one of a state of charge for each of the at least two power storage elements, and a requested power demand of the hybrid electrical storage system.

Abstract: A computer-implemented method is provided for managing a plurality of micro grids in an energy system. The method includes collecting and maintaining, in a central database, configuration information and state information for the plurality of micro grids, by a processor-based dynamic operation engine. The method further includes identifying failures in any of the plurality of micro grids and generating updated configuration information and updated state information relating to the failures, based on data analytics and diagnostic polling applied to the configuration information and the state information, by a processor-based micro grid diagnostic engine.

Abstract: Systems and methods are disclosed to control a power system with an energy generator and a hybrid energy storage system. The system includes two or more energy storage system, each with different energy storage capacity and energy discharge capacity. The system includes developing data for one or more control variables refined from expert knowledge, trials and tests; providing the control variables to a fuzzy logic controller with a rule base and membership functions; and controlling the energy generator and the hybrid energy storage system using the fuzzy logic controller.

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: Aspects of the present disclosure describe a multi-layer chiller operation management framework and associated methods for managing heating, ventilation, and air conditioning (HVAC) multi-chiller unit operation in real time serving varying system loads. According to the present disclosure, the framework includes two layers—a first layer providing 24-hour chiller operation planning thereby optimizing chiller operation using forecasted load profiles to minimize energy consumption. To this is applied a mixed-integer linear programming (MILP) based optimization. A second layer adjusts chiller operation status in real-time based on actual system load demand. Load forecasting uncertainty is cured in a hierarchical manner based on the level of load uncertainty. Two approaches are employed namely rule-based load sharing adjustment and MILP-based rolling optimization.

Abstract: System and methods for controlling an air conditioning (AC) system includes defining one or more zones to achieve control actions based on local conditions to create a localized dynamic system for localized control, wherein zones are defined by considering hot and cool areas of the room or location of heat generating equipment, wherein the zone definition changes dynamically based on time of day or based on occupancy, or wherein zones are defined in a customizable manner based on sensitivity analysis considering energy savings and comfort tradeoff or considering equipment with predetermined temperature restrictions; monitoring through sensor placement at predetermined locations in the room based on importance of the equipment, heat generation zones and proximity to the AC; determining appropriate temperature setpoints based on existing operating conditions; and applying temperature information at the predetermined locations to generate rules for the actuation of AC systems.

Abstract: A method and system are provided. The method includes synchronously reconnecting a microgrid to a main grid after islanding of the microgrid. The synchronously reconnecting step includes calculating a phase angle difference between synchrophasor measurements collected from a common coupling on the main grid and synchrophasor measurements collected from a common coupling on the microgrid. The synchronously reconnecting step further includes calculating, by a controller, a frequency reference deviation based on the phase angle difference. The synchronously reconnecting step also includes adjusting a frequency of the diesel generator based on the frequency reference deviation.

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: Systems and methods are disclosed for storing photovoltaic (PV) generation by applying reinforcement learning (RL)-based control to battery storages for PV ramp rate control; and exchanging energy dynamically to limit a ramp rate of the PV power output and maintaining a battery state of charge level at a predefined level to minimize required battery size and extend the battery life cycles.

Abstract: Aspects of the present disclosure describe methods and systems for improved control of chillers used in—for example—HVAC applications.

Abstract: Aspects of the present disclosure relate to methods and systems for improved energy storage systems employing batteries operating in a frequency regulation market.

Abstract: 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.

Abstract: A multilayer control framework for a power system includes a hybrid storage system (HSS) to store energy using a plurality of energy storage devices; a local controller coupled to the HSS to smooth output power of wind or photovoltaic energy sources while regulating a State of Charge (SoC) of the HSS; and a system-wide controller coupled to the HSS activated upon an occurrence of one or more energy disturbances with a control strategy designed to improve system dynamics to address the one or more energy disturbances.

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: A method of controlling power in a hybrid energy storage system that may include controlling power through the at least one battery storage element and the capacitor storage element using a multi-level power management system. In some embodiments, the multi-level power management system includes at least a long term battery management layer and a real time power management layer. The long term battery management layer can be for estimating and managing a life cycle for the battery. The real time power management layer can be for managing power sharing between the at least one battery storage element and the at least one capacitor storage element at each time instant dependent upon adjustments to battery performance based upon the long term battery management layer.