Forecasting energy demand and revenue

Academic Year 2021/22

Module Code: BN2230

Module Name: Business Analytics in practice

Module Leader: Dr Dimitris Giraleas

Coursework Title:

Forecasting energy demand and revenue

Task Details/Description:

Southern Energy is in the process of deciding whether to build a new gas-fired power plant that would serve specifically a large cluster of manufacturing businesses in an industrial park near London. To that end, it needs to estimate the likely costs of producing electricity over the next 5 years and the revenues that can be generated by selling the electricity produced to the business in the industrial park. However, since the costs of producing electricity are mostly dependent on the price of gas, the first task is to forecast future gas prices. Additionally, the revenue from selling electricity depends largely on electricity demand, so forecasts of electricity demand are also necessary.

Gas prices (per cubic meter- m3) are thought to depend on two major factors; the prices of alternative fuels (PF, an aggregate index measure of prices of other fuels that can generate electricity, mainly oil and coal) and changes in the general global economic environment, measured as changes in the global Gross Domestic Product (GDP). Data for those two factors together with the observed price of gas over the past 5 years, as well as the demand of the manufacturing businesses in the park is given in table 1.

Table 1: Historic gas prices, influencing factors and electricity demand

Year Quarter Gas Prices (£s per 1000’s m3) Prices of alternative fuels (PF) GDP Electricity demand (MW/h)
1 1 96.41 81.82 2.38% 458.71
1 2 116.86 104.85 2.08% 136.82
1 3 119.47 115.75 1.78% 120.94
1 4 112.06 114.65 1.58% 155.14
2 1 46.84 82.54 -1.30% 549.05
2 2 78.07 102.87 -2.22% 206.49
2 3 55.73 98.13 -2.41% 78.37
2 4 83.97 118.02 -1.30% 314.28
3 1 113.99 74.88 3.02% 538.83
3 2 168.84 92.04 4.75% 323.22
3 3 126.45 99.57 5.18% 266.30
3 4 122.76 127.85 3.89% 380.30
4 1 87.64 92.01 2.49% 608.77
4 2 140.16 132.28 2.26% 285.47
4 3 115.45 100.22 2.04% 255.69
4 4 81.72 103.97 2.26% 543.90
5 1 88.01 112.39 0.26% 541.11
5 2 77.13 98.55 0.20% 379.48
5 3 61.74 84.31 0.18% 405.90
5 4 82.23 102.05 0.16% 484.25

The first goal of Southern Energy is to create a forecast of gas prices and electricity demand for the next 5 years, under three scenarios; Base, Faster growth and Slower growth:

• The Base scenario assumes that the conditions that drive prices for alternative fuels and the trends of electricity demand in the industrial park will persist in the near future. Southern Energy also assumes that global GDP change measure will be 1.5% per quarter. The company estimates that the probability of the Base scenario taking place is 40%.
• The Faster growth scenario assumes that global demand will increase at a faster rate, which would result in increases in both the price of alternative fuels and global GDP at a rate higher than the base scenario. However, faster growth would also increase the electricity demand from the industrial park. The company estimates that the probability of the Faster growth scenario taking place is 35%.

• The Slower growth scenario assumes that global demand will slow down, which would result in slower increases in prices and demand or even periods of decrease relative to the base scenario.
The forecast values for both the Faster and Slower growth scenarios can be calculated by applying the following percentage changes to demand values and possible explanatory factors of the Base Scenario:

Table 1: Uplift parameters to Base values for the faster and slower growth scenarios
Faster Growth Slower Growth
Year PF GDP (%) Electricity demand PF GDP (%) Electricity demand
6 5% 2.5 5% 3% 1 -10%
7 10% 3 7% 0% -0.5 -8%
8 4% 3 10% -5% 0 -5%
9 3% 2.5 6% -10% 0.5 -3%
10 2% 2 4% -3% 0.7 0%
Note: For GDP, replace the base assumption with the one provided in this table.

Southern Energy’s second goal is to estimate the likely profit under these scenarios and use this information in selecting the optimal strategy to adopt for the Year 6 to Year 10 period. To do so, it has to take into account these facts:
• One cubic meter (m3) of natural gas produces 11 kW of electricity.
• The electricity demand is given in average MW per hour for a given quarter. There are 91 days in a quarter.

• The gas-fired power plant can produce electricity as demand dictates (no additional costs for fluctuating demand or running under a certain capacity). It will produce electricity for the industrial park only and its maximum generating capacity is 600 MW/h. The industrial park will absorb all electricity produced as long as there is demand. So for example, if the demand from the industrial park is 500 MW/h in a given quarter, the plant will use just enough gas to generate 500 MW/h and the industrial park will absorb the full 500 MW/h. If Demand is 700 MW/h, the plant will generate up to its capacity, ie 600 MW/h, and the remaining demand will be sourced from the national grid.

• The price of electricity has already been negotiated with the industrial park and is set at a constant £0.011 per kW produced.

• The initial investment cost for the power plant is £124 million (build cost) and will be fully operational at the start of year 6. It has fixed operating costs of £320,000 per quarter (additional to gas costs required to generate electricity). At the end of year 10, the residual life of the plant will be £103 million.
Given the above information, Southern Energy is considering three possible courses of action:

• Build the power plant as detailed above (‘standard plant’), assuming that the investment is profitable.

• Build a larger capacity power plant. The larger plant will require an additional investment of £34 million and will incur fixed operating costs of £395,000 per quarter, but will increase maximum generating capacity to 850 MW/h and the residual value to £131 million.

• Take on an additional contract to supply electricity to a nearby aluminium refinery at a price of £0.010 per kW sold. The contract stipulates that Southern Energy must supply 75 MW/h to the refinery before supplying anybody else in the industrial park. After taking on the contract, Southern Energy still needs to decide whether to build the standard plant or the larger capacity plant, as detailed above.
In all instances, the cost of capital of Southern Energy is 1.72% per quarter and all monetary values are in current prices (not taking into account the time-value of money).

Additionally, Southern Energy would also like to explore the sensitivity of the initial results to the following changes:

1) The Uplift parameters for the Faster growth scenario become:
Faster Growth
Year PF GDP (%) Electricity demand
6 7% 2.5 15%
7 12% 3 18%
8 6% 3 10%
9 3% 2.5 6%
10 4% 2 8%

2) The operating cost per quarter for the standard plant increase to £355,000.

3) From the start of year 8, the price of electricity increases to £0.012 under the Faster growth scenario and decreases to £0.010 under the Slower growth scenario. It remains at those levels until the end of Year 10. The price of electricity for the contract remain unaffected (constant at £0.010 per kW)

Module Learning Outcomes Assessed:
The analysis required for this assessment can be divided into three parts. First, you need to create a model that forecasts passenger and freight demand for the different scenarios, using all the relevant information provided by the case study. Secondly, you need to utilise (and adjust when necessary) the demand forecasts in order to estimate the likely revenues for the three options that Midlands Rail is considering, again taking into account the different demand scenarios. Thirdly, you need to make adjustments to your primary data as necessary and carry out the required sensitivity analysis.

Presentation Requirements:
This assignment has two outputs: the Excel model that you used to generate the required forecasts and explore the decision problem, and a report that summarises your findings. The report should either be a Word document or a PowerPoint presentation (I have no preference). Use a readable font and neutral colours and always keep readability in mind. There is no word limit for the report but it should not exceed 6 pages (including tables and graphs); if you opt to produce a PowerPoint presentation instead, it should not exceed 10 slides.

Both outputs need to be submitted electronically. Pay special attention when submitting your Excel model. The file submitted should be fully auditable, with no hardcoded values. PDF or Word copies of the model cannot be audited and will be given zero marks.

Submission Date & Time:
Monday 22nd August 2022, 12pm UK time.

Assessment Weighting for the Module:
Assessment Criteria
Of the two outputs you need to submit, the Excel model carries the most weight. As well as being factually correct, the model should ideally be clearly labelled, have a good ‘flow’ and a clear audit trail. Remember to use best practice (clear formatting, label your inputs and outputs, don’t be afraid to add notes, no hardcoded values, document your assumptions and your logic, keep your data separate from your calculations, have a separate area to document important outputs).
The Excel model carries 80% weight for your submission. Of that 80%:
• 70% of the mark relates to your overall implementation, ie whether your model is able to answer the questions of the coursework.
• 30% of the mark relates to the overall design of your model. The three areas in particular that I look out for are:
• model flow
• overall model structure and labelling
• no hardcoded values
The written output (report or presentation) should describe and analyse the forecasting and decision problem, describe your modelling process and the criteria you used for the analysis, summarise any interesting findings from your analysis and present your recommendations. This is a technical piece of writing, so the use of tables and/or charts to summarise and present data is strongly advisable.
The written output carries 20% weight for your submission. The report is marked on these general categories:
• Structure
• Content
• Use of language
• Use of graphs and tables
Ethical Requirements
Essential Reading for Coursework Task

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