Jan Dimon Bendtsen

Modeling of Generic Slung Load System

This paper presents the result of modelling and verification of a generic slung load system using a small-scale helicopter. The model is intended for use in simulation, pilot training, estimation, and control. The model is derived using a redundant coordinate formulation based on Gauss’ Principle of Least Constraint using the Udwadia-Kalaba equation and can be used to model all body to body slung load suspension types. The model gives an intuitive and easy-to-use way of modelling and simulating dierent slung load suspension types and it includes detection and response of wire slacking and tightening, and aerodynamical coupling between the helicopter and the load. Furthermore, it is shown how the model can be easily used for multi-lift systems either with multiple helicopers or multiple loads. A numerical stabilisation algorithm as well as a trim algorithm is presented for the complete helicopter/load system and finally the use of the model is illustrated through simulations.

Bumpless Transfer between Observer-based Gain Scheduled Controllers

This paper deals with bumpless transfer between a number of observer-based controllers in a gain scheduling architecture. Linear observer-based controllers are designed for a number of linear approximations of a non-linear system in a set of operating points, and gain scheduling control can subsequently be achieved by interpolating between each controller. The Youla-Jabr-Bongiorno-Kucera (YJBK) parameterization is used to achieve a smooth scheduling between the controllers. This approach produces a scheduled controller as a linear fractional transformation between a fixed controller and a scheduling parameter. The approach is tested on a simple, but highly non-linear model of a fossil fuel power plant.

Congestion management in a smart grid via shadow prices

Abstract We consider a distribution grid interconnecting a number of consumers with flexible power consumption. Each consumer is under the jurisdiction of exactly one balancing responsible party (BRP), who buys energy at a day-ahead electricity market on behalf of the consumer. We illustrate how BRPs can utilize the flexibility of the consumers to minimize the imbalance between the consumed and the purchased energy thereby avoiding trading balancing energy at unfavorable prices. Further we show how shadow prices on the distribution lines can be used to resolve grid congestion without information sharing between the BRPs.

A taxonomy for modeling flexibility and a computationally efficient algorithm for dispatch in Smart Grids

The word flexibility is central to Smart Grid literature, but to this day a formal definition of flexibility is still pending. This paper present a taxonomy for modeling flexibility in Smart Grids, denoted Buckets, Batteries and Bakeries. We consider a direct control Virtual Power Plant (VPP), which is given the task of servicing a portfolio of flexible consumers by use of a fluctuating power supply. Based on the developed taxonomy we first prove that no causal optimal dispatch strategies exist for the considered problem. We then present two heuristic algorithms for solving the balancing task: Predictive Balancing and Agile Balancing. Predictive Balancing, is a traditional moving horizon algorithm, where power is dispatched based on perfect predictions of the power supply. Agile Balancing, on the other hand, is strictly non-predictive. It is, however, explicitly designed to exploit the heterogeneity of the flexible consumers. Simulation results show that in spite of being non-predictive Agile Balancing can actually out-perform Predictive Balancing even when Predictive Balancing has perfect prediction over a relatively long horizon. This is due to the flexibility-synergy-effects, which Agile Balancing generates. As a further advantage it is demonstrated, that Agile Balancing is extremely computationally efficient since it is based on sorting rather than linear programming.

Plug-and-Play Control—Modifying Control Systems Online

Often, when new sensor or actuator hardware becomes available for use in a control system, it is desirable to retain the existing control system and apply the new control capabilities in a gradual fashion rather than decommissioning the entire existing system and replacing it with an altogether new control system. However, this requires that the existing controller remains in action, and the new control law component is added to the existing system. This paper formally introduces the concept of Plug-and-Play control and proposes two different methods of introducing new control components in a smooth manner, providing stability guarantees during the transition phase as well as retaining the original control structure. The applicability of the methods is illustrated on two different practical example systems, a livestock stable climate control system and a laboratory-scale model of a district heating system.

Hierarchical model predictive control for resource distribution

This paper deals with hierarchical model predictive control (MPC) of distributed systems. A three-level hierarchical approach is proposed, consisting of a high level MPC controller, a second level of so-called aggregators, controlled by an online MPC-like algorithm, and a lower level of autonomous units. The approach is inspired by smart-grid electric power production and consumption systems, where the flexibility of a large number of power producing and/or consuming units can be exploited in a smart-grid solution. The objective is to accommodate load variations on the grid, arising from varying consumption and natural variations in power production, e.g. from wind turbines. The approach presented is based on quadratic optimisation and has low algorithmic complexity as well as good scalability. In particular, the proposed design methodology facilitates plug-and-play addition of subsystems without controller redesign. The method is verified by simulating a three-level smart-grid power control system for a small isolated power grid.

Bumpless transfer between advanced controllers with applications to power plant control

This paper deals with bumpless transfer between a number of advanced controllers, e.g. in a gain-scheduling architecture. Linear observer-based controllers are designed for a number of linear approximations of the system model in a set of operating points, and gain scheduling control can subsequently be achieved by interpolating between each controller. We use the Youla-Jabr-Bongiorno-Kucera parameterization to achieve a differentiable scheduling between the controllers. This approach produces a controller as a linear fractional transformation between a controller and a scheduling parameter. In this paper we propose a systematic approach to achieve bumpless transfer between different nominal controllers. The approach is tested on a simple, but highly nonlinear model of a coal-fired power plant.

Introducing Model Predictive Control for Improving Power Plant Portfolio Performance

Abstract This paper introduces a model predictive control (MPC) approach to construction of a controller for balancing power generation against consumption in a power system. The objective of the controller is to coordinate a portfolio consisting of multiple power plant units in an effort to perform reference tracking and disturbance rejection in an economically optimal way. The performance function is chosen as a mixture of the l 1 -norm and a linear weighting to model the economics of the system. Simulations show a significant improvement of the performance of the MPC compared to the current implementation consisting of a distributed PI controller structure, both in terms of minimising the overall cost but also in terms of the ability to minimise deviation, which is the classical objective.

Exact power constraints in smart grid control

This paper deals with hierarchical model predictive control (MPC) of smart grid systems. The objective is to accommodate load variations on the grid, arising from varying consumption and natural variations in the power production e.g. from wind turbines. This balancing between supply and demand is performed by distributing power to consumers in an optimal manner, subject to the requirement that each consumer receives the specific amount of energy the consumer is entitled to within a specific time horizon. However, in order to do so, the high-level controller requires knowledge of how much energy the consumers can receive within a given time horizon. In this paper, we present a method for computing these bounds as convex constraints that can be used directly in the optimisation. The method is illustrated on a simulation example that uses actual wind data as load variation, and fairly realistic consumer models. The example illustrates that the exact bounds computed by the proposed method leads to a better power distribution than a conventional, conservative approach in case of fast changes in the load.

Observer design for boundary coupled PDEs: Application to thermostatically controlled loads in smart grids

This paper develops methods for state estimation of aggregated thermostatically controlled loads (TCLs) in smart grids, via partial differential equation (PDE) techniques. TCLs include on/off controlled devices, such as heat pumps, HVAC systems, and deep freezers. Control of aggregated TCLs provides a promising opportunity to mitigate the mismatch between power generation and demand, thus enhancing grid reliability and enabling renewable energy penetration. However, persistent communication between thousands of TCLs to a central server can be prohibitive. To this end, this paper focuses on designing a state estimation scheme for a PDE-based model of aggregated TCLs, thus reducing the required communication. First, a two-state linear hyperbolic PDE model for homogenous TCL populations is presented. This model is extended to heterogeneous populations by including a diffusive term. Next, a state observer is derived, which uses only measurements of how many TCLs turn on/off at any given time. The design is proven to be exponentially stable via backstepping techniques. Finally, the observers properties are demonstrated via simulation examples. The estimator provides system-critical information for power system monitoring and control.