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Distributed optimization methods have been extensively applied for the optimization of electric power distribution systems, especially for grid-edge coordination. Existing distributed optimization algorithms applied to power distribution systems require many communication rounds among the distributed agents and may pose convergence challenges in difficult nonlinear settings. The communication network parameters also significantly impact the algorithm's performance. In this paper, we propose a scalable, equivalent network approximation-based, distributed optimization algorithm that employs simulation within optimization using the system's digital twin (DT) to solve the optimal power problems (OPF) for a three-phase unbalanced distribution system. The proposed approach is implemented using a cyber-physical co-simulation platform to validate the robustness of the proposed distributed algorithm under stressed communication. The proposed approach is thoroughly validated using the IEEE 123-bus test system., Comment: 9 pages

The increasing penetration of intermittent renewables, storage devices, and flexible loads is introducing operational challenges in distribution grids. The proper coordination and scheduling of these resources using a distributed approach is warranted, and can only be achieved through local retail markets employing transactive energy schemes. To this end, we propose a distribution-level retail market operated by a Distribution System Operator (DSO), which schedules DERs and determines the real-time distribution-level Locational Marginal Price (d-LPM). The retail market is built using a distributed Proximal Atomic Coordination (PAC) algorithm, which solves the optimal power flow model while accounting for network physics, rendering locationally and temporally varying d-LMPs. A numerical study of the market structure is carried out via simulations of the IEEE-123 node network using data from ISO-NE and Eversource in Massachusetts, US. The market performance is compared to existing retail practices, including demand response (DR) with no-export rules and net metering. The DSO-centric market increases DER utilization, permits continual market participation for DR, lowers electricity rates for customers, and eliminates the subsidies inherent to net metering programs. The resulting lower revenue stream for the DSO highlights the evolving business model of the modern utility, moving from commoditized markets towards performance-based ratemaking.

It is of growing concern to ensure the resilience in electricity infrastructure systems to extreme weather events with the help of appropriate hardening measures and new operational procedures. An effective mitigation strategy requires a quantitative metric for resilience that can not only model the impacts of the unseen catastrophic events for complex electric power distribution networks but also evaluate the potential improvements offered by different planning measures. In this paper, we propose probabilistic metrics to quantify the operational resilience of the electric power distribution systems to high-impact low-probability (HILP) events. Specifically, we define two risk-based measures: Value-at-Risk ($VaR_\alpha$) and Conditional Value-at-Risk ($CVaR_\alpha $) that measure resilience as the maximum loss of energy and conditional expectation of a loss of energy, respectively for the events beyond a prespecified risk threshold, $\alpha$. Next, we present a simulation-based framework to evaluate the proposed resilience metrics for different weather scenarios with the help of modified IEEE 37-bus and IEEE 123-bus system. The simulation approach is also extended to evaluate the impacts of different planning measures on the proposed resilience metrics., Comment: 12 pages, 11 figures, journal