The present article focuses on the calculation of the wind capacity credit by integrating the Moroccan project on the wind energy of 1000 MW in 2020. After an introduction to the Moroccan Integrated Wind Energy Project, a wind capacity credit assessment program will be implemented on Matlab software including the whole information about “installed capacity, number of plants, failure rate, types of installed units, peak demand etc.” This program will be used to calculate the safety rate of an electrical system as well as the capacity credit of Morocco’s electricity production network. This section will be built in two phases: the first phase will examine the impact of TAZA wind farm with an installed power of 150 MW, while the second phase will focus on the generalization of this study on all the wind farms that will be injected to the Moroccan grid in 2020. The research provides conclusion according to comments and assessment of the impact of this electric energy integration based on the wind generation.
AllanRNBillintonR (2000) Probabilistic assessment of power systems. Proceedings of the IEEE88(2): 140–162.
2.
AllanRNBillintonRSjariefI, et al. (1989) A reliability test system for educational purposes-basic data. IEEE Transactions on Power Systems4(3): 1238–1244.
3.
AppinoRROrdianoJÁGMikutR, et al. (2018) On the use of probabilistic forecasts in scheduling of renewable energy sources coupled to storages. Applied Energy210: 1207–1218.
4.
AzeroualMEl MakriniAEl MoussaouiH (2018) Renewable energy potential and available capacity for wind and solar power in Morocco towards 2030. Journal of Engineering Science and Technology Review11(1): 189–198.
5.
BillintonRChenHGhajarR (1996) A sequential simulation technique for adequacy evaluation of generating systems including wind energy. IEEE Transactions on Energy Conversion11(4): 728–734.
6.
BloessASchillWPZerrahnA (2018) Power-to-heat for renewable energy integration: A review of technologies, modeling approaches and flexibility potentials. Applied Energy212: 1611–1626.
7.
BorgesCLTDiasJA (2017) A model to represent correlated time series in reliability evaluation by non-sequential Monte Carlo simulation. IEEE Transactions on Power Systems32(2): 1511–1519.
8.
CastroRMGFerreiraLAFM (2001) A comparison between chronological and probabilistic methods to estimate wind power capacity credit. IEEE Transactions on Power Systems16(4): 904–909.
9.
CatalãoJP (2016) Electric Power Systems: Advanced Forecasting Techniques and Optimal Generation Scheduling. Boca Raton, FL: CRC Press.
10.
ChoukriKNaddamiAHayaniS (2017) Renewable energy in emergent countries: Lessons from energy transition in Morocco. Energy, Sustainability and Society7: 25.
11.
ClackCTQvistSAAptJ, et al. (2017) Evaluation of a proposal for reliable low-cost grid power with 100% wind, water and solar. Proceedings of the National Academy of Sciences of the United States of America114: 6722–6727.
12.
Coopération République du Sénégal-BAD, (2011) “Étude d’interconnexion des réseaux électriques Sénégal – Mauritanie – Maroc - Espagne,” Banque africaine de développement, Département par pays region ouest B (ORWB), Bureau regional de la Banque au Sénégal (SNFO).
13.
KarkiRPoH (2007) Impact of wind power growth on capacity credit. In: 2007 Canadian Conference on Electrical and Computer Engineering (CCECE 2007), Vancouver, BC, Canada, 22–26 April. New York: IEEE.
14.
KarkiRHuPBillintonR (2006) A simplified wind power generation model for reliability evaluation. IEEE Transactions on Energy Conversion21(2): 533–540.
15.
KousksouTAllouhiABelattarM, et al. (2015) Renewable energy potential and national policy directions for sustainable development in Morocco. Renewable and Sustainable Energy Reviews47: 46–57.
16.
KousksouTBruelPJamilA, et al. (2014) Energy storage: Applications and challenges. Solar Energy Materials and Solar Cells120: 59–80.
17.
MengshoelOJChaviraMCascioK, et al. (2010) Probabilistic model-based diagnosis: An electrical power system case study. IEEE Transactions on Systems, Man and Cybernetics-Part A: Systems and Humans405: 874–885.
18.
MilliganMParsonsB (1999) A comparison and case study of capacity credit algorithms for wind power plants. Wind Engineering23: 159–166.
OumounahB (2018) Intégration des énergies renouvelables dans les systèmes électriques nationaux. ONEE-COP22. Available at: http://www.cop22.org/fr
21.
SchenkKFMisraRBVassosS, et al. (1984) A new method for the evaluation of expected energy generation and loss of load probability. IEEE Transactions on Power Apparatus and Systems PAS-103(2): 294–303.
22.
SinghCKimY (1988) An efficient technique for reliability analysis of power systems including time-dependent sources. IEEE Transactions on Power Systems33: 1090–1096.
23.
VoorspoolsKRD’haeseleerWD (2006) An analytical formula for the capacity credit of wind power. Renewable Energy31(1): 45–54.
24.
WangdeeWBillintonR (2006) Considering load-carrying capability and wind speed correlation of WECS in generation adequacy assessment. IEEE Transactions on Energy Conversion21(3): 734–741.
25.
YangYWangHSangwongwanichA, et al. (2018) Design for reliability of power electronic systems. In: RashidMH (ed.) Power Electronics Handbook (4th edn). Oxford: Butterworth-Heinemann, pp. 1423–1440.
