Barbara Glensk

Fuzzy Portfolio Optimization of Onshore Wind Power Plants

In this paper we apply fuzzy set theory to the portfolio optimization of power generation assets, using a semi-mean absolute deviation (SMAD) model as a benchmark and a fuzzy semi-mean absolute deviation (FSMAD) model for comparison. The two models are applied to five onshore wind power plants in Germany considered for the portfolio analysis. The results show that the combinations of favorable assets for efficient portfolios are very similar, although the portfolio shares are markedly different. Also, the return and risk span of the SMAD model are much broader than those of the FSMAD model. The highest returns are generated by portfolios based on the latter model. Offering less portfolio choices, the FSMAD model thus facilitates decision-making. This is in compliance with the notion that portfolio optimization by fuzzy set theory is able to better account for the decision-maker’s preferences under real-world conditions.

Investments in Flexibility Measures for Gas-Fired Power Plants: A Real Options Approach

The promotion of electricity from renewable energy in Germany by means of guaranteed feed-in tariffs and preferential dispatch leads to difficulties in the profitable operation of many modern conventional power plants. Nevertheless, conventional power generation technologies with enhanced flexibility in their operational characteristics can contribute to balancing electricity supply and demand. For this reason, the operational flexibility of conventional power plants becomes important for the system and has an inherent economic value. The focus of this research is on high efficiency gas-fired power plants; we tackle the following three research questions from a plant owner’s perspective: (1) How can already existing conventional power plants be operated more flexibly and thus be made more profitable? (2) Which flexibility measures can be taken under consideration? (3) What is the optimal timing to invest in flexibility measures? To answer these questions we propose an optimization model for the flexible operation of existing gas-fired power plants that is based on real options analysis (ROA). In the model, the economic and technical aspects of the power plant operation are explicitly taken into account. Moreover, the spark spread, which is an important source of uncertainty, is used for the definition of the flexible plant operation in terms of different load levels and corresponding efficiency factors. The usefulness of the proposed model is illustrated with a case study mimicking the retrofitting decision process.

Dynamic Portfolio Optimization for Power Generation Assets

Markowitz’s classical mean-variance approach for portfolio selection considers only single-period investments. It has, therefore, received very little attention in the context of long-term investment planning. Nevertheless, considering dynamic aspects, already Markowitz [12] mentioned the attractiveness of multi-period portfolio selection problems for portfolio readjustments during the planning horizon. The direct application of the mean-variance model to multi-stage portfolio problems, however, causes many difficulties. A number of studies have tackled such difficulties, providing suggestions on how the dynamic aspects of portfolio optimization should be considered. One of these suggestions is a reallocation methodology that is based on scenario analysis and a tree approach [14]. In this paper, we apply this methodology to power generation assets, in order to capture the continuously changing values of the economic as well as technical parameters considered when evaluating investments in power plants.

Flexibility Options for Lignite-Fired Power Plants: A Real Options Approach

Germany’s energy system transformation process “Energiewende” implies, on the one hand, the promotion of renewable energy sources and, on the other hand, difficulties in the profitable operation of many modern conventional power plants due to increasing shares of renewable electricity and decreasing electricity wholesale prices. Nevertheless, the prioritized conventional power generation technologies are still needed in times of low wind and solar power generation in order to maintain the security of electricity supply. Regarding these aspects and the specific situation in the federal state of North Rhine-Westphalia, the problem of further operation of lignite-fired power plants is of particular importance. In the study undertaken we tackled the following research questions: Should lignite-fired power plants be operated without any changes until the end of their lifetime? Can already existing lignite-fired power plants be operated more flexibly? If so, which flexibility options should be taken into consideration? What is the optimal investment time for these flexibility options? Are investments in other power generation technologies more suitable for ensuring system stability than investing in the retrofitting of existing lignite-fired power plants is? To answer these questions, we propose an optimization model that is based on real options analysis (ROA) and, more precisely, on the option of choosing. In the proposed model, the economic as well as technical aspects of the power plant operation are taken into consideration for the profitability calculations. Moreover, the results show the importance of the subsidies for lignite-fired power plants and their further operation.

On the Use of Fuzzy Set Theory for Optimizing Portfolios of Power Generation Assets

Decision-making processes need the support of analytical methods that are able to adequately capture the complexity of reality. Welldeveloped and well-established theories, such as the modern portfolio theory introduced by Harry M. Markowitz in 1952, are often based on probability theory and widely used for both financial and real assets. However, a number of empirical studies have shown that the Markowitz approach captures reality only to a very limited extent. In this paper, we propose fuzzy set theory as an alternative to the classical probabilistic approach. More specifically, we investigate the usefulness of a fuzzy portfolio selection model, where an investor’s aspiration levels of a portfolio’s return and risk are taken into account and expressed by membership functions. We define portfolio risk as a downside risk measure and introduce a fuzzy semi-mean absolute deviation portfolio selection model that is applied in order to optimize mixes of power generation plants.