The Netherlands aims to accelerate the energy transition. Accordingly, ambitious targets have been set for the industrial sector. This will require additional investments in the Dutch industry which is expected to reduce the CO2 emissions at limited costs in comparison with other sectors. However, the ambition to reduce the emissions can create a risk of loss of activity and jobs if the industrial businesses prospects are not ensured. Power-to-heat technology provides an opportunity to the industry to reduce the emissions for heating processes. This technology can be implemented in hybrid configurations to ensure the electrification of heat. Hybrid configurations are characterised by their ability to switch between natural gas and electricity which could cost-effectively reduce the emissions consuming electricity at low prices. In this research, a techno-economic evaluation of hybrid boiler systems is performed to analyse the potential of this technology to cost-effectively reduce the CO2 emissions for steam generation in a production process, in 2030. To this end, the operation of hybrid boiler systems was simulated and assessed. The performance of hybrid configurations was compared to alternative options. Finally, the hybrid systems were analysed under different scenarios for 2030. The results for the case study presented, showed that hybrid configurations saved operation costs and reduced the direct CO2 emissions by almost 20%. Therefore, the hybrid boiler could cost-effectively reduce the emissions. However the potential benefits of hybrid boilers are subjected to variation of electricity, natural gas and CO2 prices.

Multiple solutions for solving optimal power flow (OPF) have been employed, most of them using a centralised approach. However, there is another approach where every node is responsible for the local problem in order to reach a solution: the decentralised optimal power flow (D-OPF). In this thesis, the aim was to improve the speed and flexibility of a D-OPF algorithm based on the Consensus and Innovation (C+I) method using physical measurements from a direct current (DC) system. While in previous implementations, the system would work towards finding the solution for one point in time, the suggested implementation works by performing online optimisation, meaning that there is less time required in propagating information around the system and faster solutions are reached. A possible interaction between the physical and optimisation layers was suggested, to make online control feasible. Using current droop control for a DC system, it was possible to react to sudden changes the system and, in the long term, optimise the electrical resources. The improvements in speed were then demonstrated by the simulations results, where the time to reach the optimal solution was reduced, when compared to previous implementations. In order to increase the flexibility of the system, adaptive behaviour for the critical optimisation variables was suggested. To reduce the oscillatory behaviour of the system, some gains were made proportional to rate of change of said variables, meaning that the system didn't have to rely on user determine values in order to converge. It was also implemented a solution to calculate the line resistance between two nodes, further reducing the need for external inputs. These implementations were tested and it was concluded that it improved convergence speeds, while increasing the flexibility pf the system. Finally, a test case, based on a real existing lighting grid, was designed in order to test the algorithm under larger networks. The results showed that for a 25% increase in the size of the network there was no significant increase in the time required to reach a solution, indicating that the system can be scaled further, and might be dependent mainly on the network structure, and not its size.