The construction, set-up and operation of many systems of interest in sectors such as industry, supply chains and communications are complex processes, which may require significant investment of resources. For this reason, the automation of the decision making for achieving the best design and operation of such systems, which may be regarded as discrete event systems (DESs), constitutes an active research field. In this paper, we present a methodology to cope with this process in an efficient way, optimizing not only the behaviour of the DES but also its structure. This kind of problem is usually associated with the so-called combinatorial explosion, since the number of alternative configurations for the DES might be huge. We present an improved algorithm to transform a set of alternative Petri nets, representing alternative structural configurations, into a more compact model called an alternatives aggregation Petri net. In real decision-making problems, where the different alternative structural configurations may share common subnets, this compact model may allow the development of a much more efficient optimization problem than the classic approach of ‘divide and conquer’. The achievement of this objective is performed by developing a single and compact model for all of the alternative structural configurations of the DES and the simulation of the most promising of them. In this paper, the mentioned methodology is introduced and its advantages and drawbacks are described in relation with the classic approach.
Petri nets (PN) paradigm is broadly used to model discrete event systems (DES). Thanks to both, its graphical and algebraic representations, PN provide a powerful and uniform tool, with an important theoretical support for modelling and formal analysis. On the other hand, genetic algorithms constitute a metaheuristics able to cope with complex problems of combinatorial optimisation. The use of genetic algorithms to solve optimisation problems based on PN models is a classical research line; nevertheless, it has been applied mainly to decision support systems related only to the operation of DES. In this paper a general statement of decision problems is proposed, including not only the operation but also the design process of the DES. This leads to a set of undefined parameters, classified according to their role in the PN model. Moreover, under certain circumstances, the PN model can appear as a disjunctive constraint. Alternatives aggregation PN are presented as a natural formalism to afford the transformation of the disjunctive constraint and to define a single solution space that allows genetic algorithms to perform a very efficient search of the best solution in a single process. A case-study is presented exhaustively, where the proposed methodology outperforms more classical approaches.
Currently there exist several energy storage technologies that are suitable for wind energy integration services. Energy prices in several countries (such as in Spain) are set for the day ahead market, which means the hourly prices are known the day before. This represents an opportunity for wind power plant owners. Wind energy generated in hours when demand and prices are the lowest could be stored and sold in hours when demand and prices are higher. This paper analyses the benefit of wind energy storage by time shift depending on climatological (wind), technological (storage facilities), and market (power prices) factors for the Spanish case, as exemplification of a methodology to be used in any other country. Wind energy time shift has been simulated for periods of time of 1 hour up to 9 hours considering two scenarios, a day with low wind energy generation and a day with high wind generation, in order to determine in which moments a beneficial exists with the different energy storage technologies. According to the results, on a day with high wind energy levels the gain obtained by time shifting wind energy from low to high demand hours could reach 68,1%, and on a day with low wind energy levels, the gain obtained by time shifting wind energy could reach 19,3%.
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