The proposed innovation advances the ability to apply the Holomorphic Embedding Load Flow Technology (HELM) method to provide deterministic load flow modeling for spacecraft power systems. Future deep-space vehicles need intelligent, fault-tolerant and autonomous control of power management and distribution. Due to communications latency, control algorithms for future autonomous space power systems need to be very robust, highly reliable and fault tolerant. Modeling of load flows is vital both to design spacecraft power systems and to operate them autonomously. A key element is state estimationgiven the available sensors and their readings, what is the real state of the system? What action is required to maintain operation? State estimation is especially important when the system is in an off-nominal condition. Human operators draw upon experience to integrate off-nominal sensor readings and develop a gestalt of system state, but autonomous operation requires computation. Current modeling techniques (i.e., Newton-Raphson (NR) optimization) are not equal to this task due to their iterative nature and initial point dependency. Many off-nominal cases cannot be solved at all using NR. Worse, even more off-nominal cases appear to be solvable using NR, but the solutions are actually invalid. An NR-based autonomous control system faced with off-nominal conditions will reach an incorrect conclusion more often than not, with potentially catastrophic consequences for the spacecraft. By contrast, HELM provides deterministic solutions for off-nominal states, without dependence on initial solution seeds, thereby providing the level of fidelity and surety needed to develop an autonomous system. In Phase I, Gridquant Technologies LLC successfully adapted HELM to solve the non-linearity problems of a small DC micro-grid, which will enable NASA to develop and implement the advanced architectures needed for future long-term deep-space exploration.