Methods and systems for implementing experimental trials on utility grids. Variations in grid parameters are selected to introduce into utility grids to improve the value of learning from each experimental trial and promoting improved utility grid performance by computing expected values for both learning and grid performance. Those trials are used to manage the opportunity costs and constraints that affect the introduction of variations into utility grid parameters and the generation of valid data that can be attributed to particular variations in utility grid parameters.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method for injecting signals into a utility grid, comprising: receiving a spatial reach and a temporal reach for each signal injection of a plurality of signal injections; computing a learning value for each signal injection in the plurality of signal injections; computing an expected effect value for each signal injection in the plurality of signal injections; selecting, based on the learning values and expected effect values, a set of signal injections wherein the spatial reach and temporal reach of each signal injection do not both overlap the spatial reach and temporal reach of another signal injection in the set; and injecting the selected set of signal injections into a utility grid, wherein the signal injections are changes in the state of grid controls, wherein the grid controls are at least one of load tap changers, switches, and storage devices on electrical grids. 2. The method of claim 1, further comprising measuring the utility grid response to the injected signals. 3. The method of claim 2, further comprising associating data from the sensors with signal injections based on the time and location of the sensor data and the spatial reach and temporal reach of the signal injections. 4. The method of claim 1, wherein the learning value is computed based on a number of belief states that may be falsified by grid response to the signal injection. 5. The method of claim 1, wherein the learning value is computed based on a predicted change in the width of confidence intervals for grid response to the signal injection. 6. The method of claim 1, wherein the expected effect value is computed based on a database of the effects of prior signal injections. 7. A method for injecting signals into a utility grid, comprising: receiving a spatial reach and a temporal reach for each signal injection of a plurality of signal injections; computing a learning value for each signal injection in the plurality of signal injections; computing an expected effect value for each signal injection in the plurality of signal injections; selecting, based on the learning values and expected effect values, a set of signal injections wherein the spatial reach of each signal injection overlaps the temporal reach of each same signal injection; and injecting the selected set of signal injections into a utility grid with the overlapping spatial and temporal reaches, wherein the signal injections are changes in the state of grid controls. 8. The method of claim 7, further comprising measuring the utility grid response to the injected signals. 9. The method of claim 8, further comprising associating data from the sensors with signal injections based on the time and location of the sensor data and the spatial reach and temporal reach of the signal injections. 10. The method of claim 7, wherein the learning value is computed based on a number of belief states that may be falsified by grid response to the signal injection. 11. The method of claim 7, wherein the learning value is computed based on a predicted change in the width of confidence intervals for grid response to the signal injection. 12. The method of claim 7, wherein the expected effect value is computed based on a database of the effects of prior signal injections. 13. A method for injecting signals into a utility grid, comprising: receiving a spatial reach and a temporal reach for each signal injection of a plurality of signal injections; computing a learning value for each signal injection in the plurality of signal injections; computing an expected effect value for each signal injection in the plurality of signal injections; selecting, based on the learning values and expected effect values, a set of signal injections wherein the spatial reach and temporal reach of each signal injection do not both overlap the spatial reach and temporal reach of another signal injection in the set; and injecting the selected set of signal injections into a utility grid, wherein the signal injections are at least one of changing valves, pressures, and flow rates in a gas grid. 14. The method of claim 13, wherein multiple signal injections are concurrently implemented on the utility grid. 15. The method of claim 13, wherein the signal injections are coordinated by a Partially Observable Markov Decision Process.