Abstract: A reactive formulation and method for forming a thermoplastic polyurethane (TPU) having a glass transition temperature (Tg) above room temperature, preferably a Tg higher than 40° C., more preferably higher than 55° C., a flexural modulus in the range 300-15000 MPa-(measured according to ISO 178), most preferably in the range 1500-2700 MPa and a tensile strength at break (according to DIN 53504) in the range of 5 up to 150 MPa is disclosed. Said reactive formulation comprising at least an isocyanate composition and an isocyanate-reactive composition comprising at least an aromatic dicarboxylic acid based diol chain extender having a molecular weight<500 g/mol and optionally fillers. Furthermore, a TPU material having a glass transition temperature (Tg)>room temperature and a flexural modulus in the range 300-15000 MPa (measured according to ISO 178) is disclosed which is thermally recyclable and optionally made from a terephthalic acid based polyester diol chain extender made from recycled PET.

Abstract: An exemplary embodiment provides a flooring panel, comprising a core layer comprising a thermoplastic polyurethane (TPU) having a shore D hardness in the range 50-100 and a glass transition temperature above room temperature. The TPU can be formed from a reactive formulation, comprising an isocyanate composition, an isocyanate-reactive composition, optionally a catalyst compound, and optionally additives and/or fillers. The isocyanate composition can comprise at least one difunctional isocyanate compound. The isocyanate-reactive composition can comprise at least one aromatic dicarboxylic acid based diol chain extender having a molecular weight <500 g/mol. The hardblock content of the reactive formulation can be >70 wt % based on the total weight of the isocyanate and isocyanate-reactive composition, the isocyanate index can be in the range 75 up to 125, and the number average isocyanate functionality and/or the number average hydroxy functionality can be in the range of 1.8 up to 2.5.

Abstract: Disclosed is a method of determining a synchronization offset for measurement data relating to an electrical power supply network. First and second data sets of measurement data are received from a first source and a second source. Each data set includes measurements of an electrical parameter of the network measured at the respective source together with time information indicating measurement times of the measurements. Correlation measures are determined for each of a plurality of time shift values, indicating correlation between a first set of measurement values derived from the first data set and a second set of measurement values derived from the second data set, with one of the first and second sets of measurement values being derived under application of a time shift corresponding to the respective time shift value. One of the time shift values is selected in dependence on the correlation measures and used to provide a synchronization offset.

Abstract: A method of controlling assets connected to an electricity distribution grid is disclosed. For each of a plurality of assets to be configured, a trained neural network model is provided, which takes an asset configuration as input, the asset configuration specifying a response of the asset to variations in one or more operating conditions detected at the asset, and outputs one or more performance indicators relating to the operation of the asset when operated using the asset configuration. The outputs of the neural network models are provided as inputs to an optimization function. A search process optimizes the optimization function by varying asset configurations for the assets. The final asset configurations are then used to control the assets to provide a coordinated demand response service.

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.