A method for identifying a pattern of a load cycle includes: performing statistics on a daily load of a system based on smart meter data; generating a curve of the daily load of the system according to the statistics on the daily load of the system; acquiring a result of clustering curves of loads of typical days by applying shape-based time sequence clustering analysis using the curve of the daily load of the system; and identifying a pattern of a load cycle according to the result of clustering the curves of the loads of the typical days.

Devices and methods of allocating distributed energy resources (DERs) to loads connected to a microgrid based on the cost of the DERs are provided. The devices and methods may determine one or more microgrid measurements. The devices and methods may determine one or more real-time electricity prices associated with utility generation sources. The devices and methods may determine one or more forecasts. The devices and methods may determine a cost associated with one or more renewable energy sources within the microgrid. The devices and methods may determine an allocation of the renewable sources to one or more loads in the microgrid.

A cyber-secure local electrical power market for a power grid with a utility operator transmitting power where a group of participating nodes within the distribution network operate together through respective computers on a blockchain architecture. The participating nodes have controllable resources with controllers in operative communication within the blockchain architecture, such as controllable generators and controllable loads. Decentralized market software operates on computers within the blockchain architecture and shares blockchain datasets that include financial information associated with the controllable resources and operating states of the grid. One or more of the computers in the blockchain architecture calculates Locational Marginal Pricing (LMP) across the participating nodes according to the set of financial information and determines a set of energy service orders corresponding to LMP for the controllable resources to change their operating states. The computers also preferably calculate an energy balance with the transmission system in determining the energy service orders.

An energy storage system for a building includes a battery and an energy storage controller. The battery is configured to store electrical energy purchased from a utility and to discharge stored electrical energy for use in satisfying a building energy load. The energy storage controller is configured to generate a cost function including a peak load contribution (PLC) term. The PLC term represents a cost based on electrical energy purchased from the utility during coincidental peak hours in an optimization period. The controller is configured to modify the cost function by applying a peak hours mask to the PLC term. The peak hours mask identifies one or more hours in the optimization period as projected peak hours and causes the energy storage controller to disregard the electrical energy purchased from the utility during any hours not identified as projected peak hours when calculating a value for the PLC term.

An example method comprises receiving an initial topology of an electrical, receiving a selection of a region of interest, determining one or more external equivalents of the electrical network that are external to the region of interest, determining one or more internal equivalents of the region of interest, calculating a sensitivity matrix based on electrical impedances of at least one of the one or more internal equivalents and based on an amount of power exchanged when in operation, determining a subset of the sensitivity matrix as indicating highly sensitive buses, receiving historical data regarding power flows, predicting power flow for each highly sensitive buses, comparing the predicted power flow to at least one predetermined threshold to determine possible network congestion, and generating a report regarding network congestion and locations of possible network congestion in the region of interest based on the comparison.

Systems and methods for providing a report of an analytic result value based on IP data are disclosed. An example system may include a controller configured to: access a distributed ledger including a plurality of intellectual property (IP) data about a plurality of IP assets, where the IP assets include an aggregate stack of IP; tokenize the plurality of IP data; and interpret a distributed ledger operation corresponding to at least one of the IP assets. The controller may also be configured to determine an analytic result value in response to the distributed ledger operation and the tokenized plurality of IP data, where the analytic result value is one of: a number of access events, an access time; or an access processing time and to provide a report of the analytic result value; and record a transaction on the distributed ledger in response to providing the report.

One example includes a forecast engine that generates forecast data that characterizes predicted operating conditions of an energy storage system for a given time period in the future, wherein the predicted operating conditions are based on a load history for a power consuming premises coupled to the energy storage system and on a value history for power provided to and consumed from a power grid. The load history of the power consuming premises characterizes unmetered power transferred to the power consuming premises, metered powered transferred from the power grid to the power consuming premises and metered powered exchanged from the energy storage system to the power grid. In the example, a schedule manager generates an operation schedule for operating the energy storage system. The operation schedule includes charge and discharge patterns for an energy storage source that are tuned to curtail power costs and/or elevate power revenue value.

A method of operating a computer device includes obtaining measurements of one or more conditions of a power grid component associated with the computer device. The computer device forecasts a future state of the one or more conditions of the power grid component associated with the computer device. The computer device communicates with other computer devices associated with other power grid components to negotiate a behavior of the power grid component associated with the computer device using the forecast.

A facility providing systems and methods for managing and optimizing energy consumption and/or production is provided. The facility provides techniques for optimizing energy-consuming and energy-producing systems to meet specified demands or goals in accordance with various constraints. The facility relies on models to generate an optimization for an energy system. In order to use generic models to simulate and optimize energy consumption for an energy system, the generic models are calibrated to properly represent or approximate conditions of the energy system during the optimization period. After the appropriate models have been calibrated for a given situation using one or more modeling parameter sets, the facility can simulate inputs and responses for the corresponding system. The facility uses the generated simulations to generate a plan or control schedule to be implemented by the energy system during the optimization period.

A power prediction system includes a battery removably mounted on an electric power device using electric power, a charging device configured to charge the battery, and a power prediction device configured to predict an amount of electric power capable of being supplied by the charging device to outside of the charging device through machine learning on the basis of usage information indicating at least one of the usage state and the usage environment of the charging device.