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 onH∞ optimization, also referred to as structuredH∞ 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.

—We consider the improvement of dynamic security in power systems by tuning of the power plant controllers. The parameters of existing controllers are tuned like power system stabilizers in order to increase the stability reserve and oscillation damping after a dropout of power plants and power lines. The tuning is done in two steps: in the first step, the power system is stabilized after the dropout of a power plant or power line. Then, oscillation damping is increased after the dropout by an H-infinity optimization approach. Both steps use linear matrix inequality optimization methods. To perform the tuning, we introduce a modeling method for the dropout of power plants and power lines, such that the dropout of components can be considered directly as a disturbance input for the H-infinity optimization. Finally, we evaluate the approach on the IEEE 39 bus model. We show that the presented methods successfully stabilize the system and improve power oscillation damping after the dropout of different power plants and power lines.

Power system stabilizers are controllers which damp power oscillations in electrical networks. They typically reside in the automation system of the power plant. Their design and structure are typically fixed in the design of the power plant. Optimal design and tuning of these decentralized controllers such that power oscillations are avoided is a challenging task. In the first part of the paper, we outline this problem and transform it into a so called structured controller synthesis problem where the control structure is fixed and optimal controller parameters need to be found. Based on this formulation, which preserves the real controller parameters, we propose a coordinate descent method to solve the controller design and tuning problem. To this end, we consider additional steady-state constraints in the system. We show the effectiveness of the proposed approach by detailed simulations of an established power system benchmark.

As our energy systems evolve with the adoption of more variable renewable energy resources, so will our oil and gas industry play a pivotal role in what is expected to be a lengthy transitional phase to a greater mix of renewables with a reliance on fast, reliable gas peaking power generation, which have lower greenhouse gas emissions, and short delivery periods to construct. Oil and gas companies are also rapidly moving towards becoming integrated energy companies supplying a mix of gas, oil, photovoltaic power, wind power and hydrogen, coupling these into the electrical and gas grids. We discuss some of the components and tasks of a distributed energy system in its various system guises that contribute to a more cost effective, reliable and resilient energy system with lower greenhouse gas emissions. We discuss the role that hydrogen will play in the future as oil and gas companies explore alternatives to fossil fuels to address their need to reduce their carbon footprint, substituting or supplementing their conventional gas supply with renewably produced hydrogen. We talk about how Australia with its excellent renewable resources and the opportunity to potentially develop a new industry around the production of renewable fuels, power-to-X, such as hydrogen, with the potential for the oil and gas industry to leverage its existing assets (i.e. gas pipelines) and future embedded renewable assets to produce hydrogen through electrolysis with the intention of supplementing their liquefied natural gas exports with a portion of renewably produced hydrogen.

Oscillatory modes in power systems will become more variable due to the increasing share of volatile renewable generation. Adding new or replacing existing controllers in power systems in order to damp such oscillations is an expensive and difficult task. Thus, new functions are required in order to optimally use existing controllers for the damping of variable oscillations. This paper presents a method for automatic parameter tuning of existing controllers in AC/DC power systems for optimal oscillation damping. For this purpose, we first present a detailed dynamic model of AC/DC grids with explicit dependencies on controller parameters. We then use H-infinity optimization to tune the controllers in the system. The efficiency of the proposed method is demonstrated with detailed rounded-mean-square simulations of a modified IEEE 39 bus power system.

Robust optimal dispatch, secondary, and primary reserve for power systems is considered in this paper based on a novel robust formulation of the well-known power flow optimization. The uncertainty of power generation and load at the power system buses is modeled as nominal, expected power supply and demand, uncertain load and generation variations, as well as tunable dispatch, secondary, and primary reserves. The power transmission between the buses is modeled with algebraic, linearized power flow equations. The challenge is how to distribute dispatch as well as secondary and primary reserve at minimal cost, such that the power flow satisfies certain constraints even after unknown but bounded load and generation variations. These constraints reflect among others the maximal steady-state frequency deviation and the loading limits of the individual power lines. The resulting optimization problem is reformulated to different linear programming problems that can be solved efficiently even for very large systems. The applicability is shown for different IEEE test bus systems.

The rising share of renewable generation increases uncertainty in power system operation. New methods are needed for secure operation of power systems under uncertainties. We present a robust optimal power flow algorithm for mixed ac and dc power transmission systems which considers uncertain renewable generation and/or load. The uncertain infeeds are assumed to lie inside a known norm-bounded set. The presented algorithm guarantees satisfaction of various steady-state power system constraints for all uncertainties within this set. In order to satisfy these constraints at minimal cost, the algorithm modifies both the redispatch and primary reserve. This is done through the optimization of setpoints and droop gains of generators and high-voltage dc converters. The algorithm is evaluated on a test system, the Cigre B4 DC grid, the IEEE RTS 96 grid, and on the Danish transmission system. In these examples, we show that redispatch costs can be reduced by additionally optimizing primary reserve, and demonstrate primary reserve sharing between asynchronous ac grids through dc grids. We illustrate the impact of the uncertainty set choice on the worst-case redispatch costs, perform a sensitivity analysis of the approach, and evaluate the computation time as a function of the number of uncertainties.

The increasing share of renewable generation will lead to time-varying oscillatory modes in large scale power systems. Today, oscillatory modes are defined by the location of conventional power plants and by the current load level in the grid. In the future, more conventional power plants will be periodically disconnected depending on the share and location of renewable generation. Hence, the oscillatory modes will become more time variable than today, e.g. depending on the weather. Thus, new functions become necessary in order to optimally use existing controllers, e.g. power system stabilizers, for damping of inter-area oscillations. In this paper, we present a method for parameter tuning of generator controllers for optimal oscillation damping based on structured H-infinity optimization. We assume that power system stabilizers are not appropriately parametrized and use the proposed method to find the optimal parametrization of the controllers. The efficiency of the proposed method is shown with detailed rounded-mean-square simulations of a well known four generator and the IEEE 39 bus power system.

Optimal redispatch and primary reserve allocation in transmission systems is considered in this paper. The transmission system is modeled with the linearized power flow equations. The power generation and load at the buses are modeled as nominal power supply and demand, tunable redispatch and primary control action, as well as uncertain load or generation variations. The challenge is how to distribute redispatch and primary reserve at minimal cost such that the power flow satisfies certain constraints even after unknown but bounded load and generation variations. These constraints reflect among others the maximal steady state frequency deviation and the loading limits of the individual power lines. The resulting optimization problem is reformulated as a linear programming problem that can be solved efficiently even for very large systems.