Contribution to the Management of Energy in the Systems Multi ...

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ScienceDirect Energy Procedia 74 (2015) 616 – 623

International Conference on Technologies and Materials for Renewable Energy, Environment and Sustainability, TMREES15

Contribution to the Management of Energy in the Systems Multi Renewable Sources with Energy by the Application of the Multi Agents Systems “MAS” Djamel Sabaa, Fatima Zohra Laallamb, Abd Elkader Hadidia, Brahim Berbaouia a

Unité de Recherche en Energies Renouvelables en Milieu Saharien, URER-MS, Centre de Développement des Energies Renouvelables, CDER, 01000, Adrar, Algeria b

Université de Kasdi Merbah, Ouargla, Algeria

Abstract Given the current energy challenge, renewable energy appear as a real and strategic solution for electricity generation, but the intermittent nature this type of energy we forced to combine at least two power sources to ensure continuity in supply of electricity. Typically multi-source renewable energy systems are managed by centralized approaches, but the limit of these approaches in several aspects such as the dynamic aspect management of system, integration or cancellation of one or more elements we require seek other more reliable approaches for the management of multi-source renewable energy systems. The proposed solution is an integration of Multi Agent Systems "MAS" in energy management, this discipline is the connection of several fields such as artificial intelligence, distributed computing systems and software engineering. “MAS” it is discipline that focuses on collective behaviors produced by the interactions of several autonomous entities called agents, these interactions revolve around cooperation, competition or coexistence between these agents, introducing the issue of collective intelligence and the emergence of structures interactions. © Published by by Elsevier Ltd. Ltd. This is an open access article under the CC BY-NC-ND license © 2015 2015The TheAuthors. Authors. Published Elsevier (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer-review under responsibility of the Euro-Mediterranean Institute for Sustainable Development (EUMISD). Peer-review under responsibility of the Euro-Mediterranean Institute for Sustainable Development (EUMISD) Keywords: Renewable energy, Hybrid systems, Centralized approaches, Multi Agent Systems "MAS", Artificial intelligence.

* Corresponding author. Tel.: +213-668-030-454; fax: +213-499-604-92, E-mail address: [email protected]

1876-6102 © 2015 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4 .0/). Peer-review under responsibility of the Euro-Mediterranean Institute for Sustainable Development (EUMISD) doi:10.1016/j.egypro.2015.07.792

Djamel Saba et al. / Energy Procedia 74 (2015) 616 – 623

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1. Introduction Since the Seventies, energy production risks were demonstrated restful on the fossil resources exploitation following the successive oil crises [1, 2], whose reserves are badly distributed and exhaustible. Atmospheric pollution, climate warming, nuclear power risks and resources limits push becoming aware that an environment respectful economic development in which we live is necessary. Moreover, one great part of the world will never be connected to the electrical communications whose extension proves too expensive for the isolated territories, little populated or difficult to access and the supply fuel additional cost increases radically with insulation. To date two billion and half of inhabitants, mainly in the developing countries, rural areas in the world consume only 1% electricity produced [3]. Renewable energies constitute an alternative to fossil energies for several reasons: they disturb less generally the environment, do not emit a gas with greenhouse effect and do not produce waste, they are inexhaustible, and they authorize a decentralized production adapted at the same time to resources and the local needs [4]. The electrical production decentralized starting from renewable energies offers a provisioning greater safety to the consumers while respecting the environment.

This system type is touched by the site climatic data change which is characterized by the innovations in the system elements technical side. All these characteristics oblige us to call upon one artificial intelligence approaches, which is the Multi Agents Systems “MAS”, in order to ensure the more powerful results, thanks to the advantages which characterize this approach. Nomenclature

Vbat

Battery nominal voltage

η dis

Battery discharging efficiency

Capbat

Battery bank capacity (Ah)

E Pv

Photovoltaic generator power production

C dis

Battery discharging efficiency

E wt

Wind generator power production

ηch

Battery charging efficiency

SOCbat min Minimum limit of battery discharge

E ld t

Load demand (W)

SOCbat max

Maximum limit of battery charge

2. System description The system concerned by our study, is composed of two energy sources “Photovoltaic and Wind”, battery bank, load and controller equipment, see Fig.1

Fig. 1.

Synoptic diagram of multi-source renewable energy systems

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3. Energy management Problems The management or the energy control is a rather broad concept which indicates the whole techniques making it possible to decrease power consumption in a preoccupation of financial economies and an ecological print reduction [5, 6]. We propose three energy management application fields: Production and storage, Transport and distribution, Consumption, see Fig.2

Fig. 2.

Energy management application fields (Electrical engineering aspect)

Production and storage are energy sources of energy management, the transport and distribution part relating to the electricity routing between the energy sources and the consumers. Finally, the energy management for consumption for the main purpose of reducing different consumer consumptions. 4. Towards a decentralized approach for the energy management Considering the hybrid systems nature which is open and distributed the downward approach limited, a rising approach can be, by opposition and seems to be preferable for this type of problem. Indeed, the designer knows, in the majority of cases, how each element must react separately. With the rising approach, the management of energy emerges starting from relatively simple rules laid down according to the constraints of each element. This approach can be structured around the “MAS” paradigm. Methodologies of “MAS” design are many and varied [7, 8]. We present our "MAS" is appropriate to the systems nature and the energy management problem of a hybrid system. 5. Multi Agent Systems design “MAS” Our idea consists in releasing a whole of axioms; thereafter we conclude our “MAS” agents list. x Any system entity must be related at least to a system task:

e E e t Task t Connected t,e x

For any system task, it must exist at least an agent to carry out it:

(A1)

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t Task t a Agent a executed a, t

(A2)

x If an agent cannot carry out a task, it can delegate the task to another agent belonging to its dealing network: a, t Agent a Task t ̃Competence a, t a1Querable(a1 , t) Acquaintance a,a1

(A3)

x If agents want to reach the same resource, we need the presence of another agent to manage the resource sharing between them: n

a1 ,}, a n

n

Agent(a ) Resource(r) Uses(a , r) ag Agent(ag) ResourceSharing(ag, r, a ,}, a i

i 1

i

i

n)

(A 4)

i 1

From the entities list quoted previously and the axioms (A1), (A2), (A3) and (A4) we conclude the agents list, see Fig.3 Table1. System agents list Agent name

Shortened name

Agent type

Photovoltaic Agent Wind Agent Battery Agent Load Agent Updated Agent Management Agent

AgentPv AgentWind AgentBat AgentLoad AgentUpd AgentMan

Hybrid agent Hybrid agent Hybrid agent Reactive agent Reactive agent Cognitive agent

Fig. 3.

System structure “MAS”

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The “MAS” are based on their operation on the reasoning more precisely for the cognitive and hybrid agents. 6. Extract of interactions between agents

We distinguish two interactions categories: x

Interaction for the energy management, see fig.4:

Fig. 4.

Interactions between agents for energy management

Table2. Communications list for energy management Communication number 1

Description of communication “AgentLoad” informs “AgentMan” on charge.

2

“AgentMan” demand energy information produced by photovoltaic and wind power generators and energy stored in batteries.

4

The three agents: “AgentPv”, “AgentWind”, “AgentBat” respond to “AgentMan”. "AgentMan" selects the best choice and gives instructions executed by the agents: "AgentPv", "AgentWind", "AgentBat". Also responding to the "AgentLoad" agent.

R

Representing the agent's reasoning "AgentMan"

3

x

Interaction for updated data system, see fig.5:

Fig. 5.

Interactions between agents for the update

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Table3. Communications list for system update Communication number

Description of communication

1

"AgentUpd" informs agents: "AgentPv", "AgentWind", "AgentBat" on the new data: meteorological, technical, ...

R

Represents the reasoning for agents: “AgentPv”, “AgentWind” and “AgentBat”

7. The reasoning in Multi Agents Systems “MAS”

"MAS" based in its operation on the reasoning engines and inference rules [9]. An inference rule takes the following form: If the (C1), (C2) and (C3) conditions are true Then (A1) action is realizable. If (C1 C2 C3 ) Then (A1 )

(R1)

We quote some rules of inferences associated with our system: If (Socbat-min d Capbat t Socbat max ) ( Eld ( t) "0") EPv t ! 0 Ewt t ! 0 Then " battery loading", t If (Epv t

Ewt t "0") (Capbat t ! Socbat min ) ( Eld (t) ! "0")

Then"battery unloading", t If Eld t

0 CapBat t

Socbat-max Then"two generators in stopped state ", t

(R2)

(R3)

(R4)

Where Capbat(t) is the battery bank capacity at t moment, Capbat-min is the minimum limit of battery discharge, Capbat-max is the maximum limit in battery charge, Epv(t) is the photovoltaic generator power production at t moment, Ewt(t) is the wind generator power production at t moment, Eld(t) is the Load demand at t moment. 8. Communications implemented through “JADE” “JADE” (Java Agent Development framework), is the platform to further use for the "MAS" development and which is in conformity with standard FIPA (Foundation for Intelligent Physical Agents) [10], it is to develop by the language “JAVA” and the interaction between the agents made through communication protocols [11], see fig.6

Fig. 6.

Interaction between two agents

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We quote an example of communication between two agents: “AgentPv” and “AgentBat”. “AgentPv” produced of energy then send them to “AgentBat” for battery loading operation. As long as the battery is not full, it informs “AgentPv” that it is ready to receive more energy. As soon as the loading level is equal to the maximum limit, “AgentBat” informs “AgentPv” and stops. As soon as “AgentPv” receives the message of stop, it also stops. We quote maintain the algorithm which summarizes the communication described previously, see fig.7

Fig. 7.

The algorithm which represents a communication between “AgentPv” and “AgentBat”

9. Conclusion and prospects The key objective of energy management system is to achieve a high level of flexibility, not only during operation, but also during outages and during all its life cycle: the designed system should be able to adapt to most changes in the multi-source renewable energy systems, whether intentional (for example, adding a turbine to increase production capacity, or stopped for maintenance) or not (for example, after a failure). "MAS", thanks to their properties and characteristics, achieve this level of flexibility. Each element composing the multi-source renewable energy systems can be considered as an agent, with roles, constraints, and degrees of environment perception and own means of action. The agents corresponding to sources, means of storage, electrical loads, or processors, thus form an "MAS" which is the physical hybrid system image. The concept use of “MAS” in the issue of energy management is motivated by the following 3 reasons: x By its distributed structure, “MAS” are easily adaptable to a distributed system in the space such as a power grid. This particular reduces the need for bandwidth and computing power: problems (cuts, instabilities, etc.) can at


least partly be treated locally, allowing greater responsiveness. If necessary, they may also cooperate or compete. x “MAS” are flexible due to their distributed architecture and can therefore adapt to multi-source renewable energy systems changes, whether failures, adding or removing components, and that these events have been planned to system design or not. They are fault tolerant and allow degraded and plug & play operation. x “MAS” are proactive in seeking to meet their needs or goals based on their roles and constraints. A charge will therefore seek to be fed, as a central manager will seek to minimize costs while respecting its operational and legal constraints. If necessary, agents can also go for the information they need to make decisions and plan actions. So there were been a match between the characteristics of “MAS” and those required by smart grids, showing interest in their use. This contribution is a beginning for a long work; this work finality is an energy management intelligent solution. In terms of prospects, several aspects remain to be developed. Among which we quote: x x

Re-discuss the proposed design with experts in the fields of renewable energy and artificial intelligence; Development to rules Inference for “MAS”.

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