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. 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. The method of claim 1, wherein the signal injections are changes in the state of grid controls. 8. The method of claim 7, wherein the grid control is a capacitor bank. 9. The method of claim 1, wherein the signal injections are dispatching of grid personnel to perform a task. 10. The method of claim 1, wherein multiple signal injections are concurrently implemented on the utility grid. 11. The method of claim 1, wherein the signal injections are coordinated by a Partially Observable Markov Decision Process. 12. A utility grid system, comprising: a spatial reach memory, configured to store the spatial reach for each of a plurality of signal injections; a temporal reach memory, configured to store the temporal reach for each of a plurality of signal injections; a expected effect value processor, configured to compute an expected effect value for a signal injection; a learning value processor, configured to compute a learning value for a signal injection; a coordination processor, configured to generate a set of signal injections where the spatial and temporal reaches do not both overlap for any signal injections in the set; and a plurality of utility grid controls. 13. The utility grid system of claim 12, further comprising sensors located along the utility grid. 14. The utility grid system of claim 13, wherein the sensors are electrical sensors. 15. The utility grid system of claim 13, wherein the sensors are methane sensors. 16. The utility grid system of claim 12, further comprising a processor configured to associate sensor data with signal injections. 17. The utility grid system of claim 12, wherein the expected effect value processor computes expected effect value by predicting the impact of a signal injection on the utility grid. 18. The utility grid system of claim 12, wherein the learning value processor computes learning value by determining the number of belief states that may be falsified by a signal injection. 19. The utility grid system of claim 12, wherein the coordination processor coordinates the signal injections using a graphical model. 20. The utility grid system of claim 12, wherein the grid controls concurrently implement multiple signal injections.