Methodology for a selection of the optimum generation strategy

In order to find the economically optimised option from all technically possi- ble strategies several methods might be used. In the first and most simple approach one of the three possible but uncertain load development scenar- ios is chosen. Only for this scenario the optimum solution of generation is determined by contrasting the annual operation costs (OC) of each strategy as per Annex 4 with the load development. The strategy with minimum costs is chosen as optimum (see Table 2-3). Normally this is realised by Delphi-interviews of experts that choose the possibly emerging option in the future. Other development variants remain unconsidered. The uncertainties of the determination of operation costs of every generation option are ne- glected and only one single, discrete variant of operation costs is expelled, admittedly knowing that operation costs cannot be determined without fault tolerances. Also in that case one discrete variant is chosen from a variety of possible values.

Generation

Strategies GS1 GS2 GS3 GS4 GS5 GS6

Load Scenario Optimum

MGS OC12 OC22 OC32 OC42 = OCmin OC52 OC62

Table 2-3 Example for the selection of the optimum strategy by means

of Method 1 and MGS load scenario

The advantage of this methodology is the strict limitation of variants to be calculated and therefore the good clarity of results. Nevertheless the prob- lem of load development sensitivity remains unsolved.

The second methodology continues this sensitivity problem and analyses the optimum generation strategy for every possible load scenario. The so

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found solutions might coincide in best case and lead to one optimum vari- ant, i.e. from the sum of all generation strategies one single becomes ap- parent independently from load development (see Table 2-4).

Generation

Strategies GS1 GS2 GS3 GS4 GS5 GS6

Load Scenario Optimum

LGS OC11 OC21 OC31 OC41 = OCmin OC51 OC61

MGS OC12 OC22 OC32 OC42 = OCmin OC52 OC62

HGS OC13 OC23 OC33 OC43 = OCmin OC53 OC63

Table 2-4 Example for the selection of the optimum strategy by means

of Method 2 and all load scenario, “stable optimum”

Nevertheless it could be possible, too, that for every of all three forecasted load developments different generation strategies might be optimal (see Table 2-5). In that case the optimum solution needs to be defined by Del- phi-interview analysis, as well.

Generation

strategies GS1 GS2 GS3 GS4 GS5 GS6

Load Scenario Sub-

optimum 1 Sub- optimum 2 Sub- optimum 3 LGS OC11 = OCmin

OC21 OC31 OC41 OC51 OC61

MGS OC12 OC22 OC32

OC42 = OCmin OC52 OC62 HGS OC13 OC23 = OCmin

OC33 OC43 OC53 OC63

Table 2-5 Example for the finding of suboptimum strategies by means of

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Since not only load development is subject to uncertainty but the base data (the operation costs) too, the found result can be analysed concerning its stability in modification of base data (sensitivity analysis). In that way it can be seen, in which range of investment and fuel costs etc. the found opti- mum solution is stable.

This method is a significant progress in contrast to the first method. Dis- tinctly profound information about the technical-economical relation is ac- quired. The wider the ranges of basic data are, in that the result of option comparison does not change, the more stable is the found solution and therefore the confidence to this generation strategy.

The third methodology is realised as done in the second approach. How- ever a game-theoretical decision criterion is additionally utilised. The practi- cal usage of the game theory for technical-economical decision is compre- hensively described by Muschick and Müller [7].

Classical decision criteria of game theory are: - Minimax-criterion

- Bayes-Laplace-criterion - Savage-criterion

Therewith it is the most secure option to choose and use the minimax- criterion. This criterion delivers an optimum decision under the uncertainty of load development. Independent from the really emerging development that variant with the fewest economical loss is chosen. Unfortunately the minimax-criterion is characterised by the fact that strategies with high bene- fits remain unconsidered, if they cause higher losses in some load scenar- ios than the found optimum by minimax. Bayes-Laplace is more optimistic in that terms and chooses a strategy that on the one hand facilitates higher benefits but on the other hand is less secure.

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In order to receive a game-theoretical decision matrix, in that instead of operation costs benefit elements are listed, the operation costs are de- picted as negative costs. Thus the highest operation costs are converted to benefit elements with the fewest benefit.

Generation

Strategies GS1 GS2 GS3 GS4 GS5 GS6

Load Scenario Optimum

LGS OC11 OC21 OC31 OC41 OC51 OC61

MGS OC12 OC22 OC32 OC42 OC52 OC62

HGS OC13 OC23 OC33 OC43 OC53 OC63

Minmax – OC Min OC1 Min OC2 Min OC3 Min OC4 Min OC5 Min OC6

Table 2-6 Optimum strategy according to Minmax-criterion

Generation

Strategies GS1 GS2 GS3 GS4 GS5 GS6

Load Scenario Optimum

LGS OC11 OC21 OC31 OC41 OC51 OC61

MGS OC12 OC22 OC32 OC42 OC52 OC62

HGS OC13 OC23 OC33 OC43 OC53 OC63

1/3*∑OCij OC1 OC2 OC3 OC4 OC5 OC6

Max 1/3*∑OCij OC4

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