In this paper, we investigate existing and possible future power generation capacities in Switzerland from a risk-return perspective, using the Mean-Variance Portfolio Theory of Markowitz (1952). The study covers power generation technologies currently in operation, such as nuclear power, storage hydro power and run-of-river hydro power plants, and two new renewable energy technologies (photovoltaics and wind). Additionally, natural gas combined cycle (NGCC) technology, a possible extension to the current Swiss portfolio, is assessed. The technology-specific risks considered include electricity spot market price, production capacity and reliability, fuel cost, funding liabilities, and operation and maintenance outlays. These factors are implemented in a Net Present Value (NPV) model and Monte Carlo simulations are applied to assess each investment alternative. The lifetime-adjusted average return, together with the return-speci¯c variance, forms the basis for the portfolio optimization conducted in the second stage of the analysis. The minimum variance (or maximum return) optimization is performed separately for base-load and peak-load technology portfolios. By defining different scenarios for the upper and lower bound for each technology's share, we simulate different situations, enabling us both, to explain the risk-return profile of the current technology mix, and to make predictions for future portfolios. Our NPV calculations are in line with currently observed returns and, by imposing some reasonable restrictions, the model performs sufficiently well in terms of explaining past portfolio compositions. Moreover, our predicted optimal outcome matches quite nicely with the debated options for enlarging power production in Switzerland.

In this paper we empirically analyse the impact of disaggregated ICT capital on the electricity intensity in five major European industries (chemical, food, metal, pulp & paper and textile). The analysis of each industrial sector is based on an unbalanced panel including data for eight EU member countries (Denmark, Finland, Germany, Italy, Portugal, Slovenia, Sweden and the UK) for the period 1991-2005. The panel-econometric approach, in which we account for country-specific fixed effects, is based on a factor demand model that is similar to the one derived in Collard et al. (2005) [Energy Economics 27 (2): 541-550] for the French services sector. On the one hand, the analysis provides evidence for an electricity-saving effect on production induced by communication technologies in all of the sectors considered. On the other hand, the effect of computers and software on the electricity intensity of industrial production is not that clear-cut, but rather seems to be strongly dependent on the sector-specific production processes involved. Overall, the net effect of ICT diffusion on electricity intensity of production appears to be in favour of an enhancement of electricity efficiency in production.

Participatory multi-criteria evaluation (MCE) is increasingly used for the integrated assessment of future scenarios. Determining weights of the different criteria constitutes one of the biggest challenges of MCE. This paper investigates the influence of weights on the ranking of scenarios and reflects critically on the use of weights as representations of social preferences in participatory MCE. Conceptually, this exercise builds on the literature on integrated assessment and decision making under uncertainty; empirically, insights are drawn from two case studies of renewable energy scenario assessment for Austria at the national and local level. The analysis exhibits a robust ranking for the local level, especially for the highest ranked scenarios. In the national case study, the analysis finds two robust scenario clusters which never switch ranks, whereas the ranking of the scenarios within the clusters flips with minor alters in weights. This paper argues that in participatory MCE different sets of stakeholders’ priorities can be taken into account in a transparent and robust manner. The discussion explores inhowfar weights represent social preferences better than direct ranking of alternative scenarios by stakeholders on the basis of scenario presentations.

In this paper, we model the economic feasibility of compressed air energy storage (CAES) to improve wind power integration. The Base Case is a wind park with 100 MW of installed capacity and no storage facility. In Variant 1 we add a central CAES system with 90 MW of compressor and 180 MW of generation capacity. The compressed air is stored in a cavern. The CAES system is operated independently of the wind park such that profits from peak power sales at the spot market and reserve power market are maximized. Variant 2 is an integrated, decentralized CAES system, where each wind turbine is equipped with a compressor but no generator. The compressed air is stored in a cavern and converted into electricity by a turbine, again maximizing profit as a peak power plant. Both variants are modeled for conventional diabatic and the more advanced adiabatic systems.

The Integrated Energy and Climate Protection Program of the German government includes the political target to double the share of combined heat and power generation (CHP) in Germany from currently about 12% to 25% in 2020. In order to reach this goal a new CHP law was enacted to improve the framework conditions for CHP generation. In this paper we tackle the political CHP target stipulated by the German government, and aim at identifying which CHP technologies are most likely to be installed in the near future. By applying Mean-Variance Portfolio (MVP) theory, we consider return- and risk-related aspects. In our model we pay tribute to specific characteristics of CHP generation, like promotion via feed-in tariffs, additional revenues from heat sales, specific operational features and specifics concerning allocation of CO2 allowances. The investigation is carried out on the basis of four generic standard CHP technologies currently available on a commercial basis. These technologies are: large coal-fired CHP plants, combined-cycle gas turbines (CCGT-CHP), engine-CHP and micro-turbine CHP. The portfolio investigation includes a projection of fuel cost and prices for heat and power according to the BMU Leitstudie 2008. As selection criteria for the portfolio performance we take, independently from each other, the Net Present Value (NPV) of investment in CHP and the expected annual portfolio return, and compare the results obtained from both approaches with each other. Irrespective of the chosen selection criteria the analysis shows that CCGT-CHP and engine-CHP are the most attractive CHP technologies from a return perspective. A diversification of the portfolio with other kinds of CHP technologies can contribute to stabilize portfolio returns. The application of the results obtained on the further development of CHP generation in Germany leads to the conclusion that a large portion of additional new CHP capacity will probably be built in the industrial sector. We conclude further that the ambitious political target of 25% CHP generation in 2020 is not realistic under the given framework.

In this paper we introduce HECTOR, a new and advanced long-term electricity market model that simulates market behavior bottom-up through opportunistic, variable cost-based bidding of individual power plants into auction-based national markets with international interconnection capacities. Unlike most other approaches, we implement the objective function on an hourly level. This allows for a reduction of the solution space, and enables a higher modeling resolution, including opportunistic bidding behavior of power plants based on expected supply scarcity, and ex-post investment decisions based on NPV considerations. The model simulates the electricity markets of 19 European countries, with over 400 groups of power plants, and is able to closely approximate historic electricity prices. The average base load price computed by the model for 2006-2008 and across the largest regions in Europe is 54.5 €/MWh, compared to 54.8 €/MWh in reality, using 2005 as training period. In a projection until 2040, we find that conventional fossil fuel-fired power plants are replaced both by renewable energy technologies and large quantities of CCS, the latter of which almost fully utilize available CO2 storage capacities in some of the regions studied.