This work presents the design guidelines of a control for direct regulation of the voltage amplitude at the connection point of a single-phase grid-connected PV inverter. The parameters of the control are designed for ensuring local asymptotic stability for a given range of the impedance. Furthermore, the control includes a strategy of active power curtailment in order to avoid the injection of a large amount of reactive power to the grid. Numerical simulations confirm the expected voltage regulation and the control robustness with respect to the grid impedance variations without the requirement of any estimation or measure of the actual impedance.

In this paper, we study the coordination between biogas producers who can use the biogas themselves, exchange biogas with their neighbors, or deliver it to the various energy grids, such as the low pressure gas grid or the power grid. These producers are called prosumers. In this setting, gas storage, fuel cells, microcombined heat power systems, and heat buffers are all part of the prosumers' node. We aim to optimize the imbalance, profit, and comfort levels per prosumer, while taking the constraints of the energy grids into account and while allowing prosumers to exchange energy with each other. This results in a two-layer optimization problem formulation. In addition, in practice, the communication between prosumers among each other and with grid operators is done in an asynchronous manner. In this paper, we study the problem of two-layer optimization for biogas prosumers embedded in multiple energy grids, while the (bidirectional) communication between the various partners is done asynchronously. We prove the convergence of the asynchronous coordination algorithm that uses both the inputs and the states. We conduct simulations for the biogas prosumer setting, using realistic data to illustrate the convergence of the algorithm and to study its practical implementation.

In this paper, we consider the balancing problem in a hierarchical market-based structure for smart energy grids that is based on the Universal Smart Energy Framework. The large-scale introduction of renewable, intermittent energy sources in the power system can create a mismatch between the forecasted (day ahead) and the actual supply and demand. Without a proper control strategy, this deviation could lead to network overloads and commercial losses. We present a multilevel distributed optimal control formulation to the problem, in which the appliances of prosumers that can provide flexibility are optimally dispatched based on local information. The control strategy takes the capacity limitations of the distribution network into account. We provide example simulation results, obtained by distributed model predictive control. We propose a control strategy that aims to minimize the imbalance between forecasted and actual supply and demand in electricity grids. This is important, because the imbalance can lead to commercial losses for the stakeholders. Since the number of agents (i.e., households) in the power network is typically large, centralized controllers are not feasible due to scalability issues. We instead develop a distributed controller that solves the problem using only local information. We demonstrate our algorithm through simulations, which are implemented on a single computer. In practice, households can have smart meters on which the individual controllers run, thereby obtaining the solution in a parallel fashion.

Parameters of electrical machines are usually varying with time in a smooth way due to changing operating conditions, such as variations in the machine temperature and/or the magnetic saturation. This paper is concerned with robust stability analysis of controlled Doubly-Fed Induction Generators (DFIGs) that takes into account the time-varying nature of the parameter variations as well as bounds on their rate-of-variations. First, a self-scheduled Linear Parameter Varying (LPV) current controller design for the inner rotor-side loop is presented. The design is based on viewing the mechanical angular speed as an uncertain yet online measurable parameter and on subsuming the problem into the framework of LPV controller synthesis. Then the parameter-dependent model of the machine is transform into a Linear Fractional Representation, which allows to perform a stability analysis based on a specifically chosen set of Integral Quadratic Constraints (IQCs). Some simulation and analysis test results are given to demonstrate the robustness margins that result with this control algorithm.

In this paper, we derive a coordinate-free formulation of a passive controller that makes a mechanical system track reference curves in a potential field. Contrary to conventional reference tracking, we do not specify a single time-varying trajectory that the system has to track. Instead, we specify a whole curve that the system has to stay on at all times. Using tools from differential geometry, we first derive a controller that makes the system move along arbitrary (smooth enough) reference curves while keeping the kinetic energy constant. We then apply the results to the case of movement in an artificial potential field, in which case, the reference curves are completely determined by the potential field and cannot be chosen arbitrarily. Simulation then shows the performance of the controller on a benchmark robot with two degrees of freedom.