Multi-Agent Systems as Automation Platform for Intelligent Energy Systems Paulo Leitão 1,2, Pavel Vrba 3, Thomas Strasser 4 1
Polytechnic Institute of Bragança, Campus Sta Apolónia, Apartado 1134, 5301-857 Bragança, Portugal {
[email protected]} 2 LIACC - Artificial Intelligence and Computer Science Laboratory, R. Campo Alegre 102, 4169-007 Porto, Portugal 3 Czech Technical University in Prague, Technicka 2, 166 27 Prague 6, Czech Republic {
[email protected]} 4 AIT Austrian Institute of Technology, Giefinggasse 2, Vienna, Austria {
[email protected]}
Abstract–The large-scale integration of distributed energy resources into a complex cyber-physical system demands proper automation platforms for monitoring, control, optimization and reconfiguration, addressing intelligent energy systems. This paper discusses the use of multi-agent systems as a suitable solution to address this challenge by analyzing their benefits when applying them to the field of Smart Grids and surveying existing works and initiatives. The principles and use cases established in the MASGrid project illustrate the application of multi-agent technology in active power distribution systems.
integration of electric vehicles and distributed storage devices in the power distribution grids require a proper automation platform for monitoring, control and optimization [5], [6], [7], [8], [9]. The MAS approach seems to be a promising candidate in order to master the higher complexity of such a system with a distributed nature [10], [11], [12]. The objective of this paper is to discuss the benefits and challenges of using MAS technology in Smart Grid systems with a high penetration of fluctuating Distributed Generators (DER) from renewables, by surveying some existing developments and implementations. A particular attention is devoted to the presentation of the MASGrid project, which provides an overview and potential use cases for applying MAS technology in active power distribution networks. The rest of the paper is organized as follows: Section II analyses the benefits and challenges of using MAS as an ICT and automation infrastructure for Smart Grids and Section III surveys the existing projects and initiatives in this field. Section IV provides details about the MASGrid project. At last, section V rounds up the paper with the conclusions.
I. INTRODUCTION Multi-Agent Systems (MAS) [1], derived from the field of distributed artificial intelligence, is a paradigm that allows an alternative way to design large-scale distributed control systems based on autonomous and cooperative agents, exhibiting modularity, flexibility and robustness [2]. MAS exhibit the capability to distribute the control over a network of software entities, the agents, each one having its own knowledge, skills and autonomous proactive behavior. The agents co-operate with each other to achieve the global system objectives, offering a decentralized control structure, in opposite to the traditional rigid centralized solution. These principles provide a fast response to condition changes and supports reconfigurability on the fly quite naturally [2]. This innovative technology was originally applied in the business domain, making use of intensive processing and memory facilities to compute complex problems based on the distributed approach. Examples of these types of applications are the electronic commerce [3] and scheduling [4]. However, during the last years, the applicability of MAS is being spread to other (industrial) domains, ranging from manufacturing to transportation, passing by traffic control, logistics and also energy systems. All of these applications are complex engineering problems characterized by a network of a huge amount of control nodes demanding robustness and adaptation in their coordination behavior. In particular, its usage in Smart Grids is seen as a promising perspective to provide the required Information and Communication Technology (ICT) features to support their functionalities. Especially, the large scale integration of Renewable Energy Sources (RES), from solar and wind, as well as the upcoming 978-1-4799-0224-8/13/$31.00 ©2013 IEEE
II. BENEFITS AND CHALLENGES OF USING MULTI-AGENT SYSTEMS IN SMART GRIDS A Smart Grid is an electrical grid that gathers, distributes, and acts on information about the behavior of all participants (i.e., central and distributed generators, power transmission and distribution system and consumers) in order to improve the efficiency, stability, safety, reliability, economics, and sustainability of the electric energy system [13]. A Smart Grid is a complex and large-scale system due to the high number of nodes, interconnections and different topologies that can be presented. In the Smart Grid conceptual model network, defined by the IEC Strategic Group 3 (SG3) [14], these nodes are hardware/software resources that can be of different types, namely the power generation (including solar and wind energy plants), distribution, substations and users (i.e., residential, commercial shops and industry), and control components for energy management, such as smart metering, data handling and SCADA (Supervisory Control and Data Acquisition) systems. Additionally, the possibility to have consumers that are able to inject power into the grid (i.e., the 66
prosumers due to local generation) and electric vehicles that can charge their batteries at any time and serve as energy storage, increases the Smart Grids complexity [7]. The major challenge of Smart Grids is the employment of proper ICT infrastructures that will help with managing the transformation of power grids from vertically and centrally organized infrastructure into a more decentralized system. An increasing number of DERs is being installed into mediumand low-voltage networks; smart management and control devices are being employed across the network such as Distributed Generators (DG), fault passage indicators, or onload tap changers; and finally smart buildings and homes are being equipped with smart meters and intelligent devices that can response to varying energy prices by scheduling the times of energy consumption. All these factors show that the electricity grid becomes a complex large-scale cyber-physical system [6], with thousands or even millions of autonomous and interacting devices, which functionality has to be monitored and coordinated in real-time or near real-time. Additionally, as stated by [15], with this ICT apparatus, “consumers have full control and management of their own consumption and production, while companies, in agreement with end users and achieving mutual benefits, can implement advanced demand-response services such as peak shaving an load shifting, if combined with time-dependent electricity prices, strong incentives for efficient energy usage”. Searching for proper software techniques to manage such complexity leads us to the technology of MAS, which potential for deployment in the industrial domain has been already documented on several simulation studies, pilot deployments as well as real-life applications [20], [21]. In fact, the use of MAS allows the achievement of important features in large scale complex control systems, mainly in terms of modularity, distribution, flexibility, robustness and reconfigurability. Using the MAS principles, each device (e.g., generator, transformer, switch, electric appliance, sensor, storage device, electric vehicle) can be monitored and controlled by an autonomous software component (i.e., an agent). These agents interact, according to a proper coordination logic, to achieve the system goals, by combining their individual skills and knowledge, as illustrated in Fig. 1.
Using a holonic vision, each one of the agents can comprise, on its lower level, other agents managing other DERs. As an example, a domestic consumer can include a heating system, a refrigeration system, a smart metering and a photovoltaic system. This concept provides a decentralized control approach, achieving: • Modularity and plug and play, e.g., adding a new element in the system is easy and on the fly (without the need to stop, reprogram and re-start the system). • Scalability, since the system can growth in a simple way facing the growing of energy resources nodes. • Reconfigurability, since changes can be performed on the fly (e.g., an agent can stop, modify its behavior or strategy and start again without affecting the other components of the system). • Robustness, since losing one agent doesn’t imply the system failure (e.g., if an agent associated to a customer fails, the system continues running). • Reusability, allowing a faster development of the control application since existing components can be re-used to develop new agents or system. In spite of this promising perspective, several challenges arise when applying MAS in a Smart Grid, e.g., the integration of adequate control algorithms, the adequacy to real-time constraints, the connection with hardware resources and the interoperability. Moreover, domain standards covering energy and ICT issues have to be addressed as well. This ICT platform enhances several functionalities inherent to Smart Grids, namely the distributed and real-time monitoring, diagnosis, self-healing, self-maintenance and negotiation (e.g., supporting dynamic clustering formation, price evolution and grid management). The achievement of these functionalities requires the design and implementation of proper coordination mechanisms and also suitable adaptive algorithms to be embedded in the distributed agents. Of particular importance is the consideration of biological inspired methods, such as self-organization, to enhance MAS solutions with more powerful and dynamic mechanisms. The MAS solutions are usually designed not for real-time control but instead for an intermediate control level introducing intelligence, adaptation and reconfigurability. However, by proper coupling with the state-of-the-art PLCs running the IEC 61131-3 programs the real-time operation can also be guaranteed [16]. In such cyber-physical systems, the deployment of the solution usually requires the connection with the physical hardware, such as DGs, switches or PLCs. Combining agents with the IEC 61499 standard [16], [17], with agents acting at higher control level and the IEC 61499 acting at lower control level, recalling the holonic concept [18], allows for addressing the real-time constraints but also the integration of the physical hardware resources. In another dimension, combining MAS with Serviceoriented Architectures (SOA) can overcome some of its limitations, namely in terms of interoperability and ITvertical integration [19]. In fact, both SOA and MAS are based on same architectural principles – the composite
Fig. 1. Modeling a Smart Grid using Multi-Agent Systems.
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application consisting of loosely coupled entities that communicate via messaging in a networked environment. SOA is based on the concept of providing and requesting services. A service is a piece of software that encapsulates the business/control logic or resource functionality of an entity (that can be internally represented by an agent) that responds to a specific request. Enterprise Service Bus (ESB) is a SOA framework that can be used as a backbone for supporting the interoperability among distributed (agent-based) systems. Typically desirable capabilities of ESBs include, without being exhaustive, process orchestration, protocol translation, hot deployment, versioning, lifecycle management and security, supporting the IT-vertical integration. Both technologies can be advantageously combined together in terms of embedding the intelligence, possessed by agents, into web services, which are broadly accepted by industry as an interoperability standard [22].
grids, so called micro grids, which are expected to run in an islanded mode [11]. The load of house devices is intelligently managed by the Intelligent Load Controller (ILC) containing an agent running in popular the JADE agent platform. The ILC agents communicate with the micro grid central controller agent, whose task is to balance the consumption and production of electricity in the micro grid. Basically, if the demand is greater than the production the ILC agents are asked to switch off some non-important devices. The agent technology is also widely used to support the self-healing capability of a Smart Grid. In [28] the special prevention control agents are presented which are in charge of forecasting states that could lead to failures. The agentbased system for fault detection and reconfiguration in Smart Grids is presented in [12]. The agents determine the optimal network configuration in terms of identifying which switching devices should be opened or closed in order to minimize power losses in the power distribution network. In [29] the intelligent agents performing various tasks such as monitoring, performance enhancements and control to support self-healing functionality, are described. The GridAgents software, currently being developed by Infotility company, is designed as a platform for building large-scale distribution network control solutions with high share of DER. It exploits the MAS technology working in combination with web-services [10]. The pilot testing is planned to be done in Fulton Corridor Network and Madison Square Network in New York City. The EU FP7 ADDRESS project develops the technical framework enabling domestic and small commercial consumers to actively participate in the electricity market. The architecture exploits the concept of aggregation of demand flexibility provided by consumers and prosumers to form services offered to the electricity market. The communication infrastructure is designed according to SOA principles following the guidelines of IEC standards for web service implementation (i.e., IEC61968-1-1 & IEC61968-1-2). The XML messages sent among services use the IEC Common Information Model (CIM) for Smart Grids (i.e., introduced by IEC 61968 & IEC 61970) to encode their content [30]. A distributed energy management system aimed at reducing the energy wastage in camps of portable buildings used by mining companies in remote regions of Australia is presented in [31]. The power consumption at each building is controlled by an intelligent node pursuing the reduction of energy wastage without affecting the resident comfort (basically done by turning off the air condition when the inhabitant leaves). The communication between nodes and the central server is based on Internet protocols (i.e., TCP/IP and HTTP) and implemented in the form of RESTful API.
III. EXISTING INITIATIVES AND PROJECTS The purpose of this section is to provide an overview of existing applications of MAS and SOA technologies in the field of intelligent energy systems. One of the first projects employing agents in order to provide a distributed intelligent ICT infrastructure for sustainable power systems was CRISP. It devises the concept of Grid Cell, which represents a subnetwork independently managed by a software agent called Smart Grid Automation Device. Such agents make their local decisions regarding the current and estimated production and consumption, communicate with each other about the allocation of resources and also interact with the higher levels where the information is aggregated and processed [23]. The most notable results of the CRISP project is the PowerMatcher tool aimed at supply-demand balancing by use of agent-based electronic market [24]. The energy produced and consumed by corresponding devices is sold and bought by the device agents on an electronic market, where the equilibrium price is determine by the auctioneer agent. The PowerMatcher approach has been applied in many other follow-up projects like for instance in Integral project where residential houses are aggregated into the virtual power plant that interacts with the rest of the grid in terms of buying and selling the energy on the electronic market [25]. Another example is the SmartHouse/SmartGrid project aimed at delivering ICT-based solutions for technical and commercial aggregation of smart houses and their integration to the power grid [26]. The EU FP7 EcoGrid project also declares reuse of PowerMatcher to implement a real-time energy market [27]. The full scale demonstration will take place on the Danish Bornholm island, where majority of about 2.000 residential consumers will be equipped with intelligent controllers enabling them to be responsive to variable prices. The price signals will be updated every five minutes to reflect the needs for up- or down-regulation to mitigate the system imbalance. The goal of the More Microgrids project was to investigate the applicability of MAS technology to control local power
IV. SMART GRID AUTOMATION ARCHITECTURE BASED ON MAS-TECHNOLOGY: THE MASGRID APPROACH In order to show the potential of MAS technology for intelligent energy systems, the example of the Austrian
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MASGrid (Multi-Agent System for self-optimizing power distribution Grids) project has been selected [12], [33]. It deals with the application of MAS technology in active power distribution grids for the self-optimization to reduce line losses and to maintain the power quality (e.g., voltage, frequency) impacted by a high amount of fluctuating DG. Moreover, self-reconfiguration of the network topology in case of failures is also supported. These goals are mainly being achieved by using active grid components (e.g., DG inverter, on-load tap changer, charging station, controllable loads/storages) that are able to react autonomously based on their knowledge to optimize the power grid during operation. Another important goal of MASGrid is the integration of the MAS approach into existing utility DMS (Distribution Management System)/SCADA systems to support the human operators to manage the power distribution networks. In the following sections the MASGrid architecture as well as selected use cases are presented and discussed.
standards like OPC-UA or IEC 61850 are suggested [14] whereas the latter one can be in principle mapped to the general object model of the first one. • Agent layer: The optimization or reconfiguration of the grid topology is performed by this layer. It incorporates an ontology-based model1 for representing the physical environment and to enable advanced reasoning about its state. Decisions made by the automation agents have to be carried out by an underlying real-time control layer represented by Intelligent Electronic Devices (IED). For the IED communication the Agent Communication Language (ACL) is suggested but also domain standards like OPC-UA or IEC 61850 are proper candidates. • Real-time control layer: The execution of automation actions derived by the agent layer has to be carried out in a deterministic manner. In the MASGrid approach the IEC 61499 reference model for distributed automation is used. The reason for this choice is the provision of a standardized (re-)configuration interface for control functions and the distributed nature of this approach. Moreover, the mapping of IEC 61850 communication services and functional interfaces to IEC 61499 functions blocks provides a standardized automation architecture which is a key ICT requirement for the development and implementation of Smart Grids [34].
A. Multi-Agent Automation Architecture Due to the distributed nature of Smart Grids and its corresponding components, a MAS-based architecture is a quite natural approach to support advanced automation functions, especially self-optimization/reconfiguration. Fig. 2 provides an overview of using agents in MASGrid.
Fig. 3. MAS-based architecture with agent and real-time control layer.
In the MASGrid project the following automation functions are supported by using the above introduced automation architecture [33], [34]: (i) self-healing, (ii) self-optimization, (iii) self-monitoring and -diagnostics, and (iv) automatic grid (topology) reconfiguration.
Fig. 2. Applying MAS technology in Smart Grid systems.
B. Selected MASGrid Use Cases
Each component in the power distribution grid (e.g., onload tap changer, DG incl. inverter system, switch, breaker, bus/line and load) is represented as an autonomous agent. They are called automation agents as they are responsible for monitoring, control and optimization of the distribution network. In order to interact with the DMS/SCADA system and the corresponding grid components/devices, the software architecture represented in Fig. 3 has been adopted. It is divided into the following three layers: • DMS/SCADA layer: It carries out global tasks and provides high-level strategies for optimization and reconfiguration. Moreover, planning tasks (e.g., topology adaption due to planned maintenance) are carried on this layer. The visualization and interaction with the grid operator is also part of it. For the communication with the underlying MAS domain
The power of using MAS technology for Smart Grids is illustrated with the description of three MASGrid use cases. 1. Power Distribution Grid Topology Reconfiguration This example motivates the usage of the MAS approach for the automatic topology reconfiguration in power distribution grids. Fig. 4 shows the topology of a meshed network which is common for urban areas. It is characterized by generators that can be connected to the distribution grid and loads that can be supplied with electricity using different feeders by switching on/off different breakers and switches. This means that a kind of redundancy exists in the grid that can be used to optimize it or to maintain the power quality in case of failures. 1 The IEC CIM provided by the IEC 61970/61968 standards families can be used as basis to develop a domain ontology.
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a photovoltaic system as well as a parking lot with charging stations for electric vehicles. Larger commercial customers are also expected to include more controllable loads, which make the local energy management more challenging than for a private home. On the right hand feeder a commercial building representing a critical infrastructure (e.g., hospital) with a battery back-up system is shown. Using agents for the operation of the grid components as shown above undesired grid states can be observed and detected. In order to keep the distribution system or parts of it operating in case of general distribution grid failures, each customer (e.g., smart building with local generation and/or storage capability) can be disconnected from the grid forming a so-called island (i.e., a kind of a micro grid). The agents representing the local micro grid are then responsible for the local energy management and optimization. After the failure in the distribution grid has been solved, the local agent system will reconnect the micro grid to the distribution system and recover automatically to the normal state. 3. Logical Reconfiguration of Automation Functions
Fig. 4. Automatic Power Distribution Grid Topology Reconfiguration [12].
The management and optimization becomes even more complicated for utility and distribution system operators for such grids since the components will change over their life time. Due to the large scale integration of DGs, electric vehicles and controllable loads makes it hard to manage and optimize the corresponding grids. This use case therefore focuses on the topology reconfiguration/adaptation of meshed distribution grids in urban areas in order to automate the grid management (which is currently often manually performed) and to optimize the grid operation. An agent-based system as represented in the previous section can help to describe the necessary automation functions and to optimize/reconfigure such a distribution grid. 2. Micro Grid: Controlled Islanding
Besides the grid topology reconfiguration and the islanding case also the logical reconfiguration of automation functions will be a future necessary requirement. As already stated in the previous sections, the integration of distributed generators using renewable and therefore fluctuating energy resource can result in undesired grid states. Maintaining the power quality is one of the most important requirements. The usage of inverter-based systems in DGs is quite common. The adjustment/update of DG inverter controller functions during operation can be used to solve voltage or frequency problems in Smart Grids. Such a use case is depicted in Fig. 6 whereas a droop control function in a PV inverter is adjusted during operation in order to solve a local voltage or frequency problem [34]. The trigger for this onlinereconfiguration as well as the parameters for the droop control can be derived from the corresponding generator agent.
Another use case of the MASGrid project is depicted in Fig. 5, which shows a small low-voltage distribution network with several customers (i.e., smart loads) connected. It is assumed that each customer can operate in a controlled islanding mode. Thus, each customer can be connected or disconnected from the grid. Each customer has usually a different number of distributed generators, storages and loads.
Fig. 5. Controlled Islanding [33].
Fig. 6. Local Reconfiguration of Automation Functions.
On the left hand side in this figure a typical future customer from a residential area (i.e., a private home consuming and producing energy) is depicted, including an electric vehicle, a photovoltaic system and the building. In the middle of the figure a commercial customer is depicted, also equipped with
Using such an automation and control architecture supporting the online-reconfiguration due to agent interaction is derived from the holonic control concept [21]. It provides a flexible and scalable architecture and supports modularity and reconfigurability as introduced in Section II. 70
V. CONCLUSIONS
[13] “Smart grid: Trainer Guide for ICTTEN4229A Design, Install and Configure a Customer Smart Grid Network”, NSW Department of Education and Communities (DEC), 2011. [14] SMB Smart Grid Strategic Group (SG3), “IEC Smart Grid Standardization Roadmap,” International Electrotechnical Commission (IEC), Geneva, Switzerland, Tech. Rep. Ed. 1.0, June 2010. [15] V. Gungor, D. Sahin, T. Kocak, S. Ergüt, C. Buccella, C. Cecati, G. Hancke, “Smart Grid and Smart Homes: Key Players and Pilot Projects”, IEEE Industrial Electronics Magazine, December, pp.18-34, 2012. [16] A. Zoitl, T. Strasser, C. Sünder, T. Baier, “Is IEC 61499 in Harmony with IEC 61131-3?”, IEEE Industrial Electronics Magazine, 3(4), pp. 49-55, 2009. [17] V. Vyatkin, “IEC 61499 Function Blocks for Embedded and Distributed Control Systems Design”, 2nd Edition, ISA, 2011. [18] A. Koestler, “The Ghost in the Machine”, Arkana Books, London, 1969. [19] F. Jammes, H. Smit, “Service-oriented Paradigms in Industrial Automation”, IEEE Transactions on Industrial Informatics, 1(1), pp. 6270, 2005. [20] M. Pěchouček, V. Mařík, “Industrial Deployment of Multi-Agent Technologies: Review and Selected Case Studies”, Autonomous Agents and Multi-Agent Systems, 17(3), pp. 397-431, 2008. [21] P. Vrba, P. Tichý, V. Mařík, K. H. Hall, R. J. Staron, F. P. Maturana, P. Kadera “Rockwell Automation’s Holonic and Multi-agent Control Systems Compendium”, IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews, 41(1), pp. 14-30, 2011. [22] J.M. Mendes, P. Leitão, F. Restivo, A.W. Colombo, “Service-Oriented Agents for Collaborative Industrial Automation and Production Systems”, V. Mařík, V. Strasser, T., and Zoitl, A. (eds.), HoloMAS 2009, LNAI 5696, Springer-Verlag, Berlin Heidelberg, pp. 13-24, 2009. [23] G. J. Schaeffer, H. Akkermand, (Eds.), CRISP Final Summary Report, 2006. [24] J.K. Kok, C.J. Warmer, I.G. Kamphuis, "PowerMatcher: Multiagent Control in the Electricity Infrastructure", 4th International Joint Conf. on Autonomous Agents and Multiagent Systems, pp. 75-82, 2005. [25] G. Peppink, R. Kamphuis, K. Kok, A. L. Dimeas, E. Karfopoulos, N. D. Hatziargyriou, N. Hadjsaid, R. Caire, R. Gustavsson, J. M. Salas, H. Niesing, J. van der Velde, L. Tena, F. Bliek, M. Eijgelaar, L. Hamilton, H. Akkermans, “INTEGRAL: ICT-Platform Based Distributed Control in Electricity Grids with a Large Share of Distributed Energy Resources and Renewable Energy Sources”, Energy-Efficient Computing and Networking, Springer Berlin Heidelberg, pp. 215–224, 2011. [26] K. Kok, “Multi-agent Coordination in the Electricity Grid, from Concept Towards Market Introduction”, 9th International Conference on Autonomous Agents and Multiagent Systems: Industry Track, (AAMAS’10), Richland, pp. 1681–1688, 2010. [27] J. M. Jorgensen, S. H. Sorensen, K. Behnke, P. B. Eriksen, “Ecogrid eu: A Prototype for European Smart Grids”, IEEE Power and Energy Society General Meeting, pp. 1-7, 2011. [28] S. Bou Ghosh, P. Ranganathan, S. Salem, Jingpeng Tang, D. Loegering, K.E. Nygard, "Agent-Oriented Designs for a Self Healing Smart Grid", 1st IEEE International Conference on Smart Grid Communications (SmartGridComm), pp. 461-466, 2010. [29] K. Moslehi, A.B.R. Kumar, D. Shurtleff, M. Laufenberg, A. Bose, P. Hirsch, ”Framework for a Self-healing Power Grid“, IEEE Power Engineering Society General Meeting, vol. 3, pp. 3027, 2005. [30] C. Effantin, T. Kostic, E. Lambert, "Documentation of Software Architecture and Encoding in UML Including Compiled Software with API Description - Models for Interoperable Information Exchanges between ADDRESS Actors", ADDRESS project deliverable D4.1, 2011. [31] M. Lanthaler, C. Gutl, "A Web of Things to Reduce Energy Wastage", 10th IEEE International Conference on Industrial Informatics (INDIN’12), pp. 1050-1055, 2012. [32] I. Hegny, T. Strasser, M. Melik-Merkumians, M. Wenger, A. Zoitl, “Towards an Increased Reusablity of Distributed Control Applications Modeled in IEC 61499”, 17th IEEE Int. Conf. on Emerging Technologies and Factory Automation (ETFA’12), Kraków, Poland, 2012. [33] M. Merdan, A. Prostejovsky, I. Hegny, W. Lepuschitz, F. Andrén, T. Strasser, “Recent Advances in Robotics and Automation, Studies in Computational Intelligence”, Power Distribution Control using MultiAgent Systems, vol. 480, Springer Berlin Heidelberg, pp. 323-333, 2013. [34] T. Strasser, F. Andrén, M. Merdan, A. Prostejovsky, “Review of Trends and Challenges in Smart Grids: An Automation Point of View”, Holonic and Multi-Agent Systems for Manufacturing, V. Marík, P. Skobelev and J.L. Lastra (eds.), Lecture Notes in Computer Science, Springer Berlin / Heidelberg, 2013.
Multi-agent systems offer an alternative approach to develop complex, distributed, large-scale systems, where modularity, scalability, robustness and reconfigurability are key issues. Smart Grids, as a cyber-physical system integrating a large amount of DERs, such as PV systems, controllable loads, electric vehicles and storage devices, requires an automation platform for monitoring, control, optimization and reconfiguration. Having this in mind, the paper overviews the applicability of MAS technology to address the referred challenges and surveys the existing works and initiatives in the area, namely some projects that use MAS to achieve the Smart Grids functionalities. The architecture and use cases defined in the MASGrid project were presented as an example of using MAS in Smart Grids. As conclusion, MAS technology is a suitable approach to develop such large-scale intelligent energy systems since it is aligned with the imposed requirements by providing modularity, scalability, flexibility and reconfigurability. ACKNOWLEDGMENT Partially funding of this work has been received by the Grant Agency of the Czech Technical University in Prague, grant No. SGS12/188/OHK3/3T/13 and the Austrian Federal Ministry of Economy, Family and Youth (BMWFJ) for the MASGrid project (No. 832037) under the RSA programme. REFERENCES [1] M. Wooldridge, “An Introduction to Multi-Agent Systems”, John Wiley & Sons, 2002. [2] P. Leitão, “Agent-based Distributed Manufacturing Control: A State-ofthe-art Survey”, Engineering Applications of Artificial Intelligence, 22(7), pp. 979-991, 2009. [3] H.L. Cardoso, E. Oliveira, M. Schaefer, “A Multi-Agent System for Electronic Commerce including Adaptive Strategic Behaviours", EPIA, pp. 252-266, 1999. [4] A. Glaschenko, A. Ivaschenko, G. Rzevski, P. Skobelev, “Multi-Agent Real Time Scheduling System for Taxi Companies”, 8th International Conf. on Autonomous Agents and Multiagent Systems, pp. 29-36, 2009. [5] H. Farhangi, “The Path of the Smart Grid”, IEEE Power Energy Management, 8(1), pp. 18-28, 2010. [6] S. Karnouskos, “Cyber-Physical Systems in the SmartGrid”, 9th IEEE International Conference on Industrial Informatics (INDIN’11), pp. 2023, 2011. [7] X. Yu, C. Cecati, T. Dillon, M.G. Simões, “The New Frontier of Smart Grids”, IEEE Ind. Electronics Magazine, September, pp. 49-63, 2011. [8] S. Rafiei, A. Bakhshai, “A Review on Energy Efficiency Optimization in Smart Grid," 38th Annual Conference on IEEE Industrial Electronics Society (IECON’12), pp.5916-5919, 2012. [9] P. Palensky, D. Dietrich, “Demand Side Management: Demand Response, Intelligent Energy Systems, and Smart Loads”, IEEE Transactions on Industrial Informatics, 7(3), pp.381-388, 2011. [10] D.A. Cohen, ”GridAgents™: Intelligent Agent Applications for Integration of Distributed Energy Resources within Distribution Systems,” IEEE Power and Energy Society General Meeting, pp. 1-5, 2008. [11] N. Hatziargyriou, “Smart Agent Technology to Help DER Integrate Markets: Experiments in Greece”, 3rd International Conference on Integration of Renewable and Distributed Energy Resource, Nice, 2010. [12] M. Merdan, W. Lepuschitz, T. Strasser, F. Andrén, “Multi-Agent System for Self-Optimizing Power Distribution Grids”, 5th IEEE International Conference on Automation, Robotics and Applications (ICARA’11), 68, December, Wellington, New Zealand, 2011.
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