EU carbon emission targets related to climate change has set in motion a process of transition towards an environmentally clean and sustainable power system. A central focus on this process is the transition from fossil fuel based energy sources to clean Renewable Energy Sources (RES). However, the intermittency of RES (e.g. solar and wind) presents a formidable challenge to achieve a stable and reliable supply-demand balance in grid oper- ations. To achieve high levels of RES deployment, increasing the power system flexibility will be central to accommodate large fluctuations in supply and to cope with peak demand. Prospects of electricity storage technologies have emerged as a potential key technology to manage high levels of RES in the power system.

Recent projections on the cost of electricity storage show a high decrease in the next five years ([1],[2], [3]). As the commercial maturity of batteries might become a reality within the next decade, many questions remain on the role of batteries in the power system, where batteries should be located? What capacity will be optimal? What kind of battery services are the most valuable? How do batteries contribute to the large deployment of distributed RES installations? Significant research has been done on estimating sizing and sitting of storage in power systems. Yet, most of this research treat storage capacity as continuous instead of discrete, i.e. allocating storage by percentages of a total allowed capacity, wherever necessary in the grid. Despite these previous studies have provided interesting contributions on the value of storage in the power system, many of them lack the modelling of power flows, technical limits, or voltage considerations.

This thesis focuses on battery flexibility in medium voltage grids. Specifically, how to define cost-effective strategies to deploy batteries in a medium voltage grid? What is the optimal battery location in a distribution grid? And how do the technical limits of the power grid influence the allocation of storage? To address these questions, an optimization model was developed to simulate half-hourly operational decisions for a distribution grid. The model is multi-period and includes: power flows, diverse technical consideration for different battery sizes, high RES penetration levels, time of use electricity prices (half-hour dynamic prices), load data of actual customers and battery costs. To decide on the battery location, the model employs binary variables to determine the investment and sitting of the battery in a distribution grid. That is, the model is a mixed integer linear program with multi-period features which provides an investment analysis for the cost-effective sitting of batteries in a time horizon of 10 years. The model is implemented to the IEEE 33 bus test system. Results show that in general battery location and size strategies are driven by multiple factors, which can be either fixed or dynamic, like thermal limits and power load consumption, respectively. Some relevant findings are: First, flexibility in terms of power arbitrage delivers costs reductions of around 4% when RES production is low, compared to a No-Batteries case. Moreover, when RES production is high, the reductions in total costs can ramp up to 12% below the No-Batteries case. Also, this model decides to al-

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locate batteries only if they are economically feasible for a 10-year time horizon. These results indicate a potential revenue up to 2.1 million pounds (GBP) based on the investment in batteries. And they are based on battery cost prices from 2008, together with several optimistic projections for the next decade. Furthermore, depending on battery size, RES penetration, RES generation and technical limits, batteries tend to be located for buses at the entrance of branches with high loads. Likewise, line limits and voltage limits proved to be decisive in the election of buses and the number of batteries placed.

On one hand, results show that optimal allocation strategies depend on grid topology features and technical limits, on the other hand, they also have a high dependency on time- varying and unsteady factors (e.g. power generation and loads). The optimal location strategies tend to change and adapt to the dynamics of the system. Moreover, with the exception of the slack bus, every bus in the system turned out to be an optimal location, at least once. These results indicate that batteries might be useful in every bus of the dis- tribution grid, but only if each battery is operated in coordination and cooperation with one another. These insights support the idea of designing local electricity markets. Based on a reflection of this work, we recommend a market design that retrieves day-ahead and intraday DSO-reports of the battery operations and the flexibility that is available in the distribution grid. And also a subsequent structure of market incentives and penalties that maximizes the value of flexibility while keeping non-optimal operations to a minimum.

In short, this thesis contributes with a novel modelling approach that can shed some lights on the optimal battery allocation problem for distribution grids. Moreover, it pro- vides insights on how location affects the value of storage, how optimal locations are affected by multiple technical factors. And finally, it also provide some reflections on the need to collectively, cooperatively and coordinately operate the storage resources in the grid by considering market-based solutions.

European countries are showing their willingness to reduce their greenhouse gases emissions in the coming years. For example, the Paris agreement within the United Nations Framework Convention on Climate Change (UNFCCC) is one of the most crucial steps that countries have signed recently. The aim is to cause a lower global temperature increase, and thus, to reduce the resulting climate risks.
North Sea countries are working on a greener future in a national level, as well; and these are in fact, the countries which are the most concerned about the climate change globally. Moreover, these are affluent in terms of opportunities for using greener energy sources, due to their climatic and geographic conditions. Clear examples are hydropower plants in the Nordic region or offshore-wind in the North Sea.
Nevertheless, these countries need also of infrastructure to achieve their objectives, such as a Power System which will enable to integrate green generation sources properly and to satisfy the societies’ energy needs successfully. Power Systems are in fact, needed key enablers of this energy transition.
The topic of this Master Thesis is transmission expansion planning in the North Sea for the year 2030. PowerGIM and PowerGAMA are used. The objective is to find the socio-economically beneficial grid design which will help achieve these future ambitions that the North Sea countries have and at the same time, which will be robust w.r.t. renewable energy sources’ development uncertainty. The main finding of this Master Thesis is that Dogger Bank hub is obtained as part of the most socio-economically beneficial offshore-grid layout for the year 2030, in all implemented scenarios. Each scenario refers to one implemented ENTSO-E Vision (Visions 1-4) with some additional assumptions.
In short, in this Master Thesis, four different offshore-grid layouts are obtained, one per each implemented scenario; and all of them have the same core. The core is Dogger Bank hub’s interconnection with Great Britain, Belgium, Germany, Netherlands, Norway and Denmark. Nevertheless, there are some variations which depend on the implemented scenario and on the implemented assumptions. Overall, the obtained Dogger Bank hub could also integrate between 13-32 GW offshore-wind in the North Sea. Then, a reference grid layout is created, as well. This design embraces the previously mentioned four grid layouts in a conservative way, i.e. the repeated lines in all four obtained grid layouts are taken with the lowest capacity value among all four designs.
The grid layout obtained after the implementation of the second scenario is the most robust grid layout w.r.t. different future energy prospects. Its operational cost saving throughout the lifetime of 30 years w.r.t. the reference grid layout is of 20-33 bn €, depending on the implemented future scenario; and the investment cost increase is of 5.5 bn € w.r.t. the reference grid layout.