A method tests the configuration of an aggregated DERs system using distributed asset managers in a decentralized hardware-in-the-loop (“HIL”) scheme. The managers contain the model of the asset they are meant to control. The method programs an asset manager with a model of a DERs asset. A plurality of asset managers are connected to a central controller. The plurality of asset managers are also connected to a simplified hardware-in-the-loop platform. The simplified HIL platform is configured to solve a network model, a load model, a non-controllable asset model, and a grid model. The method tests the DERs system control structure by using: (a) the simplified HIL platform to solve the network model, the load model, the non-controllable asset model, and the grid model, and (b) the asset manager to solve the model of the DERs asset, without any simulation between the central controller and the distributed asset managers.

Systems and methods are described herein that improve grid performance by smoothing demand using thermal reserves. The smoothed demand can reduce peak loads as well as the ramp rate of demand that will otherwise require the use of inefficient, expensive generation sources. These improvements are tied to the selective switching on or off electrical loads that are coupled to thermal reserves, effectively using the thermal reserves as an energy storage mechanism. Historical data of past usage can be used to create load model and ensure that effects on customer comfort are minimized while still accomplishing the beneficial effects for the overall grid, which enables grid owners to both reduce their operational cost by avoiding expensive generation and improve system reliability by achieving more predictable power demand.

Systems include one or more critical datacenter connected to behind-the-meter flexible datacenters. The critical datacenter is powered by grid power and not necessarily collocated with the flexboxes, which are powered “behind the meter.” When a computational operation to be performed at the critical datacenter is identified and determined that it can be performed at a lower cost at a flexible datacenter, the computational operation is instead routed to the flexible datacenters for performance. The critical datacenter and flexible datacenters preferably shared a dedicated communication pathway to enable high-bandwidth, low-latency, secure data transmissions.

A system for the orchestration and scheduling of access to power sources includes a plurality of smart devices, each comprising a resource manager, and a resource orchestrator that communicates with each resource manager, generates a power resource schedule that includes at least one available power block associated with a power source, transmits the schedule to each resource manager of each smart device, receives at least one power request from a resource manager associated with at least one of the smart devices, the at least one power request including a requested available power block and an associated price, allocates the requested available power block to the resource manager that sent the at least one power request, and sends an allocation indication to the resource manager that sent the at least one power request, the allocation indication indicating that the requested available power block has been allocated to the resource manager.

A system includes a flexible datacenter and a power generation unit that generates power on an intermittent basis. The flexible datacenter is coupled to both the power generation unit and grid power through a local station. By various methods, a control system may detect a transition of the power generation unit into a stand-down mode and selectively direct grid power delivery to always-on systems in the flexible datacenter.

Provided are energy device control systems for distributed grid subsystem that control a first power demand of a plurality of appliances. The control system comprises a graphical user interface configured to accept a user input indicative of a first demand and dynamic allocation flexibility associated with the a respective energy device; a communication interface configured to aggregate dynamic allocation values from a plurality of system nodes including at least the user input indicative of a first demand and the dynamic allocation flexibility; and at least one processor programmed to: generate a learning model for evaluating dynamic future allocation with future energy execution prediction, wherein the dynamic future allocation includes at least energy operational information based on a categorization of energy usage at a plurality of respective energy devices; and trigger energy generation on the energy grid at respective generator nodes according to the learning model and dynamic projections.

A system includes control circuitry configured to determine a maximum electrical load for one or more electrical circuits on one or more electrical panels, determine preference information for allocating the maximum electrical load, automatically set a charge rate for charging the electric vehicle using current from at least one of the one or more electrical circuits based on the maximum electrical load and on the preference information, and cause the electric vehicle to be charged at the charge rate.

A method of operating a power grid includes: generating, by a power management system of the power grid, an intermediate resource allocation schedule that provides a tentative schedule of resource allocation for power grid resources operating within the power grid; determining, by the power management system, whether the intermediate resource allocation schedule is feasible by checking whether the intermediate resource allocation schedule satisfies coupling constraints of a power grid resource allocation profile of the power grid indicative of an operation of the power grid constrained by power grid operational information; and in response to determining that the intermediate resource allocation schedule is infeasible, repairing, by the power management system, the intermediate resource allocation schedule to generate a feasible resource allocation schedule, where the repairing comprises determining whether a first dispatch solution obtained by solving a dispatch problem with fixed integer decisions from the intermediate resource allocation schedule is feasible.

In an aircraft seat power system, a Smart Power Distribution Connector (PDC) connects a power supply to multiple power outlets. Power to each outlet is monitored and controlled directly by the PDC that is in turn monitored and controlled by the power source. The power source and the PDC share a communication bus through which instructions and responses are communicated. The PDC has the ability to communicate with the outlets by sending instructions and monitoring and acting on the responses from the outlets. The system allows for power management from a supply of limited power. The PDC can provide current limiting, allowing the use of lighter weight and smaller wire and other components.

An asset manager controls power distribution within an aggregated distributed energy resources system (“DERs system”) having a plurality of assets. The asset manager is configured to operate with a given asset. As such, the asset manager has 1) an interface to receive asset information relating to the given asset and to communicate with another asset manager in the DERs system, and 2) a function generator configured to produce a local cost function using data relating to the given asset only. The local cost function represents a portion of a system cost function for the DERs system. The asset manager also has 3) a controller configured to use the local cost function for the given asset to manage operation of the given asset in the DERs system. In addition, the controller also is configured to determine, using the local cost function, an operating point for the given asset.