Complementary Modelling Approaches for Analysing Several Effects of Privatization on Electricity Investment

Investment Propensities under Carbon Policy Uncertainty

“…pacity planning models in use by policy makers, to the authors' awareness, do not include the real options (delaying investment, retrofitting CCS, and earlier debt payments), financing details (cash vs. debt financed), and accounting considerations (depreciation and taxation). Furthermore, earlier work by Bunn et al (1993) suggested that, in the context of a traditional large scale (non-stochastic) capacity expansion model, financial details are second order effects, but that analysis did not incorporate all of the three elements at the same time, nor, more importantly did it focus more precisely upon the propensities to invest. Thus, it seems that all of the three behavioral specification elements beyond simple economic NPV are important to include simultaneously in a precise analysis of investment inclinations.…”

Prospective long-term overall economic and environmental impact of a country’s electric power system policies : a behavioral dynamic systems thinking approach

“…(Bunn & Larsen, 1992) provide insights regarding how investments in generation capacity may evolve according to different regulatory conditions, economic assumptions, degrees of competition and strategic behavior of utilities in England and Wales. (Bunn, et al, 1993) show that optimization techniques are more suited for simulating rate of return, capital structure and tax implications due to the deregulation of power systems while SD is more useful for simulating the regulatory, uncertainty and competitive effects. (Ford, 1999) and (Ford, 2001a) uses SD in order to assess the impact of capacity payments on investment boom and bust cycles and analyses California's 2000 and 2001 power market crises.…”

“…(Hasani & Hosseini, 2011). For example, (Bunn & Larsen, 1992) and (Bunn, et al, 1993) include specific degrees of foresight in their models ranging from 0 (myopic) to 4 years while (Kadoya, et al, 2005) include trend extrapolations of past average values.…”

“…So, the project start rate is computed as: In contrast to other works where a single overall threshold IRR is set based on estimated industry ROI requirements (Olsina, et al, 2006;Hasani & Hosseini, 2011), different threshold IRRs and proportionality factors by technology are empirically determined, based on historical values as described in section 7.3. (Bunn, et al, 1993) provides examples of required IRRs required by both the public and the private sector as well as a description on how these requirements have evolved with the privatization of the power industry. Technology-specific threshold IRRs aim at reflecting the different risk profiles inherent to each technology.…”

“…By visually inspecting the charts, it can be also observed a significant difference in gas CC as actual investments seem to take place sooner than the model forecast. This may be due to the "dash for gas" phenomenon by which, after deregulation, utilities invested heavily in gas CC power plants not because of profitability reasons but because of strategic considerations (Gary & Larsen, 2000) as well as for the increasing interest in low capital investment technologies which are less penalized by the increasing discount rates required by the private sector (Bunn, et al, 1993).…”