Resources are required to satisfy various needs and wants of people, businesses, and machines. Resources come in the forms of time, talents, money, materials, energy, services, people, knowledge, communication, and other tangible and intangible assets. When both the capacities and the needs of multiple resources are stored in a way that allows for them to be connected together using computers, they can be efficiently and effectively matched. This matching creates shared value, which has potential academic, economic, societal and philanthropic benefits. Connected computer system(s) can query and match resources together in a way that is mutually beneficial. While a common lexicon is the simplest way to perform the matching, natural language processing, machine translation, or use of similar technologies may be optimal. Any method of collecting these inputs should be able to handle one or multiple capacities, and one or multiple needs.

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.

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.

The present invention provides a control apparatus (100) that includes a remaining capacity change model determination unit (120) that determines a remaining capacity change model that estimates a temporal change of a remaining capacity which is based on charging/discharging in accordance with a first energy service for each of a plurality of energy storage systems that perform charging/discharging in accordance with energy services; and an operation planning unit (130) that computes a charging/discharging plan of each of the plurality of energy storage systems based on the remaining capacity change model of each of the plurality of energy storage systems.

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.

Architectures, apparatuses, methods, systems, and techniques for controlling electrical power distribution network are disclosed. Distributed, hierarchical controls including layered locational energy service control variables may be utilized to determine and control the provision of energy services, including real power, reactive power (VAR), and capacity reserves, by DERs in a distribution network. In a first ex-ante iteration a simulation may be performed to calculate a set of subnetwork-specific control variables based on subnetwork locational energy service prices and a plurality of sets of DER-specific control variables based on DER locational energy service prices. In a second ex-ante iteration a set of actual subnetwork-specific control variables based on subnetwork locational energy service prices and a plurality of sets of actual DER-specific control variables based on DER locational energy service prices. Provision of energy services by DERs in a distribution network occur in response to the determined control variables.

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.

Methods include iterating a price vector between a coordinator and a plurality of participants, wherein the price vector comprises prices for energy services and ancillary services to be produced or consumed by the participants during a plurality of time periods that form power transmission time sequence of power transmission over a power transmission network, until a convergent price vector is obtained, and responsive to the convergent price vector, producing or consuming power over the power transmission network and providing ancillary services to the power transmission network during at least one of the time periods of the power transmission time sequence. Related apparatus are also disclosed.

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.