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.

The ambitious energy policy goals set by the European Union have accelerated the pace at which energy efficiency and clean energy measures are being adopted. The Netherlands, lagging behind by a massive margin in transitioning to a green energy infrastructure, feel the need, now more than ever, to push the integration of renewable energy into the grid. The state of the electricity industry has remained almost the same ever since its inception, but has undergone relatively significant changes in the past two decades. The main advancements being decreasing costs of clean energy technologies like solar panels and storage solutions, increasing electricity demand, changing policy & regulations to achieve decarbonization and the active attempt to phase out fossil fuel energy sources like coal. However, changing the energy mix in the electricity grid is bound to make grid management more cumbersome, owing to the intermittency posed by the renewable energy sources and their lack of flexibility, leading to extreme volatility in the electricity market prices. The inertial characteristics of the heavy rotating masses of fossil-powered plants allow traditional power plants to provide flexibility in the grid. Flexibility, in this thesis, is defined as the system’s ability to respond to uncertain generation and demand, while maintaining a constant energy balance. Inertia, in the simplest terms, refers to the resistance provided by an object to a sudden change in motion. As renewables begin replacing conventional power plants, the flexibility required will have to be provided by solutions that allow the electricity generated from the renewables to be stored and used later, which also provides an avenue to hedge the risks from volatile electricity prices. Energy Storage is identified by the Dutch government as one of the most important techniques to provide flexibility surpassing alternatives like Demand Response and Energy Efficiency. This thesis proposes a new approach to design a hedging strategy using energy storage systems. It aims to hedge the risks arising from daily price volatility in the electricity market, that the various stakeholders in the distribution grid are subjected to, caused by factors like increasing grid penetration of renewables, increasing carbon prices etc. “Hedging” in this study, is the act of risk aversion by taking a certain action in the present to avoid a future consequential risk. The ability to store electricity after purchasing from the wholesale market, when looked at from an economic point of view, directly points to being able to store electricity when its in excess (accordingly also cheapest) and to sell it when it is most expensive. This is referred to as “energy arbitrage via electricity prices” and is carried out in the research presented in this work. Along with arbitrage, the strategy also proposes the idea of also being self-sufficient during period of high electricity prices. The significance of emphasizing electricity prices in the term “energy arbitrage via electricity prices” is because energy arbitrage can also be used for technical services like peak shaving that looks into the capacity constraints of the grid, which is beyond the scope of this thesis. The scope of this thesis is to find an analytical approach to design a hedging strategy, to monetize on volatility in the day-ahead electricity market to its maximum potential, for the stakeholders like energy suppliers (that do not own generation assets) in the distribution grid.

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.

There are many methods for solving an optimal power flow (OPF) problem. Most of them employ one central unit to solve the problem (centralized) while in others, every node/area computes for its coverage and send information to its neighbor(s) every time. The mentioned method is called Distributed OPF. In this thesis, the Distributed OPF aims for a fast and resilient algorithm to solve a DC-OPF problem based on Consensus + Innovation (C+I) method.
The research focuses on developing a faster algorithm in solving an OPF problem for a DC distribution grid. The current C+I method has tuning parameters, all of which are determined by trial and error for every case. They are sensitive parameters that determine the speed of the iteration to converge to the solution. This thesis attempts to form adaptive tuning parameters by understanding their function in every equation for a variable update. By understanding the purpose of each tuning parameter and the physical interpretation, some parameters can be formulated while the other is still determined by estimating the value. The losses and congestion are also taken into account. In the results, by formulating these parameters, they are shown that the iteration number has dropped significantly compared to the previous research.

Regarding the resilience of the algorithm, the distributed approach relies on the information exchange between the nodes and delay on the information exchange will stall the whole iteration process. Therefore, an asynchronous algorithm is implemented to resolve the problem by setting a timeout duration. The timeout duration enables the algorithm to wait for the new information only for the desired duration, and therefore the calculation converges in faster.