In this paper, an approach is presented to calculate the reserve capacity of Pump-Hydro Combined Energy Storage (PHCES) integrated with wind generation. The proposed approach utilizes Monte Carlo methods to obtain all the reasonable capacity of PHCES based on the power system reliability requirement. The pumping and hydro period of PHCES will be taken into consideration to estimate the reliability of wind power generation with EES. Finally this approach is applied in a RBTS system to calculate the minimum capacity of PHCES

Lo, Kwoklun

Lu, Jianfeng

Shi, Shuai

Wang, Su

Su Wang

Shuai Shi

Kwoklun Lo

Jianfeng Lu

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World Journal of Engineering and Technology

An Approach to Calculate the Capacity of Pump-Hydro Combined Energy Storage  with Wind Power Integration

University of Strathclyde Institutional Repository

Strathprints Institutional RepositoryWang, Su and Shi, Shuai and Lo, Kwoklun and Lu, Jianfeng (2016) An approach to calculate the capacity of pump-hydro combined energy storage with wind power integration. World Journal of Engineering and Technology, 4 (3). pp. 43-49. ISSN 2331-4222 , http://dx.doi.org/10.4236/wjet.2016.43D006This version is available at http://strathprints.strath.ac.uk/59707/Strathprints is  designed  to  allow  users  to  access  the  research  output  of  the  University  of Strathclyde. Unless otherwise explicitly stated on the manuscript, Copyright © and Moral Rights for the papers on this site are retained by the individual authors and/or other copyright owners. Please check the manuscript for details of any other licences that may have been applied. You may  not  engage  in  further  distribution  of  the  material  for  any  profitmaking  activities  or  any commercial gain. You may freely distribute both the url (http://strathprints.strath.ac.uk/) and the content of this paper for research or private study, educational, or not-for-profit purposes without prior permission or charge. Any  correspondence  concerning  this  service  should  be  sent  to  Strathprints  administrator: strathprints@strath.ac.ukWorld Journal of Engineering and Technology, 2016, 4, 43-49 http://www.scirp.org/journal/wjet ISSN Online: 2331-4249 ISSN Print: 2331-4222 DOI: 10.4236/wjet.2016.43D006  October 20, 2016    An Approach to Calculate the Capacity of Pump-Hydro Combined Energy Storage  with Wind Power Integration Su Wang, Shuai Shi, Kwoklun Lo, Jianfeng Lu  Department of Electronic and Electrical Engineering, University of Strathclyde, Glasgow, UK      Abstract In this paper, an approach is presented to calculate the reserve capacity of Pump- Hydro Combined Energy Storage (PHCES) integrated with wind generation. The proposed approach utilizes Monte Carlo methods to obtain all the reasonable capac-ity of PHCES based on the power system reliability requirement. The pumping and hydro period of PHCES will be taken into consideration to estimate the reliability of wind power generation with PHCES. Finally this approach is applied in a RBTS sys-tem to calculate the minimum capacity of PHCES.  Keywords Wind Power, PHCES Cooperation System, Reliability, Comprehensive Benefits  1. Introduction The integration of wind power generation into grid networks is increasing around the world. Wind power generation is taken as an energy resource instead of a capacity re-source because that wind source output is unpredictable and intermittent. Capacity re-source is a kind of resource that could be useful for generating power to meet the load demand [1]. Since conventional designing methods are concentrated on capacity and reliability designing, the challenges of wind power is to balance the effective generation output and the gird demand. In order to reduce the incongruity between the time va-rying wind generation and the load demand, a method that PHCES integrated has been built [2]. Several wind speed prediction methods have been summarized in papers [3]-[5], which could be utilized to simulate the power output from a wind turbine generator. Papers [6] [7] define the wind capacity credit by comparing the system reliability be-Wang, S., Shi, S., Lo, K. and Lu, J.F. (2016) An Approach to Calculate the Capacity of Pump-Hydro Com- bined Energy Storage with Wind Power In- tegration. World Journal of Engineering and  Technology, 4, 43-49. http://dx.doi.org/10.4236/wjet.2016.43D006    July 20, 2016 October 13, 2016 October 20, 2016  S. Wang et al.  44 tween the conventional unit and wind power generator. Papers [8]-[10] proposed si-mulation methods to calculate the reliability of wind power system and are based on continuous and discontinuous Monte Carlo methods.  In this paper, the Monte Carlo method is used to evaluate the reliability of the power system and some reliability parameters are obtained. Then the change of the reliability before and after the wind power addition is analyzed and the effect of wind power on power system reliability is elaborated. After that, by comparing the reliability index of conventional power system and system with wind power, the minimum capacity of PHCES can be evaluated. 2. Wind Speed and Wind Farm Output Model 2.1. Study System 1 explains a sinle line schematic diagram of the study WP-PHCES unit, inte-grated to a distribution network. WP-PHCES is based on a wind farm which contains several double-fed induction generators (DFIG) which are controlled by the connection of a grid-side converter (GSC) and rotor-side converter (RSC); and a PHCES is con-nected to store the redundant wind power. In this figure, (1)ijP  and (2)ijP  stand for ac-tive power extracted by wind power unit and conventional generators respectively. The total power, (1) (2)ij ijP P  is denoted hereafter by ijP . In this study model, phesP  is au-tomatically controlled through a voltage/frequency regulation scheme to compensate for the difference between ijP  and the total load power, LP . 2.2. Wind power Predict of Study System Wind energy output is stochastic and is often predicted by several probabilistic tech-niques [8]. In power systems, two distinguishing methods have been used to evaluate the reliability indices [11]. There are analytical method and the simulation method re-spectively. This paper utilizes F & D method and Weibull probability density function to predict wind speed based on computer simulation. Weibull distribution when com-bined with Monte Carlo simulation method shows an excellent performance on wind speeds modeling. It could accommodate the wind speed fluctuation by modifying the    Simple line schematic diagram of the study system. S. Wang et al.  45 parameters. The expression of individual Weibull probability density function is as Eq-uation (1).   1 exp[ ]kkkkv vf v cc § · ¨ ¸© ¹                      (1) where:  f v : probability of occurrence of wind speed v, c and k: Weibull scale and shape parameter respectively. And Equation (2) below could model the wind speed. 1[ ( ) ]kn nv c In U                           (2) where: nv : wind speed at hour n (m/s) nU : a uniform distributed random number generated at hour n. In this paper c and k has been set to standard values that are 7 and 2 respectively. So the wind speed could be generated as shown in  2. The power curve of wind turbine in this paper is obtained by using above wind speed which is substtued in Equation (3). 2(1)0( * * ) *0wind ciwind wind rate ci wind rijrate r wind cowind cov vA B v C v P v v vPP v v vv vd­°    d° ®  d°° !¯        (3) where: ,ci cr cov v v : designed cut-in speed, rate speed and cut-out speed of wind turbine (m/s) windv : wind speed (m/s) rateP : rated power output of wind turbine (MW) The constants of A, B, C can be calculated busing the equation in [12].   Wind speed generated by Weibull distribution random. S. Wang et al.  46 3. Reliability of Study System Reliability Index ELCC (effective load carrying capability) is a kind of reliability index. It is used to cal-culate the value of the capacity of added renewable and traditional power plant. Loss of load expectation (LOLE) is the most common used index utilized to evaluate ELCC, which means the risk standard of the additional power plant, and in this paper, LOLE is considered as the reliability index, also LOEE (loss of energy expectation) will be used. LOLE could been determined by Equation (4), and LOEE could be determined by Equ-ations (5) and (6) respectively 1( [ ] [ ])Nij LtLOLE p P h P h  ¦                     (4) 0,,ij LiL ij ij LP PLOEEP P P Pt­° ®  °¯                    (5) 87601NitLOEE LOEE   ¦                        (6) where, [ ]ijP h : the available generation capacity during period h (MW); [ ]LP h : the load demand during period h (MW). Then, the reliability indices calculations are based on Monte Carlo simulation. Load response of PHCES is very fast which needs just 5 minutes from full stop to full gene-rating capacity and 1 minute from full stop to full pumping, so time of PHES to trans-late between charge and discharge is not considered. In the electrical power system, if the failure rate and the repair rate of a certain repairable component are O  and ߤre-spectively. O  and P  are the basic parameters used for measurement of analogue element duration and state transition characteristics in Monte Carlo Algorithm, while the mean time to failure and the mean time to repair being MTTF and MTTR respec-tively, the following important relation comes into effect: 1 /MTTE O                           (7) 1 /MTTR P                           (8) The forced outage rate (FOR) of the component reflects the unavailability of the sys-tem that is the basic parameter of non-sequential simulation algorithm measuring the system status by sampling, can be calculated with equation 9: MTTRFORMTTR MTTFOO P                     (9) The FOR and MTTR of conventional and wind generators will be considered, be-cause wind turbine will meet an average of 170 hours per year per turbine downtime and an average 1.5 times failure per year per turbine. This failure will actually influence the system reliability, and PHCES can be recognized as a conventional generator. There are five steps to evaluate the reliability index of test system: 1) Utilizing certain load variation pattern and adjustable annual peak load demand to determine load model LP  in one year. S. Wang et al.  47 2) Assume conventional generators used for computations are considered as a two state model (2)ijP . 3) Evaluating LOLE of conventional power system by comparing LP  and (2)ijP . 4) Adding a certain capacity of wind power and at the same time remove the same size of conventional power units. Generating the output of wind power by using Weibull distribution using Equation (3). 5) Evaluating LOLE of test system by comparing ijP  and LP . 4. Results and Discussion Assume wind penetration level is 10% and recognize wind power is must take power output which means that the output of conventional generators decreases 10%. Gene-rating wind speed by using Weibull distribution, and then time series of wind speed generated by matlab input to the wind turbine characteristic functions (3). After that, total power generation (1) (2)ij ij ijP P P   could be obtained by combining the outputs of conventional generators. shows the snap shot of simulated wind power output in 300 hours.  shows the superimposition of chronological available test system capacity and system load, an outage is counted when the load exceeds the available system ca-pacity. From the figure and the simulation results in this figure, the system capacity is less than the load in twice. It means that LOLE is 2 in this simulation. In fact, Monte Carlo Method is a continuous varying convergence process and it cannot provide real value for the expected reliability index but can approach it. It is important to keep Monte Carlo Simulation sampling repeatedly for many times within a long test period to achieve certain accuracy level of the simulation results. In this paper, the setting of the number of samples is 10,000. After 10,000 sampling, LOLE of general power system and system with wind power are 1.0717 and 5.4601 respectively. The PHCES combines the advantages of the com-bined flexibility of PHCES units and reliability assessment method to reduce the im-pacts of wind power fluctuation on power outputs and maintain the reliability level.    Snap shot of simulated wind power output. S. Wang et al.  48   Superimposition of test system capacity and load.  The operation of PHCES will be considered when evaluate the reliability index of WP-PHCES system. First, the control logic of the WP-PHCES, the pumping stored energy, > @1PHESE h   is calculated for all hours, expressed by equation (10). > @> @ > @> @ > @ > @ > @> @ > @1min PHES PHES minPHES PHES PHES min PHES PHES maxmax max bat PHESE if E h TP h EE h E h TP h if E E h TP h EE if E E h TP h­  d°   d  d®° d ¯  (10) where: minE  and maxE  are, respectively, the lower and upper limits of the PHES. T: the duration of state Then based on > @1PHESE h  , the PHES available for export, PPHES [h], and subse-quently the “effective load power” > @> @ > @> @ > @ > @ > @> @ > @   hydro max ij L hydro maxPHES ij L hydro max ij L pump maxpump max ij L pump maxP if P h P h PP h P h P h if P P h P h PP if P h P h P   ­   d °   d  d®°  t¯   (11) where: maxhydroP  : the maximum capacity of PHCES during hydro period. (MW) maxpumpP  : the maximum capacity of PHCES during pumping period (MW) The simulation results of reliability indices for WP-PHCES are summarized in 1. As can be seen from , when the capacity of PHCES is larger than 12 MW, the test system could meet the reliability requirement. 4. Conclusions and Future Work This paper proposed an approach to calculate the minimum capacity of PHCES when wind power is integrated to power grid. The methodology is based on Monte Carlo method that is used to analyze the power system reliability, aims to analyze the change of power system reliability involving wind farm power. The force outage rate of gene-rating units and operation of PHCES has been considered. The test system example shows that the capacity of the wind farms power has a negative effect on the reliability  S. Wang et al.  49 LOLE result of test system at different values of PHCES.  2.4 4.8 7.2 9.6 12 14.4  2.7598 2.1875 1.6715 1.3048 1.1347 0.9023  of the power grid. As can be seen form the reliability index of wind power and test sys-tem, PHCES is effective to overcome the wind power fluctuation problem. Future work will focus on PHCES capacity optimization, which can be described as using the optimal capacity of PHCES to achieve the required level of reliability and highest benefits. This paper has described the minimum capacity of PHCES to meet the required reliability, in the next paper; comprehensive benefits and cost should be taken into consideration to find out the optimal capacity of PHCES. References [1] US Department of Energy (2008) 20% Wind Energy by 2030, Increasing Wind Energy’s Contribution to U. S. Electricity Supply. Oak Ridge. [2] Tuohy, A. and O’Malley, M. (2009) Impact of Pumped Storage on Power Systems with In-creasing Wind Penetration. Power & Energy Society General Meeting, PES’09. IEEE. [3] Shuai, S. and Lo, K.L. (2012) Reliability Assessment of Power System Considering the Im-pact of Wind Energy. 47th International Universities Power Engineering Conference (UPEC). [4] Billinton, R. and Dange, H. (2011) Incorporating Wind Power in Generating Capacity Re-liability Evaluation Using Different Models. IEEE Transactions on Power Systems, , 2509- 2517. http://dx.doi.org/10.1109/TPWRS.2011.2120633 [5] Billinton, R., et al. (2012) Adequacy Assessment Considerations in Wind Integrated Power Systems. IEEE Transactions on Power Systems, , 2297-2305.  http://dx.doi.org/10.1109/TPWRS.2012.2205022 [6] Wang, H.C., Lu, Z.X. and Zhou, S.X. (2005) Resarch on the Capacity Credit of Wind Ener-gy Resources. Proceedings of the CSEE, , 103-106. [7] Chen, S.Y., et al. (2000) Reliability Model of Wind Power Plants and Its Application. Pro-ceedings of the Csee, , 26-29. [8] Bakirtzis, A.G. (1992) A Probabilistic Method for the Evaluation of the Reliability of Stand Alone Wind Energy Systems. IEEE Trans. Energy Convers, 7, 99-107.  http://dx.doi.org/10.1109/60.124548 [9] Roy Billinton, R.N.A. (1992) Reliability Evaluation of Engineering Systems: Concepts and Techniques. Second Edition, Plenum Press, New York and London.  http://dx.doi.org/10.1007/978-1-4899-0685-4 [10] Billinton, R. and Gan, L. (1991) Use of Monte Carlo Simulation in Teaching Generating Capacity Adequacy Assessment. IEEE Transactions on Power Systems, 6, 1571-1577.  http://dx.doi.org/10.1109/59.117004 [11] Billinton, R. and Allan, R.N. (1996) Reliability Evaluation of Powersystems. Plenum Press, New York. [12] Zhou, Y. (2009) Some Issues of Integrating Wind Power Generation into Electric Networks. Department of Electronic and Electrical Engineering, University of Strathclyde, UK, 252.      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An approach to calculate the capacity of pump-hydro combined energy storage with wind power integration

An approach to calculate the capacity of pump-hydro combined energy storage with wind power integration

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