Small-Signal Stability Analysis for Droop-Controlled Inverter-based Microgrids with Losses and Filtering

An islanded microgrid supplied by multiple distributed energy resources (DERs) often employs droop-control mechanisms for power sharing. Because microgrids do not include inertial elements, and low pass filtering of noisy measurements introduces lags in control, droop-like controllers may pose significant stability concerns. This paper aims to understand the effects of droop-control on the small-signal stability and transient response of the microgrid. Towards this goal, we present a compendium of results on the small-signal stability of droop-controlled inverter-based microgrids with heterogeneous loads, which distinguishes: (1) lossless vs. lossy networks; (2) droop mechanisms with and without filters, and (3) mesh vs. radial network topologies. Small-signal and transient characteristics are also studied using multiple simulation studies on IEEE test system

Structured Neural-PI Control with End-to-End Stability and Output Tracking Guarantees

We study the optimal control of multiple-input and multiple-output dynamical systems via the design of neural network-based controllers with stability and output tracking guarantees. While neural network-based nonlinear controllers have shown superior performance in various applications, their lack of provable guarantees has restricted their adoption in high-stake real-world applications. This paper bridges the gap between neural network-based controllers and the need for stabilization guarantees. Using equilibrium-independent passivity, a property present in a wide range of physical systems, we propose neural Proportional-Integral (PI) controllers that have provable guarantees of stability and zero steady-state output tracking error. The key structure is the strict monotonicity on proportional and integral terms, which is parameterized as gradients of strictly convex neural networks (SCNN). We construct SCNN with tunable softplus-$\beta$ activations, which yields universal approximation capability and is also useful in incorporating communication constraints. In addition, the SCNNs serve as Lyapunov functions, giving us end-to-end performance guarantees. Experiments on traffic and power networks demonstrate that the proposed approach improves both transient and steady-state performances, while unstructured neural networks lead to unstable behaviors.Comment: arXiv admin note: text overlap with arXiv:2206.0026

Online Cooperative Feedback Control of Residential Community Microgrids with 100% Renewable Energy

The emerging of renewable distributed energy resources (DER) in the residential community opens the door to forming a residential community microgrid for enhancing energy resiliency when the main grid is out of service. However, traditional microgrid controls via the hierarchical feedforward tertiary, secondary, and primary control framework may not be effective for such residential community microgrids, because of high volatility, low inertia, and insufficiency of DERs along with limited supporting facilities. This paper discusses an online feedback scheme, which cooperates the three control layers in real time to ensure operational stability of the microgrid. Besides, to economically dispatch scarce DERs in the tertial feedback control, this paper deduces an increment cost model of battery storage assets based on their degradation costs and depth of discharges. The model is of low computational complexity, thus can be naturally embedded in the proposed online cooperative feedback control scheme to calculate marginal price in real-time. Small-signal analysis and Simulink simulation are conducted to illustrate stability of the proposed online cooperative feedback control scheme, and its economic advantages over the traditional feedforward control scheme

Distributed Generator and Load-Side Secondary Frequency Control in Power Networks

We design a distributed secondary frequency control scheme for both generators and controllable loads. The proposed scheme operates via local sensing and computation, and neighborhood communication. Equilibrium and stability analysis of the closed-loop system is performed with a power network model including turbines and governors of generators and nonlinear AC power flows. After a change in power supply or demand, the proposed scheme is able to stabilize the system, restore bus frequencies and net inter-area power exchanges, and minimize total generation cost minus user utility at equilibrium

Control of Synchronization in two-layer power grids

In this work we suggest to model the dynamics of power grids in terms of a two-layer network, and use the Italian high voltage power grid as a proof-of-principle example. The first layer in our model represents the power grid consisting of generators and consumers, while the second layer represents a dynamic communication network that serves as a controller of the first layer. In particular, the dynamics of the power grid is modelled by the Kuramoto model with inertia, while the communication layer provides a control signal $P_i$ for each generator to improve frequency synchronization within the power grid. We propose different realizations of the communication layer topology and different ways to calculate the control signal. Then we conduct a systematic survey of the two-layer system against a multitude of different realistic perturbation scenarios, such as disconnecting generators, increasing demand of consumers, or generators with stochastic power output. When using a control topology that allows all generators to exchange information, we find that a control scheme aimed to minimize the frequency difference between adjacent nodes operates very efficiently even against the worst scenarios with the strongest perturbations