Optimization of Wind-Pumped Storage Hydro Power System

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1 International Journal of Engineering Technology, Management and Applied Sciences Optimization of Wind-Pumped Storage Hydro Power System 1 Mahesh Kumar P.G. Student Mechanical Engg.Dept. MMMT Gorakhpur 2 Prashant Saini Assistant Professor Mechanical Engg.Dept. MMMT Gorakhpur 3 Neeraj Kumar P.G. Student Mechanical Engg.Dept. MMMT Gorakhpur ABSTRACT In this study it has been attempted to use wind energy supported with pumped storage for the better utilization of available wind energy. This study is related with profit maximization after coupling of the wind farm with the pumped storage system. In the pumped storage, two reservoirs at different elevations are used, where water is pumped from lower reservoir to upper reservoir at the time of excess wind energy and during the time of high demand water is released from upper basin to lower basin to generate hydro power energy. Pumped storage plant is added to electricity system to achieve high economic gain from wind energy system having variable and intermittent characteristics. A short term (hourly) optimization problem related with the cost has been developed for optimum daily operational strategy and we optimize it in optimization software. Keywords Pump storage system, Profit maximization and Hydro power energy. INTRODCTION Power is very significant infrastructure in overall growth of any nation in the world. It is the tool to forge the profitable growth of the country. There has been an ever-increasing require for power generation recently in all countries of the world. In the true global perspective of the power demand it can be laid with certainty that many of the developing countries of the world are facing the energy crises and busy in formulating methods and devices to discover the various potential of energy generation for satisfying the growing demand. As such there has to be a fresh appraisal of energy producing resources and formulation of program for the implementation of plans with maximum efficiency. It is being realized that renewable energy sources can augment the availability of energy and provide a viable option in wide range of applications and can play an increasing important role in solving the twin problem of energy shortage as well as supply to the decentralized applications. Nowadays, small hydropower is considered to be the promising source among renewable energy, being a proven, mature, reliable, easily available energy technology. CONCEPTS OF WIND- PMP STORAGE HYDRO POWER SYSTEM Wind - pumped storage power plants Wind pump storage hydro power plants are nothing but storing wind energy in the form of hydro energy by using pumped storage power plants. As wind energy is not consistent through the time, it should be stored when it is available more than the demand. Demand goes high when the wind energy is can t supply the whole power that is required, and then the shortage power is taken from pumped storage plant which was stored during off peak hours. The figure 1 shows the layout of wind-pumped storage plant in which wind energy is used to drive the pumps of the PSP during pumping mode. In the above figure it is shown that the pump and hydro generator separate, but now a day s both pumping and generation is performed by reversible turbine only this means that there will be no separate components for pumping and generation. 67

2 International Journal of Engineering Technology, Management and Applied Sciences Figure 1: Wind-Pumped Storage plant In general, a PHPS system operates by using the excess power generation at times of low electrical demand to pump water to a reservoir at a higher elevation. When there is a higher demand of power then water is released back into the lower reservoir through hydraulic turbine which generates electricity that can be run through the grid to satisfy the peaks during high load demand. A typical PHPS system consists of an upper and lower reservoir, connecting lines, pump turbine, and motor-generator set. Optimization of wind-pumped storage systemwind -hydro storage model The model was developed exploiting two ideas. 1) The capability of controlling a portion of the wind park power production, assuring a minimum power delivery to the grid, no matter the wind speed conditions and 2) The utilization of a stochastic variable to represent the wind speed forecasting. The available wind energy should be known 2 periods ahead in order to increase its competitiveness in the energy market. The accuracy of the forecasted wind power and the energy storage ability should enable us for determining the width of the output generation interval for the hours ahead. To improve the output controllability of the wind generation, a hydro system is added to the wind park, consisting of: 1) A water pump station that elevates water from a source (i.e., river, lake, and reservoir) to an upper water reservoir, using exclusively the electric power produced by the wind generators; 2) A mini hydroelectric power plant (eventually, these latter two equipment can be replaced by a single reversible hydraulic pump/turbine); and 3) Penstock and pumping pipes. Although the hydraulic equipment and the wind park can be located in different places, in the present work an electrical proximity between them is assumed. PROBLEM FORMLATION In this work, the maximization of the 2-h operational profit of the W-H power plant is wanted. For that purpose, an optimization problem was formulated, through the maximization of the economic gain that results from the energy delivered to the grid, considering the main operational restrictions of the W-H system for 2 hourly periods. The solution of this problem provides an operational strategy to be followed by the wind, hydro generator/pumping units during the next hours. In the present work wind potential P v is taken as input data. The optimization problem which is formulated is given below 68

3 International Journal of Engineering Technology, Management and Applied Sciences Objective function: n Max i=1 (c i P i c p P pi ) nc (1) Subjected to: P i = Pw i + Ph i (2) Pv i = Pw i + Pp i + P DLi (3) E i+1 = E i + t(η p Pp i Ph i ) η h () E 1 = E 1 (5) E n+1 = E n+1 αp L i P i P i P g L (Pw i + P pi ) P g (8) P h L Ph i P h (9) P p L Pp i P p (1) E i E (11) 1. ` (12) P DLi > i = 1,, n (13) (6) (7) Where the variables are vectors describing as P v = Hourly vector of available wind power in the considered scenario. P = Hourly active powers delivered to the network by the W-H facility. P w = Hourly active powers delivered to the network by the wind generator. P p = Hourly average active power consumed by the water pump station. P h = Hourly active powers produced by the hydro generator. P DL = Hourly dumping power loads. E = Energy storage levels in the reservoir in each hour. P L = Hourly vectors of minimum power output limits related with market requirements and network restrictions. P = Hourly vectors of maximum power output limit related with market requirements. c = Vector of hourly active power prices. α = Variable that represents a decrease factor in the output lower limit. c p = Pump operation cost. c α = Penalty for generation below the lower output limit. η p = Efficiency of water pump station and water pipes network. η h = Efficiency of the water reservoir and hydro generator. E = Reservoir storage capacity. E 1 = Initial level of the reservoir. E n+1 = Final level of the reservoir. L P g = Lower power capacity limit of the wind farm. P g = pper power capacity limit of the wind farm. L P h = Lower production power of the hydro generator. P h = pper production power of the hydro generator. L P p = Lower physical power limit of the pump station. 69

4 International Journal of Engineering Technology, Management and Applied Sciences = pper physical power limit of the pump station. t = Duration of each interval (1 h, in this case). n = Number of discrete intervals. Scheduling is done by solving the optimization problem as mention above for both wind and wind-hydro system. From the observation of the objective function (1), one can identify two terms: P p 7 (c i P i c p P pi ) nc n i=1 The first aims to maximize the profit in the active hourly power (energy) delivered by the W-H plant to the grid, considering the internal pumping cost; The second component represent the penalty term for generating power below the lower limit. Here represents a decrease factor in the output lower limit, which decides the range of penalty. The limits of were set in between and 1. If the output lower limit is equal to the minimum power output limits (P L ) then the value of will zero. The value of will be increased for every when there is decrease in output power below lower limit. As the lower limit should be rected in all the intervals, the expression c is multiplied by the number of discrete intervals. P i = Pw i + Ph i The above constrain represents the output of hourly active power of W-H plant that is supplied to the grid, which is equal to the summation of both the hydro production and the portion of the available wind power which is directly delivered to the grid. Pv i = Pw i + Pp i + P DLi The above constrain represents the wind power in which a fraction of the hourly available wind power is directly supplied to the grid during the considered interval (Pw i ). Another portion of this can be stored (by using the hydro components) and delivered in subsequent intervals (Pp i ). In some particular cases, it may happen that a part of the available wind energy could not be used (P DLi ). E i+1 = E i + t(η p Pp i Ph i η h ) The constrain describes the energy balance in the reservoir. Energy at the beginning of the (i+1) interval is equal to the energy in the reservoir is the initial level in the i -interval plus the pumped energy, minus the energy supplied to the grid by the hydro generation during that same interval. E 1 = E 1 The above constrain represent initial level of the reservoir. The initial level is known, because it is the final level of the previous day. E n+1 = E n+1 The above constrain represent final level of the reservoir. However, the optimal final level of the current day is unknown and depends on the expected operation strategy to be defined for the next day. αp L i P i P i The above constrain represents power output limits related to market requirement. α is the decrease factor in the output lower limit. It is the value of α which decides the range of penalty for generation below the lower output limit. That s why α is multiplied with the lower output limit as shown in above equation. P g L (Pw i + P pi ) P g The above constrain represents the power capacity limit of the wind farm. The power capacity of the wind form is nothing but the summation of the fraction of the hourly available wind power is directly supplied to the grid during the considered interval (Pw i ) and the fraction of wind power that is stored in the hydro components (P pi ) P h L Ph i P h

5 Wind power potential [MW] International Journal of Engineering Technology, Management and Applied Sciences The above constrain represents the hydro generation limits of a pumped storage plants. The maximum hourly hydro generation level depends on the generation equipment limits and on the available energy in the reservoir for that interval. P p L Pp i P p The above constrain represents power limits of the pump station of pumped storage plant. Plant operates at its maximum power limit when there is excess of wind power. E i E The above constrain represents the reservoir storage capacity in terms of energy. 1. The above constrain represents the upper and lower limits of the variable that represent a decrease factor in the output lower limit. Value of will be high if there is great decrease in power than output lower limit and vice versa. P DLi > i = 1,, n The above constrain represent the fraction of wind power which is not used for any purpose which is also known as dumping power load. Its value will never be negative. In the above formulation, (1) (13) represent a linear optimization problem. This problem is solved using a Knitro of AMPL solver. However, any other linear optimization methods could also be used. INPT DATA Besides the physical characteristics of the W-H system, the model needs the following input data to calculate the estimated operation in the study horizon. As an input data a wind speed or wind potential power is required, the wind potential required is calculated from wind speed collected from the wind world plant Daman. The hourly wind power is given in the below figure. As it is observed from the figure that wind power fluctuates, it can t supply to the grid directly. That s why a pumped storage plant is integrated with this Time [hours] Figure 2: Available Wind power Potential Data In Table 1, η L = η p * η h is the global hydraulic circuit efficiency, here assumed to be 75%, a typical value with the nowadays technology. Both maximum hydro generation and pump nominal capacity are considered 25% of the wind park installed capacity. The penalty for generation below the lower output limit (c α ) should be a large value, aiming to verify this restriction. In the present work, a value approximately three to four times the best power price is used. The cost of the pumping operation (c p ) only represents internal operational costs. To test the quality of the proposed methodology, the W-H generation facility described in Table 1 is used. 71

6 Power output [MW] International Journal of Engineering Technology, Management and Applied Sciences P g P h P p Table 1: W-H Power Plant Characteristics c α c p E csp csp E 1 E n+1 η L Turbines [MW] [MW] [MW] [Rs./MWh] [Rs./MWh] [MWh] [MWh] [MWh] x2[MW] The power tariffs taken into consideration while solving the optimization problem is given below, the following prices for wind energy were used for the i th hours of the day: Table 2: Power Tariffs i (hr s) i<7 2 7 i<9 9 i< i< i<2 2 c i [Rs/kWh] The solution of the optimization problem (1) (13) provides the hourly active power to be generated by the hydro and wind generators during each of the 2 h. Storage levels and the pump operational strategy in the period are also determined, assuming that wind power is supposed constant during each period. RESLTS AND DISCSSIONS The solution of the optimization problem (1) (13) provides the hourly active power to be generated by the hydro and wind generators during each of the 2 h. A case is considered for output power operational band restrictions in all periods of the day Case: ( 3) MW P i 8MW, Figure 3: Power supplied to the Grid by W-H system

7 Energy [MWhr] Power[MW] International Journal of Engineering Technology, Management and Applied Sciences Figure 3 showing the power output that is supplied to the grid which was plotted from the results obtained after solving the optimization problem with different constrains. The power that is supplied to the grid by wind-hydro system is the combined power of the fractional wind power (P w ) and the power generated by the hydro generator (P h ) Without PSP With PSP Figure : Power supplied by wind farm with and without PSP Figure showing the graph of power generation with PSP and without PSP with this graph it is clear that the penalty in case of without PSP is more than with PSP which results in lowering the profit gain by the without PSP system Figure 5:represents the hourly Energy storage levels in the Reservoir 73

8 Power [MW Storage level [MWhr] Pump[MW] International Journal of Engineering Technology, Management and Applied Sciences The above Figure shows the variation of storage levels in reservoir in terms of MWhr for 2 hours time steps Storage level Pump Figure 6:Energy storage levels in the Reservoir Vs Pumping Energy Figure 6 shows the variation of storage levels in MWhr and pump consumption in MW. From graph it is clear that the storage level increases when the pump station is in operation mode and if the storage level decrease when the hydro station is in operation mode. Pump station operation is zero means that the plant is operation in generation mode. From the graph it is clear that when the pump station consumption is zero there is considerable fall in the storage level these means the hydro plant is in generation Pumping Hydro gen Figure 7: Represents operation of PSP in different modes for given time intervals The above Figure shows the operation of pump storage power plant in pumping as well as in generation mode at different hours. 7

9 Power [MW] International Journal of Engineering Technology, Management and Applied Sciences Wind gen to grid Power deliver Hydro gen Figure 8:represents the total power supplied to the grid by wind-hydro system As shown in the above Figure the power output supplied to the grid is the summation of the power delivered by wind generation to the grid and power produced by hydro generation. P i = Pw i + Ph i PROFITS Table 3 presents the gains obtained when using the W-H operation strategy comparatively to the OW operation, for the first 2 h of simulation. Table 3: Profits: Only wind vs Wind Hydro Power Plant POWER GENERATION OPTIONS Without PSP With PSP AMONT (Rs.) 3827(daily) 32597(daily) Net Gain 9897(daily) Percentage Gain 13.3% Annual Gain 182 lacs After solving the optimization problem for both cases i.e. only wind and integrated wind hydro power plants, daily profits were compared. The daily profit of only wind plant operation gives Rs. 3,82,7 and daily profit of Wind integrated with hydro plant was obtained as Rs.,32,597. The daily Gain in profit for installing wind-hydro power plant instead of only wind plant is around Rs. 9, 897. The percentage daily gain in installing wind hydro plant instead of only wind plant is about 13.3%. If we extend for annually then the gain will be approximately around Rs. 182 lacs as mentioned in above table. 75

10 International Journal of Engineering Technology, Management and Applied Sciences CONCLSION In this work, an optimization approach is developed to find out the optimum operation strategy of hydro generation and pumping for a small scale hydro system is taken. The water storage ability allows enhanced profits for wind energy installation. During peak hours, the available wind energy is complemented with hydro generation. During off peak hours a part of wind energy is used to store energy by pumping water. A small scale system is tested and analyzed. The predicted yearly average economic gain of the wind-hydro strategy is found around Rs. 182 lacs. FTRE SCOPE OF WORK 1) A small capacity wind energy system is considered in this work, this approach can also be attempted to very large wind-hydro storage facility. 2) In this work the levels of the reservoir were taken in terms of energy, in further study levels of reservoirs may be taken in terms volume of water in reservoir to get the discharge limits of the pumps and turbines. 3) nit commitment of wind as well as pumped storage plants is also recommended for further study. REFERENCES Tang J., Huang W.V, Hydrothermal Scheduling Problems with Pumped-Storage Hydro Plants Computers and lmb~trial Engineering Vol. 23, Nos 1-, pp , Jeng L.N, Chang K. K. Chen A linear programming method for the scheduling of pumped-storage units with oscillatory stability constraints IEEE Transactions on Power Systems, Vol. 11, No., November Kazempour S.J, Yousefi A., Zare K., Moghaddam M.P., Haghifam M.R., Yousefi G.R. A MIP-Based Optimal Operation Scheduling of Pumped-Storage Plant in the Energy and Regulation Markets TarbiatModares niversity (TM), Tehran, Iran. Castronuovo E. D. And Lopes J. A. P., Optimal operation and hydro storage sizing of a wind hydro power plant, Int. J. Elect. Power and Energy Syst., vol. 26, no. 1, pp , Dec 2. Castronuovo E. D. and. Lopes J. A. P, On the optimization of the daily operation of a wind-hydro power plant, IEEE Trans. Power Syst., vol. 19, no. 3, pp , Aug 2. Chen P.H., Pumped-Storage Scheduling sing Evolutionary Particle Swarm Optimization IEEE transactions on energy conversion, vol. 23, no. 1, march 28. Suul J.R., hlen K., ndeland T., Variable speed pumped storage hydropower for integration of wind energy in isolated grids case description and control strategies Nordic Workshop on Power and Industrial Electronics, June 9-11, 28. Kazempour S.J *, Moghaddam M.P, Haghifam M.R., Yousefi G.R., Risk-constrained dynamic self-scheduling of a pumped-storage plant in the energy and ancillary service markets Department of Electrical Engineering, TarbiatModares niversity (TM), Tehran, Iran 29. Khatod D.K., Pant P, Sharma J.D., Optimized Daily Scheduling of Wind-Pumped Hydro Plants for a Day-Ahead Electricity Market System 29 Third International Conference on Power Systems, Kharagpur, INDIA, December

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