MASCEM improvement for agent based coalitions

This paper presents MASCEM - Multi-Agent Simulator for Electricity Markets improvement towards an enlarged model for Seller Agents coalitions. The simulator has been improved, both regarding its user interface and internal structure. The OOA, used as development platform, version was updated and the multi-agent model was adjusted for implementing and testing several negotiations regarding Seller agents’ coalitions. Seller coalitions are a very important subject regarding the increased relevance of Distributed Generation under liberalised electricity markets.

A multi-agent based approach for intelligent smart grid management

The spread and globalization of distributed generation (DG) in recent years has should highly influence the changes that occur in Electricity Markets (EMs). DG has brought a large number of new players to take action in the EMs, therefore increasing the complexity of these markets. Simulation based on multi-agent systems appears as a good way of analyzing players’ behavior and interactions, especially in a coalition perspective, and the effects these players have on the markets. MASCEM – Multi-Agent System for Competitive Electricity Markets was created to permit the study of the market operation with several different players and market mechanisms. MASGriP – Multi-Agent Smart Grid Platform is being developed to facilitate the simulation of micro grid (MG) and smart grid (SG) concepts with multiple different scenarios. This paper presents an intelligent management method for MG and SG. The simulation of different methods of control provides an advantage in comparing different possible approaches to respond to market events. Players utilize electric vehicles’ batteries and participate in Demand Response (DR) contracts, taking advantage on the best opportunities brought by the use of all resources, to improve their actions in response to MG and/or SG requests.

Modeling group decision meeting participants with an Agent-based approach

This paper aims to present a multi-agent model for a simulation, whose goal is to help one specific participant of multi-criteria group decision making process.This model has five main intervenient types: the human participant, who is using the simulation and argumentation support system; the participant agents, one associated to the human participant and the others simulating the others human members of the decision meeting group; the directory agent; the proposal agents, representing the different alternatives for a decision (the alternatives are evaluated based on criteria); and the voting agent responsiblefor all voting machanisms.At this stage it is proposed a two phse algorithm. In the first phase each participantagent makes his own evaluation of the proposals under discussion, and the voting agent proposes a simulation of a voting process.In the second phase, after the dissemination of the voting results,each one ofthe partcipan agents will argue to convince the others to choose one of the possible alternatives. The arguments used to convince a specific participant are dependent on agent knowledge about that participant. This two-phase algorithm is applied iteratively.

Distributed Intelligent Management of Microgrids using a Multi-Agent Simulation Platform

Multi-agent approaches have been widely used to model complex systems of distributed nature with a large amount of interactions between the involved entities. Power systems are a reference case, mainly due to the increasing use of distributed energy sources, largely based on renewable sources, which have potentiated huge changes in the power systems’ sector. Dealing with such a large scale integration of intermittent generation sources led to the emergence of several new players, as well as the development of new paradigms, such as the microgrid concept, and the evolution of demand response programs, which potentiate the active participation of consumers. This paper presents a multi-agent based simulation platform which models a microgrid environment, considering several different types of simulated players. These players interact with real physical installations, creating a realistic simulation environment with results that can be observed directly in the reality. A case study is presented considering players’ responses to a demand response event, resulting in an intelligent increase of consumption in order to face the wind generation surplus.

DYNAMOD – A dynamic agent based modelling framework for digital businesses

Digital Businesses have become a major driver for economic growth and have seen an explosion of new startups. At the same time, it also includes mature enterprises that have become global giants in a relatively short period of time. Digital Businesses have unique characteristics that make the running and management of a Digital Business much different from traditional offline businesses. Digital businesses respond to online users who are highly interconnected and networked. This enables a rapid flow of word of mouth, at a pace far greater than ever envisioned when dealing with traditional products and services. The relatively low cost of incremental user addition has led to a variety of innovation in pricing of digital products, including various forms of free and freemium pricing models. This thesis explores the unique characteristics and complexities of Digital Businesses and its implications on the design of Digital Business Models and Revenue Models. The thesis proposes an Agent Based Modeling Framework that can be used to develop Simulation Models that simulate the complex dynamics of Digital Businesses and the user interactions between users of a digital product. Such Simulation models can be used for a variety of purposes such as simple forecasting, analysing the impact of market disturbances, analysing the impact of changes in pricing models and optimising the pricing for maximum revenue generation or a balance between growth in usage and revenue generation. These models can be developed for a mature enterprise with a large historical record of user growth rate as well as for early stage enterprises without much historical data. Through three case studies, the thesis demonstrates the applicability of the Framework and its potential applications.

An agent-based simulator to estimate domestic energy use

Throughout recent years, there has been an increase in the population size, as well as a fast economic growth, which has led to an increase of the energy demand that comes mainly from fossil fuels. In order to reduce the ecological footprint, governments have implemented sustainable measures and it is expected that by 2035 the energy produced from renewable energy sources, such as wind and solar would be responsible for one-third of the energy produced globally. However, since the energy produced from renewable sources is governed by the availability of the respective primary energy source there is often a mismatch between production and demand, which could be solved by adding flexibility on the demand side through demand response (DR). DR programs influence the end-user electricity usage by changing its cost along the time. Under this scenario the user needs to estimate the energy demand and on-site production in advance to plan its energy demand according to the energy price. This work focuses on the development of an agent-based electrical simulator, capable of: (a) estimating the energy demand and on-site generation with a 1-min time resolution for a 24-h period, (b) calculating the energy price for a given scenario, (c) making suggestions on how to maximize the usage of renewable energy produced on-site and to lower the electricity costs by rescheduling the use of certain appliances. The results show that this simulator allows reducing the energy bill by 11% and almost doubling the use of renewable energy produced on-site.

An agent-based model to assess the attractiveness of industrial estates

This article describes the main approaches adopted in a study focused on planning industrial estates on a sub-regional scale. The study was supported by an agent-based model, using firms as agents to assess the attractiveness of industrial estates. The simulation was made by the NetLogo toolkit and the environment represents a geographical space. Three scenarios and four hypotheses were used in the simulation to test the impact of different policies on the attractiveness of industrial estates. Policies were distinguished by the level of municipal coordination at which they were implemented and by the type of intervention. In the model, the attractiveness of industrial estates was based on the level of facilities, amenities, accessibility and on the price of land in each industrial estate. Firms are able to move and relocate whenever they find an attractive estate. The relocating firms were selected by their size, location and distance to an industrial estate. Results show that a coordinated policy among municipalities is the most efficient policy to promote advanced-qualified estates. In these scenarios, it was observed that more industrial estates became attractive, more firms were relocated and more vacant lots were occupied. Furthermore, the results also indicate that the promotion of widespread industrial estates with poor-quality infrastructures and amenities is an inefficient policy to attract firms.

Policy Diffusion: An Agent-Based Approach

Methods like Event History Analysis can show the existence of diffusion and part of its nature, but do not study the process itself. Nowadays, thanks to the increasing performance of computers, processes can be studied using computational modeling. This thesis presents an agent-based model of policy diffusion mainly inspired from the model developed by Braun and Gilardi (2006). I first start by developing a theoretical framework of policy diffusion that presents the main internal drivers of policy diffusion - such as the preference for the policy, the effectiveness of the policy, the institutional constraints, and the ideology - and its main mechanisms, namely learning, competition, emulation, and coercion. Therefore diffusion, expressed by these interdependencies, is a complex process that needs to be studied with computational agent-based modeling. In a second step, computational agent-based modeling is defined along with its most significant concepts: complexity and emergence. Using computational agent-based modeling implies the development of an algorithm and its programming. When this latter has been developed, we let the different agents interact. Consequently, a phenomenon of diffusion, derived from learning, emerges, meaning that the choice made by an agent is conditional to that made by its neighbors. As a result, learning follows an inverted S-curve, which leads to partial convergence - global divergence and local convergence - that triggers the emergence of political clusters; i.e. the creation of regions with the same policy. Furthermore, the average effectiveness in this computational world tends to follow a J-shaped curve, meaning that not only time is needed for a policy to deploy its effects, but that it also takes time for a country to find the best-suited policy. To conclude, diffusion is an emergent phenomenon from complex interactions and its outcomes as ensued from my model are in line with the theoretical expectations and the empirical evidence.Les méthodes d'analyse de biographie (event history analysis) permettent de mettre en évidence l'existence de phénomènes de diffusion et de les décrire, mais ne permettent pas d'en étudier le processus. Les simulations informatiques, grâce aux performances croissantes des ordinateurs, rendent possible l'étude des processus en tant que tels. Cette thèse, basée sur le modèle théorique développé par Braun et Gilardi (2006), présente une simulation centrée sur les agents des phénomènes de diffusion des politiques. Le point de départ de ce travail met en lumière, au niveau théorique, les principaux facteurs de changement internes à un pays : la préférence pour une politique donnée, l'efficacité de cette dernière, les contraintes institutionnelles, l'idéologie, et les principaux mécanismes de diffusion que sont l'apprentissage, la compétition, l'émulation et la coercition. La diffusion, définie par l'interdépendance des différents acteurs, est un système complexe dont l'étude est rendue possible par les simulations centrées sur les agents. Au niveau méthodologique, nous présenterons également les principaux concepts sous-jacents aux simulations, notamment la complexité et l'émergence. De plus, l'utilisation de simulations informatiques implique le développement d'un algorithme et sa programmation. Cette dernière réalisée, les agents peuvent interagir, avec comme résultat l'émergence d'un phénomène de diffusion, dérivé de l'apprentissage, où le choix d'un agent dépend en grande partie de ceux faits par ses voisins. De plus, ce phénomène suit une courbe en S caractéristique, poussant à la création de régions politiquement identiques, mais divergentes au niveau globale. Enfin, l'efficacité moyenne, dans ce monde simulé, suit une courbe en J, ce qui signifie qu'il faut du temps, non seulement pour que la politique montre ses effets, mais également pour qu'un pays introduise la politique la plus efficace. En conclusion, la diffusion est un phénomène émergent résultant d'interactions complexes dont les résultats du processus tel que développé dans ce modèle correspondent tant aux attentes théoriques qu'aux résultats pratiques.

Are agent-based simulations robust? The wholesale electricity trading case

Agent-based computational economics is becoming widely used in practice. This paperexplores the consistency of some of its standard techniques. We focus in particular on prevailingwholesale electricity trading simulation methods. We include different supply and demandrepresentations and propose the Experience-Weighted Attractions method to include severalbehavioural algorithms. We compare the results across assumptions and to economic theorypredictions. The match is good under best-response and reinforcement learning but not underfictitious play. The simulations perform well under flat and upward-slopping supply bidding,and also for plausible demand elasticity assumptions. Learning is influenced by the number ofbids per plant and the initial conditions. The overall conclusion is that agent-based simulationassumptions are far from innocuous. We link their performance to underlying features, andidentify those that are better suited to model wholesale electricity markets.

Spatial behavior in groups: an agent-based approach

We present an agent-based model with the aim of studying how macro-level dynamics of spatial distances among interacting individuals in a closed space emerge from micro-level dyadic and local interactions. Our agents moved on a lattice (referred to as a room) using a model implemented in a computer program called P-Space in order to minimize their dissatisfaction, defined as a function of the discrepancy between the real distance and the ideal, or desired, distance between agents. Ideal distances evolved in accordance with the agent's personal and social space, which changed throughout the dynamics of the interactions among the agents. In the first set of simulations we studied the effects of the parameters of the function that generated ideal distances, and in a second set we explored how group macrolevel behavior depended on model parameters and other variables. We learned that certain parameter values yielded consistent patterns in the agents' personal and social spaces, which in turn led to avoidance and approaching behaviors in the agents. We also found that the spatial behavior of the group of agents as a whole was influenced by the values of the model parameters, as well as by other variables such as the number of agents. Our work demonstrates that the bottom-up approach is a useful way of explaining macro-level spatial behavior. The proposed model is also shown to be a powerful tool for simulating the spatial behavior of groups of interacting individuals.

Emergent patterns of social organization in captive Cercocebus torquatus: Testing the GrooFiWorld agent-based model

We empirically applied the GrooFiWorld agent-based model (Puga-González et al. 2009) in a group of captive mangabeys (Cercocebus torquatus). We analysed several measurements related to aggression and affiliative patterns. The group adopted a combination of despotic and egalitarian behaviours resulting from the behavioural flexibility observed in the Cercopithecinae subfamily. Our study also demonstrates that the GrooFiWorld agent-based model can be extended to other members of the Cercopithecinae subfamily generating parsimonious hypotheses related to the social organization.

Multi-agent simulation of sawmill yard operations

This paper reports the findings of using multi-agent based simulation model to evaluate the sawmill yard operations within a large privately owned sawmill in Sweden, Bergkvist Insjön AB in the current case. Conventional working routines within sawmill yard threaten the overall efficiency and thereby limit the profit margin of sawmill. Deploying dynamic work routines within the sawmill yard is not readily feasible in real time, so discrete event simulation model has been investigated to be able to report optimal work order depending on the situations. Preliminary investigations indicate that the results achieved by simulation model are promising. It is expected that the results achieved in the current case will support Bergkvist-Insjön AB in making optimal decisions by deploying efficient work order in sawmill yard.

Business competition dynamics: agent-based modeling simulations of firms in search of economic performance

A intenção deste trabalho é explorar dinâmicas de competição por meio de “simulação baseada em agentes”. Apoiando-se em um crescente número de estudos no campo da estratégia e teoria das organizações que utilizam métodos de simulação, desenvolveu-se um modelo computacional para simular situações de competição entre empresas e observar a eficiência relativa dos métodos de busca de melhoria de desempenho teorizados. O estudo também explora possíveis explicações para a persistência de desempenho superior ou inferior das empresas, associados às condições de vantagem ou desvantagem competitiva

Exploration of the parameter space in an agent-based model of tuberculosis spread: emergence of drug resistance in developing vs developed countries

In this work we present an agent-based model for the spread of tuberculosis where the individuals can be infected with either drug-susceptible or drug-resistant strains and can also receive a treatment. The dynamics of the model and the role of each one of the parameters are explained. The whole set of parameters is explored to check their importance in the numerical simulation results. The model captures the beneficial impact of the adequate treatment on the prevalence of tuberculosis. Nevertheless, depending on the treatment parameters range, it also captures the emergence of drug resistance. Drug resistance emergence is particularly likely to occur for parameter values corresponding to less efficacious treatment, as usually found in developing countries.

Performance Availability and Anticipatory Change Planning of Intralogistics Systems: A Simulation-Based Approach

In recent years, the ability to respond to real time changes in operations and reconfigurability in equipment are likely to become essential characteristics for next generation intralogistics systems as well as the level of automation, cost effectiveness and maximum throughput. In order to cope with turbulences and the increasing level of dynamic conditions, future intralogistics systems have to feature short reaction times, high flexibility in processes and the ability to adapt to frequent changes. The increasing autonomy and complexity in processes of today’s intralogistics systems requires new and innovative management approaches, which allow a fast response to (un)anticipated events and adaptation to changing environment in order to reduce the negative consequences of these events. The ability of a system to respond effectively a disruption depends more on the decisions taken before the event than those taken during or after. In this context, anticipatory change planning can be a usable approach for managers to make contingency plans for intralogistics systems to deal with the rapidly changing marketplace. This paper proposes a simulation-based decision making framework for the anticipatory change planning of intralogistics systems. This approach includes the quantitative assessments based on the simulation in defined scenarios as well as the analysis of performance availability that combines the flexibility corridors of different performance dimensions. The implementation of the approach is illustrated on a new intralogistics technology called the Cellular Transport System.