Crosswind Kite Control - A Benchmark Problem for Advanced Control and Dynamic Optimization

This article presents a kite control and optimization problem intended as a benchmark problem for advanced control and optimization. We provide an entry point to this exciting renewable energy system for researchers in control and optimization methods looking for a realistic test bench, and/or a useful application case for their theory. The benchmark problem in this paper can be studied in simulation, and a complete Simulink model is provided to facilitate this. The simulated scenario, which reproduces many of the challenges presented by a real system, is based on experimental studies from the literature, industrial data and the first author’s own experience in experimental kite control. In par- ticular, an experimentally validated wind turbulence model is included, which subjects the kite to realistic disturbances. The benchmark problem is that of controlling a kite such that the average line tension is maximized. Two different models are provided: A more comprehensive one is used to simulate the ’plant’, while a simpler ’model’ is used to design and implement control and optimization strategies. This way, uncertainty is present in the form of plant-model mismatch. The outputs of the plant are corrupted by measurement noise. The maximum achievable average line tension for the plant is calculated, which should facilitate the performance comparison of different algorithms. A simple control strategy is implemented on the plant and found to be quite suboptimal, even if the free parameters of the algorithm are well tuned. An open question is whether or not more advanced control algorithms could do better.

A Directional Modifier-Adaptation Algorithm for Real-Time Optimization

Dominique Bonvin, Sean Costello, Grégory François

The steady advances of computational methods make model-based optimization an increasingly attractive method for process improvement. Unfortunately, the available models are often inaccurate. The traditional remedy is to update the model parameters, but this generally leads to a difficult parameter estimation problem that must be solved on-line. In addition, the resulting model may poorly represent the plant when there is structural mismatch between the two. The iterative optimization method called Modifier Adaptation overcomes these obstacles by directly incorporating plant measurements into the optimization framework, principally in the form of constraint values and gradients. However, the experimental cost (i.e. the number of experiments required) to estimate these gradients increases linearly with the number of process inputs, which tends to make the method intractable for processes with many inputs. This paper presents a new algorithm, called Directional Modier Adaptation, that overcomes this limitation by only estimating the plant gradients in certain privileged directions. It is proven that plant optimality with respect to these privileged directions can be guaranteed upon convergence. A novel, statistically optimal, gradient estimation technique is developed. The algorithm is illustrated through the simulation of a realistic airborne wind-energy system, a promising renewable energy technology that harnesses wind energy using large kites. It is shown that Directional Modifier Adaptation can optimize in real time the path followed by the dynamically flying kite.

Elsevier2016

A Propagation/Local Search Hybrid for Distributed Optimization

Boi Faltings, Adrian Petcu

We present a new hybrid algorithm for local search in distributed combinatorial optimization. This method is a mix between classical local search methods in which nodes take decisions based only on local information, and full inference methods that guarantee completeness. In general, classical inference methods are time and space exponential in a parameter of the graph called the induced width, thus they may be infeasible for dense problems. On the other hand, local search methods suffer from the fact that the nodes take myopic decisions based only on local information available to them, and thus can easily get stuck in local optima. Their advantage is that they require linear memory, and in many cases provide good solutions with a small amount of effort. We propose a hybrid method that combines the advantages of both these approaches. This method is a utility propagation algorithm controlled by a parameter maxDim which specifies the maximal allowable amount of inference. The maximal space requirements are exponential in this parameter. In the dense parts of the problem, where the required amount of inference exceeds this limit, the algorithm executes a local search procedure guided by as much inference as allowed by maxDim. This hybrid can be seen as a large neighborhood search, where exponential neighborhoods are rigorously determined according to problem structure, and polynomial efforts are spent for their complete exploration at each local search step. The algorithm explores independent parts of the problem simultaneously and asynchronously, and then combines the results, all in a distributed fashion. We show the efficiency of this approach with experimental results from the distributed meeting scheduling domain.

2005

Design of heliostat fields using a multi-objective evolutionary algorithm

Daniel Favrat, Xavier Pelet

The paper discusses the interest of the multi-objective optimization approaches for the design of complex energy systems and the basics of the original evolutionary algorithm used. The specific problems linked to the design of the concentrators of solar tower power plants are then introduced to illustrate the objective of the work presented. The first analysis deals with the optimization of the x-y positioning of each of the 500 rectangular reflectors of an existing predesigned field. In spite of the large number of variables (1000) the algorithm successfully calculated an optimum positioning of each concentrators and the main induced variations are shown. The second part of the paper describes a new program, which was made and coupled with the same algorithm to optimize the whole design in a two-objective approach (specific energy cost versus investment cost). The main hypothesis made, to keep the number of variables within a reasonable domain, was to distribute identical reflectors along a set of concentric ellipses around the tower. The interest of the method is illustrated for one given period with the Pareto curve showing all the optimum solutions (lowest specific energy cost at any given investment). The investment cost includes the cost of the heliostats themselves, the cost of land, the cost of the tower, etc. Variables include the x-y positioning, the height of the support and the dimensions of each rectangular reflectors as well as the height of the tower and the number of reflectors. These decision variables being given for each solution, the extracted energy is calculated by a program of CIEMAT [Sanchez, Romero, 2003]. Details of the design parameters are illustrated for selected optima along the Pareto curve References M. Sanchez, M. Romero Optimisation of heliostat field layout in central receiver systems based on yearly normalized energy surfaces. ISES, 2003, Goteborg

2006

Design of heliostat fields using a multi-objective evolutionary algorithm

Daniel Favrat, Xavier Pelet

2006