Difference between revisions of "Projects:2019s2-25201 Evaluating the Capabilities of the Existing Synchronous Generators for Ancillary Services Provision in the NEM in various Renewable Penetration Scenarios"
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[[File:CorrelationFCAS1.png|thumb|right|Figure 3: Correlation of intermittent renewable generation with FCAS prices]] | [[File:CorrelationFCAS1.png|thumb|right|Figure 3: Correlation of intermittent renewable generation with FCAS prices]] | ||
[[File:FCAS2.png|thumb|right|Figure 4: Correlation of intermittent renewable generation with FCAS requirements]] | [[File:FCAS2.png|thumb|right|Figure 4: Correlation of intermittent renewable generation with FCAS requirements]] | ||
+ | [[File:Output table of Python.png|400px|thumb|center|Figure 5: Output table of Python]] | ||
+ | [[File:Linear graph.jpg|thumb|center|Figure 6: The Linear Graph]] | ||
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Due to aiming for better resolution of data, 5-minutes intervals of data are chosen. Hence, there will be 3 years of historical data of 5-minutes intervals for each FCAS products. From this, our interest is to study the most critical intervals for each product. Hence, the calculation of the critical factor of each FCAS services is required. To find the most critical intervals, the data sorting process is required. This sorting process is done using Python. The lowest critical factor followed by big capacity of FCAS requirements will prove the criticality of that time intervals. This can be done using assessment table method by categorising it into safe and critical zone. The figure 5, this is the example output from assessment table method resulted from analysis made using Python. | Due to aiming for better resolution of data, 5-minutes intervals of data are chosen. Hence, there will be 3 years of historical data of 5-minutes intervals for each FCAS products. From this, our interest is to study the most critical intervals for each product. Hence, the calculation of the critical factor of each FCAS services is required. To find the most critical intervals, the data sorting process is required. This sorting process is done using Python. The lowest critical factor followed by big capacity of FCAS requirements will prove the criticality of that time intervals. This can be done using assessment table method by categorising it into safe and critical zone. The figure 5, this is the example output from assessment table method resulted from analysis made using Python. | ||
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After that, create a data modelling based on the highest critical zone for each products. This data modelling is create by applying a linear function to the 3 years average data. Figure 5 shows the graph with the linear equation for one of the product. This linear equation is used to predict the future requirement of FCAS products and observe whether availability in the FCAS still can support when the penetration of intermittent increase until 70%. | After that, create a data modelling based on the highest critical zone for each products. This data modelling is create by applying a linear function to the 3 years average data. Figure 5 shows the graph with the linear equation for one of the product. This linear equation is used to predict the future requirement of FCAS products and observe whether availability in the FCAS still can support when the penetration of intermittent increase until 70%. | ||
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<small>Note:The data proportion of intermittent energy in while NME is lower than South Australia(16.2% VS 36.2% in 2019). Also, there are not critical time interval for both regulation services (result from data soring and analysis). Therefore, the outcome of the regulation services will be different with the contingency services.</small> | <small>Note:The data proportion of intermittent energy in while NME is lower than South Australia(16.2% VS 36.2% in 2019). Also, there are not critical time interval for both regulation services (result from data soring and analysis). Therefore, the outcome of the regulation services will be different with the contingency services.</small> | ||
Revision as of 11:01, 9 June 2020
Abstract here
Contents
Introduction
Our power system is going through dramatic changes. With renewable generation resources in the grid, more synchronous generators are retiring. Traditionally, the synchronous machines were the main sources of ancillary services to compensate imbalances between generation and demand in order to keep the frequency within the acceptable range. However, renewable resources (such as photovoltaic and wind) are very uncertain, unpredictable, and representing huge up and down ramping events. In this study, we want to see if the existing synchronous generators of different types and properties (such as coal- and gas-fired and hydro power plants) are able to provide the kind of AS that is needed in different penetration levels of renewables. We use the information from AEMO to identify the existing synchronous machines, their availability for AS (eight FCAS markets), and specific characteristics related to providing AS. We also analyse the ramping requirements under various renewable (PV + wind) generation scenarios.
Project team
Project students
- Khairul Azwari Adnan
- Aina Afrina Hasram
- Wenkang Li
Supervisors
- Ali Pourmousavi Kani
- David Vowles
Objectives
- Study technical characteristics of existing synchronous generators available in the market.
- Quantifying the ancillary services requirements of the system under different scenarios of intermittent renewable generation.
- Analyze the ramping requirements of under various intermittent generation scenarios.
Background
Supply & Demand
Basically, the electric supply is from renewable and non-renewable sources. Electricity demand is the electricity used by the consumers and the amount is varies. The balancing of supply and demand is very important to make sure the performance of the power system is in stable state. Frequency is one of the important parameters in the power system and it is totally depending on the balancing of supply and demand. The standard frequency limit in the power system is ±50 Hz. As the supply is higher than demand, the frequency is lower and vice versa.
Intermittent Generation
Intermittent energy is a kind of energy cannot continuously generate power, cannot accurately set or plan the capacity of power generation. In renewable energy specifically refers to wind and solar power. These two intermittent energy source increasing is the main factor of the Supply & Demand unbalance and the frequency deviation. Thus, this project will not study others renewable energy soucre(such as Hydropower).The figure 1 is installed wind and solar capacity in the NEM for 2019, with 2025 and 2040 forecasts from the Draft 2020 ISP Central and Step Change generation builds[1]. Obviously, the proportion of wind and solar will continuously increasing. Hence, due to uncertainty and variability characteristics from these two sources, there will be more occurrences in frequency drop/rise event.
Ancillary Services
Ancillary service is functioning to help in maintaining the performance of the power system. There are three types of ancillary services, Frequency Control Ancillary Service (FCAS), Network Support Control Ancillary Service (NSCAS) and System Restart Ancillary Service (SRAS). For this project, the team is focusing on the FCAS only.
Frequency Control Ancillary Services (FCAS)
Frequency control is necessary in order to guarantee that the system frequency in the grid system is in the nominal frequency. FCAS is required to get back the frequency to its standard frequency and make sure the frequency is always in the range. Other than that, Australia has a huge number of intermittent sources such as wind and solar, that supply electricity to the power system in the grid. These intermittent sources have affected the performance of the frequency in the power system.
There are two types of FCAS services, regulation services and contingency services. Regulation services is provided by the participated generators based on the Automatic Generator Control (AGC) while contingency is provided based on the frequency deviation in the power system [1]. From these two services, there are eight products in the FCAS market, Raise Regulation, Raise 6-Second, Raise 60-Second, Raise 5-Minute, Lower Regulation, Lower 6-Second, Lower 60-Second, and Lower 5-Minute. All these products perform a different services. Table 1 shows the description of each product in the FCAS market.
For every FCAS products, there are different characteristics and different purposes. It will depends on the situation of contingency event. In Regulation service, there are two which are raise and lower. A signal from AGC will alter the generation output so that it can match with the supply and demand. For contingency, there are three speed, 6 seconds(fast), 60 seconds (slow), and 5 minutes (delayed). For the fast service, it will arrest the major drop/rise frequency issue immediately. While the 60 seconds is to stabilise following major drop/rise in frequency and the last one, 5 minutes will recover frequency to the normal operating band following the major drop/rise in frequency.
Methodology
Figure 2 shows the project planning of the project. Date retrieving is taken from NEMreview apps https://app.nemreview.info/index.html#/ for 3 years of all FCAS products. The data for regulation services is observe in the whole NEM which are South Australia, Victoria, New South Wales, Queensland, and Tasmania, while for contingency, the observation is only focus in the South Australia since the contingency services is being controlled by local generator. Once the data is completely being retrieved, correlation testing is conducted to all parameters to find the relationship with the intermittent sources generation over past three years. There are two factors being tested which are FCAS prices and FCAS requirements. This correlation testing is done in MATLAB. Figure 2 and Figure 3 show the correlation between requirement and availability of raise contingency. It can be seen that the correlation for for intermittent renewable generation with the FCAS requirements are stronger comparing to FCAS prices. Hence, to proceed the next stage, we just focus on FCAS requirements data and analyse from it.
Due to aiming for better resolution of data, 5-minutes intervals of data are chosen. Hence, there will be 3 years of historical data of 5-minutes intervals for each FCAS products. From this, our interest is to study the most critical intervals for each product. Hence, the calculation of the critical factor of each FCAS services is required. To find the most critical intervals, the data sorting process is required. This sorting process is done using Python. The lowest critical factor followed by big capacity of FCAS requirements will prove the criticality of that time intervals. This can be done using assessment table method by categorising it into safe and critical zone. The figure 5, this is the example output from assessment table method resulted from analysis made using Python.
After that, create a data modelling based on the highest critical zone for each products. This data modelling is create by applying a linear function to the 3 years average data. Figure 5 shows the graph with the linear equation for one of the product. This linear equation is used to predict the future requirement of FCAS products and observe whether availability in the FCAS still can support when the penetration of intermittent increase until 70%.
Note:The data proportion of intermittent energy in while NME is lower than South Australia(16.2% VS 36.2% in 2019). Also, there are not critical time interval for both regulation services (result from data soring and analysis). Therefore, the outcome of the regulation services will be different with the contingency services.
Results
Table 2 shows the result of prediction for requirement based on the linear equation of 3 years data. The prediction is done by using the data of availability for 2019. So, as the penetration of intermittent sources increase, the requirement also increase. The percentage of how many the FCAS availability can support the requirement is based on the assumption of area under the distribution curve minus the area of histogram. Figure 6 shows the distribution curve for requirement of FCAS that has been plot on the histogram of availability.
Conclusion
As the conclusion, the availability of raise regulation can support until 30% penetration of intermittent sources, while the availability of lower regulation can support until 100% penetration of intermittent sources. The availability of raise contingency can support until 50% penetration of intermittent sources, while the availability of lower contingency can support until 40% penetration of intermittent sources.
Future Planning
Analyze the ramping requirements under various intermittent generation scenarios.
References
[1] Renewable Integration Study: Stage 1 report, AMEO
[2] Guide to Ancillary Services in the National Electricity Market, AEMO