'Optimization & Applications' (see information & program)
Feb 18 - May 27, 2013
Liberalization of power markets in Europe has completely changed the environment for companies active in those markets. Despite the advantages of deregulation, players today are facing the problem of market power resulting from congestions on transmission lines. This fact, combined with the instorability of electricity makes pricing of contracts difficult. It has been observed that prices for contracts such as for virtual storages vary with the method being used for valution. The reason is that the replication of those contracts is not risk free -- the risk of the underlying cannot be fully hedged and consequently hedging has to be seen as an action necessary to reduce risk.
In this work we will not continue along the line of financial mathematics but exploit the flexibility embedded in a utility's portfolio in order to value those contracts. Thereby a managerial view will be adopted by using flexibility in order to control the firm's profit and loss distribution. In the following flexibility will be interpreted in terms of risk reduction and a coherent principle will be given to price flexibility internally. The link between financial positions and the production portfolio's flexibility will be drawn by deriving a coherent framework that jointly optimizes both aspects. To be able to compare generation assets and contracts on a unified basis we identify power plants as sets of contracts in order to replicate those assets -- flexible power plants are regarded as options and inflexible ones as futures contracts. This joint optimization leads to the well-known problem in finance of portfolio optimization, which however needs to be tailored for the power market. As a result of the portfolio optimization we obtain trading and dispatch policies, i.~e.~dynamic decision rules that are specified today but determine the portfolio's composition at each point in time over the whole time horizon -- similar to a series of American put options with exercise boundary. Those so-called policies not only maximize the expected profit but also dynamically control the risk over time. This will be done with a dynamic risk engineering approach based on Conditional Value-at-Risk. The advantage of our approach is linearization resulting in a linear program that can even be solved for large instances which is truly the case for utilities.
It turns out in the performed case study that the value of flexibility can be substantial. Especially one observes that the power market does not price the embedded flexibility correctly -- it is underestimated. Therefore utilities that highly value flexibility need to buy inflexibility, such as wind farms, up to a certain degree in order to increase their value. This observation only becomes apparent when looking at the portfolio of a utility as a whole and not on an aggregated level -- subsequently flexibility is a portfolio property.
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