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Nicolas Höning

Bio: Nicolas Höning is an academic researcher. The author has contributed to research in topics: Smart grid & Electricity market. The author has an hindex of 1, co-authored 1 publications receiving 45 citations.

Papers
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DOI
27 May 2016
TL;DR: In this paper, an agent-based model and a stochastic solution were proposed to plan and balance in future electricity systems, which can deal with the problem of price fluctuations by consumers.
Abstract: In contemporary societies, industrial processes as well as domestic activities rely to a large degree on a well-functioning electricity system. This reliance exists both structurally (the system should always be available) and economically (the prices for electricity affect the costs of operating a business and the costs of living). After many decades of stability in engineering principles and related economic paradigms, new developments require us to reconsider how electricity is distributed and paid for. Twowell-known examples of important technological developments in this regard are decentralised renewable energy generation (e.g. solar and wind power) and electric vehicles. They promise to be highly useful, for instance because they allow us to decrease our CO2 emissions and our dependence on energy imports. However, a widespread introduction of these (and related) technologies requires significant engineering efforts. In particular, two challenges to themanagement of electricity systems are of interest to the scope of this dissertation. First, the usage of these technologies has significant effects on howwell (part of) supply and demand can be planned ahead of time and balanced in real time. Planning and balancing are important activities in electricity distribution for keeping the number of peaks low (peaks can damage network hardware and lead to high prices). It can become more difficult to plan and balance in future electricity systems, because supply will partly depend on intermittent sunshine and wind patterns, and demand will partly depend on dynamic mobility patterns of electric vehicle drivers. Second, these technologies are often placed in the lower voltage (LV) tiers of the grid in a decentralised manner, as opposed to conventional energy sources, which are located in higher voltage (HV) tiers in central positions. This is introducing bi-directional power flows on the grid, and it significantly increases the number of actors in the electricity systems whose day-to-day decisionmaking about consumption and generation (e.g. electric vehicles supplying electricity back to the network) has significant impacts on the electricity system. In this dissertation, we look into dynamic pricing and markets in order to achieve allocations (of electricity and money) which are acceptable in future electricity systems. Dynamic pricing and markets are concepts that are highly useful to enable efficient allocations of goods between producers and consumers. Currently, they are being used to allocate electricity between wholesale traders. In recent years, the roles of the wholesale producer and the retailer have been unbundled in many countries of the world, which is often referred to as “market liberalisation”. This is supposed to increase competition and give end consumers more choice in contracts. Market liberalisation creates opportunities to design markets and dynamic pricing approaches that can tackle the aforementioned challenges in future electricity systems. However, they also introduce new challenges themselves, such as the acceptance of price fluctuations by consumers. The research objective of this dissertation is to develop market mechanisms and dynamic pricing strategies which can deal with the challenges mentioned above and achieve acceptable outcomes. To this end, we formulate three major research questions: First, can we design pricing mechanisms for electricity systems that support two necessary featureswell, which are not complementary—namely to encourage adaptations in electricity consumption and generation on short notice (by participants who have this flexibility), but also to enable planning ahead of electricity consumption and generation (for participants who can make use of planning)? Second, the smart grid vision (among others) posits that in future electricity systems, outcomeswill be jointly determined by a large number of (possibly) small actors and allocations will be mademore frequently than today. Which pricing mechanisms do not require high computational capabilities from the participants, limit the exposure of small participants to risk and are able to find allocations fast? Third, automated grid protection against peaks is a crucial innovation step for network operators, but a costly infrastructure program. Is it possible for smart devices to combine the objective of protecting network assets (e.g. cables) from overloading with applying buying and selling strategies in a dynamic pricing environment, such that the devices can earn back parts of their own costs? In order to answer the research questions, our methods are as follows: We consider four problems which are likely to occur in future electricity systems and are of relevance to our research objective. For each problem, we develop an agent-based model and propose a novel solution. Then, we evaluate our proposed solution using stochastic computational simulations in parameterised scenarios. We thus make the following four contributions: In Chapter 3,we design a market mechanism in which both binding commitments and optional reserve capacity are explicitly represented in the bid format, which can facilitate price finding and planning in future electricity systems (and therefore gives answers to our first research question). We also show that in this mechanism, flexible consumers are incentivised to offer reserve capacity ahead of time, whichwe prove for the case of perfect competition and showin simulations for the case of imperfect competition. We are able to show in a broad range of scenarios that our proposed mechanism has no economic drawbacks for participants. Furthermore (giving answers to our second research question), the mechanism requires less computational capabilities in order to participate in it than a contemporary wholesale electricitymarket with comparable features for planning ahead. In Chapter 4, we consider the complexity of dynamic pricing strategies that retailers could use in future electricity systems (this gives answers to our first, but foremost to our second research question). We argue that two important features of pricing strategies are not complementary—namely power peak reduction and comprehensibility of prices—and we propose indicators for the comprehensibility of a pricing strategy from the perspective of consumers. We thereby add a novel perspective for the design and evaluation of pricing strategies. In Chapter 5, we consider dynamic pricing mechanisms where the price is set by a single seller. In particular, we develop pricing strategies for a seller (a retailer) who commits to respect an upper limit on its unit prices (this gives answers to both our first and second research question). Upper price limits reduce exposure of market participants to price fluctuations. We show that employing the proposed dynamic pricing strategies reduces consumption peaks, although their parameters are being simultaneously optimised for themaximisation of retailer profits. In Chapter 6, we develop control algorithms for a small storage device which is connected to a low voltage cable. These algorithms can be used to reach decisions about when to charge and when to discharge the storage device, in order to protect the cable from overloading as well as to maximise revenue from buying and selling (this gives answers to our third research question). We are able to show in computational simulations that our proposed strategies perform well when compared to an approximated theoretical lower cost bound. We also demonstrate the positive effects of one of our proposed strategies in a laboratory setupwith real-world cable hardware. The results obtained in this dissertation advance the state of the art in designing pricing mechanisms and strategies which are useful for many use cases in future decentralised electricity systems. The contributions made can provide two positive effects: First, they are able to avoid or reduce unwanted extreme situations, often related to consumption or production peaks. Second, they are suitable for small actors who do not have much computation power but still need to participate in future electricity systems where fast decision making is needed.

45 citations


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Journal Article

329 citations

07 Feb 2018
TL;DR: This thesis addresses research challenges in which a multi-perspective view on processes is needed and that look beyond the control-flow perspective, which defines the sequence of activities of a process.
Abstract: Process mining methods analyze an organization’s processes by using process execution data. During the handling of a process instance data about the execution of activities is recorded. Process mining uses such data to gain insights about the real execution of processes. In this thesis, we address research challenges in which a multi-perspective view on processes is needed and that look beyond the control-flow perspective, which defines the sequence of activities of a process. We consider problems in which multiple interacting process perspectives — in particular control-flow, data, resources, time, and functions — are considered together. The contributed methods span several types of process mining: two are concerned with conformance checking, two are process discovery techniques, and one is a decision mining method. All methods have been implemented, evaluated, and applied in the context of four case studies.

95 citations

Journal ArticleDOI
TL;DR: Semiparametric Theory and Missing Data is an excellent addition to the literature and is suitable for an advanced graduate course or for self-study by doctoral students or researchers in statistics and biostatistics.
Abstract: methodology for obtaining regular asymptotic linear AIPWCC (augmented inverse probability weighted complete case) estimators. Additionally, Chapter 9 includes a presentation of the relationship between monotone coarsening and censoring. Chapters 10 and 11 develop methodology for obtaining efficient and robust estimators. Initially optimal influence functions, whose structure yields to the space of double robust influence functions, are identified. Then the discussion is divided into three parts, according to the structure of coarsening (namely two levels of missingness), monotone coarsening, and nonmonotone coarsening. The concepts are promoted nicely for every different case and the corresponding estimators are developed via both theory and intuition. Chapter 11 considers efficient estimation in the class of double robust estimators. The ideas are based on AIPWCC estimators, but as the author shows there are computational problems for their implementation. Motivated by the preceding results, Chapter 12 develops estimation within a restricted class of AIPWCC estimators. In particular, detailed proof of the form of the estimating equations is provided in two cases. Initially, it is assumed that the estimating function belongs to the q-replicating linear subspace of the space of influence functions and the q-replicating linear subspace of the augmentation space, where both of the spaces are assumed to be linear and finite dimensional. The second case considers the q-replicating linear subspace of the space of influence functions to be finite dimensional, while there is no restriction on the augmentation space. Examples are worked out in great detail for both cases. Chapter 13 demonstrates the theory to the problem of estimating the average causal treatment effect. The idea is based on the so-called stable unit treatment value assumption, which implies that estimation of the average causal treatment effect is equivalent to estimation with missing data. Estimators are derived in great detail, reinforcing in this way the previous results. The last chapter examines the asymptotic properties of multiple imputation estimators. The prerequisites vary in level and depth as the material advances, but a graduate class in statistics at the level of Casella and Berger (1990) suffices to follow the exposition. The writing style is excellent, and all the main concepts and ideas are presented in a clear and pedagogical way. For instance, a detailed study of both restricted moment and logistic models throughout the book is instrumental in illustrating the abstract theory to some standard data analysis tools. At the end of almost each chapter, there are problems and a summary, which recapitulates notation usage and important results. The author should have put more emphasis on real data examples—applications are missing from the presentation. This book is suitable for an advanced graduate course or for self-study by doctoral students or researchers in statistics and biostatistics. It provides a valuable resource because it contains an up-to-date literature review and an exceptional account of state of the art research on the necessary theory. Overall, Semiparametric Theory and Missing Data is an excellent addition to the literature and, without any hesitation, I recommend it to any professional statistician.

93 citations

01 Jan 2017
TL;DR: In this paper, the authors present a take down policy to remove access to the work immediately and investigate the claim. But they do not provide details of the claim and do not discuss the content of the work.
Abstract: Users may download and print one copy of any publication from the public portal for the purpose of private study or research You may not further distribute the material or use it for any profit-making activity or commercial gain You may freely distribute the URL identifying the publication in the public portal Take down policy If you believe that this document breaches copyright, please contact us providing details, and we will remove access to the work immediately and investigate your claim.

80 citations

DissertationDOI
10 Mar 2017
TL;DR: In this paper, the authors explore computational approaches to text analysis for studying cultural and social phenomena and focus on two emerging areas: computational sociolinguistics and computational folkloristics, both of which share the recognition that variation in text is often meaningful and may provide insights into social and cultural phenomena.
Abstract: Massive digital datasets, such as social media data, are a promising source to study social and cultural phenomena. They provide the opportunity to study language use and behavior in a variety of social situations on a large scale and often with the availability of detailed contextual information. However, to fully leverage their potential for research the social sciences and the humanities, new computational approaches are needed. This dissertation explores computational approaches to text analysis for studying cultural and social phenomena and focuses on two emerging areas: computational sociolinguistics and computational folkloristics. Both areas share the recognition that variation in text is often meaningful and may provide insights into social and cultural phenomena. This dissertation develops computational approaches to analyze and model variation in text.

75 citations