Case Study 3 – Intelligent competition

This case study presents one simulation using the test scenario, with the purpose of analyzing the performance of ALBidS when competing with players with actual intelligent behaviour, since the majority of the tests done in the scope of this work use a scenario where the competing negotiating players do not present much intelligence in the definition of their bids.

An interesting way to analyze this is to watch a player using ALBidS competing with another player also supported by ALBidS. In order to do this, the test scenario is used, but this time both Seller 2 and Seller 3 use the ALBidS support, with the same characteristics and strategy parameters, and the same amount of power to sell. This way they both start in equal ground, and it is possible to see how they anticipate each other and react to the higher success of their competitor. Both players’ definition is presented in Figure 4.13.

Simulation 1 Simulation 2 Simulation 3 Execution Time 80944,21 81374,63 4030,28

Figure 4.13 – ALBidS definitions for Seller 2 and Seller 3.

Both players use the Roth-Erev reinforcement learning algorithm, with a weight value for past experience of 0.4. They are both supported by the Context Analysis mechanism, and by all the available strategies, with full preference for the effectiveness of the method. The simulation is performed for 61 days, and both players’ bids for the twelfth period of each day are presented in Figure 4.14.

Figure 4.14 – Seller 2 and Seller 3’s bids in the twelfth period of the considered 61 days.

As presented in Figure 4.14, the two players’ bids are always very close, and constantly changing whose is above and whose is below. It is even difficult to analyze their performance, so many are the changes in their behaviour, to make it unpredictable, anticipating the other’s actions, and reacting to the environment. In fact, in order to understand which of the two performed better, it is necessary to take a look at their obtained incomes, which are presented in Figure 4.15. Note that the range of the graph may be misleading, since the axes’ range had to be small, in order to be able to properly show the differences.

Figure 4.15 – Seller 2 and Seller 3’s incomes in the twelfth period of the considered 61 days.

A better visualization of this equilibrium can be achieved by analysing Figure 4.16.

Figure 4.16 – Comparison between Seller 2 and Seller 3’s incomes in the twelfth period of the considered 61 days.

As can be seen by Figure 4.16, the total incomes of both considered players concerning this period are very close, resulting from the constant variations and reaction in both players’ actions. The closeness in both players’

results is supported by Figure 4.17.

Figure 4.17 – Seller 2 and Seller 3’s total incomes in the twelfth period of the considered 61 days.

From Figure 4.17 the difference between both players’ achieved incomes is almost imperceptible. In fact the results for this period are as close as: Seller 2: 22,710.24 €; Seller 3: 22,756.36 €.

The results equilibrium is a constant factor, not only for the period in matter, but for all. Figure 4.18 presents the total incomes achieved by both players in the total of the 24 hourly periods of the 61 considered days.

Figure 4.18 – Seller 2 and Seller 3’s incomes in the total of the 24 periods of the considered 61 days.

Analyzing Figure 4.18 the remark goes for the equilibrium of both players’ achieved incomes. The total income results for all periods are: Seller 2: 555,040.21 €; Seller 3: 526,152.74 €. The two players’ equilibrium through competition was expected. However, such close results can be surprising, even though the two players were equipped with the same exact tools.

The most relevant conclusion to take from this test is that a player using ALBidS can, in fact, deal with the competition of intelligent opposition, and still be able to adequately achieve high incomes and adapt its behaviour to anticipate changes, react to other’s plays, and make its own behaviour as unpredictable as possible.

4.5. Final Remarks

The case studies presented in this chapter supported the demonstration of the ALBidS system’s advantages in supporting the decisions of an electricity market’s negotiating player.

As showed by the first case study, the use of ALBidS’ support resulted in the achievement of higher incomes by the test subject, when compared to the results of the same player when using other negotiating strategies. The system also proved to be able to make the adequate choices in what concerns the selection of the most appropriate supporting strategies, as showed by ALBidS ability to change its selected negotiating approach depending on the expectable results of each considered strategy throughout time, depending on the circumstances.

The advantage of using the Context Definition mechanism was showed by the results improvement on weekends. This proved to be a simple, but effective case study in what concerns the demonstration of the required feature. This demonstration also included the evaluation of the Efficiency/Effectiveness Management mechanism’s suitability in managing the balance between the system’s execution time and its achieved results. This mechanism proved to be able to guarantee high incomes when the preference for effectiveness is more accentuated. It also showed that it can drastically reduce the ALBidS system’s execution time, when such reduction is required, nevertheless leading to the consequent smaller achievement of incomes.

Regarding the competition against players provided with intelligent behaviour, ALBidS proved to be able to appropriately adapt to such environment, while still being able to provide the supported player with high incomes.