Time Series and Renewable Energy Forecasting

Reliability is a key important criterion in every single system in the world, and it is not different in engineering. Reliability in power systems or electric grids can be generally defined as the availability time (capable of fully supplying the demand) of the system compared to the amount of time it is unavailable (incapable of supplying the demand). For systems with high uncertainties, such as renewable energy based power systems, achieving a high level of reliability is a formidable challenge due to the increased penetrations of the intermittent renewable sources such as wind and solar. A careful and accurate planning is at the utmost importance to achieve high reliability in renewable energy based systems. This chapter will assess wind-based power system’s reliability issues, and provide a case study that proposes a solution to enhance the reliability of the system

Time Series and Renewable Energy Forecasting

Renewable energy generation has been constantly increasing during recent years. Wind and solar have had the most significant growths among all renewable resources. Wind and solar resources are highly intermittent and dependent on meteorological parameters and climatic conditions. The power output of wind turbines is subject to various meteorological parameters, such as wind speed, wind direction, air temperature, relative humidity, etc., among which the wind speed is the most direct and influential factor in wind power generation. Solar photovoltaic (PV) power is a function of solar radiation. Wind speed and solar radiation time series data exhibit unique features which complicate their prediction. This makes wind and solar power forecasting challenging. Accurate wind and solar forecasting enhances the value of renewable energy by improving the reliability and economic feasibility of these resources. It also supports integrating solar and wind power into electric grids by reducing the integration and operation costs associated with these intermittent generation sources. This chapter provides an overview of the time series methods that can be used for more accurate wind and solar forecasting

Optimizing Hybrid Renewable Energy Systems: A Review

With the fast progression of renewable energy markets, the importance of combining different sources of power into a hybrid renewable energy system (HRES) has gained more attraction. These hybrid systems can overcome limitations of the individual generating technologies in terms of their fuel efficiency, economics, reliability and flexibility. One of the main concerns is the stochastic nature of photovoltaic (PV) and wind energy resources. Wind is often not correlated with load patterns and may be discarded sometimes when abundantly available. Also, solar energy is only available during the day time. A hybrid energy system consisting of energy storage, renewable and nonrenewable generation can alleviate the issues associated with renewable uncertainties and fluctuations. Large number of random variables and parameters in a hybrid energy system requires an optimization that most efficiently sizes the hybrid system components to realize the economic, technical and designing objectives. This chapter provides an overview of optimal sizing and optimization algorithms for hybrid renewable energy systems as well as different objective functions considered for designing such systems

Pattern Recognition and Its Application in Solar Radiation Forecasting

As intermittent renewable energy sources such as wind and solar proliferate, the power systems operation and planning become more complicated due to increased uncertainties and variabilities. Accurate forecasting of these sources facilitates planning and operating the electric grid to integrate wind/solar power more reliably and efficiently. The neural network learning process can be disrupted by anomalies of wind/solar time-series data, which results in less accurate forecasting. By processing and analyzing wind/solar time-series data, machine learning and pattern recognition methods such as data clustering and classification can significantly enhance the forecast accuracy. This chapter reviews the various machine learning and pattern recognition methods proposed in the literature for time-series forecasting of solar radiation

Childhood Epilepsy; Prognostic Factors in Predicting the Treatment Failure

How to Cite This Article: Taghdiri MM, Omidbeigi M, Asaadi S, Mohebbi M, Azarghashb E, Ghofrani M. Childhood Epilepsy; Prognostic Factors in Predicting the Treatment Failure. Iran J Child Neurol. Winter 2017; 11(1):21-28.AbstractObjectiveWe aimed to find the prognostic factors to detect the patients who fail the treatment of epilepsy, in the early stages of the disease.Materials & MethodsThis study was done on the epileptic patients attending the Neurology Clinic of Mofid Children’s Hospital, Tehran, Iran from September 2013 to October 2014. After defining the criteria for exclusion and inclusion, the patients were divided to two groups based on responding to the medical treatment for their epilepsy and indices were recorded for all the patients to be used in the statistical analyses.ResultsThe patients’ age ranged from 1 to 15 yr. There was 188 patients with refractory seizure in group 1 (experimental group) and 178 patient with well controlled seizure in group 2(control group).There was a significant different between serum drug level in both groups and patients with refractory seizure group had a lower serum drug level than control group. In both groups tonic-clonic was the most common type of seizure. Also the prevalence of brain imaging Abnormalityand other neurologic disorders was significantly higher in patients with refractory seizure in compare with control group.ConclusionChildren with seizure who suffer from refractory epilepsy need more attention and exact observation by the medical staff. References 1. Kozyrskyj AL, Prasad AN. The burden of seizures in Manitoba children: a population-based study. Can J Neurol Sci 2004;31:48-52. 2. Camfield PR, Camfield CS, Gordon Kandet al. If a first antiepileptic drug fails to control a child’s epilepsy, what are the chances of success with the next drug? J Pediatr 1997; 131:821-4.3. Arts WF, Brouwer OF, Peters ACet al. Course and prognosis of childhood epilepsy: 5-year follow-up of the Dutch study of epilepsy in childhood. Brain 2004;127:1774–84.4. Berg AT, Shinnar S, Levy SR, et al. Early Development of intractable epilepsy in children: a prospective study. Neurology 2001;56:1445–52.5. Berg AT, Vickrey BG, Testa FM, et al. How long does it take for epilepsy to become intractable? A prospective investigation. Ann Neurol 2006;60:73–9. 6. Kwan P, Brodie M. Early identification of refractory epilepsy. N Eng J Med2000;342:314–9.7. Mohanraj R, Brodie MJ. Diagnosing refractory epilepsy: response to sequential treatment schedules. Eur J Neurol 2006;13:277–82.8. Berg A. Identification of Pharmacoresistant Epilepsy. Neurol Clin 2009;27(4):1003-1013.9. Luciano AL, Shorvon SD. Results of treatment changes in patients with apparently drug-resistant chronic epilepsy. Ann Neurol 2007;62:375–381. 10. Carpay HA, Arts WF, GeertsAT, et al. Epilepsy in childhood: An audit of clinical practice. Arch Neurol 1998;55:668–73.11. Dudley RW, Penney SJ, Buckley DJ. First-drug treatment failures in children newly diagnosed with epilepsy. Pediatr Neurol 2009;40:71–7.12. Berg AT, Vickrey BG, Testa FM, et al. How long does it take epilepsy to become intractable? A prospective investigation. Ann Neurol 2006;60:73–79.13. Spooner CG, Berkovic SF, Mitchell LA, et al. New onset temporal lobe epilepsy in children: lesion on MRI predicts poor seizure outcome. Neurology 2006;67:2147–2153. 14. Robinson RO, Baird G, Robinson Get al. Landau– Kleffner syndrome: course and correlates with outcome. Dev Med Child Neurol2001;43:243-7.15. Berg AT, Shinnar S, Levy SR, et al. Defining early seizure outcomesin pediatric epilepsy: the good, the bad and the in-between. Epilepsy Res 2001;43:75-84.16. Shinnar S, Berg AT. Does antiepileptic drug therapy prevent the development of ‘‘chronic’’ epilepsy? Epilepsia 1996;37:701-8.Neurol Clin 2009;27(4):1003-1013.17. Engel J. The goal of epilepsy therapy: no seizures, no side effects,as soon as possible. CNS Spectrums 2004;9:95–97.18. Mathern GW, Pretorius JK, Babb TL. Influence of the type ofinitial precipitating injury and at what age it occurs on courseand outcome in patients with temporal lobe seizures. J Neurosurg1995;82:220 –227.19. Cross JH, Jaykar P, Nordli D and et al. Propose criteria for referraland evaluation of children for epilepsy surgery: recommendations of the Subcomission for Pediatric Epilepsy Surgery. Epilepsia2006;47:953–959.20. Weiner HL, Carlson C, Ridgway EBet al. Epilepsy surgery inyoung children with tuberous sclerosis: results of a novel approach. Pediatrics 2006;117:1494 –1502.21. Del Felice A, Beghi E, Boero G, La Neve A, Bogliun G, De Palo A, et al. Early versus late remission in a cohort of patients with newly diagnosed epilepsy. Epilepsia 2010;51(1):37-42.22. Levy SR, Novotny EJ, Shinnar S. Predictors of intractable epilepsy in childhood: a case–control study. Epilepsia 1996;37:24–30.23. Berg AT, Shinnar S, Levy SR and et al. Smith- Rappaport S, Beckerman B. Early development of intractable epilepsy in children: a prospective study. Neurology2001;56:1445–52.24. Casetta I, Granieri E, Monetti VC et al. Early predictors of intractability in childhood epilepsy: a community-basedcase–control study in Copparo, Italy. Acta Neurologica Scandinavica 1999;99:329–33.25. Chawla S, Aneja S, Kashyap Ret al. Etiology and clinical predictors ofintractable epilepsy. Pediatric Neurology 2002;27:186–91.26. Ko TS, Holmes GL. EEG and clinical predictors of medically intractable childhood epilepsy. Clin Neurophysiol 1999;110:1245–51. 27. Kwong KL, Sung WY, Wong SN, et al. Early predictors of medical intractability in childhood epilepsy. Pediatr Neurol2003;29:46–52.28. Oskoui M, Webster RI, Zhang X and et al. Factors predictive of outcome inchildhood epilepsy. J Child Neurol 2005;20:898–904.29. Seker Yilmaz B, Okuyaz C, Komur M. Predictors of Intractable Childhood Epilepsy. Pediatr Neurol 2013;48(1):52-55.30. Kim S, Park K, Kim S, Kwon O, No S. Presence of epileptiform discharges on initial EEGs are associated with failure of retention on first antiepileptic drug in newly diagnosed cryptogenic partial epilepsy: A 2-year observational study. Seizure 2010;19(9):536-539.31. Callaghan B, Anand K, Hesdorffer D, Hauser W, French J. Likelihood of seizure remission in an adult population with refractory epilepsy. Ann Neurol 2007;62(4):382- 389.32. Arhan E, Serdaroglu A, Kurt A, Aslanyavrusu M. Drug treatment failures and effectivity in children with newly diagnosed epilepsy. Seizure 2010;19(9):553-557

V2G Services for Renewable Integration

With the proliferation of renewable energy sources (RES) and the growing consumer demand for plug-in hybrid (PHEV) and total electric vehicles (EV), the limitations of the aging electrical grid distribution infrastructure is becoming more and more apparent. The development of better infrastructure, therefore, is at the forefront of research. The development of a smart grid, a bidirectional distribution infrastructure, will allow for two-way “communication” of power distributors and aggregators with multiple smart platforms, such as smart buildings, homes, and vehicles. The focus of this chapter is to outline the means of (electrical) vehicle to (smart) grid (V2G) interactions and how attaining a synergistic relationship is vital to improving the way power is distributed. The ability of fleets of EVs to act as a unit for excess power storage allows for the increased integration of RES into existing grid infrastructure and smart grids in the future through the bidirectional communication; providing support, giving back stored power into the grid to lessen the load felt by generation utilities, augment stochastic RES when generation is not meeting demands, lowering costs for both sellers and buyers, and above all, working toward the betterment of Earth

Zonisamide Efficacy as Adjunctive Therapy in Children With Refractory Epilepsy

How to Cite This Article: Karimzadeh P, Ashrafi MR, Bakhshandeh Bali MK, Nasehi MM, Taheri Otaghsara SM, Taghdiri MM, Ghofrani M. ZonisamideEfficacy as Adjunctive Therapy in Children With Refractory Epilepsy. Iran J Child Neurol. 2013 Spring; 7(2):37-42.ObjectiveApproximately one third of epileptic children do not achieve complete seizure improvement. Zonisamide is a new antiepileptic drug which is effective as adjunctive therapy in treatment of intractable partial seizures.The purpose of the current study was to evaluate the effectiveness, safety, and tolerability of Zonisamide in epileptic children.Materials & MethodsFrom November 2011 until October 2012, 68 children who referred to Children’s Medical Center and Mofid Children Hospital due to refractory epilepsy (failure of seizure control with the use of two or more anticonvulsant drugs) entered the study. The patients were treated with Zonisamide by dose of 2- 12 mg/kg daily in addition to the previous medication. We followed the children every three to four-weeks intervals based on daily frequency, severity and duration of seizures. During the follow-up equal and more than fifty percent reduction in seizurefrequency or severity known as response to the drug.ResultsIn this study 68 patients were examined that 61 children reached the last stage.35 (57.4%) were male and 26 (42.6%) patients were female.After first and six months of Zonisamide administration daily seizure frequency decreased to 2.95±3.54 and 3.73±3.5 respectively. There was significant difference between seizure frequency in first and six month after Zonisamide toward initial attacks. After six months ZNS therapy a little side effects were created in 10 patients (16.4%) including stuttering(4.9%), decreased appetite (4.9%), hallucination (1.6%), dizziness(1.6%), blurred vision(1.6%) and suspiring(1.6%) as all of them eliminated later dosage reduction.ConclusionThis study confirms the short term efficacy and safety of Zonisamide in children with refractory epilepsies. References1. Michal V. Johnston. Seizure in childhood. In: Robert M. Kliegman, Richad E. Behrman. Nelson Text book of pediatrics.18th edition; Philadelphia:Saunders,2010,p 2457-70.2. Icardi J.Epilepsy in children .3th Ed. Lippincott Williams &Wilkins .edition .2004:38.3. Barbara Olson.Treatment of refractory epilepsy.Adv stumed 2005:Vol 5;470-473.4. Berto P. Quality of life in patients with epilepsy and impactof treatments. Pharmacoeconomics 2002;20:1039-59.5. Lepikk IE. Zonisamide: chemistry, mechanism of action,and pharmacokinetics. Seizure 2004;13S: S5-9.6. Sobieszek G, Borowicz KK, Kimber-Trojnar Z, Małek R, Piskorska B, Czuczwar SJ. Zonisamide: a new antiepileptic drug. Pol J Pharmacol 2003 Sep- Oct; 55(5): 683-9.7. Ohtahara, S. Zonisamide in the management of epilepsyJapanese experience. Epilepsy Res 2006;68 (Suppl. 2):25-33.8. Baulac M. Introduction to zonisamide. Epilepsy Res 2006;68(Suppl. 2):S3-S9.9. Hwang H, Kim KJ. New antiepileptic drugs in pediatric epilepsy. Brain Dev 2008;30(9):549-55.10. Kyoung Heo, Byung In Lee, Sang Do Yi, Yong Won Cho, Dong Jin Shin, Hong Ki Song, et al. Shortterm efficacy and safety of zonisamide as adjunctive treatment for refractory partial seizures: A multicenter open-label single-arm trial in Korean patients. Seizure 2012;21:188-193.11. Schulze-Bonhage A. Zonisamide in the treatment of epilepsy. Expert Opin Pharmacother 2010;11(1):115-26.12. Lee YJ, Kang HC, Seo JH, Lee JS, Kim HD. Efficacy and tolerability of adjunctive therapy with zonisamide in childhood intractable epilepsy. Brain Dev 2010;32(3):208-12.13. Marmaroua A, Pellockb JM. Zonisamide: Physician and patient experiences. Epilepsy Res 2005 Mar-Apr;64(1-2):63-9.14. Fallah R, Divesalar S, Babaei A. The efficacy and safety of zonisamide as an add-on drug in the treatment of lennox–gastaut syndrome. Iran J Child Neurol 2010 Nov;l4(3):45-50.15. Shah J, Shellenberger K, Canafax DM. Zonisamide: chemistry, biotransformation, and pharmacokinetics. Healthcare, Philadelphia (2002), pp. 873-879.16. Baulac M. Introduction to zonisamide. Epilepsy Res2006 Feb;68 (Suppl 2):S3-9. 17. Baulac M, Ilo E. Leppik. Efficacy and safety of adjunctive zonisamide therapy for refractory partial seizures. Epilepsy Research 2007;75:75-83. 18. Coppola G, Grosso S, Verrotti A, Parisi P, Luchetti A,Franzoni E, et al. Zonisamide in children and young adults with refractory epilepsy: An open label, multicenter Italian study. Epilepsy Research 2009;83:112-116.19. Tan HJ, Martland TR, Appleton RE, Kneen R. Effectiveness and tolerability of zonisamide in children with epilepsy: A retrospective review. Seizure 2010;19:31-35.20. Stephen LJ, Kelly K, Wilson EA, Parker P, Brodie MJ. A prospective audit of adjunctive zonisamide in an everyday clinical setting. Epilepsy Behav 2010 Apr;17(4):455-60.21. Catarino CB, Bartolini E, Bell GS, Yuen AWC, Duncan JS, Sander JW.The long-term retention of zonisamide in a large cohort of people with epilepsy at a tertiary referral centre. Epilepsy Research 2011;96:39-44.22. Loscher W, Schmidt D. Experimental and clinical evidence for loss of effect(tolerance) during prolonged treatment with antiepileptic drugs. Epilepsia 2006;47(8):1253-84.23. Eun SH, Kim HD, Eun BL, Lee IK, Chung HJ, Kim JS, et al. Comparative trial of low- and high-dose zonisamide as monotherapy for childhood epilepsy.Seizure 2011;20(7):558-63

Energy Storage Applications in Power Systems with Renewable Energy Generation

In this dissertation, we propose new operational and planning methodologies for power systems with renewable energy sources. A probabilistic optimal power flow (POPF) is developed to model wind power variations and evaluate the power system operation with intermittent renewable energy generation. The methodology is used to calculate the operating and ramping reserves that are required to compensate for power system uncertainties. Distributed wind generation is introduced as an operational scheme to take advantage of the spatial diversity of renewable energy resources and reduce wind power fluctuations using low or uncorrelated wind farms. The POPF is demonstrated using the IEEE 24-bus system where the proposed operational scheme reduces the operating and ramping reserve requirements and operation and congestion cost of the system as compared to operational practices available in the literature. A stochastic operational-planning framework is also proposed to adequately size, optimally place and schedule storage units within power systems with high wind penetrations. The method is used for different applications of energy storage systems for renewable energy integration. These applications include market-based opportunities such as renewable energy time-shift, renewable capacity firming, and transmission and distribution upgrade deferral in the form of revenue or reduced cost and storage-related societal benefits such as integration of more renewables, reduced emissions and improved utilization of grid assets. A power-pool model which incorporates the one-sided auction market into POPF is developed. The model considers storage units as market participants submitting hourly price bids in the form of marginal costs. This provides an accurate market-clearing process as compared to the `price-taker' analysis available in the literature where the effects of large-scale storage units on the market-clearing prices are neglected. Different case studies are provided to demonstrate our operational-planning framework and economic justification for different storage applications.A new reliability model is proposed for security and adequacy assessment of power networks containing renewable resources and energy storage systems. The proposed model is used in combination with the operational-planning framework to enhance the reliability and operability of wind integration. The proposed framework optimally utilizes the storage capacity for reliability applications of wind integration. This is essential for justification of storage deployment within regulated utilities where the absence of market opportunities limits the economic advantage of storage technologies over gas-fired generators. A control strategy is also proposed to achieve the maximum reliability using energy storage systems. A cost-benefit analysis compares storage technologies and conventional alternatives to reliably and efficiently integrate different wind penetrations and determines the most economical design. Our simulation results demonstrate the necessity of optimal storage placement for different wind applications.This dissertation also proposes a new stochastic framework to optimally charge and discharge electric vehicles (EVs) to mitigate the effects of wind power uncertainties. Vehicle-to-grid (V2G) service for hedging against wind power imbalances is introduced as a novel application for EVs. This application enhances the predictability of wind power and reduces the power imbalances between the scheduled output and actual power. An Auto Regressive Moving Average (ARMA) wind speed model is developed to forecast the wind power output. Driving patterns of EVs are stochastically modeled and the EVs are clustered in the fleets of similar daily driving patterns. Monte Carlo Simulation (MCS) simulates the system behavior by generating samples of system states using the wind ARMA model and EVs driving patterns. A Genetic Algorithm (GA) is used in combination with MCS to optimally coordinate the EV fleets for their V2G services and minimize the penalty cost associated with wind power imbalances. The economic characteristics of automotive battery technologies and costs of V2G service are incorporated into a cost-benefit analysis which evaluates the economic justification of the proposed V2G application. Simulation results demonstrate that the developed algorithm enhances wind power utilization and reduces the penalty cost for wind power under-/over-production. This offers potential revenues for the wind producer. Our cost-benefit analysis also demonstrates that the proposed algorithm will provide the EV owners with economic incentives to participate in V2G services. The proposed smart scheduling strategy develops a sustainable integrated electricity and transportation infrastructure

Micro-grids - Applications, Operation, Control and Protection

The integration of recent and emerging energy technologies in the existing electric grid requires modifications in several aspects of the grid, including its architecture, protection, operation, and control. Micro-grid provides a solution for integrating distributed energy resources such as renewable energy generation, energy storage systems, electric vehicles, controllable loads, etc. and delivers flexibility, security, and reliability by operating in both grid-connected and isolated modes. This book provides an overview of micro-grid solutions, applications, and implementations. State-of-the-art methods for micro-grid operation, optimization, and control are presented. Distributed energy resources and their interactions in micro-grids are also studied. In addition, micro-grid designs, architectures, and standards are covered, as are micro-grid protection strategies and schemes for different operation modes