K. Vanthournout

Bio: K. Vanthournout is an academic researcher from United States Department of Energy. The author has contributed to research in topics: Smart grid & Demand response. The author has an hindex of 2, co-authored 2 publications receiving 99 citations.

LINEAR breakthrough project: Large-scale implementation of smart grid technologies in distribution grids

Abstract: The LINEAR project (Local Intelligent Networks and Energy Active Regions) focuses on the introduction and implementation of innovative smart-grid technologies in the Flanders region, and aims at a breakthrough in the further development and deployment of these solutions. It consists of a research component and a large-scale residential pilot, both focusing on active demand-side management of domestic loads. This paper describes the unique approach, the main objectives and the current status of this project. Selected business cases and target applications are discussed in detail.

Effect of Demand Response on transformer lifetime expectation

Abstract: Demand Response is seen as important to support the integration of renewable energies into the grid. In Flanders, a residential Demand Response setup is realized in the Linear pilot. The aim is to assess the potential benefits and ways of technical realization of residential Demand Response. In this paper, household devices, like washing machines, are used to offer a flexible load. These are also devices which are used in the pilot. The effect of using flexible loads on the lifetime of a low-voltage transformer is assessed. An IEEE transformer model is used to calculate the lifetime. To calculate the effect of Demand Response, aging is first calculated based on the load of a group of customers and then based on their load being optimized by Demand Response. In this paper, devices are scheduled based on the transformer temperature. The temperature is optimized by using a simulation model based on a mixed integer quadratic programming (MIQP) scheduler. To assess the effect of Demand Response on the transformer lifetime, aging for the improved load curve is compared with aging for the initial load curve. To demonstrate the impact, realistic data for household load curves and the usage of household devices are employed. Results for this input data show reductions in aging of up to 75 % for transformers operating at rated load. The setup will be used to calculate a benchmark for the setup in the Linear pilot, which will use an on-line scheduler. It will be also used to determine potential outcome of a business case.

A review of demand side management business models in the electricity market

Abstract: Demand side management (DSM) can be defined as modifications in the demand side energy consumption pattern to foster better efficiency and operations in electrical energy systems. DSM activities, which are classified into “energy efficiency (EE)” and “demand response (DR)” are becoming more popular due to technological advances in smart grids and electricity market deregulation. However, it can be argued that ensuring DSM sustainability requires creating suitable business models. Business models are influenced by different factors such as electricity market regulation, mechanisms, power system characteristics and infrastructure. The proliferation of smart grid infrastructure, distributed generation, intermittent renewable energy resources and energy storage devices has affected DSM business models considerably. Therefore, in this paper, possible business models for EE and DR providers in different electricity market segments are analyzed and reviewed. The analysis covers three types of characteristics: DSM transaction characteristics, renewable energy correlation and DSM load control characteristics. In DSM transaction characteristics, the value proposition of DSM such as added value offered to the DSM purchaser and transaction triggers are discussed. In renewable energy correlation, the effect of increased renewable energy penetration on the business model is evaluated. In DSM load control characteristics, load control and aggregation aspects such as response speed, duration, advance notice, location sensitivity and actual usage frequency are analyzed.

Residential Demand Response of Thermostatically Controlled Loads Using Batch Reinforcement Learning

Abstract: Driven by recent advances in batch Reinforcement Learning (RL), this paper contributes to the application of batch RL to demand response. In contrast to conventional model-based approaches, batch RL techniques do not require a system identification step, making them more suitable for a large-scale implementation. This paper extends fitted Q-iteration, a standard batch RL technique, to the situation when a forecast of the exogenous data is provided. In general, batch RL techniques do not rely on expert knowledge about the system dynamics or the solution. However, if some expert knowledge is provided, it can be incorporated by using the proposed policy adjustment method. Finally, we tackle the challenge of finding an open-loop schedule required to participate in the day-ahead market. We propose a model-free Monte Carlo method that uses a metric based on the state-action value function or Q-function and we illustrate this method by finding the day-ahead schedule of a heat-pump thermostat. Our experiments show that batch RL techniques provide a valuable alternative to model-based controllers and that they can be used to construct both closed-loop and open-loop policies.

Large-scale grid integration of residential thermal energy storages as demand-side flexibility resource: A review of international field studies

Abstract: Power imbalances from fluctuating renewable electricity generators are counteracted by often expensive flexibility services. Heating, cooling, and air-conditioning (HVAC) of buildings, or domestic power-to-heat (P2H), are end uses of electricity that allow flexible load patterns due to the inertia of an attached thermal storage while meeting their quality constraints. Compared to smart appliances or electric vehicle charging, P2H exhibits large and predictable capacities of demand response (DR), because buildings in many countries account for 30–40% of the final energy demand, a large part of which is thermal. Yet, its practical flexibility potential remains largely unknown: is DR from P2H a mature technology for mass usage; is it cost-efficient, socially attractive, and ready to make key contributions to flexibility comparable to backup generators or battery storage? In the present paper, we review recent international field studies that are paving the way from research to practice. These field trials include real customers but have a broader research focus and a wider outreach than rolling out a new DR tariff or program or a specific new technology for DR. Their experience mirrors the technology readiness beyond revenue or policy studies, optimization frameworks or laboratory-scale micro-grids. We analyze the adequacy of the pricing mechanisms deployed for incentivization and remuneration and review the coordination mechanisms for balancing on different timescales including fast ancillary services. We conclude that current control and information technology and economic and regulatory frameworks which have been field-tested do not yet meet the flexibility challenges of smart grids with a very high share (>50%) of intermittent renewable generation.

Residential Electrical Load Model Based on Mixture Model Clustering and Markov Models

Abstract: Detailed large-scale simulations require a lot of data Residential electrical load profiles are well protected by privacy laws Representative residential electrical load generators get around the privacy problem and allow for Monte Carlo simulations A top-down model of the residential electrical load, based on a dataset of over 1300 load profiles, is presented in this paper The load profiles are clustered by a Mixed Model to group similar ones Within the group, a behavior model is constructed with a Markov model The states of the Markov models are based on the probability distribution of the electrical power A second Markov model is created to randomize the behavior A load profile is created by first performing a random-walking of the Markov models to get a sequence of states The inverse of the probability distribution of the electrical power is used to translate the resulting states into electrical power