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A crucial challenge in future smart energy grids is the large-scale coordination of distributed energy generation and demand. In the last years several Demand Side Management approaches have been developed. A major drawback of these approaches is that they mainly focus on realtime control and not on planning, and hence cannot fully exploit the flexibility of e.g. electric vehicles over longer periods of time. In this chapter we investigate the optimization of charging an electric vehicle (EV). More precisely, the problem of charging an EV overnight is formulated as a Stochastic Dynamic Programming (SDP) problem. We derive an analytic solution for this SDP problem which in turn leads to a simple short-term bidding strategy. From an MDP point of view this solution has a number of special features: • It leads to analytic optimal results based on order statistics. • It allows for a more practical rule which can be shown to be nearly optimal. • It is robust with respect to the modeling assumptions, showing little room for further improvement even when compared to a solution with perfect foresight. Numerical results with real-world data from the Belgium network show a substantial performance improvement compared to standard demand side management strategies, without significant additional complexity. (This chapter is based on Kempker et al. (Proceedings of the 9th EAI international conference on performance evaluation methodologies and tools, Valuetools 2015, Berlin, 14–16 December 2015, pp 1–8, 2016).)

An approach is presented to compare two Markov Chains, particularly Continuous-Time Markov Chains (CTMC) such as to model Queueing Networks (QN). Here one may typically think of one CTMC or QN to be a solvable modification (e.g. a product form QN) of the other one, say the original, which is of practical interest but unsolvable. The approach is essentially based upon evaluating performance measures by cumulative reward structures and analytically bounding so-called bias-terms, also known as relative gains or fundamental matrix elements. A general comparison and error bound result will be provided. The approach, referred to as Markov Reward approach, is related to Stochastic Dynamic programming and • may lead to analytic error bounds for the discrepancy, and • may still apply while stochastic comparison fails To motivate and illustrate the approach, the presentation will contain an instructive finite tandem queue example and a practical result for a real-life application of an Operation Theater-Intensive care unit system. Some remaining questions for research will be addressed briefly.

Een bloedtransfusie is een veilige, veel voorkomende en soms levensreddende medische behandeling, waarbij rode bloedcellen afkomstig van een donor worden ingebracht in de bloedbaan van een transfusieontvanger die deze nodig heeft vanwege bijvoorbeeld een chirurgische ingreep (orgaantransplantatie, hartchirurgie) of een hematologische aandoening (leukemie, sikkelcelanemie, thalassemie). Hierbij is het van belang dat de bloedgroepen van de donor en transfusieontvanger zoveel mogelijk overeenkomen, of anders gezegd, dat hun bloedgroepen matchen.