Optimization of a Multi-source System with Renewable Energy Based ...

Available online at www.sciencedirect.com

ScienceDirect Energy Procedia 74 (2015) 608 – 615

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

Optimization of a Multi-Source System with Renewable Energy Based on Ontology

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 This article presents a contribution to the optimization of a multi sources system with renewable energy system. Following the limits of conventional design methods in the reasoning aspect, also renewable energy hybrid systems are distributed nature, open and includes huge detail information needs a formal representation and a shared conceptualization by dynamic contribution to the change in weather conditions of installation site of hybrid system. We propose an approach based on hybridization between sizing techniques and ontology domain. The introduction of ontology in such systems allows the shared knowledge representation of a common area of a unit. Moreover, these will conceptualization of knowledge that can be updated without changing the system goals. The aim of the work presented in this paper is essentially the conceptualization of the ontology after the presentation of the proposed approach. “Protege2000” is used for editing the ontology. The purpose of this work is a knowledge base which contains a representation of all concepts and informational system details. © 2015 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license © 2015 The Authors. Published by Elsevier Ltd. (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: Optimization, Multi sources to renewable energy system, Reasoning system, Distributed system, Formal and common representation, Conditions weather, Sizing techniques, Domain ontology, Protege2000

* 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.2015.07.787

609

Djamel Saba et al. / Energy Procedia 74 (2015) 608 – 615

1. Introduction The renewable energies appear in our days and for a long-term as the adequate solution which covers this energy need by decreasing the major inconvenience emitted by the fossil fuels. The work presented in this article is a part of the optimization and the engineering of the knowledge for the renewable energy multi-Sources systems. The ontology is the most used approach for the formal representation and the knowledge capitalization in the modeling field [1, 2]. The deployment of ontology in modeling allows the intelligent integration of information and the knowledge management. The ontology allows supplying a shared and common knowledge on a given domain. They define primitive and essential for their representation, as well as their semantics in a particular context [3, 4]. Besides, the reuse and the division of the knowledge, allow facilitating the communication between the actors of the various bodies, in particular the realization of the interoperability between the various systems. They allow not only the creation of system with knowledge but also to argue about this knowledge and to contribute in providing its current news [5, 6]. This article is organized as follows: the section 2 presents the domain and the type of systems concerned by our study. It contains, in particular, an overview on the essential elements of these systems. The section 3 is dedicated to the presentation of the approach proposed for our conception. The section 4 illustrates the techniques of sizing specifically. The section 5 is reserved for the construction of ontology of the domain. We end with a conclusion and perspectives.

Nomenclature Ci

Initial cost

Ci_wt

Initial cost of the wind system

Ci_pv

Initial cost of the Photovoltaic system

Ș pvg

Ci_bat

Initial cost of the storage system

Șr

Ci_inv

Initial cost of the inverter

N bat

Battery number

Sinv mw

Apparent power of the inverters Maintenance cost Maintenance cost of the wind system

m pv

Maintenance cost of the Photovoltaic system

m bat

Ppv Gt A pvg

Photovoltaic power generation (W) Global solar irradiation (W/m2) Surface of photovoltaic generator (m2)

Efficiency of conversion Photovoltaic module reference efficiency Temperature coefficient ȕ Solar cell temperature (° C) Tc Reference solar cell temperature (° C) Tc ref Ambient temperature (° C) Ta NOCT Nominal operating cell temperature Wind speed in anemometer level(m/s) V(Z) V(Za ) Wind speed in the desired level (m/s) Coefficient characterized wind cutting Į Za , Z Height wind turbine (m) Cap bat Battery bank capacity(Ah) Load demand (W) E ld Number of autonomy days Nja Battery discharging efficiency Șdis Installation voltage U Wind speed (m/s) V Șinv Inverter efficiency Photovoltaic generator power production E pv

Cm

minv

Maintenance cost of the storage system Maintenance cost of the inverter system

dvsys

Life time of the system (years)

dv wt

dv bat

Life time of the Wind generator system (years) Life time of the Photovoltaic generator system (years) Life time of the storage system (years)

dvinv

Life time of the inverter system

Cg

System global cost

dv pv

(years)

610


2. System description The renewable energy multi-Sources systems concerned by our study. See fig.1, consists of two renewable sources energy (photovoltaic, wind), battery bench for energy storage, load and control current equipment’s.

Fig. 1.

Synoptic diagram for multi-sources system

3. The proposed approach 3.1

Approach principle

Sizing hybrid energy systems techniques “HES” are numerous. This approach principle is to select the most appropriate technique with the available climate data of the site concerned by the installation from the 'nӋ1' techniques existing in the literature, see Fig.2.

Fig. 2.

The proposed approach for system optimization


3.2

Proposed solution steps

Refers to all development stages of proposed solution, from design to its implementation, see: Fig.3 and Fig.4.

Fig. 3.

Fig. 4.

Proposed solution stages

Proposed solution stages (continued)

611

612


The objective of such a division is to minimize errors and set interim milestones that validate the development of a future application. 4. Sizing techniques The design is an indispensable tool for the analysis and comparison of different possible combinations of sources used in HES (hybrid energy system). [7, 8] The main design factors are: site environmental conditions (wind speed, irradiance, temperature and humidity), load profile, requirements and customer preferences and financial resources. 4.1

Photovoltaic generator power

The performance of photovoltaic modules depends on several parameters, namely the illumination temperature and the charge state. In the case of the hybrid system we use a simple model of the “PV” array that can calculate the power produced at any time if the temperature and irradiation Gt (W/m2) are known [9]:

Ppv

K pvg Apvg Gt

(1)

Where Apvg (m2) is the Surface of photovoltaic generator (m2), K pvg is the efficiency of conversion [10]:

K pvg

Kr ª1- E Tc - Tc ref º ¬

(2)

¼

Where Kr is the photovoltaic module reference efficiency, E is the temperature coefficient which is assumed to be constant and for photovoltaic cells based on silicon E is in the range 0.004 to 0.006 (1 / ° C), Tc ref is the reference solar cell temperature (°C), Tc is the solar cell temperature (° C), given by: Tc

§ NOCT - 20 · Ta ¨ ¸ Gt 800 © ¹

(3)

Where Ta (°C) is the ambient temperature (° C), NOCT (°C) is the nominal operating cell temperature. 4.2

Wind generator power

A simple model can simulate the output power:

Pwt V

V Vmin ° Pn V V n min °° ® Pn ° 0 ° °¯

Vmin d V Vn V Vmax V

d Vn d Vmax Vmin

(4)

Where Pn is the nominal power, V is the wind speed, Vn is the nominal speed, Vmin is the minimum speed, Vmax is the maximum speed. 4.3

Storage battery capacity

Battery capacity in terms of “Wh” energy depends mainly on the number of day’s autonomy of energy for each day by renewable resources without the storage system, and the daily energy consumption by the load.

613


Capbat

Eld .Nja Cdis .U

(5)

Where Capbat is the battery bank capacity “Ah”, Eld is the load demand (W), Nja is the number of autonomy days, Cdis is the discharge coefficient, U is the installation voltage. 5. Knowledge of domain ontology Knowledge in ontology is mainly formalized using five types of components namely: Concepts (or classes), relations (or properties), functions, axioms (or rules) and bodies (or individuals). 5.1

Classes List and their attributes

We will mention some classes of our system: Table 1. Photovoltaic generatorclass Class Name

Class Description

Attribute of Class

Attributes description

Attributety pe

Ppv

Photovoltaic power generation

Float

Photovoltaic generator power production

Float

Photovoltaic

Renewable energy

E pv

Generator

source

K pvg

Efficiency of conversion

Float

A pvg

Surface of photovoltaic generator

Float


Attributety pe

Table 2. Wind generatorclass Class Name Wind Generator

Class Description

Attribute of Class

Vn

Nominal speed

Float

Renewable

Vmin

Minimum speed

Float

energy source

D

Helix diameter Wind power generation

Float

Pwt

Float

Table 3. Storage class Class Name

Storage (Battery)

Class Description

Energy storage system

Attribute of Class


Attributet ype

Cap bat

Battery bank capacity

Float

Șch

Battery charging efficiency

Float

SOCbat min

Minimum allowable storage capacity

Float

SOCbat max

Maximum allowable storage capacity

Float

Șdis

Battery discharging efficiency

Float

Attribute of Class

Attributes description Inverter efficiency

Attributet ype

Table 4. Inverterclass Class Name

Class Description

Șinv Inverter

A system converter

Float

Ci inv

Initial cost of the inverter

Float

minv

Maintenance cost of the inverter system

Float

dvinv

Life time of the inverter system

Integer

614

Djamel Saba et al. / Energy Procedia 74 (2015) 608 – 615 Table 5. Load class Class Name Load

5.2

Class Description Required energy

Attribute of Class


Attribute type

Pld

Power demand

Float

E ld

Load demand

Float

Nja Min

Minimum number of autonomy days

Integer

Relations List

We will mention some relations of our system: Table 6. Some relations of our system Relationship

5.3

Associated Classes

Supply-Storage-Load

Storage, Load

Supply- Photovoltaic Generator-Storage

Photovoltaic Generator, Storage

Supply- Photovoltaic Generator- Load

Photovoltaic Generator, Load

Supply- Wind Generator – Storage

Wind Generator, Storage

Influence-Irradiation-Photovoltaic Generator

Irradiation, Photovoltaic Generator

Edition of the ontology

For editing the ontology, the “Protege 2000” software “Version 3.4.4” is selected as the editing tool, our choice is justified by the benefits that characterize [11, 12]. The graphical ontology editor can encode formal ontology obtained in the previous step. Graphically extract ontology, see fig.5:

Fig. 5.

Graphic presentation “first version” of system ontology


615

6. Conclusion and prospects This work focuses on building ontology for a renewable energy hybrid system. This will thus be a knowledge base for the hybrid systems optimization (photovoltaic, wind). We based on mathematical models of each system element to define the concepts, attributes and relationships between concepts; we also based on conventional sizing techniques in the calculation of the optimal configuration, but due to the limit of these techniques in the reasoning part we thought to domain ontology. In terms of outlook, we quote: x Revisit the approach proposed; x Develop the necessary rules for good reasoning system. References [1]

Vijayan Sugumaran, Veda C. Storey, “Ontologies for conceptual modeling: their creation, use, and management”, Elsevier, Data & Knowledge Engineering 42 (2002) 251–271. [2] Farouk Belkadi, Nicolas Dremont, Alban Notin, NadegeTroussier, MouradMessadia, “A meta-modelling framework for knowledge consistency in collaborative design”, Elsevier,Annual Reviews in Control 36 (2012) 346–358. [3] N. Lasierra, A. Alesanco, D. O'Sullivan, J. García, “An autonomic ontology-based approach to manage information in home-based scenarios: From theory to practice”, Elsevier, Data & Knowledge Engineering 87 (2013) 185-205. [4] Gunjan Mansingh, Kweku-MuataOsei-Bryson, Han Reichgelt, “Using ontologies to facilitate post-processing of association rules by domain experts”, Elsevier, Information Sciences 181 (2011) 419-434. [5] Khouri Selma, Boukhari Ilyès, Bellatreche Ladjel, Sardet Eric, Jean Stéphane, Baron Michael, “Ontology-based structured web data warehouses for sustainable interoperability : requirement modeling, design methodology and tool”, Elsevier, Computers in Industry 63 (2012) 799–812. [6] Ioannis Savvas, Nick Bassiliades, “A process-oriented ontology-based knowledge management system for facilitating operational procedures in public administration”, Elsevier, Expert Systems with Applications 36 (2009) 4467–4478. [7] I.Vichiu,''Modélisation et analyse de l'intégration des énergies renouvelable dans un réseau autonome'', PhDThesis, University of Le Havre, December, 2005. [8] A .Kaabeche, M. Belhamel, R. Ibtiouen, “Sizing optimization of grid-independent hybrid photovoltaic/wind power generation system”, Elsevier, Energy 36 (2011) 1214-1222. [9] M.S. Ismail, M. Moghavvemi, T.M.I. Mahlia, “Techno-economic analysis of an optimized photovoltaic and diesel generator hybrid power system for remote houses in a tropical climate”, Elsevier,Energy Conversion and Management 69 (2013) 163–173. [10] S.Diaf, M.Belhamel, M.HaddadietA.Louche,"Technical and economic asservissement of hybrid photovoltaic-wind system with battery storage in Corsica island", ScienceDirect (Energy Policy), PP.743-754, 2007. [11] John H. Gennari, Mark A. Musen, Ray W. Fergerson, William E. Grosso, Monica Crubézy, Henrik Eriksson,Natalya F. Noy, Samson W. Tu, “The evolution of Protégé: an environment for knowledge-based systems development”, Elsevier, Int. J. Human-Computer Studies 58 (2003) 89–123. [12] Chieh feng (Cliff) Chen, Kung Chen, Chien-Yeh Hsu, Yu-Chuan (Jack) Li, “Developing guideline-based decision support systems using protégé and jess”, Elsevier,computer methods and programs in biomedicine 102 (2011) 288–294.