Tom Verschueren

Optimizing smart energy control strategies for plug-in hybrid electric vehicle charging

The electrification of the vehicle fleet will result in an additional load on the power grid. Adequately dealing with such pluggable (hybrid) electrical vehicles (PHEV) forms part of the challenges and opportunities in the evolution towards Smart Grids. In this paper, we investigate the potential benefits of using control mechanisms, that could be offered by a Home Energy control box, in optimizing energy consumption stemming from PHEV charging in a residential use case. We present smart energy control strategies based on quadratic programming for charging PHEVs, aiming to minimize the peak load and flatten the overall load profile. We compare two strategies, and benchmark them against a business-as-usual scenario assuming full charging starting upon plugging in the PHEV. The first, local strategy only uses information at the home where the PHEV is charged: as a result the charging is optimized for local loads. The local strategy is compared to a global iterative strategy which controls the charging of multiple vehicles based on global load information over a residential area. Both strategies control the duration and rate of charging and result in charging schedules for each vehicle. We present quantitative simulation results over a set of 150 homes, and discuss the strategies in terms of complexity and performance (esp. resulting energy consumption), as well as their requirements concerning infrastructure and communication.

Integrated simulation of power and communication networks for smart grid applications

Innovative architectures, control mechanisms and network technologies are being proposed to realize the future smart grid. To assess their impact and effectiveness, simulation is key. Simulation in both areas of communication networks as well as power systems has been widely adopted. However, the coupling of those two worlds calls for tools able to address both. In this paper, we propose an innovative integrated framework that models and simulates both the communication network and power networks. We discuss the design and operation of the simulation environment, and illustrate this by means of a case study that employs it.

Exploiting V2G to optimize residential energy consumption with electrical vehicle (dis)charging

The potential breakthrough of pluggable (hybrid) electrical vehicles (PHEVs) will impose various challenges to the power grid, and esp. implies a significant increase of its load. Adequately dealing with such PHEVs is one of the challenges and opportunities for smart grids. In particular, intelligent control strategies for the charging process can significantly alleviate peak load increases that are to be expected from e.g. residential vehicle charging at home. In addition, the car batteries connected to the grid can also be exploited to deliver grid services, and in particular give stored energy back to the grid to help coping with peak demands stemming from e.g. household appliances. In this paper, we will address such so-called vehicle-to-grid (V2G) scenarios while considering the optimization of PHEV charging in a residential scenario. In particular, we will assess the optimal car battery (dis)charging scheduling to achieve peak shaving and reduction of the variability (over time) of the load of households connected to a local distribution grid. We compare (i) a business-as-usual (BAU) scenario, without any intelligent charging, (ii) intelligent local charging optimization without V2G, and (iii) charging optimization with V2G. To evaluate these scenarios, we make use of our simulation tool, based on OMNeT++, which combines ICT and power network models and incorporates a Matlab model that allows e.g. assessing voltage violations. In a case study on a three-feeder distribution network spanning 63 households, we observe that non-V2G optimized charging can reduce the peak demand compared to BAU with 64%. If we apply V2G to the intelligent charging, we can further cut the non-V2G peak demand with 17% (i.e., achieve a peak load which is only 30% of BAU).

Architectures for smart end-user services in the power grid

The increase of distributed renewable electricity generators, such as solar cells and wind turbines, requires a new energy management system. These distributed generators introduce bidirectional energy flows in the low-voltage power grid, requiring novel coordination mechanisms to balance local supply and demand. Closed solutions exist for energy management on the level of individual homes. However, no service architectures have been defined that allow the growing number of end-users to interact with the other power consumers and generators and to get involved in more rational energy consumption patterns using intuitive applications. We therefore present a common service architecture that allows houses with renewable energy generation and smart energy devices to plug into a distributed energy management system, integrated with the public power grid. Next to the technical details, we focus on the usability aspects of the end-user applications in order to contribute to high service adoption and optimal user involvement. The presented architecture facilitates end-users to reduce net energy consumption, enables power grid providers to better balance supply and demand, and allows new actors to join with new services. We present a novel simulator that allows to evaluate both the power grid and data communication aspects, and illustrate a 22% reduction of the peak load by deploying a central coordinator inside the home gateway of an end-user.

Assessment and mitigation of voltage violations by solar panels in a residential distribution grid

Distributed renewable electricity generators, such as solar cells and wind turbines introduce bidirectional energy flows in the low-voltage power grid, possibly causing voltage violations and grid instabilities. The current solution to this problem comprises automatically switching off some of the local generators, resulting in a loss of green energy. In this paper we study the impact of different solar panel penetration levels in an residential area and the corresponding effects on the distribution feeder line. To mitigate these problems, we assess how effective it is to locally store excess energy in batteries. A case study on a residential feeder serving 63 houses shows that if 80% of them have photo-voltaic (PV) panels, 45% of them would be switched off, resulting in 482 kWh of PV-generated energy being lost. We show that providing a 9 kWh battery at each house can mitigate some voltage violations, and therefor allowing for more renewable energy to be used.

Distributed multi-agent algorithm for residential energy management in smart grids

Distributed renewable power generators, such as solar cells and wind turbines are difficult to predict, making the demand-supply problem more complex than in the traditional energy production scenario. They also introduce bidirectional energy flows in the low-voltage power grid, possibly causing voltage violations and grid instabilities. In this article we describe a distributed algorithm for residential energy management in smart power grids. This algorithm consists of a market-oriented multi-agent system using virtual energy prices, levels of renewable energy in the real-time production mix, and historical price information, to achieve a shifting of loads to periods with a high production of renewable energy. Evaluations in our smart grid simulator for three scenarios show that the designed algorithm is capable of improving the self consumption of renewable energy in a residential area and reducing the average and peak loads for externally supplied power.

DYAMAND: DYnamic, Adaptive MAnagement of Networks and Devices

Consumer devices increasingly are “smart” and hence offer services that can interwork with and/or be controlled by others. However, the full exploitation of the inherent opportunities this offers, is hurdled by a number of potential limitations. First of all, the interface towards the device might be vendor and even device specific, implying that extra effort is needed to support a specific device. Standardization efforts try to avoid this problem, but within a certain standard ecosystem the level of interoperability can vary (i.e. devices carrying the same standard logo are not necessarily interoperable). Secondly, different application domains (e.g. multimedia vs. energy management) today have their own standards, thus limiting trans-sector innovation because of the additional effort required to integrate devices from traditionally different domains into novel applications. In this paper, we discuss the basic components of current so-called service discovery protocols (SDPs) and present our DYAMAND (DYnamic, Adaptive MAnagement of Networks and Devices) framework. We position this framework as a middleware layer between applications and discoverable/controllable devices, and hence aim to provide the necessary tool to overcome the (intra- and inter-domain) interoperability gaps previously sketched. Thus, we believe it can act as a catalyst enabling transsector innovation.

Design and evaluation of an architecture for future smart grid service provisioning

The increase of distributed renewable electricity generators, such as solar cells and wind turbines, requires new energy management systems where real-time measurements and communication between end users, suppliers and utilities are vital. To address this need, we propose a common service architecture that allows houses with renewable energy generation and smart energy devices to plug into a distributed energy management system, integrated with the public power grid. The presented architecture facilitates end-users to optimize their energy consumption, enables power network operators to better balance supply and demand, and creates a platform where new market players (e.g. ESCOs) can easily provide new services. This service architecture has been implemented and is currently evaluated in a field trial with 21 users, of which we present the initial results.

Deploying the ICT architecture of a residential demand response pilot

The Flemish project Linear was a large scale residential demand response pilot that aims to validate innovative smart grid technology building on the rollout of information and communication technologies in the power grid. For this pilot a scalable, reliable and interoperable ICT infrastructure was set up, interconnecting 240 residential power grid customers with the backend systems of energy service providers (ESPs), flexibility aggregators, distribution system operators (DSOs) and balancing responsible parties (BRPs). On top of this architecture several business cases were rolled out, which require the sharing of metering data and flexibility information, and demand response algorithms for the balancing of renewable energy and the mitigation of voltage and power issues in distribution grids. The goal of the pilot is the assessment of the technical and economical feasibility of residential demand response in real life, and of the interaction with the end-consumer. In this paper we focus on the practical experiences and lessons learnt during the deployment of the ICT technology for the pilot. This includes the real-time gathering of measurement data and real-time control of a wide range of smart appliances in the homes of the participants. We identified a number of critical issues that need to be addressed for a future full-scale roll-out: (i) reliable in-house communication, (ii) interoperability of appliances, measurement equipment, backend systems, and business cases, and (iii) sufficient backend processing power for real-time analysis and control.