Case study 1 – Ancillary Services Market model

This case study refers to the model developed in section 3.2. After a brief introduction to the considered scenarios with the corresponding input data, the results are presented and some conclusions are made.

Three scenarios compose the case study. The first scenario corresponds to the base scenario of the case study and refers to the market simulation. The usefulness of the relaxation variables is proven in the second scenario, in which the ISO has an obligation to contract other resources through bilateral contracts, ensuring the feasibility of ancillary services dispatch. An ancillary services market simulation with the inclusion of the AS

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92 October 2013

cascade process is presented in the last scenario. Moreover, this scenario also provides a comparison between the AS market simulation with and without AS cascade process.

4.2.1. Outline

The ancillary services model described in section 3.2 considers simultaneous optimization of AS market. In order to simulate the developed model, a set of input data relevant to the type of problem was used.

In this case study, three scenarios are considered in order to observe carefully the results from the proposed methodology.

Therefore, the input data for each scenario are available in the Annex A, tables A1, A2 and A3, and they include essential features for the simulation. These features include the following aspects:

 Bids that each player performs in the market

 AS requirements imposed by the System Operator (SO)

The input data for the first scenario consists of real data available at the CAISO web site [CAISO-2007b].

4.2.2. Results

The results of this case study are based on the referred scenarios and approaches.

The first scenario is the baseline scenario of the case study, which besides being analyzed individually, is compared with another scenario in order to know the benefits of the implemented methodology.

4.2.2.1. Scenario 1 – Baseline case

AS cascading is important in order to make the market more competitive, and achieve lower overall cost in contracting power required for the AS dispatch.

Table 4.2 shows the results of the AS dispatch, to the simplest methodology of the AS market simulation.

Regarding to the RD dispatch, the Regulation Down requirement is about 150 MW, from which 111 MW are dispatched by “Bid 5” and the remaining 39 MW are supplied by

“Bid 8”. “Bid 8” has a bid price of 4.8 m.u./MW, corresponding to the market clearing price for this service. Although “Bid 4” contains a bid price of 4.0 m.u./MW, the generation minimum limit of this bid is 60 MW. Thus, the “Bid 4” is not dispatched for not fitting the requirement of 39 MW, resulting that a bid with a higher price to be dispatched.

Each of the AS dispatches is related to the market clearing price. This market clearing price is used to calculate the final cost of the market. Each player is remunerated at the market clearing price of each ancillary service.

Table 4.2 – Ancillary services dispatch in scenario 1 of AS model.

Bids Regulation Down Regulation Up Spinning Reserve Non-Spinning

Reserve Total

Section 4.2.2.3 shows a comparison between scenarios in which it is evidenced the differences between the method with or without AS cascading.

4.2.2.2. Scenario 2 – Relaxation variables

This scenario demonstrates the use of certain variables directed to the relaxation of the dispatch problem when there are special negative events for market stability, in which the forecast capability of these events is poor.

The variables RLXD and RLXU are penalties to the optimization problem for not meeting the AS requirements imposed by the SO. Therefore, the RLXD penalty is applied when the minimum power requirement of an increase generation AS is higher than the sum of maximum power offer by all agents in AS market, i.e., when there is a lack of power to achieve the requirement. In turn, the RLXU penalty is applied when the maximum power requirement of increased generation AS is less than the minimum power of the agent with lower minimum limit of power, i.e., when there is the limit of minimum power of the most lower agent is higher than the maximum AS requirement.

The penalty for each variable has a very high price. For this case study it was established for both variables a penalty of 100 m.u./MW.

The results of the AS simulation considering the use of the relaxation variables are presented in Table 4.3. In this table, it is possible to see the use of RLXD variable by NS service, as well as the contribution of the RLXU variable to the SP dispatch.

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Table 4.3 – AS dispatch considering relaxation variables.

Bids Regulation

Down Regulation Up Spinning

Reserve Non-Spinning

The power of these penalties is contracted by the SO to special agents, with special contracts for this kind of emergency service.

When penalties are applied, they are not included in the cost of each service to be hired by the SO, i.e., not increasing even more the market clearing price. These penalties are applied to all agents who participate in the service. In several real markets, these penalties costs are accounted for each AS, in which the SO share the cost by the number of agents participating in that service [CAISO-2009].

4.2.2.3. Scenario 3 – Ancillary Services Cascade process

This scenario shows the results of simultaneous optimization of all AS. The simulation model provides the possibility of AS cascading when it is economically more efficient, which implies that a high quality reserve can replace a lower quality one.

In this context, the present subsection compares the scenario presented in section 4.2.2.1 (Scenario 1) related to the scenario shown in Table 4.4 (Scenario 3) of this present section.

In this way, it is clear the use of AS cascade shown in Table 4.4. Through Table 4.4, one can verify that the RU service hired more power than the necessary to meet their

needs. This happened as it was triggered the AS substitution through the slack variables.

This implies that players who offered their bids in the RU service, have partially satisfied the SP service and fully satisfied the NS service, thus making the AS joint dispatch more economical for the SO. Therefore, to meet their own needs and those of other services, the RU service (SP and NS), resulted in a higher market clearing price for the RU service, than if it had just met their own needs. However, as the bids for SP service and especially for NS service are clearly more expensive, the increase in the market price of the RU service reward makes the system more economically advantageous, regarding the market price that would be charged on NS service if there was no possibility of AS cascade.

Table 4.4 – Ancillary services dispatch considering AS cascade mechanism.

Bids Regulation Down Regulation Up Spinning

Reserve Non-Spinning scenario, these requirements are not equal to the ones presented in the first scenario. With this, there is leeway for the slacks to be used. The results in Table 4.4 show the AS dispatch with clearances resulting in a total cost of approximately 3630 m.u., while the total cost of the AS dispatches without slacks is around 4315 monetary units, as shown in Table 4.5 to compare the scenarios.

As seen in Table 4.5 the information of dispatch and market clearing prices was obtained for each AS and each simulated scenario. Comparing the two scenarios it is noticeable the difference between the total cost of both scenarios. This comparison

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validates the formulation implemented, taking into account that the AS cascade process can obtain a lower final cost, which meets the main goal to SO for ancillary services, that is to guarantee AS quality at the lowest possible cost.

Table 4.5 – AS dispatch comparison related to scenarios 1 and 3 of AS model.

Market Dispatch

Regulation Down Regulation Up Spinning Reserve Non-Spinning Reserve Total Cascade No substitution, which enables a more economical global AS dispatch, without affecting the reliability of power systems.

4.2.3. Results analysis

In this case study, the simultaneous simulation model of AS market was applied. The main goal of the problem is to minimize costs for the SO. The optimization was performed in GAMS using the MINLP model. In order to know the characteristics and abilities of the model, three scenarios able to express the advantages and disadvantages were developed.

In the first scenario shows the simulation model in a simple way, which lies in a AS simulation, considering only the players involved in these service.

In the second scenario, importance was given to the use of relaxation variables of the problem. These variables guarantee that the AS requirement is satisfied and related to a penalty, regardless the ability of the players who participate in the service.

The third and final scenario shows the AS simultaneous optimization model considering the advantage of using the AS cascade process outlined in implemented model.

Also in the section, on the last scenario, it is established a comparison between the results obtained in the first scenario and the last scenario. In this way, one can verify the difference between the possible variations of the model, in which it is clear that considering the AS substitution results in a more economic optimization for the SO.