Abstract: A method of assessing a log-file is described. A log-file to be assessed is received. A data storage is accessed that includes several log-files. A group of log-files from the several log-files stored in the data storage is identified. The group of log-files includes log-files that are nearest to the log-file to be assessed. The identified group of log-files is returned to a user for further analysis. Further, a method of grouping several log-files as well as a system for processing at least one log-file are described.

Abstract: A method of controlling assets connected to an electricity distribution grid operating at a predetermined grid frequency is disclosed. The assets comprise assets supplying electrical energy to and/or consuming electrical energy from the grid, with at least some of the assets being configurable to adjust energy flow to or from the grid to counteract grid frequency fluctuations. The method includes configuring an aggregate response by a group of assets to fluctuations in the grid frequency, wherein the aggregate response comprises respective adjustments of energy flow between each of the group of assets and the grid. The adjustments are arranged in combination to counteract a fluctuation in grid frequency. A divergence is detected between a first grid frequency measured in a portion of the grid comprising a first asset of the group of assets and a second grid frequency measured in a portion of the grid comprising a second asset of the group of assets.

Abstract: 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.

Abstract: 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.