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Decision Making under Uncertainty and Competition for Sustainable Energy Technologies

Maurovich Horvat, L; (2015) Decision Making under Uncertainty and Competition for Sustainable Energy Technologies. Doctoral thesis , UCL (University College London). Green open access

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This dissertation addresses the main challenges faced in the transition to a more sustainable energy sector by applying modelling tools that could design more effective managerial responses and provide policy insights. To mitigate the impact of climate change, the electric power industry needs to reduce markedly its emissions of greenhouse gases. As energy consumption is set to increase in the foreseeable future, this can be achieved only through costly investments in more efficient conventional generation or in renewable energy resources. While more energy-efficient technologies are commercially available, the deregulation of most electricity industries implies that investment decisions need to be taken by private investors with government involvement limited to setting policy measures or designing market rules. Thus, it is desirable to understand how investment and operational decisions are to be made by decentralised entities that face uncertainty and competition. One of the most efficient thermal power technologies is cogeneration, or combined heat and power (CHP), which can recover heat that otherwise would be discarded from conventional generation. Cogeneration is particularly efficient when the recovered heat can be used in the vicinity of the combustion engine. Although governments are supporting on-site CHP generation through feed-in tariffs and favourable grid access, the adoption of small-scale electricity generation has been hindered by uncertain electricity and gas prices. While deterministic and real options studies have revealed distributed generation to be both economical and effective at reducing CO2 emissions, these analyses have not addressed the aspect of risk management. In order to overcome the barriers of financial uncertainties to investment, it is imperative to address the decision-making problems of a risk-averse energy consumer. Towards that end, we develop a multi-stage, stochastic mean-risk optimisation model for the long-term and medium-term risk management problems of a large consumer. We first show that installing a CHP unit not only results in both lower CO2 emissions and expected running cost but also leads to lower risk exposure. In essence, by investing in a CHP unit, a large consumer obtains the option to use on-site generation whenever the electricity price peaks, thereby reducing significantly its financial risk over the investment period. To provide further insights into risk management strategies with on-site generation, we examine also the medium-term operational problem of a large consumer. In this model, we include all available contracts from electricity and gas futures markets, and analyse their interactions with on-site generation. We conclude that by swapping the volatile electricity spot price for the less volatile gas spot price, on-site generation with CHP can lead to lower risk exposure even in the medium term, and it alters a risk-averse consumer’s demand for futures contracts. While extensive subsidies have triggered investments in renewable generation, these installations need to be accompanied by transmission expansion. The reason for this is that solar and wind energy output is intermittent, and attractive solar and wind sites are often located far away from demand centres. Thus, to integrate renewable generation into the grid system and to maintain a reliable and secure electricity supply, a vastly improved transmission network is crucial. Finding the optimal transmission line investments for a given network is already a very complex task since these decisions need to take into account future demand and generation configurations, too, which now depend on private investors. To address these concerns, our third study models the problem of wind energy investment and transmission expansion jointly through a stochastic bi-level programming model under different market designs for transmission line investment. This enables the game-theoretic interaction between distinct decision makers, i.e., those investing in power plants and those constructing transmission lines, to be addressed directly. We find that under perfect competition only one of the wind power producers, the one with lower capital cost, makes investment and to a lower degree under a profit-maximising merchant investor (MI) than under a welfare-maximising transmission system operator (TSO), as the MI reduces the transmission capacity to increase congestion rent. In addition, we note that regardless of whether the grid expansion is carried out by the TSO or by the MI, a higher proportion of wind energy is installed when power producers exercise market power. In effect, strategic withholding of generation capacity by producers prompts more transmission investment since the TSO aims to increase welfare by subsidising wind and the MI creates more flow to maximise profit. Under perfect competition, a higher level of wind generation can be achieved only through mandating renewable portfolio standards (RPS), which in turn results also in increased transmission investment.

Type: Thesis (Doctoral)
Title: Decision Making under Uncertainty and Competition for Sustainable Energy Technologies
Open access status: An open access version is available from UCL Discovery
Language: English
UCL classification: UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Statistical Science
URI: https://discovery.ucl.ac.uk/id/eprint/1460562
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