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Southern Energy Forecasting and Profit Estimation

## Forecasting Gas Prices and Electricity Demand

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.

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.

The analysis required for this assessment can be divided into three parts. First, you need to create a model that forecasts gas prices and electricity 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 forecasts in order to estimate the likely profit for the three options that Southern Energy is considering, again taking into account the different growth scenarios. Thirdly, you need to make adjustments to your primary data as necessary and carry out the required sensitivity analysis.

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.