This thesis demonstrates that heterogeneous spatio-temporal demand profiles are required for a realistic representation of urban energy systems. This is needed to prepare them for the energy transition. Therefore, existing and future urban energy system models should be expanded with more detailed spatio-temporal local demand data that account for both household and non-household consumers, in particular for the thus far omitted service sector consumers. This thesis describes methods and approaches that allow for such detailed modelling of urban demand profiles based on the few publicly available data sources. Using the developed detailed spatio-temporal demand profiles, this thesis provides new insights in the impact of renewable energy resources in realistic, heterogeneous urban areas. The presented results can support governments, communities, and companies in their endeavours to bring the energy transition to fruition.@en

The European Union considers demand response to be an integral part of its future energy vision, in particular as a supporting mechanism for renewable resource integration. To achieve high demand response participation, the European Union recognises the need for adequate financial incentives for all consumers, especially for residential and service sector consumers. However, the European Energy Tax Directive, which regulates energy taxation in the European Union, is currently not in alignment with this vision, as it does not provide any financial incentives for demand response participation. This paper explores the potential of energy taxes to provide such incentives. First, through an analysis of the current energy taxation and demand response literature. Second, by quantifying the difference in financial incentives between two tax designs (per-unit and ad valorem taxes) in a simulation case study of consumers heat pumps in the Netherlands. Results show that financial incentives are 3.5 times higher for the ad valorem tax than for the per-unit tax. The paper concludes with recommendations for policy makers for the design of energy taxes that provide residential and service sector consumers with adequate financial incentives for demand response participation.@en

The high level of uncertainty of renewable energy sources generation creates differences between electricity supply and demand, endangering the reliable operation of the power system. Demand response has gained significant attention as a means to cope with uncertainty of renewable energy sources. Demand response of residential and service sector consumers, when accumulated and managed by aggregators, can play a role in existing electricity markets. This paper addresses the question to what extent aggregator-mediated demand response can be used to deal with the impacts of the uncertainty of solar generation. Uncertain solar generation leads to imbalances of an aggregator. These imbalances can be reduced by shifting flexible loads, which is called demand response for internal balancing. The aim of this paper is to assess the impact of demand response from loads in residential and service sectors for internal balancing to reduce the imbalances of an aggregator, caused by uncertain solar generation. For this purpose, a Model Predictive Control model which minimizes the imbalances of the aggregator through load shifting is presented. The model is applied to a realistic case study in the Netherlands. The results show that demand response for internal balancing succeeds in reducing imbalances. Even though this is favorable from the power system's perspective, economic analysis shows that the aggregator is not financially incentivized to implement demand response for internal balancing.

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Cities and communities worldwide are seeking to become more sustainable by transitioning to renewable energy resources, and by introducing electric transportation and heating. The impact and suitability of such technologies for a given area heavily depend on local conditions, such as characteristics of local demand. In particular, the shape of a local demand profile is an important determinant for how much renewable energy can be used directly, and how charging of electric vehicles and use of electric heating affect a local grid. Unfortunately, a systematic understanding of local demand characteristics on different urban scales (neighbourhoods, districts and municipalities) is currently lacking in literature. Most energy transition studies simplify local demand to household demand only. This paper addresses this knowledge gap by providing a novel data-driven classification and analysis of demand profiles and energy user compositions in nearly 15000 neighbourhoods, districts and municipalities, based on data from the Netherlands. The results show that on all urban scales, three types of areas can be distinguished. In this paper, these area types are termed “residential”, “business” and “mixed”, based on the most prevalent energy users in each. Statistic analysis of the results shows that area types are pairwise significantly different, both in terms of their profiles and in terms of their energy user composition. Moreover, residential-type demand profiles are found only in a small number of areas. These results emphasise the importance of using local detailed spatio-temporal demand profiles to support the transition of urban areas to sustainable energy generation, transportation and heating. To facilitate the implementation of the obtained insights in other models, a spreadsheet modelling tool is provided in an addendum to this paper.@en

Aggregators are considered essential to extend demand response (DR) to small residential and service sector consumers. Both sectors currently have untapped load flexibility, which is considered key to support renewable resource integration. Aggregators can offer this flexibility in bulk to other power system parties. This paper addresses the question under which conditions DR can be profitable for both aggregators and end-consumers. The paper builds further on existing research that shows end-consumer preference for flat-rate tariffs. The aim is to find the range of flat-rate retail prices for different photovoltaic (PV) feed-in-Tariffs which make DR profitable for both aggregator and end-consumers. For this purpose, an optimisation model which minimises costs through load scheduling is presented. The model is applied using two approaches: optimising from aggregator's and from end-consumers' perspective. The results show that only the aggregator's perspective yields a range of flat-rate retail prices that are profitable for both actors. However, both the price range and the expected profits of DR are small.

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As the focus of the energy transition within cities worldwide moves towards local communities and neighbourhoods, the need for insights in the dynamics of local electricity demand increases. Detailed local electricity demand information is, however, often not available. This paper proposes a statistical data-driven method to model local electricity demand for mixed urban areas, using a combination of other openly available datasets. Such datasets however are mutually incompatible without further conversion. The proposed method over- comes this problem. Linear regression is used to combine these different datasets, whereby the regression coefficients have the meaning of scaling factors for different types of electricity consumers (households, offices, shops, etc.). The method is calibrated and validated using respectively a training and a test dataset of Dutch municipalities, yielding R-squared values for most consumer types between 61% and 98%. The application of the method for local electricity demand modelling is illustrated for three Dutch municipalities with different consumer compositions.@en

As renewable power generation gains importance, balancing of power demand and supply becomes more and more challenging. This paper addresses this challenge by exploring the potential of individually-owned storage units in decentralised power systems with a high share of renewables. The focus is on the influence of coordination and peak-shaving operation of these individual units in realistic urban areas. Currently extensive amount of research exits on specific applications related to storage coordination. However, in these studies often simplified consumer models are used. This study considers a representative mixed residential and commercial neighbourhood in Amsterdam. The influence of storage coordination and peak-shaving operation on the neighbourhood's energy autonomy and on the peakiness of the power exchanged with the main grid are addressed. Results show that, compared to individual storage operation, coordinated storage operation increases renewable energy utilisation by 39%, decreases the excess energy transferred to the grid by almost threefold and increases the neighbourhood self-sufficiency by 21%. Peak-shaving operation reduces the highest power peak of the year by 55%. These results are statistically significant (p-value < 10^-4). Thus, in realistic urban areas storage coordination improves local energy autonomy, while peak-shaving operation reduces peaks in power flows exchanged with the main grid.

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The integration of distributed renewable energy resources in urban power systems requires locally tailored approaches. This study analyses the impact of storage penetration and its coordination in three representative urban areas in Amsterdam: a residential, a business and a mixed area. Results show considerable benefits of storage and its coordination in all three areas, assuming a high (50%) penetration of solar panels. Self-consumption of locally generated renewable energy increases from 70% without storage to 80% with individually used storage and to over 90% with coordinated storage. Self-sufficiency increases from 17% without storage to almost 40% with coordinated storage. These results make a case for coordinated use of storage units to support the integration of renewable resources in future distribution grids in a variety of urban areas.@en

Urban areas consist of a mix of households and services, such as offices, shops and schools. Yet most urban energy models only consider household load profiles, omitting the service sector. Realistic assessment of the potential for renewable resource integration in cities requires models that include detailed demand and generation profiles. Detailed generation profiles are available for many resources. Detailed demand profiles, however, are currently only available for households and not for the service sector. This paper addresses this gap. The paper (1) proposes a novel approach to devise synthetic service sector demand profiles based on a combination of a large number of different data sources, and (2) uses these profiles to study the impact of the service sector on the potential for renewable resource integration in urban energy systems, using the Netherlands as a case study. The importance of the service sector is addressed in a broad range of solar and wind generation scenarios, and in specific time and weather conditions (in a single scenario). Results show that including the service sector leads to statistically significantly better estimations of the potential of renewable resource integration in urban areas. In specific time and weather conditions, including the service sector results in estimations that are up to 33% higher than if only households are considered. The results can be used by researchers to improve urban energy systems models, and by decision-makers and practitioners for grid planning, operation and management.@en