Maximizing Sensor Measurement Data Through Adaptive Real-Time Control

The increasing penetration of distributed energy resources (DERs) in the distribution networks has turned the conventionally passive load buses into active buses that can provide grid services for the transmission system. To take advantage of the DERs in the distribution networks, we formulate a transmission-and-distribution systems co-optimization problem that achieves economic dispatch at the transmission level and optimal voltage regulation at the distribution level by leveraging large generators and DERs.

Learn more in Economic Dispatch With Distributed Energy Resources: Co-Optimization of Transmission and Distribution Systems.

Distribution network operation is becoming more challenging because of the growing integration of intermittent and volatile distributed energy resources. This motivates the development of new distribution system state estimation paradigms that can operate at fast timescale based on real-time data stream of asynchronous measurements enabled by modern information and communications technology. To solve the real-time distribution system state estimation with asynchronous measurements effectively and accurately, this paper formulates a weighted least squares distribution system state estimation problem and proposes an online stochastic gradient algorithm to solve it. The performance of the proposed scheme is analytically guaranteed and is numerically corroborated with realistic data on IEEE 123-bus feeder.

Learn more in Online Distribution System State Estimation via Stochastic Gradient Algorithm.

Conventional optimal power flow solvers assume full observability of the involved system states. However, in practice, there is a lack of reliable system monitoring devices in the distribution networks. To close the gap between the theoretic algorithm design and practical implementation, this work proposes to solve the optimal power flow problems based on the state estimation feedback for the distribution networks where only a part of the involved system states is physically measured. The state estimation feedback increases the observability of the undermeasured system and provides more accurate system states monitoring when the measurements are noisy. We analytically investigate the convergence of the proposed algorithm. The numerical results demonstrate that the proposed approach is more robust to large pseudo measurement variability and inherent sensor noise in comparison to the other frameworks without state estimation feedback.

Learn more in Solving Optimal Power Flow for Distribution Networks With State Estimation Feedback.

This work investigates the real-time optimization of networked systems without explicit knowledge of the system model and develops online algorithms that steer the system toward the optimal trajectory. The problem is modeled as a dynamic optimization problem with time-varying performance objectives and engineering constraints. The design of the algorithms leverages the online zero-order primal-dual projected-gradient method. In particular, the primal step that involves the gradient of the objective function (and hence requires a networked systems model) is replaced by its zero-order approximation with two function evaluations using a deterministic perturbation signal. The evaluations are performed using the measurements of the system output, hence giving rise to a feedback interconnection, with the optimization algorithm serving as a feedback controller.

Learn more in Model-Free Primal-Dual Methods for Network Optimization With Application to Real-Time Optimal Power Flow.

The new generation of power systems involve many autonomous operating units with the flow of data being restricted by privacy concerns or infrastructure hurdles. The systems are also time-varying, so the control should adjust in a timely manner to avoid failures that usually have very negative economic implications. Adaptive neurocontrol takes elements from adaptive control (great for time-varying problems) and model identification (could be in the form of neural network) by using local available data. Those properties make adaptive neurocontrol a compelling tool for power system applications. This work is about leveraging adaptive neurocontrol for decentralized control of grid-following inverters. Some encouraging results are found, and we expect that adaptive neurocontrol can also work on many other online power system control problems.

Learn more in Adaptive Neurocontrol for Grid-Following Inverters.

Failures in distribution systems contribute the most to power supply unavailability to end users. As distribution systems get more complex because of the large integration of distributed energy resources (many of them renewable generators), the possibility of fault currents or voltage fluctuation has become higher. Ideally, identification of the fault locations and fault currents can be done with full observability of distribution systems. However, such sensing and monitoring infrastructures (especially with real-time capabilities) come with impractically high cost. In this line of work, we assume that only parts of the buses have sensors, e.g., µPMU, installed and lay out how to identify the fault locations with those real-time sensor data.

The next-generation power grid is envisioned to integrate a high level of distributed energy resources—such as rooftop solar PVs, electric vehicles, batteries, and flexible loads—which introduces both challenges (uncertainty) and opportunities (flexibility and controllability) into power systems. There is a critical need to understand the flexibility in residential homes that can be used to support grid resilience. This work proposes a sparse-sensing-based state estimation method to characterize the aggregate flexibility of residential homes. The idea is to randomly sample the flexibility of residential homes and use that to estimate the aggregate system flexibility. As a main result, we find the unbiased estimator for the aggregate flexibility, with the estimation error quantified by a Gaussian distribution.