Zofia Lukszo

Network Impacts and Cost Savings of Controlled EV Charging

This paper investigates the distribution system impacts of electric vehicle (EV) charging. The analysis is based on a large number of operational distribution networks in The Netherlands. Future load profiles have been constructed by adding different EV charging profiles to household loads and solving the power flows to assess the network impacts on various network levels. The results indicate that controlled charging of EVs leads to significant reduction of overloaded network components that have to be replaced, but the impact varies per network level. Overall, in the uncontrolled charging scenarios roughly two times more replacements are needed compared to the controlled charging scenario. Furthermore, it was shown that for the controlled charging scenario the overall reduction in net present value due to energy losses and the replacement of overloaded network components is approximately 20% in comparison with the uncontrolled charging scenario. The results suggest that the deployment of a flexible and intelligent distribution network is a cost-beneficial way to accommodate large penetrations of EVs.

Agent-Based Modelling of Socio-Technical Systems

Decision makers in large scale interconnected network systems require simulation models for decision support. The behaviour of these systems is determined by many actors, situated in a dynamic, multi-actor, multi-objective and multi-level environment. How can such systems be modelled and how can the socio-technical complexity be captured? Agent-based modelling is a proven approach to handle this challenge. This book provides a practical introduction to agent-based modelling of socio-technical systems, based on a methodology that has been developed at TU Delft and which has been deployed in a large number of case studies. The book consists of two parts: the first presents the background, theory and methodology as well as practical guidelines and procedures for building models. In the second part this theory is applied to a number of case studies, where for each model the development steps are presented extensively, preparing the reader for creating own models.

Renewable Energy Sources and Responsive Demand. Do We Need Congestion Management in the Distribution Grid

Possible congestion management mechanisms for price-responsive electric vehicle demand in electricity distribution networks are investigated. Because a high penetration of renewable energy sources weakens the correlation between wholesale electricity prices and network demand, cost-minimizing electric vehicles may cause high peaks in network load. Managing congestion is not costly in theory but difficult to implement efficiently. Grid tariffs that are fixed ex ante, based on network load, were found to make the problem worse compared to the base-case scenario of flat tariffs. An optimal dynamic grid tariff yields desirable outcomes but is difficult to determine in case of realistic forecasting uncertainties. An iterative approach of a distribution grid capacity market has practical barriers related to IT infrastructure and computational requirements. Advance capacity allocation is more straightforward to implement, but the inter-temporal constraints of the electric vehicles continue to pose a challenge.

Optimizing the Location of Intermodal Freight Hubs: An Overview of Agent Based Modelling Approach

This paper aims at developing an integral model for the evaluation of road-rail intermodal freight hub location decisions. The model comprises four dominant agents, namely, hub owners or operators; transport network infrastructure providers; hub users; and communities. An agent based modelling approach is introduced to allow such negotiation to happen to achieve a global objective. The paper outlines the methodology to be used. It also presents an initial location selection process, a testing with individual objective functions, and a design for an agent based model using a case study of intermodal freight hub location decisions in South East Queensland of Australia.

The impact of controlled electric vehicle charging on residential low voltage networks

This papers investigates the impact of electric vehicle charging on residential low-voltage networks. The analysis is based on real life data of both driving patterns and a large number of electricity networks. The focus is on comparing uncontrolled an controlled charging scenarios. The results indicate that a significant number of LV-transformers will be overloaded if no charge control is applied. Moreover, a substantial portion of LV-feeder cables will be overloaded in the case of uncontrolled charging, although much less cables are overloaded than transformers. Controlled charging of EVs can reduce the number of overloaded tranformers and cables with approximately 25 % and 8% compared to the base case scenario of 1% growth of household electricity consumption.

Agent-Based Control of Distributed Electricity Generation with Micro Combined Heat and Power: Cross-Sectoral Learning for Process and Infrastructure Engineers

For the distributed control of an electricity infrastructure incorporating clusters of residential combined heat and power units (micro-CHP or ?CHP) a Multi-Agent System approach is considered. The network formed by households generating electricity with ?CHP units and the facilitating energy supplier can be regarded as an electricity production system, analogous to a (flexible) manufacturing system. Next, the system boundary is extended by allowing the trade of electricity between networks of households and their supplier. A methodology for designing an agent-based system for manufacturing control is applied to both cases, resulting in a conceptual design for a control system for the energy infrastructure. Because of the analogy between production systems and infrastructures Process Systems Engineering (PSE) approaches for optimisation and control can be applied to infrastructure system operations. At the same time we believe research on socio-technical infrastructure systems will be a valuable contribution to PSE management strategies.

Performance analysis of a multi-plant specialty chemical manufacturing enterprise using an agent-based model

Modern day manufacturing enterprises consist of networks of worldwide production sites, each of which has its own supply chain. There are complex interactions between the decisions at various levels of such enterprises that lead to intricate dynamics. To make holistic decisions, it is necessary to measure and analyze performance of the enterprise and its constituents under various conditions. Such performance analysis calls for appropriate modeling and simulation tools. Agent-based modeling has been demonstrated as a promising approach for modeling such complex networks of distributed actors. In this paper, we demonstrate how an agent-based model can be developed to explicitly capture the interactions among the various constituents including the plants, functional departments, and external entities. As an illustrative case, an agent-based model of a lube additive manufacturing supply chain is introduced and the performance of the system studied under a significant range of behaviors, business policies, and environmental events.

Comparing different EV charging strategies in liberalized power systems

Electric vehicles (EVs) can be an important building block of future power systems due to the flexibility of the charging process. In liberalized power systems, the objectives of using this flexibility vary for different actors in the system. In this paper we formulate the optimization problem of EV charging from the perspective of three different actors: 1) consumers (represented by an aggregator), 2) distribution network operators and 3) wind power producers. The respective objectives of these actors can be summarized as minimizing charge costs, minimizing network losses and maximizing profits on a day-ahead market. The results show that the three different perspectives lead to markedly different EV demand profiles. These differences are partly caused by the fact that, due to its stochastic nature, wind power generation weakens the correlation between electricity prices, network load and imbalance volumes.

Deriving electric vehicle charge profiles from driving statistics

The impacts of EV charging on electricity grids is becoming an increasingly important subject of study, but detailed knowledge about the future charging profiles of EVs appears to be missing. In this study we construct EV charge profiles based upon a large dataset of driving patterns. We consider both controlled and uncontrolled charging scenarios, where the main rationale of the controlled charging scenario is to shift the EV electricity demand away from the standard household peak. We show that applying charge control results in only slightly higher peaks compared to the situation without EVs, whereas in the uncontrolled case, the peaks will be significantly higher. Moreover, it is shown that the aggregated charge profiles give a good approximation for the demand of approximately 50 EVs or more. The EV charge profiles can be used as a tool for future network planning and EV impact studies.

Modelling Energy and Transport Infrastructures as a Multi-Agent System using a Generic Ontology

Considering infrastructures as socio-technical systems, the multi-agent paradigm is a good way to model these systems in order to study new control concepts to improve the efficiency and reliability of infrastructures. Agent Based Models (ABM) allow the modeller to build models in a bottom-up way, thus having the freedom to experiment with different designs of the network emerging from local decision making. This paper describes a generic approach for building such models using a generic ontology of the infrastructure domain that can be extended and specialised by including domain specific concepts. This approach has been used in proof of concept model and is currently being applied to both energy and transport infrastructures.