With the penetration of distributed energy resources, peer-to-peer (P2P) energy trading is becoming a promising way to harmonize the decarbonization and decentralization transformations in the energy sector. P2P markets give peers the autonomy to make individual decisions and cooperative behaviors between the peers may emerge. However, existing studies on cooperative behaviors in P2P markets focus mostly on the electricity sector, P2P multi-energy markets are rarely studied. In fact, other energy carriers not only constitute a large part of the total energy demand, but their coupling can potentially benefit the system as well as the end-users. In this paper, we propose a P2P multi-energy market mechanism that allows peers to trade both electricity and heat, where the peers can join two predefined trading coalitions. The proposed system model thus explores the integrated effects of the multi-energy coupling and the cooperative behaviors in the P2P market. In order to maximize the net benefits of the peers, price conditions are derived, based on which the peers will join either of the coalitions and determine their trading volumes. Then, the electricity and the heat markets are cleared separately by a market operator. Lastly, the market mechanism is illustrated by a case study on a neighborhood in the Netherlands using realistic data.

The large-scale integration of variable renewable energy sources (VRES) brings about challenges related to the stochastic characteristics in supply. In this light, a recent and promising approach which has received increasing attention is that of multi-energy systems (MES). A MES is defined as an energy system whereby different energy sectors interact with each other, and builds on the notion of considering the optimization of the whole energy system instead of focussing on specific energy sectors. The increase in substitutions between different energy carriers offer increases in overall efficiency and system flexibility, compared to non-integrated energy system (NIES). This thesis presents a MES investment model. The model is able to systematically find the cost-optimal investment portfolio for meeting three types of demand (electricity, heating, and hydrogen). The outcomes consist of the capacities and locations of generation and conversion technologies, as well as electricity and hydrogen network lines and storage technologies, simultaneously, whilst adhering to balance and RES target constraints. The model is an extension of the current cost-optimal energy investment Greenfield Renewables Investment Model (GRIM) and allows the incorporating of spatial characteristics of VRES supply, leading to a more realistic representation of the real energy system. Furthermore, the model is able to account for limitations on maximum available land for onshore wind and solar photovoltaics (PV).
A case study was conducted on the energy system of the Netherlands. Fifteen scenarios were run, for increasing RES targets, different levels of sector integration and land-use restriction for solar and onshore. The results show that increasing sector integration can lead to a cost reduction of 25% in a fully renewable scenario. Increasing connections between electricity, heating and hydrogen sectors provides system flexibility, resulting in a decrease in generation, network, and storage investment. Furthermore, the results highlight the importance of considering the spatial components: both limitations on land-use and spatial-temporal VRES supply profiles have significant impact on the optimal solution. For the Netherlands, imposing land-use constraints resulted in a decrease in market share from onshore and an increase in solar, since the maximum amount of available land for onshore wind is reached, when including three types of demands. The academic relevance of this thesis is found in the modelling approach developed. In addition, this thesis builds on existing energy planning literature by providing a case study of the future energy system of the Netherlands. Future research should analyse different objectives and include additional generation technologies.

Energy transition goals to reduce carbon emissions are a driver key for an increase share of variable renewable energy sources (vRES). To achieve 49% of CO2 reduction by 2030 compared to 1990, the Netherlands set a plan to generate 84 TWh of electricity from renewable energy sources, where 35 TWh need to be generated exclusively from onshore renewable energy sources (large -scale solar PV and wind onshore). Since large renewable energy projects require inter-municipal decision-making rather than a decision-making at a local level, the Netherlands introduced an instrument called regional energy strategy (RES) in the climate agreement. RES consists of dividing the country into thirty regions,
in where each region needs to identify the necessary installed capacity of vRES and storage units along with the necessary investments in the grid. So far, the energy regions set their vRES plans, where 26 TWh of electricity generation from large-scale solar PV and wind onshore is expected. The regional transition entails many uncertainties. On one side, the electrification of different sectors such as industry and transport will lead to an increase in electricity demand. On the other side, the electricity grid has reached it’s maximum capacity in some regions. Therefore, the vRES plans (large-scale solar PV) set by the energy regions might not be achieved as planned. Therefore, in order to implement the energy region’s plans into the Dutch power system optimally, uncertainties in electricity supply and demand need to be taken into account. The approach adopted in this thesis consists first of the modelling of the Dutch power system as a thirty-region power system reflecting both the electricity grid and the energy regions. Second, developing a high spatio-temporal resolution electricity supply and demand profiles. Third, creating different scenarios to capture uncertainty in electricity supply and demand, where a two-phase scenario planning is developed. Generation type and capacity uncertainty (achievement of 50% and 100% of large-scale planned solar PV projects by the energy regions) are presented in the first phase and the allocation of the installed capacities to segments of two different electricity load shapes (medium growth and high growth) as a second phase decision. Last, optimizing the investment costs in generation and transmission expansion with energy storage units under the different scenarios. The optimization problem is formulated as a two-stage optimization problem. In the first stage, the investment costs in energy storage units along with the transmission lines to incorporate the 26 TWh planned electricity generation are minimized under the different scenarios in electricity supply and demand. In the second stage, the outcomes of the first optimization problem (the required energy storage and transmission lines capacities) are used as input in the second optimization problem, where the investment costs in generation, transmission and storage units to meet the 35 TWh electricity generation are minimized under the same scenarios. The generation expansion consists of expanding the generation from large-scale solar PV and wind onshore from 26 TWh to 35 TWh. The results of the first optimization problem show that under a medium growth of electricity demand, the target to reduce CO2 emissions by 49% can be reached under the achievement of both 50% and 100% of planned large-scale solar PV. However, under a high growth of electricity demand, the national target to reduce CO2 emissions by 49% by the achievement of 50% of large-scale solar PV is not reached. Both transmission lines (at different voltage levels) and storage units (battery and hydrogen) need to be expanded to incorporate the 26 TWh electricity generation. The best technology to generate the remaining 9 TWh according to the results of the second optimization problem is wind onshore. Moreover, the best location is Rotterdam-Den Haag region. As a result, the 35 TWh electricity generation can be integrated into the electricity grid in a cost-optimal way by using energy storage systems, flexible gas supply and the expansion of several transmission lines at the 380kV and 150kV voltage level. This work can be extended to explore other directions such as the variations of both CO2 cap and price, the coupling to other sectors such as gas network and the interconnection between surrounding countries.

The energy system is undergoing a grand transition. In 2019, the Dutch government presented the national climate accord stating that in 2030, 70% of all energy needs to come from a renewable source. In this accord, strong emphasis is put on a regional approach to the energy transition. Across the country, regions are picking up the gauntlet and 141 municipalities in The Netherlands have formulated ambitions to become energy neutral by 2050 or earlier. The road to reach these regional ambitions, however, is not always clear. One of the key issues in energy planning is defining the optimal mix of generation methods to fulfill the electricity demand. Historically, this challenge has been approached only from a least cost perspective. Different stakeholders, however, have a different view on what defines the ‘optimal’ situation and care about more than cost. It is found that minimizing land use and minimizing the visual impact of wind turbines are important objectives to consider when designing an energy system. This research presents a multi-objective optimization that employs a genetic algorithm (NSGA-II) to find the set of pareto-optimal solutions for an optimal generation mix for a regional energy system in The Netherlands minimizing costs, land use and visual impact. Three scenarios are investigated: reducing the total emissions by 70%, 90% and 98%. The results of the optimization are analyzed from a multi-actor perspective to provide insight into the most ideal solutions for different stakeholders. The results show that there are significant trade-offs to be made in designing an energy system: governments, investors and local residents all have a different view about the optimal generation mix. This research presents an average optimal solution: one that may work best for all actors. It shows that by finding a Pareto-optimal set, many optimal solutions can be compared on their desirability, leading to more insight into the functioning of the system and a more feasible design.

During the next few decades, a significant increase in the use of intermittent renewable energy sources is expected in the Netherlands as well as a general increase in electricity consumption. Due to the increased demand as well as a more uncertain, volatile supply, substantial upgrades and redesign of the current Dutch electricity grids are needed. These upgrades will inevitably require large investments as the design and installation of electricity grids is costly. Additionally, the investments are lumpy and irreversible. It is therefore important that the investments will be made in such a way that the future grids will operate successfully, supplying consumers with the demanded electricity at sufficient quality with a low rate of interruptions. Recently, the Netherlands has been divided into 30 energy regions, which allows the Netherlands to work on its climate agreements both from a regional and from a national level. These regions will work on generation and consumption of electricity and heating as well as on the energy infrastructures needed to supply this energy. This aim of this research paper has been to create a method that can be used to design suitable electricity distribution networks for the energy regions in the Netherlands. One approach to designing electricity grid topologies is with the use of graph theory heuristics, which has shown to be a useful way of approaching the electricity design problem by discretising plots of land into a graph. The research paper has shown that by taking into account spatial constraints specific to the Dutch regions, more valid networks can be created. This further leads to increased implementability of the final networks in addition to a reduction in the possibility for unforeseen costs related to building on certain plots of land. The proposed method aims to minimise the investment costs of the future regional electricity distribution networks in the Netherlands, taking both cable lengths and capacities into account. A radiality constraint is applied, ensuring that the network is connected but does not contain any cycles. A flexible way of ensuring that the final networks do not overlap with unavailable land is thereafter applied and demonstrated. A heuristic method aiming to minimise network length is applied before assigning the required capacities to the network. An improvement procedure is performed in order to further reduce investment costs. The cost function is formulated as a non-linear function, incorporating the characteristic that savings can be made by combining lines in order to create a shorter, high-capacity network instead of a longer, low-capacity network. The proposed method has thereafter been verified with respect to the problem formulated and demonstrated using a case study on the energy region Goeree-Overflakkee. Experimental results have also been generated in order to assess the effectiveness of the method. In comparison to an alternative simultaneous topology and production optimisation, it has been found that the proposed method leads to a shorter final network that additionally leads to lower total investments costs.