Multi-agent system based active distribution networks

This thesis gives a particular vision of the future power delivery system with its main requirements. An investigation of suitable concepts and technologies which creates a road map forward the smart grid has been carried out. They should meet the requirements on sustainability, efficiency, flexibility and intelligence. The so called Active Distribution Network (ADN) is introduced as an important element of the future power delivery system. With an open architecture, the ADN is designed to integrate various types of networks, i.e., MicroGrid or Autonomous Network, and different forms of operation, i.e., islanding or interconnection. By enabling an additional local control layer, these so called cells are able to reconfigure, manage local faults, support voltage regulation, or manage power flow. Furthermore, the Multi-Agent System (MAS) concept is regarded as a potential technology to cope with the anticipated challenges of future grid operation. Analysis of benefits and challenges of implementing MAS shows that it is a suitable technology for a complex and highly dynamic operation and open architecture as the ADN. By taking advantages of the MAS technology, the AND is expected to fully enable distributed monitoring and control functions. This MAS-based ADN focuses mainly on control strategies and communication topologies for the distribution systems. The transition to the proposed concept does not require an intensive physical change to the existing infrastructure. The main point is that inside the MAS-based ADN, loads and generators interact with each other and the outside world. This infrastructure can be built up of several cells (local areas) that are able to operate autonomously by an additional agent-based control layer. The ADN adapts a MAS hierarchical control structure in which each agent handles three functional layers of management, coordination, and execution. In the operational structure, the ADN addresses two main function parts: Distributed State Estimation (DSE) to analyze the network topology, compute the state estimation, and detect bad data; and Local Control Scheduling (LCS) to establish the control set points for voltage coordination and power flow management. Under the distributed context of the controls, an appropriate method for DSE is proposed. The method takes advantage of the MAS technology to compute iteratively the local state variables through neighbor data measurements. Although using the classical Weighted Least Square (WLS) as a core, the proposed algorithm based on an agent environment distributes drastically computation burden to subtasks of state estimation with only two interactive buses and an interconnection line in between. The accuracy and complexity of the proposed estimation are investigated through both off-line and on-line simulations. Distributed and parallel working of processors improves significantly the computation time. This estimation is also suitable for a meshed configuration of the ADN, which includes more than one interconnection between each pair of the cells. Depending on the availability of a communication infrastructure, it is able to work locally inside the cells or globally for the whole ADN. As a part of the LCS, the voltage control function is investigated in both steady-state and dynamic environments. The autonomous voltage control within each network area (cell) can be deployed by a combination of active and reactive power support of distributed generation (DG). The coordinated voltage control defines the optimal tap setting of the on-load tap changer (OLTC) while comparing amounts of control actions in each area. Based on the sensitivity factors, these negotiations are thoroughly supported in the distributed environment of the MAS platform. To verify the proposed method, both steady-state and dynamic simulations are developed. Simulation results show that the proposed function helps to integrate more DG while mitigating voltage violation effectively. The optimal solution can be reached within a small number of calculation iterations. It opens a possibility to apply the proposed method as an on-line application. Furthermore, a distributed approach for the power flow management function is developed. By converting the power network to a represented graph, the optimal power flow is understood as the well-known minimum cost flow problem. Two fundamental solutions for the minimum cost flow, i.e., the Successive Shortest Path (SSP) algorithm and the Cost-Scaling Push-Relabel (CS-PR) algorithm, are introduced. The SSP algorithm is augmenting the power flow along the shortest path until reaching the capacity of at least one edge. After updating the flow, it finds another shortest path and augments the flow again. The CS-PR algorithm approaches the problem in a different way which is scaling cost and pushing as much flow as possible at each active node. Simulations of both meshed and radial test networks are developed to compare their performances in various network conditions. Simulation results show that the two methods can allow both generation and power flow controller devices to operate optimally. In the radial test network, the CS-PR needs less computation effort represented by a number of exchanged messages among the MAS platform than the SSP. Their performances in the meshed network are, however, almost the same. Last but not least, this novel concept of MAS-based AND is verified under a laboratory environment. The lab set-up separates some local network areas by using a three-inverter system. The MAS platform is created on different computers and is able to retrieve data from and to hardware components, i.e., the three-inverter system. In this set-up, a configuration of the power router is established in a combination of the three-inverter system with the MAS platform. Three control functions of the inverters, AC voltage control, DC bus voltage control, and PQ control, are developed in a Simulink diagram. By assigning suitable operation modes for the inverters, the set-up successfully experiments on synchronizing and disconnecting a cell to the rest of the grid. In the MAS platform, an obvious power routing strategy is executed to optimally manage power flow in the lab set-up. The results show that the proposed concept of the ADN with the power router interface works well and can be used to manage electrical networks with distributed generation and controllable loads, leading to active networks

A distributed knowledge-based approach to flexible automation : the contract-net framework

Includes bibliographical references (p. 26-29)

Power flow management in active networks

This paper proposes a new method to manage the active power in the distribution systems, a function under the framework of the active network (AN) concept. An application of the graph theory is introduced to cope with the optimal power generation (DGs/Cells dispatch) and interarea power flows. The algorithm is implemented in a distributed way supported by the multi-agent system (MAS) technology. Simulations show how the method works in cases of optimal operation, congestion management, and power generation cost change

Energy-aware routing protocols in wireless sensor networks

Saving energy and increasing network lifetime are significant challenges in the field of Wireless Sensor Networks (WSNs). Energy-aware routing protocols have been introduced for WSNs to overcome limitations of WSN including limited power resources and difficulties renewing or recharging sensor nodes batteries. Furthermore, the potentially inhospitable environments of sensor locations, in some applications, such as the bottom of the ocean, or inside tornados also have to be considered. ZigBee is one of the latest communication standards designed for WSNs based on the IEEE 802.15.4 standard. The ZigBee standard supports two routing protocols, the Ad hoc On-demand Distance Vector (AODV), and the cluster-tree routing protocols. These protocols are implemented to establish the network, form clusters, and transfer data between the nodes. The AODV and the cluster-tree routing protocols are two of the most efficient routing protocols in terms of reducing the control message overhead, reducing the bandwidth usage in the network, and reducing the power consumption of wireless sensor nodes compared to other routing protocols. However, neither of these protocols considers the energy level or the energy consumption rate of the wireless sensor nodes during the establishment or routing processes. (Continues...)

Intelligent Product Agents for Multi-Agent Control of Manufacturing Systems

The current manufacturing paradigm is shifting toward more flexible manufacturing systems that produce highly personalized products, adapt to unexpected disturbances in the system, and readily integrate new manufacturing system technology. However, to achieve this type of flexibility, new system-level control strategies must be developed, tested, and integrated to coordinate the components on the shop floor. One strategy that has been previously proposed to coordinate the resources and parts in a manufacturing system is multi-agent control. The manufacturing multi-agent control strategy consists of agents that interface with the various components on the shop floor and continuously interact with each other to drive the behavior of the manufacturing system. Two of the most important decision-making agents for this type of control strategy are product agents and resource agents. A product agent represents a single product and a resource agent represents a single resource on the plant floor. The objective of a product agent is to make decisions for an individual product and request operations from the resource agents based on manufacturer and customer specifications. A resource agent is the high-level controller for a resource on the shop floor (e.g., machines, material-handling robots, etc.). A resource agent communicates with other product and resource agents in the system, fulfills product agent requests, and interfaces with the associated resource on the plant floor. While both product agents and resource agents are important to ensure effective performance of the manufacturing system, the work presented in this dissertation improves the intelligence and capabilities of product agents by providing a standardized product agent architecture, models to capture the dynamics and constraints of the manufacturing environment, and methods to make improved decisions in a dynamic system. New methods to explore the manufacturing system and cooperate with other agents in the system are provided. The proposed architecture, models, and methods are tested in a simulated manufacturing environment and in several manufacturing testbeds with physical components. The results of these experiments showcase the improved flexibility and adaptability of this approach. In these experiments, the model-based product agent effectively makes decisions to meet its production requirements, while responding to unexpected disturbances in the system, such as machine failures or new customer orders. The model-based product agent proposed in this dissertation pushes the fields of manufacturing and system-level control closer to realizing the goals of increased personalized production and improved manufacturing system flexibility.PHDMechanical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/162893/1/ikoval_1.pd

The DIDAM framework Disaggregated demand and assignment models for combined passengers and freight transport

The objective of this paper is to present the methodological developments in the DIDAM (Disaggregated demand and assignment models for combined passengers and freight transport) research project, which aims at advancing fundamental research in transportation modelling and analysis, using two complementary methodologies. The first is to develop a joint methodological approach for both freight and passenger transport, and the second is to base this common approach on the use of models that are as disaggregate and realistic as possible. Furthermore, the methods will be designed to cope with the important question of competition for infrastructure. The project is mainly organized around two themes. Each of them covers both freight and passengers aspects, in a manner which promotes collaborative work between the partners of the project. The first theme is concerned with disaggregate demand modelling issues, the second deals with innovative aspects of (joint) traffic assignment. Working at a fully disaggregate level is however not always easy. If such models are already available for passenger demand and traffic, disaggregate tools are still largely missing for freight transport. This is not only true at the operational level, but, more crucially, at the conceptual level. This is why our research program adopts, in this domain, a progressive approach that introduces disaggregation gradually into existing methods and models. This entails research in a full spectrum of issues, ranging from concepts definition (who are the actors, how can they be characterized ...) to validation exercises using available data source

Knowledge and agent-based system for decentralised scheduling in manufacturing

The aim of the research paper is to develop algorithms for manufacturers’ agents that would allow them to sequence their own operation plans and to develop a multi-agent infrastructure to allow operation pair agents to cooperatively adjust the timing of manufacturing operations. The scheduling problem consisted of jobs with fixed process plans and of manufacturers collectively offering the necessary operations for the jobs. Manufacturer agents sequenced and pair agents timed each operation as and when required. Timing an operation triggered a cascade of conflicts along the job process plan that other pair agents would pick up on and would take action accordingly. The conventional approach performs conflict resolution in series and manufacturer agents as well as pair agents wait until they are allowed to sequence and time the next operation. The limiting assumption behind that approach was systematically removed, and the proposed approach allowed manufacturers to perform operation scheduling in parallel, cutting down tenfold on the computation time. The multi-agent infrastructure consists of the Protégé knowledge base, the Pellet semantic reasoner and the Workflows and Agent Development Environment (WADE). The case studies used were the MT6, MT10 and LA19 job shop scheduling problems; and an industrial use case was provided to give context to the manufacturing environment investigated. Although there were benefits from the decentralised manufacturing system, we noted an optimality loss of 34% on the makespans. However, for scalability, our approach showed good promise

An enhanced ant colony optimization approach for integrated process planning and scheduling

An enhanced ant colony optimization (eACO) meta-heuristics is proposed in this paper to accomplish the integrated process planning and scheduling (IPPS) in the jobshop environments. The IPPS problem is graphically formulated to implement the ACO algorithm. In accordance with the characteristics of the IPPS problem, the mechanism of eACO has been enhanced with several modifications, including quantification of convergence level, introduction of pheromone on nodes, new strategy of determining heuristic desirability and directive pheromone deposit strategy. Experiments are conducted to evaluate the approach, while makespan and CPU time are used as measurements. Encouraging results can be seen when comparing to other IPPS approaches based on evolutionary algorithms. © 2013 International Institute for Innovation, Industrial Engineering and Entrepreneurship - I4e2.published_or_final_versio