Harnessing Heterogeneity: Understanding Urban Demand to Support the Energy Transition

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 theirendeavours to bring the energy transition to fruition.System Engineerin

Service Sector and Urban-Scale Energy Demand: Dataset Accompanying the PhD Thesis “Harnessing Heterogeneity - Understanding Urban Demand to Support the Energy Transition”

This is the dataset accompanying the PhD thesis: N. Voulis. Harnessing Heterogeneity - Understanding Urban Demand to Support the Energy Transition. PhD Thesis. Delft University of Technology. 2019. https://doi.org/10.4233/uuid:9b121e9b-bfa0-49e6-a600-5db0fbfa904e. Real urban areas consist of a mix of households, services (such as schools, offices, shops, etc.), and industry. However, most literature concerned with local energy demand simplifies it to household demand only. This is, to a large extent, cause by a lack of detailed (e.g., hourly) service sector and urban-scale energy demand data. This dataset and the accompanying thesis seek to resolve this issue. The primary focus of this dataset and the accompanying thesis is therefore on service sector and urban-scale demand data. Households are also taken into account, albeit in less detail. Households and services are often collocated in urban areas, but extensive research and data already exist for households. Industry is left out of scope. The dataset contains: - Demand profiles of 13 types of service sector consumers (hourly resolution, full year). - Demand profile of 1 type of average household consumer (hourly resolution, full year). - Demand profile of an average mix of 100 000 households and associated services, with a total annual demand of 710 GWh (hourly resolution, full year). - Demand profile of 203 005 households only, also with a total annual demand of 710 GWh (hourly resolution, full year). - Demand profiles of archetype residential, business, and mixed urban areas. Urban areas include neighbourhoods, districts, and municipalities (hourly resolution, average weekday and average weekend). - Composition of archetype residential, business, and mixed urban areas. Urban areas include neighbourhoods, districts, and municipalities. - Spreadsheet tool to estimate the average hourly demand profile of an urban area of interest, based solely on annual demand data of different consumer types. This tool is also published as an addendum to publication. All profiles pertain to the Netherlands and to the year 2014. The modelled year can be adapted as described in the input data and assumptions part. The geographic region can be adapted by repeating the research described in the thesis for another region

Statistical Data-Driven Regression Method for Urban Electricity Demand Modelling

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.Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.System Engineerin

Understanding spatio-temporal electricity demand at different urban scales: A data-driven approach

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.System Engineerin

Storage coordination and peak-shaving operation in urban areas with high renewable penetration

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.System Engineerin

The case for coordinated energy storage in future distribution grids

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.System Engineerin

Critical Analysis of the Profitability of Demand Response for End-Consumers and Aggregators with Flat-Rate Retail Pricing

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.Accepted Author ManuscriptEnergy & IndustrySystem Engineerin

Simulating Solar Forecasting for Energy Market Decision Models

System EngineeringEnergy & Industr

Critical Analysis of the Profitability of Demand Response for End-Consumers and Aggregators with Flat-Rate Retail Pricing

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.</p

Aggregator-mediated demand response: Minimizing imbalances caused by uncertainty of solar generation

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.Energy & IndustrySystem Engineerin