Rising shares of renewable generation raises uncertainty and thus the number of possible power flow scenarios in the power system, which in turn increases the possibility for unforeseen contingencies, such as power line or generator failures and their combinations. Therefore, operators cannot longer rely only on operational experience to deal with every contingency. Our proposed method involves identifying the most effective countermeasures to minimize the impact of contingencies on the power system. We take into account various options, such as load shedding, adjusting phase-shifting transformer angles, and injecting active power using fast elements. The proposed approach considers primary control of generators and its limitations in order to compensate for power imbalances in the system. The problem is formulated as a mixed-integer linear optimization problem, employing DC power flow equations. The applicability of the approach is evaluated on the IEEE 39-bus system, and the scalability of the approach is shown on five systems with up to 6470 buses.

Many large-scale systems are composed of subsystems operated by decentralized controllers, which are fixed in their structure, yet have parameters to tune. Initial tuning or subsequent adjustments dof those parameters ue to varying operating conditions or changes in the network of interconnected systems, while ensuring stability, performance, and security, pose a challenging task due to the overall complexity and size. Subsystems may not be willing or allowed to expose detailed information for safety and privacy reasons. In some cases, a comprehensive system model might not be available for global tuning, or the resulting problem might be computationally infeasible. To enable meaningful global parameter tuning while allowing for data privacy and security, we propose that the subsystems themselves should provide reduced-order models. These models capture the parametric dependency of the subsystem dynamics on the controller parameters. Specifically, we present a method to construct a region in the subsystems’ parameter space in which the deviation of the subsystem and the reduced-order model stays below a specified error bound and in which both systems are stable. A necessary and sufficient condition for such regions is derived using robust control theory. Notably, sufficiency can be expressed in terms of a linear matrix inequality. We demonstrate the approach by considering the temperature control of a large-scale building complex.

Reliable power system operation with 100% inverter-based resources (IBRs) is an unsolved and challenging problem. One of the most challenging factors is ensuring power system stability after N-1 contingencies. This paper presents a promising solution using an operator support system (OSS) to enable stable operation of power system with up to 100% IBR generation. The OSS consists of two components. First is dynamic security assessment to evaluate the system resiliency, and identify critical N-1 contingencies that could endanger the system. The second component, as the key technology behind the OSS, is dynamic security optimization (DSO). The DSO optimizes the control parameters of generators and inverters to improve the stability of the system towards the identified N-1 contingencies. The key to system with 100% IBRs, as emphasized in many recent studies, is to establish the grid frequency reference using grid-forming (GFM) inverters. We show through high-fidelity Electro-Magnetic-Transient (EMT) simulations of the future generation models of Hawai‘i Island system with 100% IBR capacity that a system with 100% IBRs can be operated stably with the help of GFM inverters, and appropriate controller parameters can be found by DSO for the inverters. The DSO is verified via 28 critical N-1 contingencies of Hawai‘i Island system identified by Hawaiian Electric. The simulation results verify the effectiveness of DSO, and show significant stability improvement from DSO.

The integration of renewable generation in electrical power systems is exponentially increasing for multiple reasons. First, a fast decarbonization of the electrical energy system is a critical milestone to slow climate change and facilitate the decarbonization of other energy sectors, such as transportation and heat. Second, renewable generation from wind and solar have become much cheaper compared to conventional sources like gas, coal, and nuclear. Third, renewable generation is in many cases decentralized, which increases the resilience of the energy system, for example, in the face of natural disasters.

This paper presents some of our first experiences and findings in the ARPA-E project ReNew100, which is to develop an operator support system to enable stable operation of power system with 100% non-synchronous (NS) generation. The key to 100% NS system, as found in many recent studies, is to establish the grid frequency reference using grid-forming (GFM) inverters. In this paper, we demonstrate in Electro-Magnetic-Transient (EMT) simulations, based on Hawai'i big island system with 100% NS capacity, that a system can be operated stably with the help of GFM inverters and appropriate controller parameters for the inverters. The dynamic security optimization (DSO) is introduced for optimizing the inverter control parameters to improve stability of the system towards N-1 contingencies. DSO is verified for five critical N-1 contingencies of big island system identified by Hawaiian Electric. The simulation results show significant stability improvement from DSO. The results in this paper share some insight, and provide a promising solution for operating grid in general with high penetration or 100% of NS generation.

Abstract This article discusses how to use optimization-based methods to efficiently operate microgrids with a large share of renewables. We discuss how to apply a frequency-based method to tune the droop parameters in order to stabilize the grid and improve oscillation damping after disturbances. Moreover, we propose a centralized real-time feasible nonlinear model predictive control (NMPC) scheme to achieve efficient frequency and voltage control while considering economic dispatch results. Centralized NMPC for secondary control is a computationaly challenging task. We demonstrate how to reduce the computational burden using the Advanced Step Real-Time Iteration with nonuniform discretization grids. This reduces the computational burden up to 60 % compared to a standard uniform approach, while having only a minor performance loss. All methods are validated on the example of a 9-bus microgrid, which is modeled with a complex differential algebraic equation.

Abstract Accurate state-estimation is a vital prerequisite for fast feedback control methods such as Nonlinear Model Predictive Control (NMPC). For efficient process control, it is of great importance that the estimation process is carried out as fast as possible to provide the feedback mechanism with fresh information and enable fast reactions in case of any disturbances. We discuss how Multi-Level Iterations (MLI), known from NMPC, can be applied to the Moving Horizon Estimation (MHE) method for estimating the states and parameters of a system described by a Differential Algebraic Equation model. A challenging field of application for the proposed MLI-MHE method are electric microgrids. These push current control approaches to their limits due to the rising penetration of volatile renewable energy sources and the fast electrical system dynamics. We investigate the closed-loop control performance of the proposed MLI-MHE algorithm in combination with an NMPC controller for a realistic sized microgrid as a numerical example.

We present a real-time feasible Nonlinear Model Predictive Control (NMPC) scheme to control a microgrid described by a detailed Differential Algebraic Equation (DAE). Our NMPC formulation allows to consider secondary voltage and frequency control, steady-state equal load sharing, economic goals and all relevant operational constraints in a single optimization problem. The challenge is to control the fast and large dynamical system in real-time. To achieve this goal, we use the recently introduced Advanced Step Real-Time Iteration (AS-RTI) scheme and its efficient implementation in the acados software package. We present an NMPC scheme which delivers feedback in the range of milliseconds. Thereby, the controller responds efficiently to large disturbances and mismatches in the predictions and effectively controls the fast transient dynamics of the microgrid. Our NMPC approach outperforms a state-of-the-art I-controller usually used in microgrid control and shows minor deviation to a fully converged NMPC approach.

The increasing share of renewable generation leads to new challenges in reliable power system operation, such as the rising volatility of power generation, which leads to time-varying dynamics and behavior of the system. To counteract the changing dynamics, we propose to adapt the parameters of existing controllers to the changing conditions. Doing so, however, is challenging, as large power systems often involve multiple subsystem operators, which, for safety and privacy reasons, do not want to exchange detailed information about their subsystems. Furthermore, centralized tuning of structured controllers for large-scale systems, such as power networks, is often computationally very challenging. For this reason, we present a hierarchical decentralized approach for controller tuning, which increases data security and scalability. The proposed method is based on the exchange of structured reduced models of subsystems, which conserves data privacy and reduces computational complexity. For this purpose, suitable methods for model reduction and model matching are introduced. Furthermore, we demonstrate how increased renewable penetration leads to time-varying dynamics on the IEEE 68-bus power system, which underlines the importance of the problem. Then, we apply the proposed approach on simulation studies to show its effectiveness. As shown, similar system performance as with a centralized method can be obtained. Finally, we show the scalability of the approach on a large power system with more than 2500 states and about 1500 controller parameters.

Reliable and secure operation of power systems becomes increasingly challenging as the share of volatile generation rises, leading to largely changing dynamics. Typically, the architecture and structure of controllers in power systems, such as voltage controllers of power generators, are fixed during the design and buildup of the network. As replacing existing controllers is often undesired and challenging, setpoint adjustments, as well as tuning of the controller parameters, are possibilities to counteract large disturbances and changing dynamics. We present an approach for fast and computationally efficient adaptation of parameters of structured controllers based on H-infinity optimization, also referred to as structured H-infinity controller synthesis, tailored towards power systems. Conditions are established that guarantee that the approach leads to stability.The results are verified in a testbed microgrid consisting of six inverters and a load bank, as well as simulation studies. The proposed method improves the system robustness, as well as the time-response to step disturbances and allows structured controller tuning even for large networks.