Abstract
In this paper short term power forecast of wind and solar power is proposed to evaluate the available output power of each production component. In this model, lead acid batteries used in proposed hybrid power system based on wind-solar power system. So, before the predicting of power output, a simple mathematical approach to simulate the lead–acid battery behaviors in stand-alone hybrid wind-solar power generation systems will be introduced. Then, the proposed forecast problem will be evaluated which is taken as constraint status through state of charge (SOC) of the batteries. The proposed forecast model includes a feature selection filter and hybrid forecast engine based on neural network (NN) and an intelligent evolutionary algorithm. This method not only could maintain the SOC of batteries in suitable range, but also could decrease the on-or-off switching number of wind turbines and PV modules. Effectiveness of the proposed method has been applied over real world engineering data. Obtained numerical analysis, demonstrate the validity of proposed method.
Similar content being viewed by others
References
Abedinia O, Ghadimi N (2013) Modified harmony search algorithm based unit commitment with plug-in hybrid electric vehicles. J Artif Intel Electr Eng 2(6):49–62
Abedinia O, Amjady N, Ghasemi A (2014) A new meta-heuristic algorithm based on shark smell optimization. Complex J. doi:10.1002/cplx.21634 2014.
Ahmadian I, Abedinia O, Ghadimi N (2014) Fuzzy stochastic long-term model with consideration of uncertainties for deployment of distributed energy resources using interactive honey bee mating optimization. Front Energy 8(4):412
Akbary P et al (2017) Extracting appropriate nodal marginal prices for all types of committed reserve. Comput Econ 1–26
Amjady N, Hemmati M (2009) Day-ahead price forecasting of electricity markets by a hybrid intelligent system. Eur Trans Electric Power 19(1):89–102
Buller S, Thele M, Karden E, De Doncker R (2003) Impedancebased non-linear dynamic battery modeling for automotive applications. J Power Sources 113(2):422–430
Datta M, Senjyu T, Yona A et al (2011) Photovoltaic output power fluctuations smoothing by selecting optimal capacity of battery for a photovoltaic-diesel hybrid system. Electric Power Components Syst 39(7):621–644
Eskandari Nasab M et al (2014) A new multiobjective allocator of capacitor banks and distributed generations using a new investigated differential evolution. Complexity 19(5):40–54
Fang K, Mu D, Chen S, Wu B, Wu F (2012) A prediction model based on artificial neural network for surface temperature, simulation of nickel–metal hydride battery during charging. J Power Sources 208:378–382
Ghadimi N, Firouz MH (2015) Short-term management of hydro-power systems based on uncertainty model in electricity markets. J Power Technol 95(4):265
Ghadimi N, Afkousi-Paqaleh M, Nouri A (2013) PSO based fuzzy stochastic long-term model for deployment of distributed energy resources in distribution systems with several objectives. IEEE Syst J 7(4):786–796
Ghadimi H, Akbarimajd A, Ghadimi N (2016) Optimal congestion management: strength Pareto gravitational search algorithm
Gollou AR, Ghadimi N (2017) A new feature selection and hybrid forecast engine for day-ahead price forecasting of electricity markets. J Intell Fuzzy Syst 1–15 (Preprint)
Jalili A, Ghadimi N (2016) Hybrid harmony search algorithm and fuzzy mechanism for solving congestion management problem in an electricity market. Complexity 21(S1):90–98
Karden E, Ploumen S, Fricke B, Miller T, Snyder K (2007) Energy storage devices for future hybrid electric vehicles. J Power Sources 168(1):2–11
Kumar Aggarwal S, Mohan Saini L, Kumar A (2009) Electricity price forecasting in deregulated markets: a review and evaluation. Electr Power Energy Syst 31(1):13–22
Lam AYS, Li VOK, Yu JJQ (2012) Real-coded chemical reaction optimization. IEEE Trans Evol Comput 16(3):339–353
Le Mehaute A, Crepy G (1983) Introduction to transfer and motion in fractal media: The geometry of kinetics. Solid State Ion 9/10:17–30
Liu Y, Wang W, Ghadimi N (2017) electricity load forecasting by an improved forecast engine for building level consumers. Energy
Loia V, Tomasiello S, Vaccaro A (2017) Joining fuzzy transform and local learning for wind power forecasting. In: Joint 17th World Congress of International Fuzzy Systems Association and 9th International Conference on Soft Computing and Intelligent Systems (IFSA-SCIS 2017), June 27–30, 2017, Otsu, Japan
Manla E, Nasiri A, Rentel CH, Hughes M (2010) Modeling of zinc/bromide energy storage for vehicular applications. IEEE Trans Ind Electron 57:624–632
Meissner E, Richter G (2005) The challenge to the automotive battery industry: the battery has to become an increasingly integrated component within the vehicle electric power system. J Power Sources 144(2):438–460
Milo A, Gaztanaga H, Etxeberria-Otadui I, Bilbao E, Rodriguez P (2009) Optimization of an experimental hybrid microgrid operation: reliability and economic issues. IEEE Bucharest Power Tech Conference, Bucharest, Romania, 28 June–2 July 2009, pp 1–6
Noruzi A et al (2015) A new method for probabilistic assessments in power systems, combining monte carlo and stochastic-algebraic methods. Complexity 21(2):100–110
Tsekouras GJ, Hatziargyriou ND, Dialynas EN (2006) An optimized adaptive neural network for annual midterm energy forecasting. IEEE Trans Power Syst 21(1):385–391
Usman Iftikhar M, Riu D, Druart F, Rosini S, Bultel Y, Retière N (2006) Dynamic modeling of proton exchange membrane fuel cell using noninteger derivatives. J Power Sources 160(2):1170–1182
Valenciaga F, Puleston PF (2005) Supervisor control for a stand-alone hybrid generation system using wind and photovoltaic energy. IEEE Trans Energy Convers 20(2):398–405
Zou J, Shu J, Zhang Z, Luo W (2014) An active power allocation method for wind-solar-batteries hybrid power system. Electric Power Components Syst 42:1530–1540
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Mirzapour, F., Lakzaei, M., Varamini, G. et al. A new prediction model of battery and wind-solar output in hybrid power system. J Ambient Intell Human Comput 10, 77–87 (2019). https://doi.org/10.1007/s12652-017-0600-7
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12652-017-0600-7