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Title: Medium-term planning in deregulated energy markets with decision rules
Author: Martins da Silva Rocha, Paula Cristina
ISNI:       0000 0004 2735 4271
Awarding Body: Imperial College London
Current Institution: Imperial College London
Date of Award: 2013
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The ongoing deregulation of energy markets has greatly impacted the power industry. In this new environment, firms shift their focus from cost-efficient energy supply to more profit-oriented goals, trading energy at the price set by the market. Consequently, traditional management approaches based on cost minimisation disregarding market uncertainties and financial risk are no longer applicable. In this thesis, we investigate medium-term planning problems in deregulated energy markets. These problems typically involve taking decisions over many periods and are affected by significant uncertainty, most notably energy price uncertainty. Multistage stochastic programming provides a flexible framework for modelling this type of dynamic decision-making process: it allows for future decisions to be represented as decision rules, that is, as measurable functions of the observable data. Multistage stochastic programs are generally intractable. Instead of using classical scenario tree-based techniques, we reduce their computational complexity by restricting the set of decision rules to those that exhibit an affine or quadratic data dependence. Decision rule approaches typically lead to polynomial-time solution schemes and are therefore ideal to tackle industry-size energy problems. However, the favourable scalability properties of the decision rule approach come at the cost of a loss of optimality. Fortunately, the degree of suboptimality can be measured efficiently by solving the dual of the stochastic program under consideration in linear or quadratic decision rules. The approximation error is then estimated by the gap between the optimal values of the primal and the dual decision rule problems. We develop this dual decision rule technique for general quadratic stochastic programs. Using these techniques, we solve a mean-variance portfolio optimisation problem faced by an electricity retailer. We observe that incorporating adaptivity into the model is beneficial in a risk minimisation framework, especially in the presence of high spot price variability or large market prices of risk. For a problem instance involving six electricity derivatives and a monthly planning horizon with daily trading periods, the solution time amounts to a few seconds. In contrast, scenario tree methods result in excessive run times since they require a prohibitively large number of scenarios to preclude arbitrage. Moreover, we address the medium-term scheduling of a cascaded hydropower system. To reduce computational complexity, we partition the planning horizon into hydrological macroperiods, each of which accommodates many trading microperiods, and we account for intra-stage variability through the use of price duration curves. Using linear decision rules, a solution to a real-sized hydro storage problem with a yearly planning horizon comprising 52 weekly macroperiods can be located in a few minutes, with an approximation error of less than 10%.
Supervisor: Kuhn, Daniel ; Rustem, Berc Sponsor: Fundacao para a Ciencia e a Tecnologia
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral