19.1 a framework for a multiagent-based virtual enterprise ... · pdf file19.1 a framework for...

19.1 A framework for a multiagent-based virtual enterprise with a microgrid energy market model

U. Aradag 1, B. Mert 1, G. Demirel 1, S. Uludag 2, H. O. Unver 1, S.Aradag 1 1 Department of Mechanical Engineering, TOBB University of Economics and Technology, Ankara, Turkey

2 Department of Computer Engineering, TOBB University of Economics and Technology, Department of Computer Science, Engr and Phy., University of Michigan-Flint, Michigan, U.S.A

Abstract

Within the scope of the promising Smart Grid (SG) vision, the concept of microgrids is a key facilitator, especially in order to incorporate the distributed and renewable energy resources. In manufacturing, a Virtual Enterprise (VE) is a Web-based, virtual, and temporary consortium of companies with different core competencies to fulfill product orders during a specified period of time. In this paper, we are proposing a framework and an architecture of a Microgrid-based VE to diligently and intelligently manage energy consumption in the manufacturing process and to incorporate Distributed Energy Resources. We develop a conceptual framework for computing the energy requirement of a VE and come up with an energy pricing formulation in conjunction with a process and energy scheduling methodologies to reduce the Peak-to-Average ratio of energy usage. We utilize a Multi Agent System (MAS) in VE’s clusters that is in full

compliance with FIPA specifications. Keywords: Smart grid, virtual enterprise, renewable energy, Multiagent system

1 INTRODUCTION

Under the pressure of aging and increasingly ineffectual power grid, the notion of an enhanced and improved grid, under the generally used umbrella term of Smart Grid, is gaining growing interest and attention. The Smart Grid with the bidirectional flow of energy and information is an enabling and facilitating technology. SG is a grand vision for the new generation electric power system that benefits from increased use of Information and Communication Technology (ICT) [1], [2], [3]. SG proposes to adopt and implement the recent advances and improvements of ICT onto the grid. SG is poised to upgrade the largest man-made machine in the world for the future generations considering sustainability, efficiency, reliability, security, and power quality. One of the crucial building blocks of the SG vision is microgrids [4], [5], [6], [7]. A microgrid brings about capabilities to incorporate control and monitoring techniques to the low voltage distribution network so as to facilitate conventional and renewable energy resources, energy storage and intentional islanding from the main grid and contingency-based isolation. Microgrid has mainly been studied for residential areas within the past few years. The concept of microgrids in a manufacturing domain has received little attention, such as in [8]. The potential benefits of the concept of SG, especially by means of microgrid, appear to be profound. Yet, systematic integration of microgrids has not been tapped into to analyze and evaluate its full scale of merit in the manufacturing. A collaborative approach is the best way for Small and Medium-sized Enterprises (SMEs) to exploit business opportunities more effectively. In this regard, a Virtual Enterprise (VE) is a collaboration network among multiple

SMEs to reach business goals by sharing their capabilities using information, communications, collaboration and management technologies [9]. A VE is generally based around the collaboration among the SMEs before and through the job that is going to be realized. VE may be viewed as a key catalyzer for collaborative and flexible manufacturing so that SMEs may compete with larger enterprises [10]. The Multi Agent System (MAS) structure of the VE allows multiple functionalities and makes the addition of new SMEs as part of the VE system easier. Thus, VE increases the overall system versatility and reconfigurability. In this paper, we provide a framework and an architecture for embedding energy requirements, as determined by the underlying Virtual Enterprise with Distributed Energy Resources, into the manufacturing process planning and scheduling. The energy infrastructure is modeled on top of the microgrid concept of the Smart Grid. A conceptual framework for computing the VE’s energy requirement, an

energy pricing formulation, and a process and energy scheduling methodologies to reduce the Peak-to-Average ratio of energy usage compose major parts of the overall system of our approach. We utilize a Multi Agent System (MAS) Energy Market in VE’s clusters that is in full

compliance with the Foundation for Intelligent Physical Agents (FIPA) specifications. To the best of our knowledge, ours is the first framework and architecture proposed in the literature that combines a microgrid energy market model on top of the recently developed VE-based manufacturing paradigm using an MAS infrastructure [9]. The Model consists of different agents that are responsible to fulfill the business functions that are needed by a VE to operate

G. Seliger (Ed.), Proceedings of the 11th Global Conference on Sustainable Manufacturing - Innovative Solutions ISBN 978-3-7983-2609-5 © Universitätsverlag der TU Berlin 2013

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U. Aradag , B. Mert , G. Demirel , S. Uludag , H. O. Unver , S.Aradag

efficiently. The VE structure is an effective way of collaboration for SMEs to produce more complex products that they cannot produce by themselves otherwise. SMEs benefit more from the VE by getting hooked up with an Energy Market that will provide additional advantages as detailed in Section IV. The market is constructed upon auctions. MAS is used to cope with the distributed nature of the process and the microgrid system is used to integrate DERs easily into the system at the distribution level. The rest of the paper is organized as follows: Section II provides the background on microgrids, MAS, and VE together with the related work. In Section III, we elaborate on our approach in determining the energy requirement of a VE. Energy Pricing Model for renewable sources is formulated and incorporated into our holistic system as part of the discussions in Section V. The overall energy market model, based on the preceding sections, is provided in Section IV. Finally, Section VI wraps up our approach with concluding remarks. 2 BACKGROUND AND RELATED WORK

2.1 Microgrid

With the bidirectional information and power flow provided by the Smart Grid, many new ideas are being proposed to augment and improve the current grid. One such extension is the concept of microgrid [4], [5], [6], [7]. A microgrid is a low voltage distribution network that is enhanced with Distributed Generation (DG), Combined Heat and Power (CHP), an energy storage subsystem, and/or a degree of autonomy to operate in intentional or accidental islanding mode. DG component of the microgrid might contain conventional as well as renewable sources. Microturbines, fuel cells, photovoltaic panels, wind turbines are some of the examples of the DG in the microgrid. The main benefits [6], [11] are improved reliability, integration of the distributed resources, isolation of power disturbances, and improving load and supply balance.

2.2 Multi-Agent System (MAS)

Artificial Intelligence has been applied to many real life applications, such as autonomous robots, Unarmed Aerial Vehicles (UAV), computer games, agent-based software development technologies [12], etc. An entity that reacts to the changes in its environment through a reasoning process is referred to as an agent [13]. In this context, an agent can be a hardware, i.e a circuit breaker that acts upon the changes in the power grid, or a software which may be a virtual chess player, or even a human. Any hardware or software component may fall under the definition of an agent based on their goals and autonomy [14]. Multi-Agent System (MAS) is based on computational agents interacting and communicating with each other through a network [15]. Similar to other software development methodologies, MAS makes use of the divide-and-conquer mechanism. MAS is used to tackle complex problems. Each agent evaluates data gathered from the surrounding environment into its body and responds appropriately to push the whole system towards its goals [16]. MAS in manufacturing offers a dynamic, reliable and agile mechanism to enable adaptation to changes in the system

with a faster response time in order to reduce cost and increase productivity [15]. The span of MAS application areas in power engineering includes diagnostics, condition monitoring, power system restoration, market simulation, network control and automation [17]. Along the same lines, MAS fits nicely to microgrids due to its flexibility and autonomy. Expected and incipient proliferation of microgrid systems increases the need for an interoperable platform to facilitate as seamless integration as possible among different architectures and implementations [18]. Foundation for Intelligent Physical Agents (FIPA) standards and protocols have become important players to enable interoperability in this respect. The goal of FIPA is to promote agent-based technology and interoperability of its standards with other technologies [19]. Figure 1 shows the MAS development architecture of this paper which is based on the proposal in [18].

Figure 1: Agent Design Methodology proposed in [18].

Another work, built on top of the aforementioned architecture, is reported in [20] that proposes an Energy Management System for a microgrid-based Eco-Industrial Park. However, the proposal in [20] does not consider a VE infrastructure, unlike what we propose in this paper. 2.3 Virtual Enterprise

Virtual Enterprise (VE) consists of various SMEs with different characteristics that have to be combined in order to work together in a collaborative environment, thereby leading to a synergistic relationship of overlapping characteristics with MASs [21]. Agile manufacturing targets reconfigurable structures in order to respond to the demands of a dynamic and unpredictable market for a productive cooperation. VE embeds distributed independent enterprises with various core competencies [10]. An MAS-based VE can be used to develop different aspects of manufacturing systems, such as process planning and scheduling, supply chain management, etc. An example MAS-based VE system as proposed in [9] includes the following agents: Customer agent, Task decomposer agent, Collaborative design agent, SME pool

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A framework for a multiagent-based virtual enterprise with a microgrid energy market model

agent, Environmental performance management agent, Enterprise agent, Process planning and scheduling agent, Logistics management agent, Quality management agent: Customer Agent (CA) is the agent facing the customer and collecting information regarding customer requirements. Task Decomposer Agent (TDA) decomposes downstream production process into tasks. Collaborative Design Agent (CDA) takes customer requirements and turns them into engineering specifications and enables collaboration between SMEs in the design processes: SME Pool Agent (SPA) is the responsible agent from the SME registration and elimination to the VE Environmental Performance Management Agent acts as an intermediary to reduce the ecological footprint. The responsibility of this agent is to benchmark and rank SMEs in the pool regarding their sustainability measures Enterprise Agent is responsible for the VE itself and operate in connection with the VE management subsystem. Process Planning and Scheduling Agent (PPSA) is responsible for generating detailed process plans and routing data for the components that will be manufactured. Logistic Management Agent manages the logistics be-tween SMEs located in the VE and responsible from the final assembly. Quality Management Agent is responsible for the quality control and inspection of the parts manufactured in SMEs. Four major stages of a VE are depicted in Figure 2 as opportunity capture, VE creation, VE operation and VE dissolution.

Figure 2: Virtual Enterprise Stages [9]. 3 DETERMINING VE ENERGY REQUIREMENT

As indicated earlier, our MAS-based VE framework is based on [9]. The most relevant agent for the energy requirement determination of a VE is the Process Planning and Scheduling Agent (PPSA), which is responsible for generating and scheduling detailed process plans of the VE. PPSA gathers the process plans of individual SMEs and combines them into a single large scale job plan that is represented by an hourly Gannt chart. In order to illustrate the process, below, we are using a real-life example to produce a

Francis Turbine blade design and production process which can be accomplished on a VE with different SME capabilities. The process starts with the receipt of the head and discharge values from the customer, and they will be used in the pre-design of the runner blades. The customer supplies the existing head and discharge values for a river that the hydro turbine will be used for. These pre-designs will be used for generating the solid model of the blades in a CAD program with various detailed processes. The first process will be accomplished in SME1, as shown in Figure 3. The solid model of the runner blades will be sent to SME2, as shown in Figure 4, for CAM process design and production. The output of SME2 will be the manufactured blades themselves, separately. The quality inspections will also be made in SME2 and the blades satisfying the quality specifications will be sent to SME3, see Figure 5. SME3 will assemble these blades into a turbine runner by connecting them with the auxiliary parts, two metal circular plates named hub and shroud. Therefore, the assembly phase of the VE will also be completed in SME3 and the VE will dissolve.

Figure 3: The process plan of SME 1.



The process plans of the three SMEs are different from each other and concurrent. The combined schedule of SMEs will be formed as the scheduled plan of the VE itself, as given Figure 6. Note that the figures just show the process plan in the resolution of days. The actual VE process plan will have a granularity of hours.

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Figure 6: The final process plan of VE.

Once the process plan of the VE is generated, the next step is to determine the energy needed to carry it out. Computing the energy requirement of every single SME in the VE and then combining them would give us the total needed energy. The theoretical energy that will be used for one SME is the Product Embodied Energy (PEE) which is the result of the production activities and can be calculated by relating the energy consumed by the manufacturing processes, handling and environment maintenance [22]. In the early development phases such as the Solid Modeling and Computational Fluid Dynamics phases, energy consumption is based on basically the computer power consumed. Equation 1 represents this computation:

(1) where Eprocess is the energy consumed in the manufacturing processes, i is the part feature number that is being manufactured, Ehandling is the energy consumed by the automated material handling equipment such as robots and conveyors, j is the material handling activity number, Eindirect is the allocated indirect energy consumed by the services to maintain the environment for production activities of the part and k is the source for the indirect energy. The energy requirement of every SME can be theoretically determined by the given formula above and the charts, such as Figures 3, 4 and 5 will be formed in an hourly manner for every single SME and the formula will be modified to fit the theoretical energy requirement calculation of a VE using the energy for each SME, ESME and the transportation energy, ETransport. as;

(2)

The total energy required for the job that is going to be made on a VE will be formed by the PPSA agent and will be ready to be scheduled by the scheduling agent as part of the whole VE process by means of the underlying Energy Market (EM) model to be discussed in Section IV. The energy requirement chart of the VE is then sent to the Energy Scheduling Agent (ESA). ESA will be responsible for scheduling the energy required by the VE, by considering the properties of the VE. Scheduling will be realized with the objective function of minimizing, or at least reducing, the Peak-to-Average Ratio (PAR) so that a smoother or flattened energy consumption is achieved. One significant goal of Smart Grid microgrids is the minimization of the PAR [23]. As the PAR goes down, the seller agents located in the Energy Market will supply energy easier and with smaller peak energy supply losses. That means smaller losses. So, as the seller losses decrease, it

will provide cheaper energy for the VE and VE will purchase energy for less. In addition, smaller energy losses mean a smaller impact on the environment, which is a basic goal of a VE [9]. Thus, both the VE itself and the sellers in the market will benefit from the given situation. The scheduled energy requirement of the VE will then be sent to the buyer agent of the VE that will be created on the energy market. This agent is explained in details in the next section. 4 ENERGY MARKET MODEL

In this section, we provide a synopsis of the proposed energy market model and framework.

4.1 MAS Methodology

The energy market is based on a Multi Agent Systems (MAS). MAS is composed of agents as intelligent autonomous entities which should exhibit the following characteristics [14]: Pro-Activeness: Intelligent agents are goal-directed entities. They should be able to change their behavior dynamically based on their goals. This means that an agent should look for multiple solutions and adapt to the changes in its environment to fulfill its goals. Reactivity: An intelligent agent should also react to the changes in its environment. Social Ability: Intelligent agents could communicate with each other with respect to an ontology. This communication is more complex than a simple data transfer as it involves negotiation and taking initiative. With the collaborative work and communications among intelligent agents, energy market will be formed to serve as a clearing house for the VE. In this clearing house, similar to the grid’s bid-based, security-constrained economic dispatch with locational pricing model with day-ahead and hour-ahead pricing [24], energy market contracts will be sealed between participants using similar mechanisms. Participants will be buyer agents and seller agents. Buyer agents are classified as follows:

1) Large Enterprise: The word ”Large” describes the energy need of the enterprise. Hence any agent that belongs to enterprises which consumes electrical energy larger than some predefined threshold is considered as Large Enterprise Buyer Agent. Special terms and deals may be provided to them during the negotiation phase.

2) VE Buyer : VE Agent will aggregate the total

electrical energy needs of its participants and represent them in the energy market. After purchasing the energy, it will be assigned to each participant with respect to their demands.

3) SME Buyer : The remaining buyers that do not

belong to any VE are categorized under this notation.

4.2 FIPA

The Foundation for Intelligent Physical Agents (FIPA) is an organization which defines standards for MAS and agent communications to achieve interoperability. FIPA defines Agent Communication Language (ACL) as the standard language for the agent communications [19]. Since agent

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communications is based on message transfer between agents, FIPA ACL standard predefines the structure of passing message between agents. Every FIPA ACL message should contain performative parameter which defines the communicative act.

4.3 JADE Platform

For development environment, Java Agent Development Environment (JADE) [25] will be used. JADE can also be con-sidered as a runtime environment for FIPA-compliant agents. JADE platform is a distributed platform which is composed of agent containers that can be distributed over the network. The platform that is hosting agents can be split into multiple hosts and can act as a single platform. One of these containers, the main container, is the first container that is launched. It hosts two special agents to provide management services for agents and yellow pages services: Agent Management System (AMS) and Directory Facilitator (DF) respectively. In accordance with the FIPA specifications, JADE platform provides a Message Transport Service (MTS) to exchange messages between and within the platforms. JADE also implements the standard Message Transport Protocols (MTPs) for providing interoperability between different platforms as defined by FIPA. Messaging between the agents within the same (local) platform is provided by Internal Message Trans-port Protocol (IMTP). One of the most important reasons for choosing JADE as the development framework is its consistency with FIPA standards with its built-in features, such as AMS, DF and MTS. Agent development and testing can be realized using JADE based on FIPA standards.

4.4 Market Methodology

In this study, we mainly focus on auction methodologies which are defined by FIPA standards. These are FIPA English Auction Interaction Protocol and FIPA Dutch Auction Interaction Protocol. With these interaction protocols, agents negotiate with each other for accomplishing their goals. In the energy market, negotiation cycle goes between buyer agents and seller agents. FIPA English Auction Interaction Protocol is presented in [26] to explain the negotiation in the market methodology. In the English auction interaction protocol, initiator, seller agents in this case, starts the auction and informs the participants, buyer agents. Initiator sends Call for Proposal (CFP) performative for proposing the price of the good and waits for the accept proposal performative. Each time, at least one participant, accepts the price, initiator gradually increases the price. With that negotiation method, participants try to find the market price of the good that they are selling. Auction continues until no participants accept the price. In that case, initiator sells the good with the last price that is accepted. Another interaction protocol that is provided by FIPA is Dutch Auction Interaction Protocol [27]. In that case initiator, buyer agents, starts the auction by sending CFP to the participants, seller agents. Participants send their prices as their proposals. In this interaction protocol, sellers start from a price which is higher than the market price and the price is gradually decreased until one of the buyers accepts it. First-come first-served principle is employed in this protocol, i.e. the first buyer who accepts the current price on negotiation will buy the good. Usually there is a reserve price for the good

which is the lower limit for the good. Auction terminates if no buyers accept the reserve price. Negotiation frequency will be one hour to implement hour-ahead energy market. Grid prices will be announced to the energy market before the negotiation cycle starts. Market Controller Agent (MCA) will aggregate the total demand and total available power before each cycle and decide the interaction protocol according to these aggregated demand and supply. The interaction decision is based on the following methodology:

where D is the demand and S is the supply for energy. In this methodology, the intention is to favor the lesser side. If the supply side is greater than the hourly demand side, then the sellers will start the auction with English Auction Protocol in which the energy price starts from the minimum reserve price and increases the price. In this case since the demand side is less than the supply side, energy contracts are expected to be settled at lower prices with respect to the other case. When the demand side is greater than the supply side, Dutch Auction Protocol will be applied with the intention that seller may sell their energy for higher prices. Energy market model including the SMES to show the overall work presented in this paper is shown in Fig. 7. On the user side, SMEs are located where triangles represent SMEs whose agents are SME Agents, larger triangles represent Large Enterprise Buyer and circle represents VE Agent with its SME Agents inside.

Figure 7: Energy Market Model.

After the negotiation period is ended, each agreed contract between buyer and seller is stored in the Market’s database

for future references. After all the processes ended for the next-hour energy market, all the buyer and seller agents are terminated and they are recreated before each negotiation cycle with updated information from their users. 5 ENERGY PRICING MODEL

In the literature, the cost of renewable energy usually consists of four components, as shown in Figure 8: capital costs, fuel costs, cost of decommissioning, operation and maintenance (O&M) costs [28].

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Figure 8: Cost components of renewable energy cost model.

For most renewable energy systems, the fuel costs are assumed to be zero, due mostly to government subsidy. According to the Dutch Auction Interaction Protocol, ESAs propose bids considering the time-dependent energy cost function which starts from a maximum price and works its way downward until it reaches minimum price with profit. A general form of energy price function is given below:

(3) where Pri,t is the energy price of ith renewable module at time t, Pgt is the price of the main grid which is obtained by the Microgrid Control Agent (MCA) hourly and is announced to the electricity market, Pmi,t is the minimum energy price of the ith solar module at time t, and finally, time dependent coefficient is λT .

(4) For estimating the bids price ESAs propose, the maximum and minimum prices should be determined primarily. The maximum price equals to the main grid price. Besides, minimum cost is combination of capital cost, decommissioning cost, O&M costs and the support of government. The capital cost of PV systems include the cost of PV modules, the connection cost of modules to form arrays, the array support structure costs, the cost of cabling, charge regulators, inverters and storage batteries costs, etc. [28]. All these variables can be assumed as payback cost and the Balance of System (BoS) costs [29]. Payback cost can be computed using:

(5) where Pi,t is initial cost of ith renewable at time t, i is the interest rate and N is the payback time period in years. The capital cost is sum of payback cost (Ai,t) and BoS costs (Bi,t):

(6) Substituting Ai,t from Equation 5 into Equation 6, we get:

(7) After finding the capital cost the minimum price can be written as:

(8)

where ki,j is the percentage profit of ith solar module for jth

buyer type (SMEs, large scale buyers and VE buyers), Di,t is the decommissioning cost of ith solar module at hour t, Ei,t is the energy (kW) that ith solar module produced at hour t, Ps is the price per kWh government subsidy. The minimum price can be rewritten as follows:

(9) or Equation 3 can be expressed more explicitly by substituting Equation 9 into Equation 3 as:

(10) 6 CONCLUSION

In this work, a framework of an architecture is proposed for a Microgrid-based Virtual Enterprise to intelligently manage energy consumption in collaborative design and manufacturing processes involving several SMEs. Distributed Energy Re-sources (DERs) are thereby incorporated into to the system by means of the microgrid concept; one of the crucial constituent parts of the Smart Grid. The framework proposed is conceptual and used for computing the energy requirement of a VE. We also come up with an energy pricing formulation in conjunction with a process and energy scheduling methodologies to reduce the Peak-to-Average ratio of energy usage. A Multi Agent System (MAS) is utilized in VE’s clusters that is in full compliance with the

Foundation for Intelligent Physical Agents (FIPA) specifications. To the best of our knowledge, ours is the first framework and architecture proposed in the literature that combines a microgrid energy market model on top of the recently developed VE-based manufacturing paradigm. The future work includes the implementation of the conceptual methodology developed in this paper for the collaborative design and manufacturing planning of hydro turbine blades assuming every single part of the turbine design and manufacturing as the work of a single SME. We are planning to study the impact of different energy package sizes, which is the smallest value of the energy that would be traded in the auctions. Its outcome should give us clues in terms of the required energy versus optimum package size correlations. Another dimension of an extension for our work is an aggregation scheme of the supply-side of the energy market by means of a virtual power plant concept [30]. We would like to evaluate pros and cons of such a supply-side consolidation in conjunction with our approach as outlined in this paper. 7 ACKNOWLEDGMENTS

This work is supported by Turkish Ministry of Development and TOBB University of Economics and Technology, financially.

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