Scenario planning for electricity supply

Abstract

Electricity generating systems using conventional electricity planning usually face demand-supply mismatch problems resulting from erroneous forecasting of the demand for electricity. Model simulation was used to investigate whether scenario planning would reduce such problems. It was found that scenario planning has the potential of reducing the cost of generating electricity by up to US$3000 million over a 15-year planning period for a medium-size supply system such as that of Thailand.

Introduction

Planning takes many forms. This paper is referring to planning as used in production systems, i.e. making decisions today to construct facilities to meet future demands. This use of planning holds for private goods such as motor cars, petroleum products and furniture, as well as for public goods such as roads, hospitals, schools, water reservoirs and, of course, electricity generating systems. In the conventional planning process the planners first estimate the future demand, and then generate supply options to meet this demand. Then they identify criteria for evaluating the options and apply these to select the preferred option. The problem with this procedure always is the difficulty in predicting future demand: any errors could lead to shortage or oversupply of production capacity. This problem is particularly acute for electricity supply systems for three reasons: the crucial importance of electricity to modern economies; the long time required to build base-load power stations; and the inability to store electricity.

Despite these difficulties, the conventional planning procedure is used throughout the world for both government-owned and privately owned electricity generating systems and for private companies feeding into the grid. However, whereas in a government-operated electricity system the criteria used for evaluating the options are reliability, cost and acceptable environmental impact, in a private system the only criterion is profitability.

The conventional electricity planning process begins by forecasting the annual increases in maximum demand for electricity over a chosen planning time frame, which is usually taken to be 10 or 15 years to accommodate construction times for base-load power stations (Kleinpeter, 1995). This is usually done by projecting the historical trend of growth in demand for electricity into the future. Allowance is made for predictable changes in this trend due to changes in the age profile of the population or to market saturation in particular end-use appliances. However, it is not usual to allow for unforseen events that could affect future demand, such as economic recessions or natural disasters. Next, various supply options to meet the maximum demand forecast for the various years in the planning period are identified. These supply options consist of various combinations of generator types to be built at various times. The preferred option then constitutes the initial power station construction program.

As noted above, the problem with this process is that the forecast demand will eventually differ from the actual demand, resulting in demand–supply mismatch because the construction program usually has to be started long before the electricity is required. This mismatch can be either inadequate or excess generating capacity. Because of the long construction lead times, shortages in generating capacity caused by an error in the demand forecast cannot be overcome immediately. As a result, demand cannot be met because of the inadequate generating capacity, and consumers in the industrial and commercial sectors will suffer losses in production, resulting in reduction of the nation's GDP (Sanghvi, 1991). To this has to be added social disruption, which is difficult to estimate in terms of monetary value (CRS, 1978).

Any excess supply of generating capacity due to an overforecast of demand will also cause problems. For example, total electricity supply capacity of more than 7700 MW in Indonesia and 880 MW in Thailand became in excess because of the regional economic downturn during the 1997/1998 period (EIA, 1998; EGAT, 1998). This excess generating capacity has to be either cancelled or left idle because of lack of demand, but the cost of the investment, e.g. compensation for the cancellation of construction contracts, or interest and capital repayments on idle power stations, will still have to be paid, thus increasing the cost of electricity to consumers. In the event of an economic downturn, this may cause the economic problems to become even worse.

To solve this problem, a planning method that does not rely on the forecasting of the future is needed. Scenario planning, as created for military purposes during the 1950s (Ringland, 1998), has the potential of being used to devise an electricity-supply construction plan to cope with the uncertain future demand for electricity. As Van der Heijden (1996, p. 17) puts it: ‘the first objective of scenario planning [is] the generation of projects and decisions that are more robust under a variety of alternative futures’.

In its original usage a scenario is the script for a film or play, but the early advocates of scenario planning used the word scenario to describe ‘a tool for ordering one's perceptions about alternative future environments in which decisions might be played out’ (Schwartz, 1996). Scenario planning does not rely on the forecasting of a single most likely future. Instead, it considers multiple possible futures or scenarios, all of which could occur, and examines how well alternative possible business plans (options) would perform for each of the scenarios. This permits the selection of a preferred option—the one that would be most robust, irrespective of possible future events.

Scenario planning has been used for business planning since the late 1960s (Ringland, 1998), and has proved to be successful in coping with uncertain future events, notably in the oil crises of the 1970s. Van der Heijden (1996, p. 18) gives the example of the use of scenarios by Shell: ‘When the oil crisis actually occurred in 1973 it became clear that the scenario analysis had put the company on a thinking track where traditional forecasting would never have taken it… Shell executives… recognised in developments in the Middle East the elements of the energy crisis scenario they had been discussing…so they made a number of critical strategic decisions. … Shell moved immediately from expansion of refinery capacity to upgrading the output of the refineries, well ahead of the pack. As a consequence of industry inertia, refining capacity in the industry ran into considerable oversupply, with disastrous consequences for profitability…but due to Shell's early adaptation [sic] of alternative policies they suffered much less from overcapacity and outperformed the industry by a long margin.’

Scenarios have often been used for electricity planning, but in very few cases have they gone beyond the forecasting of future electricity demand under various external environments. For example, Robinson (1988) recognized the problem inherent in conventional electricity planning, and proposed the use of scenarios to get a better picture of the future, but did not show how this would be used to make planning decisions. Baxter and Calandri (1992) used scenarios to predict how the demand for electricity might change as a result of global warming. Von Hirschhausen and Andres (2000) used scenarios to forecast demand for electricity in China under three different growth regimes—slow, moderate and rapid. This is a typical application of the use of scenarios that goes no further than demonstrating what would happen if one or other of the forecasts were to be used to formulate a plan to meet it. Ringland (1998) pointed out a problem with the ‘three-scenario’ approach: ‘Having three scenarios proved to be dangerous…[People] will usually make what seems to be the sensible assumption that the middle one is the forecast.’ Thus the planners unconsciously talk themselves into basing their construction program on the medium scenario. None of these applications went much beyond giving alternative forecasts. Wang and McDonald (1994) even categorize the use of scenarios as a forecasting method.

Scenarios have also been used for optimization of electricity-generation systems. Kunsch and Teghem (1987) used scenarios to optimize the nuclear fuel cycle. Lootsma et al. (1990) used scenarios and multi-criteria analysis to devise a long-term strategy for electricity supply. Anthanassopoulos et al. (1999) used scenarios to simulate the optimized performance of a power station. These applications do not tackle the problem of developing the most robust power generating capacity construction program.

Dansky (1986) sketched out an idea for devising an electricity plan using the original scenario concept. Thus he recognized the problem, but gave only qualitative ideas on how to deal with it. Remmer and Kaye (2001) used a variety of demand scenarios to select an appropriate method for generating electricity for consumers at the fringe of the supply grid. Apart from these two works, we have not been able to find attempts to use scenario planning to devise electricity supply construction plans to cope with the uncertain future demand for electricity. This paper constitutes a preliminary investigation of this problem.