Nec Corporation

Nec Laboratories America, Inc.

Babak Asghari

Ratnesh Kumar Sharma

Ali Hooshmand

Seyyed Ali Pourmousavi Kani

Di Shi

Rakesh Patil

Zeyu Wang

Syyed Ali Pourmousavi Kani

Mohammad Ehsan Raoufat

Amirsaman Arabali

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

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

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.

Resilient battery charging strategies to reduce battery degradation and self-discharging

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.

Management of grid-scale energy storage systems

Systems and methods for energy distribution for one or more grid-scale Energy Storage Systems (ESSs), including generating one or more time series models to provide forecasted pricing data for one or more markets, determining battery life and degradation costs for one or more batteries in or more ESSs to provide battery life and degradation costs, optimizing bids for the one or more markets to generate optimal bids based on at least one of the forecasted pricing data or the battery life and degradation costs, and distributing energy to or from the one or more ESSs based on the optimal bids generated.

Management of grid-scale energy storage systems for multiple services

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.

Method for efficiency-driven operation of dispatchable sources and storage units in energy systems

A management framework is disclosed that achieves maximum energy storage device lifetime based on energy storage device life estimation and the price of energy. The management framework includes a battery life estimation from a supercycle model, for a time window between two consecutive full charges of a battery, which allows assessing a worst case scenario impact of all partial cycles within a supercycle on the battery life. The battery life estimation then considers each supercycle as a single discharge unit instead of treating each individual discharge period separately.

Method for estimating battery life in presence of partial charge and discharge cycles

A method for managing demand or load includes, upon receipt of a demand response signal or a load management request from a utility to change the price of electricity, determining the cost of energy from a local battery, and if the cost of energy from battery is higher than the cost of electricity from the utility, shedding power from one or more devices based on the utility price, and otherwise discharging power from the battery to reduce a net load on the grid without any actual load reduction.

Reduction of operational cost using energy storage management and demand response

A method for controlling an energy storage unit having a model which tracks each charging instances includes, for each charging event, capturing energy charged into the energy storage unit and an unit energy price of the charging source(s) at the time of charging; updating a unit price of total energy stored in the storage unit at each time-step; during a discharging event, updating the unit price of energy in the storage unit on a selected cost model; comparing the unit price of energy in the storage unit with other generation sources in the microgrid and a utility tariff; selecting a lowest cost combination from generation, storage, and grid and using the lowest cost combination to supply a load; and if a controller decides to use stored energy at any time, sending a discharge command to the energy storage unit.

Method for real-time control of energy storage units to reduce electricity cost

A management framework is disclosed that achieves maximum energy storage device lifetime based on energy storage device life estimation and the price of energy.

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San Jose, CA, United States of America

Babak Asghari