Multi-Objective Predictive Control Optimization with Varying Term Objectives: A Wind Farm Case Study

Clara M. Ionescu,Mihaela Ghita,Cosmin Copot,Guillaume Crevecoeur,Constantin F. Caruntu,Ricardo Cajo

doi:10.3390/pr7110778

Abstract

This paper introduces the incentive of an optimization strategy taking into account short-term and long-term cost objectives. The rationale underlying the methodology presented in this work is that the choice of the cost objectives and their time based interval affect the overall efficiency/cost balance of wide area control systems in general. The problem of cost effective optimization of system output is taken into account in a multi-objective predictive control formulation and applied on a windmill park case study. A strategy is proposed to enable selection of optimality criteria as a function of context conditions of system operating conditions. Long-term economic objectives are included and realistic simulations of a windmill park are performed. The results indicate the global optimal criterium is no longer feasible when long-term economic objectives are introduced. Instead, local sub-optimal solutions are likely to enable long-term energy efficiency in terms of balanced production of energy and costs for distribution and maintenance of a windmill park.

Highlights

When it comes to optimization strategies, advanced control methodologies such as model based predictive control (MPC) is of great industrial relevance [1,2,3]
Model predictive control is an advanced control strategy well established over the past decades and commonly employed in the so-called money making industry, where economic costs are highly relevant [28,29,30]
To verify the result between a global and an area-wise MOOP with distributed MPC strategy, simulations are performed in Matlab/Simulink R2017a

Summary

Introduction

When it comes to optimization strategies, advanced control methodologies such as model based predictive control (MPC) is of great industrial relevance [1,2,3]. Its variant as distributed MPC has great added value in terms of numerical optimization and computational efficiency [4,5]. It requires a significantly lower amount of information than full multivariable MPC, the optimization can be accelerated. Processes 2019, 7, 778 nesting, implementation and training costs are making the MPC an even more appealing strategy for large scale processes. Chain optimization is a great example for successful stories of economic and environmental related objective based MPC applications [6,7]

Methods

Results

Discussion

Conclusion