can you only do Q3 only please

Wholesale Prices in the Australian National Electricity Market

Background

The Australian National Electricity Market (NEM) is the wholesale spot market for electricity in the Eastern states of Australia. It comprises the states of Queensland (QLD), New South Wales (NSW), Victoria (VIC), South Australia (SA) and Tasmania (TAS). The market is operated by an independent regulatory authority, the Australian Energy Market operator (AEMO). Among its other responsibilities, AEMO runs a process of dispatching generators to meet the demand of customers for electricity. Through this process the wholesale price is determined for every 30-minute interval in each of the states, each of which represents a regional market. A 24-hour day is divided into 48, 30- minute intervals. In this assignment price refers to the wholesale spot price of electricity in the NEM and interval to a 30-minute interval. Price can, and usually does, vary by state, interval as well as by season. Prices are generally higher in seasons or months of higher demand which correspond to hot or cold periods of the year driven by air conditioning or electric heating demand. You are not expected to be experts in the electricity market, this information is provided for context.

The data that you will analyse are prices for each interval in the months of February, March and April 2019 for each of NSW, VIC and SA, see the Excel file [Elec Price data _Assignment 26134 BStats]. The data has been downloaded from AEMOs website. Prices are expressed in Australian dollars per Megawatt hour (MWh). MWh is a unit of energy.

Questions

Q1- Descriptive statistics are commonly used to summarise data. Describe the main features of prices in the period using the data given for the states of NSW and VIC. You are expected to provide a numerical representation of the data.

a) In particular, comment on the measures of location, spread and skewness. You might find the link to the Introductory Data Analysis Using Excel helpful. You are expected to provide brief commentary on the measures that you present. A dumb of numbers will not earn you full marks.

b) Provide brief possible reasons (i.e. guesses) for the different descriptive statistics across states.

Q2- The states of VIC, NSW and SA are dominated by brown coal, black coal and renewable generation assets respectively. These three generation types emit greenhouse gas (GHG) in varying intensities.1 Brown coal has the highest intensity followed by black coal and then renewables. A policy maker is interested in understanding if a relationship exists between electricity price and GHG intensity and has asked you to present analysis on this question.

In your analysis, arrange the emissions intensity from highest to lowest (or vice versa) and use the following seven price categories:

1. 1) <= $30/MWh

2. 2) > $30/MWh to <= $60/MWh

3. 3) > $60/MWh to <= $90/MWh

4. 4) > $90/MWh to <= $120/MWh

5. 5) > $120/MWh to <= $150/MWh

6. 6) > $150/MWh to <= $180/MWh

7. 7) >$180/MWh

1 For information only, GHG intensity is usually expressed in tonnes of GHG per unit of energy generated. 3

Group Assignment - Business Statistics 26134 - Summer 2020

a) Using the price data for all three months (keep the three states separate) construct a table of relative frequency. The relative frequency table will have three states and seven price categories. Form two such tables, one based on observed (actual) relative frequencies and the other based on the expected (under the assumption of independence) relative frequencies. Compare the relative frequencies in the two tables. Does the data indicate that price and GHG intensity are independent or not independent?

Q3- Using the price data for VIC

a) Calculate the 95% confidence intervals for the mean price of each of the three months February, March and April. Use the month given in the first column headed Month to decide which month the data belongs to. You will get three confidence intervals for VIC, one for each month.

b) Why do the confidence intervals have different widths?

c) Without calculating the 99% confidence intervals, would you expect the width of a 99% confidence interval to be narrower, the same or wider than the width of the corresponding 95% confidence interval? Justify your answer with reference to the terms used to calculate the confidence interval.