Being at an early stage of my research career, this page looks rather empty.


Optimal Scheduling of Electric Vehicles in Distribution Networks Master’s thesis, The University of Edinburgh, 2017.

The transport sector accounts for a significant proportion of total energy consumption and is to date largely based on fossil fuels. Mitigation of greenhouse gas emissions via the large-scale electrification of road transport will likely deteriorate voltage profiles and overload network equipment in distribution networks. Controlling the charging schedule of electric vehicles in a centralised and coordinated manner provides a potential solution to mitigate the issues and could defer the investment on upgrading the network infrastructures.

In this work, a robust cost-minimising unidirectional day-ahead scheduling routine for charging electric vehicles overnight in residential low voltage distribution networks is presented that observes local network, equipment and charging demand constraints in a stochastic environment. To reduce the computational complexity, a linear power flow approximation is utilised. The modelled environment involves uncertain residential electricity demand, market prices, and the mobility behaviour of electric vehicle owners including stochastic daily trip distances, arrival and departure times. Knowledge about the probability distributions of these parameters is used to hedge risks regarding the cost of charging, network overloadings, voltage violation and charging reliability.

The results provide an insight into the impact of uncertainty and the effectiveness of addressing particular aspects of risk during optimisation. Particularly, consideration of temporally variable household-level demand peaks and planning with more conservative estimates of initial battery charge levels increased the reliability and technical feasibility of optimised schedules. It is further outlined that the introduction of dynamic grid levies, which amplify the effect of variable electricity prices, constitutes a key determinant of cost saving potential by demand side management that could incur only minor fiscal implications.

Scheduling Heatpumps Using Genetic Algorithms Bachelor’s thesis, Karlsruhe Institute of Technology, 2016.

Due to the volatility of renewable energy generation, the probability of intermittent electricity deficiency or excess rises. A stable power grid requires a balanced demand and supply of power. Especially heat pumps in connection with thermal storages that can consume electric power when demand is low and allow for a deferred usage of the stored energy by buffering, bear significant potential for intelligent demand-side management.

In this thesis a genetic algorithm is implemented that optimises the schedule of a heat pump over a specified horizon such that operation costs are minimised while thermal demands are satisfied at any given moment. It is assumed that the demand for flexible device operation resulting from a highly fluctuating supply side is expressed by dynamic electricity prices. High prices prompt a reduction of electricity use while low prices ask for more consumption of electricity. In particular, this work considers the trade-off between the flexible employment of heat pumps and a loss in efficiency due to overheating in the considered heat storages by modelling a variable coefficient of performance. With regards to the application of the genetic algorithm to the heat pump scheduling problem, an optimal set of operators and parameters is sought besides developing a suitable genotype representation and fitness evaluation function. Moreover, in order to enhance performance heuristic approaches are incorporated into the algorithm using problem-specific knowledge. The final parametrisation was evaluated regarding its robustness towards varying price profiles and uncertainty in thermal energy demand.

The operating costs after optimisation were compared to the operation costs of a heat-driven heat pump control. The results of this work reveal that the extent of savings depends on the spreading of the price signal which therefore constitutes a major option to further incentivise demand-side management.