Pieter Vingerhoets

Bio: Pieter Vingerhoets is an academic researcher from Katholieke Universiteit Leuven. The author has contributed to research in topics: Smart grid & Demand response. The author has an hindex of 9, co-authored 22 publications receiving 362 citations.

Demand response of a heterogeneous cluster of electric water heaters using batch reinforcement learning

Abstract: A demand response aggregator, that manages a large cluster of heterogeneous flexibility carriers, faces a complex optimal control problem. Moreover, in most applications of demand response an exact description of the system dynamics and constraints is unavailable, and information comes mostly from observations of system trajectories. This paper presents a model-free approach for controlling a cluster of domestic electric water heaters. The objective is to schedule the cluster at minimum electricity cost by using the thermal storage of the water tanks. The control scheme applies a model-free batch reinforcement learning (batch RL) algorithm in combination with a market-based heuristic. The considered batch RL technique is tested in a stochastic setting, without prior information or model of the system dynamics of the cluster. The simulation results show that the batch RL technique is able to reduce the daily electricity cost within a reasonable learning period of 40–45 days, compared to a hysteresis controller.

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.

Cluster Control of Heterogeneous Thermostatically Controlled Loads Using Tracer Devices

Abstract: Managing the aggregated demand of large heterogeneous clusters of thermostatically controlled loads (TCLs) is considered a sequential decision-making problem under uncertainty. Recent research indicates that using reduced-order models in combination with a broadcasted control signal offers a viable solution to the tradeoff between computational feasibility, and accurately describing the steady-state and transient cluster response. In this paper, we propose a novel control strategy based on tracer devices, which we define as a limited amount of virtual TCLs that represent the entire cluster of heterogeneous TCLs. These second-order model devices are identified in a nonintrusive manner, and capture both steady-state and transient population dynamics, as well as cluster heterogeneity. Additionally, the dispatch mechanism is included in the optimization, further improving the tracking performance. The parameterizable number of tracer devices enables a covering of the tradeoff domain. Both approaches have been evaluated in two scenarios. In the first small-scale scenario, improvements in price and power deviations are evaluated when using increasing numbers of tracer devices and integrating the dispatch dynamics. Results from the second large-scale scenario show that root mean square dispatch errors can be reduced by more than 10% when integrating the dispatch mechanism in the resulting high-fidelity model.

LV distribution network feeders in Belgium and power quality issues due to increasing PV penetration levels

Abstract: In this paper, the impact of residential distributed energy resources (DER) on the power quality is investigated in four feeder types of the electrical LV distribution network in Flanders, Belgium. The investigated power quality issues are over-voltage, under-voltage and unbalance. The results of the simulations are discussed in detail. The paper leads to an estimation of the compliance to the power quality standard EN 50160, and a summary of issues in the distribution grids when increasing the amount of DER.

On Scalability and Replicability of Smart Grid Projects—A Case Study

Abstract: This paper studies the scalability and replicability of smart grid projects. Currently, most smart grid projects are still in the RD therefore, scalability and replicability allow for or at least reduce barriers for the growth and reuse of the results of project demonstrators. The paper proposes factors that influence and condition a project's scalability and replicability. These factors involve technical, economic, regulatory and stakeholder acceptance related aspects, and they describe requirements for scalability and replicability. In order to assess and evaluate the identified scalability and replicability factors, data has been collected from European and national smart grid projects by means of a survey, reflecting the projects' view and results. The evaluation of the factors allows quantifying the status quo of on-going projects with respect to the scalability and replicability, i.e., they provide a feedback on to what extent projects take into account these factors and on whether the projects' results and solutions are actually scalable and replicable.

On-Line Building Energy Optimization Using Deep Reinforcement Learning

Abstract: Unprecedented high volumes of data are becoming available with the growth of the advanced metering infrastructure. These are expected to benefit planning and operation of the future power systems and to help customers transition from a passive to an active role. In this paper, we explore for the first time in the smart grid context the benefits of using deep reinforcement learning, a hybrid type of methods that combines reinforcement learning with deep learning, to perform on-line optimization of schedules for building energy management systems. The learning procedure was explored using two methods, Deep Q-learning and deep policy gradient, both of which have been extended to perform multiple actions simultaneously. The proposed approach was validated on the large-scale Pecan Street Inc. database. This highly dimensional database includes information about photovoltaic power generation, electric vehicles and buildings appliances. Moreover, these on-line energy scheduling strategies could be used to provide real-time feedback to consumers to encourage more efficient use of electricity.

Review on the research and practice of deep learning and reinforcement learning in smart grids

Abstract: Smart grids are the developmental trend of power systems and they have attracted much attention all over the world. Due to their complexities, and the uncertainty of the smart grid and high volume of information being collected, artificial intelligence techniques represent some of the enabling technologies for its future development and success. Owing to the decreasing cost of computing power, the profusion of data, and better algorithms, AI has entered into its new developmental stage and AI 2.0 is developing rapidly. Deep learning (DL), reinforcement learning (RL) and their combination-deep reinforcement learning (DRL) are representative methods and relatively mature methods in the family of AI 2.0. This article introduces the concept and status quo of the above three methods, summarizes their potential for application in smart grids, and provides an overview of the research work on their application in smart grids.

Assessing the Potential of Network Reconfiguration to Improve Distributed Generation Hosting Capacity in Active Distribution Systems

Abstract: As the amount of distributed generation (DG) is growing worldwide, the need to increase the hosting capacity of distribution systems without reinforcements is becoming nowadays a major concern. This paper explores how the DG hosting capacity of active distribution systems can be increased by means of network reconfiguration, both static, i.e., grid reconfiguration at planning stage, and dynamic, i.e., grid reconfiguration using remotely controlled switches as an active network management (ANM) scheme. The problem is formulated as a mixed-integer, nonlinear, multi-period optimal power flow (MP-OPF) which aims to maximize the DG hosting capacity under thermal and voltage constraints. This work further proposes an algorithm to break-down the large problem size when many periods have to be considered. The effectiveness of the approach and the significant benefits obtained by static and dynamic reconfiguration options in terms of DG hosting capacity are demonstrated using a modified benchmark distribution system.