Optimisation and Incentive Mechanisms for Robust Generation Dispatch and Capacity Investment in Electricity Markets
Time: Mon 2023-05-22 14.00
Location: Kollegiesalen, Brinellvägen 8, Stockholm
Doctoral student: Lamia Varawala , Nätverk och systemteknik
Opponent: Prof. José Manuel Arroyo, University of Castilla-La Mancha, Ciudad Real
Supervisor: Prof. György Dán, Nätverk och systemteknik; Assoc. Prof. Mohammad Reza Hesamzadeh, Elkraftteknik
Power systems constitute a large-scale critical infrastructure and therefore, it is crucial that their operation be robust to deviations from normal functioning of its independent components. Furthermore, due to their large size, any inefficiencies in electricity market design can be very costly and therefore, must be optimised.
The first part of the thesis explores how generators must be optimally dispatched while maintaining robustness of the power system, which we model as the look ahead security constrained optimal power flow (LASCOPF) problem. LASCOPF optimises the generation dispatch given any objective, typically, a generation cost minimisation, over a planning horizon of multiple dispatch intervals, subject to physical constraints on the power system such as generator ramping constraints. In addition, we consider the $N-1$ contingency criterion, which is modelled as a set of security constraints that ensure that the system can transition to a feasible operating point if an outage in any one of its components were to occur. We observe that the problem size is quadratic in the number of intervals in the planning horizon and therefore, propose a reduced LASCOPF formulation for which the dependence is linear. We extend these results to the $N-k$ contingency criterion, which requires security against multiple simultaneous contingencies and observe that the problem size depends upon the number of permutations of contingencies. To overcome this, we propose a further reduced problem for which the dependence is on the number of permutations of contingencies. We model LASCOPF specifically using DC power flow under both generator and transmission line contingencies, and AC power flow under generator contingencies. For these, we prove that, barring borderline cases, the reduced formulations are equivalent to the corresponding comprehensive formulations. Numerical results on benchmark and real systems show that the reduced formulations have a significant computational advantage over the corresponding comprehensive ones.
The second part of the thesis explores how to use incentive mechanisms in electricity market design to overcome inefficiencies. The first problem we consider is that power generation causes environmental pollution with an associated damage cost, which we model as a negative externality. By definition, negative externalities are not included in the competitive market clearing used in electricity markets and, as we show, cannot be incorporated into the price. Since producers control generation sources, we propose a Pigouvian tax on them as an incentive to incorporate their pollution damage in their costs. The second problem we consider is producers' strategic behaviour where producers can declare higher costs to increase the prices and therefore, their profits. However, we show that even if producers are forced to declare costs truthfully, they may decrease their generation capacity to achieve the same effect. To overcome strategic behaviour of both these kinds, we propose to subsidise producers with their marginal contributions to the consumer surplus as an incentive. Our tax and subsidy mechanism is derived by aligning producers' profit maximisation with the social welfare maximisation resulting in an optimal generation dispatch.
The problems solved in this thesis contribute towards improving the efficiency of electricity markets by minimising generation costs and externalities such as environmental pollution while keeping the power system robust to outages in individual components.