Energy loads, sources or batteries exchange mathematical models with each other to form clusters of devices that together provide a service (self-reliance, frequency control, etc.) to a grid operator. Models are exchanged before or after forming clusters; a particular model is used to control its own device and is also used by another load/source to influence its control policy. Heuristics and an optimization technique (using models) are used to form a cluster of devices. Exchanging models obviates the need for a central entity to directly control loads/sources, and the need to exchange real-time data between loads/sources, providing resilience against communication failure. A service manager (demand-response aggregator) sends a service or technical constraints to loads/sources to form clusters on their own. Negotiation between manager and clusters occurs to form consensus on a response. Each device in a cluster is controlled by its own control policy which may depend upon the model of another device in the cluster. If communication is lost the clusters continue to implement the service.

Provided is a bilateral transfer comprising the provision of a performance and a counter-performance, the provision of the performance necessitating the transport of a performance object by means of a distribution network, wherein a change frame indicates the range in which the performance and/or the counter-performance can be modified. Also provided is a method for controlling the transfer which includes steps of recording the performance, the counter-performance, and the change frame; transporting the performance object by means of the distribution network in dependence on an operational state of the distribution network; and determining the counter-performance based on the performance provided within the change frame.

A system for managing grid interaction with an energy storage device includes an energy exchange server, a plurality of energy storage devices, a plurality of interconnect socket adapters, and a plurality of energy exchange controllers, each energy exchange controller coupling to one of the plurality of interconnect socket adapters and dictating energy consumption based on energy pricing data received from the energy exchange server. Each interconnect socket adapter electrically couples to the power grid, one or more energy sinks, and an energy storage device, and the energy exchange server receives a real-time energy consumption data set, a real-time energy production data set, a set of environmental parameters and a starting energy price, and generates a current aggregate electricity demand value as a function of the real-time energy consumption data set and the environmental parameters, a current aggregate electricity supply value as a function of the real-time energy production dataset and the environmental parameters, and a current energy price as a function of the starting energy price, the current aggregate electricity demand value, and the current aggregate electricity supply value.

Systems and methods are described for a power aggregation system. In one implementation, a method includes establishing a communication connection with each of multiple electric resources connected to a power grid, receiving an energy generation signal from a power grid operator, and controlling a number of the electric resources being charged by the power grid as a function of the energy generation signal.

Methods and systems for an electric vehicle charging decision support system are provided. A system can include: a higher level agent configured to be connected to an energy grid and to receive a charging request from an electric vehicle and transmit the charging request to a virtual block agent; and a plurality of charging station agents connected to an energy service provider, the energy grid, and the virtual block agent. The virtual block agent can be configured to receive a respective power set-point and availability from the plurality of charging station agents and transmit a recommended energy price charged at a respective charging station to the energy service provider. The recommended price can maximize a probability of an electric vehicle agent choosing a particular charging station. The system facilitates a win-win situation for the mutual and simultaneous benefit of electric vehicles and the power grid.

Disclosed is a centralized cloud energy storage system for massive and distributed users and a transaction settlement method thereof, a storage medium, and a terminal. The system includes: a centralized energy storage facility invested and operated by a cloud energy storage service provider; the massive and distributed users; and a power network and a user energy management system connecting the centralized energy storage facility with the massive and distributed users. A user sends a charging and discharging request to the cloud energy storage service provider through the user energy management system, and the cloud energy storage service provider issues a charging and discharging instruction to the centralized cloud energy storage system.

Embodiments of systems and methods for power demand management are described herein. More specifically, embodiments comprise systems and methods for powering, controlling, and/or operating various types of controllable load for integration with power fluctuations from intermittent power generation plants, such as photovoltaic arrays and wind turbine farms.

A system for managing energy provision in a multi-tenant data center (MTDC) includes respective tenant nodes corresponding to the respective tenants of the MTDC and a landlord node corresponding to the landlord. Respective service level agreement (SLA) smart contracts are established between respective ones of the tenant nodes and the landlord nodes, the smart contracts configured to allocate energy service levels to the tenant nodes based on blockchain-based service level token exchanges between the tenant nodes and the landlord node. The tenant nodes may be configured to exchange the service level tokens among themselves in exchange for a currency other than the service level tokens. The currency other than the service level tokens may include a fiat currency or a cryptocurrency.

Some embodiments may provide a distributed optimization technology for the control of aggregation of distributed flexibility resource nodes (e.g., associated with distributed energy resources) that operates iteratively until a commanded power profile is produced by aggregated loads. Some embodiments use a distributed iterative solution in which each node solves a local optimization problem with local constraints and states, while using global qualities (e.g., associated with a Lagrange multiplier) that are based upon information from each other node. The global qualities may be determined via a secure, distributed transaction ledger (e.g., associated with blockchain) using DER-specific information obtained in a condensed form (e.g., a scalar or vector) from each node at each iteration. The global qualities may be broadcast to the nodes for each new iteration. Embodiments may provide an iterative, distributed solution to the network optimization problem of aggregated load power tracking.

A system for managing grid interaction with a solar power source includes an energy exchange server, a plurality of solar energy sources, a plurality of interconnect socket adapters, and a plurality of energy exchange controllers, each energy exchange controller coupling to one of the plurality of interconnect socket adapters and dictating energy consumption based on energy pricing data received from the energy exchange server. Each interconnect socket adapter electrically couples to the power grid, one or more energy sinks, and a solar energy source, and the energy exchange server receives a real-time energy consumption data set, a real-time energy production data set, a set of environmental parameters and a starting energy price, and generates a current aggregate electricity demand value as a function of the real-time energy consumption data set and the environmental parameters, a current aggregate electricity supply value as a function of the real-time energy production dataset and the environmental parameters, and a current energy price as a function of the starting energy price, the current aggregate electricity demand value, and the current aggregate electricity supply value.