There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

An Introduction to MultiAgent Systems.

The Object Management Group's (OMG) Model Driven Architecture (MDA) strategy envisages a world where models play a more direct role in software production, being amenable to manipulation and transformation by machine. Model Driven Engineering (MDE) is wider in scope than MDA. MDE combines process and analysis with architecture. This article sets out a framework for model driven engineering, which can be used as a point of reference for activity in this area. It proposes an organisation of the modelling 'space' and how to locate models in that space. It discusses different kinds of mappings between models. It explains why process and architecture are tightly connected. It discusses the importance and nature of tools. It identifies the need for defining families of languages and transformations, and for developing techniques for generating/configuring tools from such definitions. It concludes with a call to align metamodelling with formal language engineering techniques.

Model Driven Engineering

Plug-in vehicles can behave either as loads or as a distributed energy and power resource in a concept known as vehicle-to-grid (V2G) connection. This paper reviews the current status and implementation impact of V2G/grid-to-vehicle (G2V) technologies on distributed systems, requirements, benefits, challenges, and strategies for V2G interfaces of both individual vehicles and fleets. The V2G concept can improve the performance of the electricity grid in areas such as efficiency, stability, and reliability. A V2G-capable vehicle offers reactive power support, active power regulation, tracking of variable renewable energy sources, load balancing, and current harmonic filtering. These technologies can enable ancillary services, such as voltage and frequency control and spinning reserve. Costs of V2G include battery degradation, the need for intensive communication between the vehicles and the grid, effects on grid distribution equipment, infrastructure changes, and social, political, cultural, and technical obstacles. Although V2G operation can reduce the lifetime of vehicle batteries, it is projected to become economical for vehicle owners and grid operators. Components and unidirectional/bidirectional power flow technologies of V2G systems, individual and aggregated structures, and charging/recharging frequency and strategies (uncoordinated/coordinated smart) are addressed. Three elements are required for successful V2G operation: power connection to the grid, control and communication between vehicles and the grid operator, and on-board/off-board intelligent metering. Success of the V2G concept depends on standardization of requirements and infrastructure decisions, battery technology, and efficient and smart scheduling of limited fast-charge infrastructure. A charging/discharging infrastructure must be deployed. Economic benefits of V2G technologies depend on vehicle aggregation and charging/recharging frequency and strategies. The benefits will receive increased attention from grid operators and vehicle owners in the future.

Review of the Impact of Vehicle-to-Grid Technologies on Distribution Systems and Utility Interfaces

The current practice in multiagent systems typically associates the environment with resources that are external to agents and their communication infrastructure. Advanced uses of the environment include infrastructures for indirect coordination, such as digital pheromones, or support for governed interaction in electronic institutions. Yet, in general, the notion of environment is not well defined. Functionalities of the environment are often dealt with implicitly or in an ad hoc manner. This is not only poor engineering practice, it also hinders engineers to exploit the full potential of the environment in multiagent systems.

In this paper, we put forward the environment as an explicit part of multiagent systems.We give a definition stating that the environment in a multiagent system is a first-class abstraction with dual roles: (1) the environment provides the surrounding conditions for agents to exist, which implies that the environment is an essential part of every multiagent system, and (2) the environment provides an exploitable design abstraction for building multiagent system applications. We discuss the responsibilities of such an environment in multiagent systems and we present a reference model for the environment that can serve as a basis for environment engineering. To illustrate the power of the environment as a design abstraction, we show how the environment is successfully exploited in a real world application. Considering the environment as a first-class abstraction in multiagent systems opens up new horizons for research and development in multiagent systems.

/pdf/environment-as-a-first-class-abstraction-in-multiagent-4gdky9c0j8.pdf

Environment as a first class abstraction in multiagent systems

Intelligent electricity grids, or 'Smart Grids', are being introduced at a rapid pace. Smart grids allow the management of new distributed power generators such as solar panels and wind turbines, and innovative power consumers such as plug-in hybrid vehicles. One challenge in Smart Grids is to fulfill consumer demands while avoiding infrastructure overloads. Another challenge is to reduce imbalance costs: after ahead scheduling of production and consumption (the socalled 'load schedule'), unpredictable changes in production and consumption yield a cost for repairing this balance.To cope with these risks and costs, we propose a decentralized, multi-agent system solution for coordinated charging of PHEVs in a Smart Grid. Essentially, the MAS utilizes an "intention graph" for expressing the flexibility of a fleet of PHEVs. Based on this flexibility, charging of PHEVs can be rescheduled in real-time to reduce imbalances.We discuss and evaluate two scheduling strategies for reducing imbalance costs: reactive scheduling and proactive scheduling. Simulations show that reactive scheduling is able to reduce imbalance costs by 14%, while proactive scheduling yields the highest imbalance cost reduction of 44%.

/pdf/decentralized-coordination-of-plug-in-hybrid-vehicles-for-4rj8925cof.pdf

Decentralized coordination of plug-in hybrid vehicles for imbalance reduction in a smart grid

Research predicts that in 2030, around 30% of all vehicles in Belgium will be plug-in hybrid electric vehicles (PHEVs). Because most PHEVs are charged after working hours, the existing peak load in the evening will increase significantly. Large peak loads cause more expensive production and can even damage the electricity infrastructure. In a Smart Grid, the charging of PHEVs can be controlled to reduce peak load, denoted as demand side management (DSM). The goal of our research is to compare several solutions for DSM of PHEVs. This paper takes a first step by benchmarking a multi-agent solution against an optimal quadratic programming (QP) scheduler solution. Simulations show that a QP scheduler is able to optimally flatten peak loads while sufficiently charging the PHEV batteries. However, this solution is unfeasible in practice because it scales poorly and requires complete information on when and how much PHEVs need to charge beforehand, which is not available. The MAS solution proves to be scalable and adaptable to incomplete and unpredictable information while peaks are still reduced with an efficiency up to 95% compared to the QP scheduler.

https://lirias.kuleuven.be/bitstream/123456789/272354/1/paper.pdf

Decentralized demand side management of plug-in hybrid vehicles in a Smart Grid

Gradient Field-Based Task Assignment in an AGV Transportation System.

Assigning tasks to agents is complex, especially in highly dynamic environments. Typical protocol-based approaches for task assignment such as Contract Net have proven their value, however, they may not be flexible enough to cope with continuously changing circumstances. In this paper we study and validate the feasibility of a field-based approach for task assignment in a complex problem domain.In particular, we apply the field-based approach for task assignment in an AGV transportation system. In this approach, transports emit fields into the environment that attract idle AGVs. To avoid multiple AGVs driving towards the same transport, AGVs emit repulsive fields. AGVs combine received fields and follow the gradient of the combined fields, that guide them towards pick locations of transports. The AGVs continuously reconsider the situation of the environment and task assignment is delayed until the load is picked, improves the flexibility of the system.Extensive experiments indicate that the field-based approach outperforms the standard Contract Net approach on various performance measures, such as the average wait time of transports and throughput. Limitations of the field-based approach are an unequal distribution of wait times across different transports and a small increase of bandwidth occupation.

Gradient field-based task assignment in an AGV transportation system

For two years, we have been involved in a challenging project to develop a new architecture for an industrial transportation system. The motivating quality attributes to develop this innovative architecture were flexibility and openness. Taking these quality attributes into account, we proposed a decentralized architecture using multiagent systems (MASs). A MAS consists of multiple autonomous entities that coordinate with each other to achieve decentralized control. The typical advantages attributed to such decentralized architecture are flexibility and openness, the motivating quality attributes to apply MAS in this case.

The Architecture Tradeoff Analysis Method (ATAM) was used to provide insights wether our architecture meets the expected flexibility and openness, and to identify tradeoffs with other quality attributes. Applying the ATAM proved to be a valuable experience. One of the main outcome of applying the ATAM was the identification of a tradeoff between flexibility and communication load that results from the use of a decentralized architecture.

This paper describes our experiences in applying the ATAM to a MAS architecture, containing both the main outcomes of the evaluation and a critical reflection on the ATAM itself.

/pdf/applying-the-atam-to-an-architecture-for-decentralized-2bdyn0sbhz.pdf

Nelis Boucké

Papers

Decentralized coordination of plug-in hybrid vehicles for imbalance reduction in a smart grid

Decentralized demand side management of plug-in hybrid vehicles in a Smart Grid

Gradient Field-Based Task Assignment in an AGV Transportation System.

Gradient field-based task assignment in an AGV transportation system

Applying the ATAM to an architecture for decentralized control of a transportation system