The increasing penetration of renewable energy introduces more uncertainties and creates fluctuations in power systems than ever before, which brings great challenges for automatic generation control (AGC). It is necessary grid operators to develop an advanced AGC strategy handle uncertainties. dynamic optimization a sequential decision problem that can be formulated as discrete-time Markov pro...

The Distributed Constraint Optimization Problem (DCOP) formulation is a powerful tool for modeling multi-agent coordination problems. To solve DCOPs in dynamic environment, Dynamic (D-DCOPs) have been proposed to model the inherent dynamism present many D-DCOPs sequence of static problems by reacting changes environment as agents observe them. Such reactive approaches ignore knowledge about fut...

Abstract. A fed-batch fermentation process is examined in this paper for experimental and further dynamic optimization. The static optimization is developed for to be found out the optimal initial concentrations of the basic biochemical variables – biomass, substrate and substrate in the feeding solution. For the static optimization of the process the method of Dynamic programming is used. Afte...

The general problem of dynamic optimization of bioprocesses with unspecified final time is considered. Several solution strategies, both deterministic and stochastic, are compared based on their results for three bioprocess case studies. A hybrid (stochastic-deterministic) method is also presented and evaluated, showing significant advantages in terms of robustness and computational effort.

Resource allocation problems where tasks have to be assigned to resources in such a way that no resource gets overused can be solved using recurrent auctions. In dynamic environments where unexpected changes may occur, searching the optimal solution may not be the best choice as it would be more likely to fail. In these cases a robust solution is preferable. In this paper we present a robustnes...

OPTIMIZATION-BASED APPROXIMATE DYNAMIC PROGRAMMING