Resource control by probability tree convolution production cost valuation by iterative equivalent demand duration curve expansion (aka. tree convolution)

A smart energy grid control system to control a smart energy grid, the smart energy grid control system comprising:

• at least one processor;
• and
• at least one non-transitory processor-readable medium storing at least one of data and instructions that, when executed by the at least one processor, cause the smart energy grid control system to:
• receive input data that describes one or more operational parameters of each of a plurality of resources of the smart energy grid, the plurality of resources comprising at least a plurality of energy generation resources;
• build a probability tree based at least in part on the received input data, the probability tree comprising a plurality of leaves, each leaf of the plurality of leaves representative of one of the plurality of resources of the smart energy grid;
• select at least one initial path through the probability tree;
• determine an estimated demand duration curve based on the at least one initial path through the probability tree;
• determine whether the estimated demand duration curve for the at least one initial path satisfies one or more accuracy requirements;
• responsive to a determination that the estimated demand duration curve does not satisfy the one or more accuracy requirements, perform one or more approximation iterations in which the smart energy grid control system iteratively revises the probability tree based at least in part on an analysis of the estimated demand duration curve, iteratively selects at least one revised path through the revised probability tree, and iteratively updates the estimated demand duration curve;
• and
• responsive to a determination that the estimated demand duration curve satisfies the one or more accuracy requirements, control the smart energy grid to respectively activate for at least a first period of time at least one of the particular resources of the smart energy grid that respectively correspond to the leaves of the probability tree included in a most recently selected path.

A smart energy grid, comprising:

• a plurality of resources, at least some of the plurality of resources comprising energy generation resources;
• and
• a smart grid controller respectively controllingly coupled to the plurality of resources, the smart grid controller comprising at least one processor, wherein the smart grid controller:
• generates a plurality of operational models respectively for the plurality of resources for at least one time period, the plurality of operational models respectively descriptive of operational availability of the plurality of resources during the at least one time period;
• builds a probability tree representative of the plurality of resources during the at least one time period, the probability tree including respective probabilities of operational availability for the plurality of resources based on the respective operational models;
• evaluates an availability for each of the plurality of resources according to at least one initial path through the probability tree;
• determines a plurality of sensitivity factors respectively for the plurality of resources, wherein the sensitivity factor determined for each resource indicates a magnitude of impact that operational unavailability of such resource will have on an estimated demand duration curve for the smart energy grid for the at least one time period;
• revises a portion of the probability tree associated with at least one of the plurality of resources selected based on the sensitivity factors;
• and
• controls one or more of the plurality of resources based at least in part on the revised probability tree.