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Energy (2017) 000–000 297–307 EnergyProcedia Procedia119 00 (2017) www.elsevier.com/locate/procedia International Conference on Technologies and Materials for Renewable Energy, Environment and Sustainability, TMREES17, 21-24 April 2017, Beirut Lebanon International Conference on Technologies and Materials for Renewable Energy, Environment and Sustainability, TMREES17, 21-24 April 2017, Beirut Lebanon
Experimental typical meteorological years to study energy performance a PV grid-connected The 15th International Symposium on District Heating and Cooling Experimental typicalofmeteorological years tosystem study energy of a PV grid-connected a,b, a b a system L. Zaghba *, performance A. Borni M. Khennane , N.Terki ,the A. Fezzani , A. Bouchakoura, I. Hadj Assessing thea,feasibility of using heat demand-outdoor a Mahameda a, S. H. Oudjana a a L.temperature Zaghbaa,b, *, A. function Bornia, M. Khennane , N.Terkib,district A. Fezzani , A.demand Bouchakour , I. Hadj for a long-term heat forecast Mahamed , S. H. Oudjana *, A. Pina , P. Ferrão , J. Fournier ., B. Lacarrière , O. Le Corre
a Centre de Développement a Unité de Recherche Appliquée en Energies Renouvelables, URAER, des Energies Renouvelables, CDER, 47133, Ghardaïa, Alegria a,b,c a a b c c b a Electrical EngineeringURAER, Department, University of Biskra, Algeria Unité de Recherche Appliquée en Energies Renouvelables, Centre de Développement des Energies Renouvelables, CDER, 47133, Ghardaïa, Alegria a IN+ Center for Innovation, Technology and Policy Research - Instituto Superior Técnico, Av. Rovisco Pais 1, 1049-001 Lisbon, Portugal b Electrical Engineering Department, University of Biskra, Algeria b Veolia Recherche & Innovation, 291 Avenue Dreyfous Daniel, 78520 Limay, France c Département Systèmes Énergétiques et Environnement - IMT Atlantique, 4 rue Alfred Kastler, 44300 Nantes, France a
I. Andrić
Abstract
Abstract Elaborate a typical meteorological year applied on a system connected to the grid in order to study and analyze the energy behavior. AbstractWe then applied a hybrid intelligent technique based on sliding control and fuzzy logic for the search for a maximum global power point. Different simulations made using indices as reference yield (YR), array Elaborate a typical meteorological year applied on aMatlab system/ Simulink. connectedPerformance to the grid in ordersuch to study and analyze the energy behavior. We then applied a hybrid intelligent technique based on sliding control and fuzzy logic for the search for a maximum yield (YA), system yield (YF), system performance ratio (PR), array capture losses (LC) and system losses (LS). The daily District heating networks are commonly addressed in the literature as one of the most effective solutions for decreasing the global power point. Different simulations made using Matlab / Simulink. Performance indices such as reference yield (YR), array parameters performance computed in 2015 shows a good productivity of the system. greenhouse gas emissions from the building sector. These systems require high investments which are returned through the heat yield yield (YF), system performance (PR),renovation array capture lossesheat (LC) and system daily sales.(YA), Due system to the changed climate conditions and ratio building policies, demand in thelosses future(LS). couldThe decrease, parameters performance computed in 2015 shows a good productivity of the system. prolonging the investment return period. © 2017 The Authors. Published by Elsevier Ltd. main of this paper isby toElsevier assess the feasibility of using the heat demand – outdoor temperature function for heat demand ©The 2017 Thescope Authors. Published Ltd. Peer-review under responsibility of the Euro-Mediterranean Institute for for Sustainable Sustainable Development (EUMISD). forecast. The district of Alvalade, in Lisbon (Portugal), was used as a Development case study. The district is consisted of 665 Peer-review under responsibility of thelocated Euro-Mediterranean Institute (EUMISD). ©buildings 2017 Thethat Authors. Published by Elsevierperiod Ltd. and typology. Three weather scenarios (low, medium, high) and three district vary in both construction Keywords: Fuzzy siding MPPT meteorological years, grid connected photovoltaic system, boost converter, energy performance. Peer-review under responsibility ofstrategy, the Euro-Mediterranean Institute forTo Sustainable Development (EUMISD). renovation scenarios were control developed (shallow, intermediate, deep). estimate the error, obtained heat demand values were compared with results from a dynamic heat demand model, previously developed and validated by the authors. Keywords: Fuzzy sidingthat MPPT control strategy, meteorological years, grid connected photovoltaic boost converter,for energy The results showed when only weather change is considered, the margin of error system, could be acceptable someperformance. applications 1.(the Introduction error in annual demand was lower than 20% for all weather scenarios considered). However, after introducing renovation scenarios, the error value increased up to 59.5% (depending on the weather and renovation scenarios combination considered). 1.The Introduction Faced the programmed depletion of fossilwithin fuels the andrange theirofnegative on the environment, renewable valuewith of slope coefficient increased on average 3.8% upimpact to 8% per decade, that corresponds to the decrease are in the number of hours 22-139h during fulfillment the heating season (depending the combination of weatherand and energies becoming theheating solution forofthe sustainable of global energy on needs. Less unpredictable Faced with the programmed depletion ofhand, fossilfunction fuels and their increased negative for impact on theperenvironment, renewable renovation scenarios considered). On the other intercept 7.8-12.7% decade (depending on the coupled scenarios). The the values suggested be used to fulfillment modify the function for the Less scenarios considered,and and energies are becoming solution for could the sustainable of globalparameters energy needs. unpredictable improve the accuracy of heat demand estimations. * Corresponding author.Published Tel.: +213 98 282; fax: © 2017 The Authors. by 533 Elsevier Ltd.+213 29 870 146. E-mail address:
[email protected] Peer-review under responsibility of the Scientific Committee of The 15th International Symposium on District Heating and * Corresponding author. Tel.: +213 98 533 282; fax: +213 29 870 146. Cooling.
E-mail address:
[email protected] 1876-6102 © 2017 The Authors. Published by Elsevier Ltd. Peer-review responsibility of the Euro-Mediterranean Institute for Sustainable Development (EUMISD). Keywords: under Heat demand; Forecast; Climate change 1876-6102 © 2017 The Authors. Published by Elsevier Ltd. Peer-review under responsibility of the Euro-Mediterranean Institute for Sustainable Development (EUMISD).
1876-6102 © 2017 The Authors. Published by Elsevier Ltd. Peer-review under responsibility of the Scientific Committee of The 15th International Symposium on District Heating and Cooling.
1876-6102 © 2017 The Authors. Published by Elsevier Ltd. Peer-review under responsibility of the Euro-Mediterranean Institute for Sustainable Development (EUMISD). 10.1016/j.egypro.2017.07.085
L. Zaghba et al. / Energy Procedia 119 (2017) 297–307 L. Zaghba / Energy Procedia 00 (2017) 000–000
298 2
easier to capture than wind power, solar energy is a 100% clean and equitable source of energy that is equitable in all parts of the planet. Two main channels allow us to exploit solar energy: solar thermal and solar photovoltaic [1]. Renewable energy is emerging as a potential solution to reducing pollution. Among the promising means of production (wind, hydro, etc.), photovoltaic (PV) is today the most appropriate and the most endowed with the production of electricity of renewable origin for the habitat. Add to this the liberalization of the electricity market which introduces major changes in the field of energy [2-3]. These meteorological parameters are in general: solar radiation, air temperature, relative humidity, wind speed and direction. Hence the necessity and advantage of using meteorological year data for long term system performance evaluation. Therefore, this article we try to elaborate a typical meteorological year applied on a system connected to the grid in order to applied a hybrid intelligent technique based on sliding control and fuzzy logic for the search for a maximum global power point and to study energy performance of a PV grid-connected system in Ghardaïa site. 2. Data collection The data (global, direct and diffuse solar radiation, temperature, relative humidity, etc.) were collected since January 2006 by the Applied Research Unit for Renewable Energies (URAER), Ghardaia city situated in the south of Algeria [4-5]. The experimental data (global solar radiation and temperature) are recorded every 5 min from : 15/04/2015 to 26/04/2015 with a high precision by a CM11 Pyranometer type with a sensitivity equal to 4.57 10-6 V/W m2 was installed at the rooftop of the (URAER) as shown in Fig. 1.
Ghardaïa
Fig.1. Schematic diagram of solar photovoltaic station (Photo from authors)
3. Grid connected PV system Figure 2 shows the configuration of the photovoltaic system connected to the grid electrical distribution. The photovoltaic generator is connected to a DC / DC converter which track the optimum operating point (MMPT) and then the assembly is connected to the electrical grid via a DC / AC converter controlled by a strategy control which enables the synchronization the photovoltaic source with the network.
L. Zaghba et al. / Energy Procedia 119 (2017) 297–307 L. Zaghba / Energy Procedia 00 (2017) 000–000
PV Generator
Cpv
Boost
Cdc
Filter
Grid
Inverter
MPPT Measures
Inverter
299 3
References
Measures
Fig.2. scheme of the grid connected PV system.
4. MPPT control by fuzzy -sliding mode We will analyze in this section the behavior of the photovoltaic system connected to the network controlled by sliding mode for the maximization of the power of the panels. The model in the state space of the boost converter can be written as follows [6-9]:
0 𝐼𝐼𝐼𝐼 ̇ � 𝐿𝐿𝐿𝐿 � = �1 𝑉𝑉𝑉𝑉̇𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑 (1 − 𝑢𝑢𝑢𝑢) 𝐶𝐶𝐶𝐶
1
− (1 − 𝑢𝑢𝑢𝑢) 𝐿𝐿𝐿𝐿
−
𝐶𝐶𝐶𝐶.𝑅𝑅𝑅𝑅𝐿𝐿𝐿𝐿
Equation (1) can be written as:
�
𝑥𝑥𝑥𝑥1̇ = − 𝑥𝑥𝑥𝑥̇2 =
1−𝑢𝑢𝑢𝑢
𝐿𝐿𝐿𝐿 1−𝑢𝑢𝑢𝑢 𝐶𝐶𝐶𝐶
1
𝑥𝑥𝑥𝑥2 + 𝑉𝑉𝑉𝑉𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝
𝑥𝑥𝑥𝑥1 +
𝐿𝐿𝐿𝐿 1
𝐶𝐶𝐶𝐶.𝑅𝑅𝑅𝑅𝐿𝐿𝐿𝐿
1
��
1 𝐼𝐼𝐼𝐼𝐿𝐿𝐿𝐿 � + � 𝐿𝐿𝐿𝐿 � 𝑉𝑉𝑉𝑉𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝 𝑉𝑉𝑉𝑉𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑 0
(2)
𝑥𝑥𝑥𝑥2
Ou: 𝑥𝑥𝑥𝑥 = �𝐼𝐼𝐼𝐼𝐿𝐿𝐿𝐿̇ , 𝑉𝑉𝑉𝑉𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑 � The output power of the PV generator:
PPV = V PV .i PV
For maximum power of the PV generator [10]: The sliding surface is defined by
S=
(1)
(3) ∂PPV =0 ∂VPV
∂PPV ∂ (VPV .i PV ) ∂i = = i PV + PV VPV ∂VPV ∂VPV ∂VPV
(4) Representation in the state space of the equation (2) can be written in the general form of nonlinear system invariant in time: 𝑋𝑋𝑋𝑋̇ = 𝑓𝑓𝑓𝑓(𝑋𝑋𝑋𝑋) + 𝑔𝑔𝑔𝑔(𝑋𝑋𝑋𝑋)𝑢𝑢𝑢𝑢 (5) The equivalent control is determined from the following condition:
̇ 𝜕𝜕𝜕𝜕𝜕𝜕𝜕𝜕 𝑇𝑇𝑇𝑇 𝜕𝜕𝜕𝜕𝜕𝜕𝜕𝜕 𝑇𝑇𝑇𝑇 𝑆𝑆𝑆𝑆̇ = � � 𝑋𝑋𝑋𝑋 = � � (𝑓𝑓𝑓𝑓(𝑋𝑋𝑋𝑋) + 𝑔𝑔𝑔𝑔(𝑋𝑋𝑋𝑋)𝑢𝑢𝑢𝑢) = 0 𝜕𝜕𝜕𝜕𝜕𝜕𝜕𝜕
𝜕𝜕𝜕𝜕𝜕𝜕𝜕𝜕
(6)
After development, the expression of the equivalent component is given by:
u eq = −
f (x ) g (x )
From the equation (2) we find [10]:
(7)
L. Zaghba et al.Procedia / Energy Procedia (2017) 297–307 L. Zaghba / Energy 00 (2017)119 000–000
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�
1
𝑓𝑓𝑓𝑓(𝑥𝑥𝑥𝑥) = (𝑉𝑉𝑉𝑉𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝 − 𝑉𝑉𝑉𝑉𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑 ) 𝐿𝐿𝐿𝐿
𝑔𝑔𝑔𝑔(𝑥𝑥𝑥𝑥) =
𝑉𝑉𝑉𝑉𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑 𝐿𝐿𝐿𝐿
Therefore: 𝑢𝑢𝑢𝑢𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒 = 1 −
(8)
𝑉𝑉𝑉𝑉𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝
(9)
𝑉𝑉𝑉𝑉𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑
The final control law is given by:
u (t ) = ueq (t ) + u n (t )
(10) The structure of a controller by sliding mode consists of two parts: u éq corresponds to the equivalent command proposed by Filipov and Utkin. It corresponds to the command that guarantees the attractiveness of the variable to be controlled to the surface and satisfies the condition:
̇
