- freely available
Energies 2019, 12(16), 3200; https://doi.org/10.3390/en12163200
3.1. Definition of Agent and MAS
3.1.1. Agent and Agent-Based Modeling
- It has the ability to communicate and interact with its environment;
- It is able to perceive the (local) environment;
- It is guided by basic objectives;
- It has feedback behavior.
3.1.2. Multi-Agent Systems
- Large numbers of actors are able to interact, in competition or in cooperation;
- Local agents focusing on local interests and negotiating with more global agents;
- Implementation of distributed decision making, through negotiation processes between different local or global agents;
- Communication between actors is minimized to generic information exchange between agents: only the information necessary for their functioning is sent between agents.
3.2. MAS Applied in Energy Domains
3.2.1. MAS for Grid Control
3.2.2. MAS for Electricity Markets
3.2.3. MAS for Demand-Side and Building Systems
3.2.4. MAS Tools for the Energy Domain
- Multi-agent simulators for smart grid:
- MASGriP (Multi-Agents Smart Grid Simulation Platform): models the internal operation of a smart grid with the consideration of all involved players .
- Multi-agent simulators for the grid communication, monitoring, and control:
- Global Event-Driven Co-Simulation framework (GECO): models and simulates the control, monitoring, and protection of the power systems and communication network .
- Multi-agent simulators for electricity markets:
- Agent-based Modeling of Electricity Systems (AMES) (http://www2.econ.iastate.edu/tesfatsi/AMESMarketHome.htm): simulates wholesale power market operation including load, market participants, grid .
- Multi-Agent Negotiation and Risk Management in Electricity Markets (MAN-REM): simulates electricity markets, and emphases the bilateral contracting and risk management .
- Adaptive Learning strategic Bidding System (ALBidS): aims to integrate market strategies, evaluate performances under different contexts of negotiation, and provides decision support to electricity markets negotiating players .
3.3. Ontology and Defined Ontologies in the Energy Domain
3.3.1. Definition of Ontology
- Communication between people. Here, an unambiguous but informal ontology may be sufficient.
- Inter-operability among systems achieved by translating between different modeling methods, paradigms, languages and software tools;
- Communication: ontology can provide common glossaries to communication among different individuals.
- Interoperation: ontology can freely interpret and map among various modeling methods, languages and software tools.
- Reuse: the ontology’s analyses clarify the structure of the field’s knowledge in order to lay a good foundation for knowledge representation. Ontology can be reused, so the repetitious knowledge analyses can be avoided.
- Knowledge acquisition and sharing: to construct the system based on knowledge, the available ontology can be used as origination and foundation to supervise the acquisition of knowledge, which can improve its velocity and reliability.
3.3.2. Defined Ontologies in the Energy Domain
- Cognitive ontology: the activity that agents analyze power systems.
- Physical entity ontology: the equipment that is used for transmitting electric energy and its connecting topology.
- Data ontology: the magnitude that cognitive agent has apperceived to respond to physical entities.
- State ontology: the generalization of the current operation mode in an electric power grid.
- Event ontology: all aspects that create changes of state.
- Operation Ontology: the combination of all actual actions that a cognitive agent does on physical entities.
3.3.3. Ontology Design
- Categories of ontologies
- Ontology mapping
- Ontology development tools
- Ontology development process
- Step 1.
- Determine the domain and scope of the ontology
- Step 2.
- Consider reusing existing ontologies
- Step 3.
- Enumerate important terms in the ontology
- Step 4.
- Define the classes and the class hierarchy
- Step 5.
- Define the properties of classes—slots
- Step 6.
- Define the facets of the slots
- Step 7.
- Create instances
3.4. MAS Design and Architectures
3.4.1. MAS Design Methodologies
- A conceptualization phase where the problem to be solved is specified;
- An analysis phase;
- A design phase that uses the results of the analysis phase to produce agent designs of varying detail
- Requirements and knowledge capture stage: the MAS design usually begins with a particular problem. To solve this problem, this stage specifies the system requirements and capture the knowledge needed to fulfill those requirements. The system requirements and captured knowledge is the input to the next stage.
- Task decomposition stage: it transforms the requirements specification and captured knowledge from the previous stage into a hierarchy of tasks and subtasks. These tasks may include the functions performed by legacy systems.
- Ontology design
- Agent modeling stage: based on the task hierarchy and ontology design, it identifies a group of autonomous agents performing the tasks in the task hierarchy. Each task in the hierarchy must be attributed to at least one agent and one agent can encapsulate one or more tasks. The outcome is a set of agent models that specify the tasks the agents perform. The tasks attributed to legacy systems and generated new codes are also identified at this stage.
- Agent interaction modeling stage: it defines the interactions the identified agents support. The output usually is the interaction diagrams.
- Specification of agent behaviors stage: it specifies the interaction functionality of the agent and the control functionality of the agent.
- MAS Development environment
3.4.2. MAS Architectures
- Agent types
- Agent management framework/ system architecture
3.5. The Application of Ontology in MAS Development
3.5.1. MAS Interoperability and Ontology
3.5.2. Agent Communication and Ontology
- Standards for agent communication and interoperability
- Ontology-based agent communication design
- Definition of agent and MAS. The definitions of agent, intelligent agent and MAS and the introduction of an agent structure are given in some selected publication. However, some publications do not differentiate the agent-based system and multi-agent-based systems.
- MAS applied energy domains. The applied energy domains include grid control (also, microgrids), electricity markets, demand-side and building systems. The applied MAS tools are also introduced in some selected publication.
- Defined ontologies in the energy domain. Definition of ontology, functions of ontology and the defined ontologies in the energy domain are introduced. The ontology design is introduced usually together with the agent communication model. Although generic ontology and the case-specific ontology, upper-level, and lower-level ontology, and ontology hierarchical are introduced, a systematic discussion on the categories of ontologies (upper ontologies, domain ontologies, and application ontology) is missing. Meanwhile, although ontology mapping for inter-MAS communication and ontology development tools are introduced, the ontology development process is not yet discussed in the selected literature.
- MAS Design and architectures. The MAS design methodology-Gaia methodology is introduced and applied in some selected publication, and MAS design methodology proposed by the IEEE PES MAS working group is introduced but not well discussed or applied in the selected publication. The MAS Development environment, JADE, and its extensions are introduced but the design detail with JADE is missing. The MAS architecture is commonly introduced with the description of agent types and agent management framework/ system architecture.
- Ontology in the MAS development. The importance of ontology for the MAS interoperability is emphasized and the application of ontology in the agent communication design is well discussed in the majority of the selected publication. The standards for agent communication and interoperability are discussed with two dimensions: standards for domain-specific, e.g., the SGAM reference model, the power systems CIM and SEAS knowledge model in the energy domain are discussed; The FIPA-ACL is applied for almost all MAS design in the selected publication.
5.1. Recommendation of the Ontology-Driven MAS Development for the Energy Domain
- The ontology development process in MAS design
- The detail design process and realization of the ontology-driven MAS development
- Open standard implementation and adoption
- Higher intelligent MAS development
- Inter-domain MAS development
- Agent listing
5.2. Limitations and Future Work
Conflicts of Interest
|Energy Domain||Ontology||MAS Design|
|2004||A policy-driven multi-agent system for OGSA-compliant grid control||||Grid control||Policy ontology|
|2005||Issues in integrating existing multi-agent systems for power engineering applications||||Grid control||Upper ontology|
|2006||Modeling energy and transport infrastructures as a multi-agent system using a generic ontology||||Grid control||Generic and case-specific ontologies||ABM|
|2007||Multi-agent architecture of energy management system based on IEC 61970 CIM||||Management system||IEC 61970 Standard||Agent structure|
|2009||Multi-agents for energy efficient comfort agents for the energy infrastructure of the built environment: Flexergy||||Buildings/demand side||Ontology for the design process||Agent type|
|2011||Intelligent multi-agent framework for power system control and protection||||Grid control||Ontology structure||Agent type|
|2011||Multi-agent system for self-optimizing power distribution grids||||Grid control||Domain ontology in the world model||Agent type|
|2013||An architecture for a microgrid-based eco industrial park using a Multi-Agent System||||Microgrid||Ontology in the agent design process||Agent types|
MAS architecture, Negotiation methodology
|2013||Demonstration of a multi-agent-based control system for active electric power distribution grids||||Grid control||An ontology with four levels||Agent type|
|2013||Power transformer condition monitoring and fault diagnosis with multi-agent system based on ontology reasoning||||Grid control||Ontology reasoning||MAS architecture|
|2013||Upper ontology for multi-agent energy systems’ applications||||Power system||Upper ontology and standards||Agent types|
|2013||Smart grid - building energy management system: an ontology multi-agent approach to optimize comfort demand and energy supply||||Buildings/demand side||Ontology hierarchical||Agent UML diagrams|
|2014||Energy efficient automation model for office buildings based on ontology, agents and IEC 61499 function blocks||||Buildings/demand side||Translator agent and ontology agent||Agent type|
|2014||Realistic multi-agent simulation of competitive electricity markets||||Electricity market||Upper ontology||Agent types|
|2015||Multi-agent simulation of competitive electricity markets: Autonomous systems cooperation for European market modeling||||Electricity market||Upper ontology||MAS interoperability and UML diagrams|
|2016||Optimal real-time dispatch for integrated energy systems: an ontology-based multi-agent approach||||Grid control||Ontology-based FIPA-ACL||Agent type|
|2016||Ontology-based demand-side flexibility management in smart grids using a multi-agent system||||Buildings/demand side||Standard of data models in the power system||Gaia methodology|
|2016||An ontology-driven approach for modeling a multi-agent-based electricity market||||Electricity market||Ontology-Driven Conceptual Modelling||Model-driven development|
MAS organizational structure
|2016||Enabling communications in heterogeneous multi-agent systems: electricity markets ontology||||Electricity market||Electricity Markets OntologyDescription logic||MAS interoperability|
|2017||A multi-agent-based energy management solution for integrated buildings and microgrid system||||Management systemMicrogrid Buildings/demand side||Ontology for message content||Agent types|
Agent goals, MAS architecture
|2017||EPEX ontology: enhancing agent-based electricity market simulation||||Electricity market||Lower ontology||MAS interoperability|
|2017||Nord Pool ontology to enhance electricity markets simulation in MASCEM||||Electricity market||Lower ontology||MAS interoperability|
|2018||Power systems simulation using ontologies to enable the interoperability of multi-agent systems||||Power system||SEAS knowledge model||MAS interoperability|
|2018||Multi-agent decision support tool to enable interoperability among heterogeneous energy systems||||Power systemMicrogrid||Ontology in Tools Control Center (TOOCC) framework||MAS interoperability|
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|Web of Science||355|
|Grid Control||Power System||Energy Management System||Microgrid||Buildings/Demand Side||Electricity Market|
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