Technological learning modelling towards sustainable energy planning

2019 ◽  
Vol 18 (1) ◽  
pp. 84-101 ◽  
Author(s):  
Najmeh Neshat ◽  
Hengameh Hadian ◽  
Somayeh Rahimi Alangi
Keyword(s):  
Integrated Approach ◽  
Electric Power System ◽  
Content Type ◽  
Agent Based ◽  
Technological Learning ◽  
Agent Based Modelling ◽  
Planning Approach ◽  
Multi Agent ◽  
Renewable Technology

Purpose Obviously, the development of a robust optimization framework is the main step in energy and climate policy. In other words, the challenge of energy policy assessment requires the application of approaches which recognize the complexity of energy systems in relation to technological, social, economic and environmental aspects. This paper aims to develop a two-sided multi-agent based modelling framework which endogenizes the technological learning mechanism to determine the optimal generation plan. In this framework, the supplier agents try to maximize their income while complying with operational, technical and market penetration rates constraints. A case study is used to illustrate the application of the proposed planning approach. The results showed that considering the endogenous technology cost reduction moves optimal investment timings to earlier planning years and influences the competitiveness of technologies. The proposed integrated approach provides not only an economical generation expansion plan but also a cleaner one compared to the traditional approach. Design/methodology/approach To the best of the authors’ knowledge, so far there has not been any agent-based generation expansion planning (GEP) incorporating technology learning mechanism into the modelling framework. The main contribution of this paper is to introduce a multi-agent based modelling for long-term GEP and undertakes to show how incorporating technological learning issues in supply agents behaviour modelling influence on renewable technology share in the optimal mix of technologies. A case study of the electric power system of Iran is used to illustrate the usefulness of the proposed planning approach and also to demonstrate its efficiency. Findings As seen, the share of the renewable technology agents (geothermal, hydropower, wind, solar, biomass and photovoltaic) in expanding generation increases from 10.2% in the traditional model to 13.5% in the proposed model over the planning horizon. Also, to incorporate technological learning in the supply agent behaviour leads to earlier involving of renewable technologies in the optimal plan. This increased share of the renewable technology agents is reasonable due to their decreasing investment cost and capability of cooperation in network reserve supply which leads to a high utilization factor. Originality/value To the best of the authors’ knowledge, so far there hasn’t been any agent-based GEP paying attention to this integrated approach. The main contribution of this paper is to introduce a multi-agent based modelling for long-term GEP and undertakes to show how incorporating technological learning issues in supply agents behaviour modelling influence on renewable technology share in the optimal mix of technologies. A case study of the electric power system of Iran is used to illustrate the usefulness of the proposed planning approach and also to demonstrate its efficiency.

Agent-Based Modelling in Multicellular Systems Biology

2020 ◽  
pp. 369-389
Author(s):  
Sara Montagna ◽  
Andrea Omicini
Keyword(s):  
Developmental Biology ◽  
Drosophila Melanogaster ◽  
Systems Biology ◽  
Computational Models ◽  
The Self ◽  
Agent Based Simulation ◽  
Agent Based ◽  
Agent Based Modelling ◽  
Multi Agent

This chapter aims at discussing the content of multi-agent based simulation (MABS) applied to computational biology i.e., to modelling and simulating biological systems by means of computational models, methodologies, and frameworks. In particular, the adoption of agent-based modelling (ABM) in the field of multicellular systems biology is explored, focussing on the challenging scenarios of developmental biology. After motivating why agent-based abstractions are critical in representing multicellular systems behaviour, MABS is discussed as the source of the most natural and appropriate mechanism for analysing the self-organising behaviour of systems of cells. As a case study, an application of MABS to the development of Drosophila Melanogaster is finally presented, which exploits the ALCHEMIST platform for agent-based simulation.

Agent-Based Modelling in Multicellular Systems Biology

2017 ◽  
pp. 159-178 ◽  
Author(s):  
Sara Montagna ◽  
Andrea Omicini
Keyword(s):  
Developmental Biology ◽  
Drosophila Melanogaster ◽  
Systems Biology ◽  
Computational Models ◽  
The Self ◽  
Agent Based Simulation ◽  
Agent Based ◽  
Agent Based Modelling ◽  
Multi Agent

This chapter aims at discussing the content of multi-agent based simulation (MABS) applied to computational biology i.e., to modelling and simulating biological systems by means of computational models, methodologies, and frameworks. In particular, the adoption of agent-based modelling (ABM) in the field of multicellular systems biology is explored, focussing on the challenging scenarios of developmental biology. After motivating why agent-based abstractions are critical in representing multicellular systems behaviour, MABS is discussed as the source of the most natural and appropriate mechanism for analysing the self-organising behaviour of systems of cells. As a case study, an application of MABS to the development of Drosophila Melanogaster is finally presented, which exploits the ALCHEMIST platform for agent-based simulation.

scholarly journals Visualising Ostia’s Processional Landscape Through a Multi-Layered Computational Approach: Case Study of the Cult of the Magna Mater

2019 ◽  
Vol 5 (1) ◽  
pp. 444-467
Author(s):  
Katherine A. Crawford
Keyword(s):  
Network Analysis ◽  
Computational Approach ◽  
Urban Network ◽  
Agent Based ◽  
Agent Based Modelling ◽  
Existing Problems ◽  
Layered Approach ◽  
Religious Landscape ◽  
Insight Into

AbstractOstia, the ancient port of Rome, had a rich religious landscape. How processional rituals further contributed to this landscape, however, has seen little consideration. This is largely due to a lack of evidence that attests to the routes taken by processional rituals. The present study aims to address existing problems in studying processions by questioning what factors motivated processional movement routes. A novel computational approach that integrates GIS, urban network analysis, and agent-based modelling is introduced. This multi-layered approach is used to question how spectators served as attractors in the creation of a processional landscape using Ostia’s Campo della Magna Mater as a case study. The analysis of these results is subsequently used to gain new insight into how a greater processional landscape was created surrounding the sanctuary of the Magna Mater.

An agent-based supplier selection framework

2015 ◽  
Vol 8 (2/3) ◽  
pp. 180-205 ◽  
Author(s):  
Alireza Jahani ◽  
Masrah Azrifah Azmi Murad ◽  
Md. Nasir bin Sulaiman ◽  
Mohd. Hasan Selamat
Keyword(s):  
Supply Chain ◽  
Supplier Selection ◽  
Information Overload ◽  
Case Based Reasoning ◽  
Multi Agent Systems ◽  
Content Type ◽  
Agent Based ◽  
Common Framework ◽  
Multi Agent ◽  
Case Based

Purpose – The purpose of this paper is to propose an approach that integrates three complementary perspectives, multi-agent systems, fuzzy logic and case-based reasoning. Unsatisfied customers, information overload and high uncertainty are the main challenges that are faced by today’s supply chains. In addition, a few existing agent-based approaches are tied to real-world supply chain functions like supplier selection. These approaches are static and do not adequately take the qualitative and quantitative factors into consideration. Therefore, an agent-based framework is needed to address these issues. Design/methodology/approach – The proposed approach integrates three complementary perspectives, multi-agent systems, fuzzy logic and case-based reasoning, as a common framework. These perspectives were rarely used together as a common framework in previous studies. Furthermore, an exploratory case study in an office furniture company is undertaken to illustrate the value of the framework. Findings – The proposed agent-based framework evaluates supply offers based on customers’ preferences, recommends alternative products in the case of stock-out and provides a collaborative environment among agents who represent different supply chain entities. The proposed fuzzy case-based reasoning (F-CBR) approach reduces the information overload by organizing them into the relevant cases that causes less overall search between cases. In addition, its fuzzy aspect addresses the high uncertainty of supply chains, especially when there are different customers’ orders with different preferences. Research limitations/implications – The present study does not include the functions of inventory management and negotiation between agents. Furthermore, only the case description and case retrieval phases of the case-based reasoning approach are investigated, and the remaining phases like case retaining, case reusing and case revising are not included in the scope of this paper. Originality/value – This framework balances the interests of different supply chain structural elements where each of them is represented by a specific agent for better collaboration, decision-making and problem-solving in a multi-agent environment. In addition, the supplier selection and order gathering mechanisms are developed based on customers’ orders.

A Comparison between Agent-Based and GIS-Based Tsunami Evacuation Simulations: A Case Study for Tofino, BC

2021 ◽  
Author(s):  
Joseph Kim ◽  
Tomoyuki Takabatake ◽  
Ioan NISTOR ◽  
Tomoya Shibayama
Keyword(s):  
Information Systems ◽  
Geographical Information Systems ◽  
Mortality Rates ◽  
Geographical Information ◽  
Evacuation Planning ◽  
Loss Of Life ◽  
Agent Based ◽  
Agent Based Modelling ◽  
Validation Tool

Soft measures such as evacuation planning are recommended to mitigate the loss of life during tsunamis. Two types of evacuation models are widely used: (1) Agent-based modelling (ABM) defines sets of rules that individual agents in a simulation follow during a simulated evacuation. (2) Geographical information systems (GIS) are more accessible to city planners, but cannot incorporate the dynamic behaviours found in ABMs. The two evacuation modelling methodologies were compared through a case study by assessing the state of evacuation preparedness and investigating potential mitigation options. The two models showed different magnitudes for mortality rates and facility demand but had similar trends. Both models agreed on the best solution to reduce the loss of life for the community. GIS may serve as a useful tool for initial investigation or as a validation tool for ABMs. ABMs are recommended for use when modelling evacuation until GIS methodologies are further developed.

Towards a Multi-Agent-Based Modelling of Obsidian Exchange in the Neolithic Near East

2013 ◽  
Vol 21 (2) ◽  
pp. 461-485 ◽  
Author(s):  
David Ortega ◽  
Juan José Ibañez ◽  
Lamya Khalidi ◽  
Vicenç Méndez ◽  
Daniel Campos ◽  
...  
Keyword(s):  
Near East ◽  
Agent Based ◽  
Agent Based Modelling ◽  
Multi Agent

scholarly journals A case study on availability of sensor data in agent cooperation

2010 ◽  
Vol 7 (3) ◽  
pp. 597-615
Author(s):  
Christian Johansson ◽  
Fredrik Wernstedt ◽  
Paul Davidsson
Keyword(s):  
Production Costs ◽  
Sensor Data ◽  
Energy Market ◽  
Physical Processes ◽  
Agent Based ◽  
Production And Distribution ◽  
Multi Agent ◽  
Problem Characteristic ◽  
Agent Cooperation

Multi-agent cooperation can in several cases be used in order to mitigate problems relating to task sharing within physical processes. In this paper we apply agent based solutions to a class of problems defined by their property of being predictable from a macroscopic perspective while being highly stochastic when viewed at a microscopic level. These characteristic properties can be found in several industrial processes and applications, e.g. within the energy market where the production and distribution of electricity follow this pattern. Another defining problem characteristic is that the supply is usually limited as well as consisting of several layers of differentiating production costs. We evaluate and compare the performance of the agent system in three different scenarios, and for each such scenario it is shown to what degree the optimization system is dependent on the level of availability of sensor data.

Improving the Efficiency of Large-Scale Agent-Based Models Using Compression Techniques

2012 ◽  
pp. 301-326
Author(s):  
Mitchell Welch ◽  
Paul Kwan ◽  
A.S.M. Sajeev ◽  
Graeme Garner
Keyword(s):  
Large Scale ◽  
Agent Based Models ◽  
Agent Based ◽  
Agent Based Modelling ◽  
Livestock Disease ◽  
Interacting Agents ◽  
National Model ◽  
Complex Phenomena ◽  
Modelling System

Agent-based modelling is becoming a widely used approach for simulating complex phenomena. By making use of emergent behaviour, agent based models can simulate systems right down to the most minute interactions that affect a system’s behaviour. In order to capture the level of detail desired by users, many agent based models now contain hundreds of thousands and even millions of interacting agents. The scale of these models makes them computationally expensive to operate in terms of memory and CPU time, limiting their practicality and use. This chapter details the techniques for applying Dynamic Hierarchical Agent Compression to agent based modelling systems, with the aim of reducing the amount of memory and number of CPU cycles required to manage a set of agents within a model. The scheme outlined extracts the state data stored within a model’s agents and takes advantage of redundancy in this data to reduce the memory required to represent this information. The techniques show how a hierarchical data structure can be used to achieve compression of this data and the techniques for implementing this type of structure within an existing modelling system. The chapter includes a case study that outlines the practical considerations related to the application of this scheme to Australia’s National Model for Emerging Livestock Disease Threats that is currently being developed.

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