Description: The Holomorphic Embedding Load Flow Method (HELM) is a breakthrough that brings significant advances to the field of power systems. It provides a non-iterative procedure to compute, with mathematically proven guarantees even near voltage collapse, the correct operative power flow solution, to the desired accuracy. Unlike iterative methods, which are inherently prone to non-deterministic convergence failures, HELM can be used as the fundamental block for building reliable real-time network applications. The most advanced applications, which rely on optimal search techniques in the state-space of the power system and perform thousands of exploratory power flows, would be unfeasible with any of the iterative methods. This proposal addresses one of the needs of Topic S3.03, namely the need for intelligent, fault-tolerant PMAD technologies to efficiently manage system power for deep space missions. It does so at a foundational level, as it lays down the algorithmic technology that will enable a new class of real-time intelligent algorithms based on reliable, model-based computation. An example of this in terrestrial grids, which has been proven in actual deployments at some large utilities, is a Restoration plan builder, able to compute detailed restoration plans in real time, equaling or surpassing the abilities of human operators. The approach for Phase I consists in applying the new HELM power flow technology to the relevant models for the micro-grids present on current and projected spacecraft power systems, validating and benchmarking the simulation results against other current power flow technologies. This will demonstrate how this technology is better than the state of the art. By highlighting the mathematical properties of the method (unequivocal results, 100% reliability) on the models specific to autonomous DC spacecraft, we will establish the validity and also the status of HELM as the building block of future intelligent applications.

Benefits: The project, when completed (Phase I, II & III) will provide NASA with: (1) Reliable and fast State Estimator that will improve grid observability; (2) Optimization algorithms for load management under variable load demand and constrained capacity, yielding reliable results that have been power-flow checked; (3) Auto-healing modules providing optimal (power-flow checked) action sequences for reconfiguration, in order to minimize brownouts and blackouts; (4) Modules for training, system design, forensic analysis, and diagnostics and identification of errors in modeling. These applications are the basic ingredients upon which a truly intelligent autonomous power system can be built. Such a system is undoubtedly a pre-requisite for successful Deep Space missions requiring long-term complex PMAD operations with minimal or no human intervention.

The achievement of managing electric power systems with minimal human intervention, with the ensuing reduction in costs and improved operational efficiencies and grid stability, will place these products as an appealing technological option to grid operators whether large or small. The technology developed for spacecraft micro-grids, with the necessary adaptations, will easily be transferred to the Smart Grid environments. The Smart Grid Demonstrator at NASA's Glenn Research Center is the ideal place where demonstration protocols for terrestrial grids could be carried out. The results achieved will be disseminated in conferences in collaboration with industry leaders such as the Battelle foundation, and demonstrations will be provided.