Thermal Unit Commitment Strategy Integrated with Solar Energy System

Thermal Unit Commitment Strategy Integrated with Solar Energy System K. Selvakumar 1, B. Vignesh 2, C.S.Boopathi 3 and T. Kannan 4 1,3 Assistant Professor, Dept. of EEE, SRM University, Kattankulathur-603203, Tamil Nadu, India. 2 PG Research Scholar, Dept. of EEE, SRM University, Kattankulathur-603203, Tamil Nadu, India. 4 Assistant Professor, Dept. of ECE, Tamilnadu College of Engg., Coimbatore-641659, Tamil Nadu, India. Abstract This paper presents a methodology for solving unit commitment problem for thermal units integrated with solar energy system. The Solar energy source is included in this model due to low electricity and environmental issues like Global warming, Greenhouse effect, etc., There will be changes in the generation by considering the solar energy and this paper explains about the differences by considering solar and non-considering it. The main objective of this paper is to reduce the total production of the generation system. The IEEE 39 bus system and forecasted solar radiation with 24 hour load demand is taken as input data and the simulation done by using the MATLAB 12.10 version. This method is verified by Forward Dynamic programming method and the results are compared with and without solar energy. Keywords: Unit Commitment (UC), Economic Dispatch (ED), Forward Dynamic Programming (FDP), Solar Energy (SE). Introduction In planning and operation of power system the generation scheduling or commitment of generating units plays a very important role. Before committing a unit, the unit scheduling has to be carried out because load pattern will vary for each day. On considering load forecasting for a week the demand will be high on weekdays and low during weekend due to commercial loads. In order to get no loss, the generation of power should be equal to the demand in a day. The ultimate aim is to reduce the generation and for that it is significant to know that committing which unit on what time will give us economic result [1]. In recent researches the renewable energy sources are getting importance over lower electricity generation price and positive effect on environment. On these resources solar energy is investigated and integrated with thermal power system. For solving this technique many methods are available but the accuracy of result will be none and also in most cases the constraints are avoided. The minimization of production can be done by all algorithms but it will be more effective if committing is done with a perfect idea of using peak load supplier not only during peak load but also during less demand. The above can be executed by Dynamic programming method including generator constraints and solar energy system constraints to makeup with all the disadvantages faced. There are various meta-heuristic optimisation techniques are used to solve UC problem such as Priority List (PL) [2], Extended Priority List (EPL) [3], Lagrangian Relaxation (LR) [4], Dynamic Programming (DP) [5-7], Genetic Algorithm (GA) [8], evolutionary programming (EP) [9], Particle Swarm Optimisation (PSO) [10], Shuffled Frog Leaping Algorithm (SFLA) [11-13] have been applied to get optimal solutions for solving Unit Commitment problem. In this paper we proposed for solving the short term solar thermal Unit Commitment problem using Dynamic Programming method. The organization of paper is follows. Section I describes the introduction and section II gives detailed mathematical formulation of UC problem with considering the various thermal and solar constraints. The proposed DP method with flow chart is explained in section III. Section IV and V gives the result and discussion and conclusion of our proposed work. Problem Formulation More number of constraints are available in Unit commitment which has to be included before starting a unit and the solar thermal constraints are power demand, generation limit constraint, minimum uptime, minimum downtime, ramp up and ramp down constraints, spinning reserve and others. Apart from these the solar energy constraints are also be considered. By considering these constraints and preparing a scheduling scheme an optimal output must be obtained which should be economic. Objective Function The main objective is to minimize the total of generation and it includes start up and shut down which can be calculated from the available data for each generator. On the time of shutting down the generator, the generator will run above the minimum running time so that the shutting down is always considered as zero. N T TC = [F(P i,t ) + SC i (1 I i,t 1 )]I i,t i=1 t=1 (1) F(P i,t) = a+ b*p i,t+ c*p i,t*p i,t (2) P i,t= Thermal power output of unit i at hour t a,b,c = Fuel coefficients I i,t = Binary status of i th unit at hour t N = Number of units T = Number of time horizon The start-up is defined as follows: 6856

off off off h SC i = { i T i Xi Hi off off off off ci X i >Hi, Hi =Ti +cshouri where T i off = Minimum down time h i = heart start at unit i c i = cold start at unit i cshour i = cold start hour at unit i Operational constraints The condition of generator for the previous day or week may not be known for that initial condition of generator should be known and it will help to scheduling units. Initial conditions The information available in the initial conditions are about the generator, how many hours a generator was running and for how many hours before the generator was shut down. By this information we can follow the constraints. Power balance constraints N P i,t + P s,t = D t i=1 P i,t= Thermal power output of unit i at hour t P s,t = Solar power output at hour t D t = Demand at hour t (3) (4) method done in Dynamic programming. The ability to maintain solution feasibility was the main disadvantage in priority list method of DP. For reducing the overall, the algorithm will generate large number of possible decisions for large power systems that occur dimensional problem. The assumptions made in the DP are[19]: 1. An array of units with defined units operating and the offline is available in a state. 2. The for shutting down a unit is nil. 3. For a unit the startup is independent of time and it has been offline. 4. There is a strict priority order and in each interval a defined minimum amount of capacity must be operating. The unit commitment algorithm based on DP consists of different combinations of units for each time interval which render feasible solutions to the scheduling problem are consider at each stage and on every feasible unit combination performs an economic dispatch to calculate its generation. By calculating the generation includes transitional s associated with the units startup and shutdown thus the algorithm could proceed in a forward direction to cover entire horizon. The algorithm to run from initial to final hour can be setup in forward DP approach. The advantages in forward DP approach are: 1. The initial and state conditions are known. 2. The startup of the unit is a function of time. Solar energy system: Solar energy is included whenever the radiation data will have produced. By considering the radiation data, the solar power output is calculated [14,15]. For getting the solar power output the following equation is used PsnS(t) 2 S std Rc P s,t = { P sns(t) S std 0<S(t)<R c S(t)>R c (5) S std = Solar radiation for standard environment as 1000 W/m 2 R c = cut in radiation point set as 150 W/m 2 P sn = rated power output of solar S(t) = solar radiation data Dynamic Programming Method By the characterisation of sequential decision process the solution for optimization problem can be obtained with utilization of principle of optimality is known as Dynamic programming. In this paper Unit commitment was solved by using the forward dynamic programming methodology. The founder of this technique is Dr.Richard bellman and the optimization is meant for planning or a tabular method[16-18]. The sequence of decision is a result that may be viewed as a solution for an optimization problem or a stage wise search Figure 1: Flow Chart for Forward Dynamic programming 6857

Result and Discussion The proposed technique has been implemented in MATLAB on a 12.10 GHz core i5 processor laptop. The IEEE 39 bus 10 unit system and forecasted hourly solar Irradiance (shown in the Appendix) is taken as input parameters. The performance of the proposed method has been evaluated through MATLAB coding. The shutdown of the thermal generating units is not considered in this calculation. The short term generation scheduling of solar and thermal generating units and the total production for the given load is tabulated in Table 1 and Table 2. From the results obtained, it is observed that the proposed solar thermal generation scheduling gives good results for both total and emission. The comparison of total fuel and production for every hour obtained from the thermal and solar thermal for 10 units test system is presented in Figure 2. Figure 2: Comparison between thermal and solar thermal fuel Conclusion The Unit Commitment Problem (UCP) with Thermal power and Solar energy has been studied and executed. The model of UCP is done by using Priority list method and Dynamic Programming is presented. By the usage of Forward Dynamic Programming approach, there will be improvement in the computation efficiency without losing much calculating accuracy. In the dispatching cycle the most -optimal units will be maintained on-line status and then the comparison of UCP with Solar energy is better than UCP without Solar energy was showed in the results. The demonstration of case study of IEEE 39 bus with 10 units shows that there is a raise in benefit with the joining of Solar energy into the scheduling. References [1] A. J. Wood, and B. F. Wollenberg, Power generation, operation and control, New York: John Wiley & Sons, Inc, 1996. [2] T. Senjyu, T. Miyagi, A. Y. Saber, N. Urasaki, and T. Funabashi, Emerging solution of large-scale unit commitment problem by stochastic priority list, Elect. Power Syst. Res., Volume 76, pp. 283-292, Mar. 2006. [3] T. Senjyu, K. Shimaukuro, K. Uezato, and T. Funabashi, A Fast Technique for Unit Commitment Problem by Extended Priority List," IEEE Transactions on Power Systems, Volume 18, No. 2, pp0882-888, May 2003. [4] K. Karthick Kumar, K. Lakshmi, LR-PSO Method of Generation Scheduling Problem for Thermal-Wind- Solar Energy System in Deregulated Power System in proc. ICGHPC, pp.1-7, 2013. [5] P. G. Lowery, Generating Unit Commitment by Dynamic Programming, IEEE Transactions on Power Apparatus and Systems, Volume PAS-85, Issue 5, pp. 422-426, May 1966. [6] Boopathi C.S, Dash S.S, Selvakumar K, Venkadesan A, Subramani C and Vamsikrishna. D, Unit Commitment Problem with POZ Constraint Using Dynamic Programming Method, International Review of Electrical Engineering, (9)1, pp.218-225, 2014. [7] W. L. Snyder, H.D. Powel, J.C. Rayburn, Dynamic programming approach to unit commitment, IEEE Transactions on Power Systems, No. 2, pp. 339-350, May 1987. [8] B.Xiaomin, and S. M. Shaidehpour, Extended neighbourhood search algorithm for constrained unit commitment, Electric Power and Energy Systems, Volume 19, No.5 pp. 349-356, 1997. [9] H. Y. Yamin, Review on methods of generation scheduling in electric power systems, Electric Power System Research., Volume 69, pp. 227-248, 2004. [10] K selvakumar, Santhoshkumar.G and c.sakthivel, optimal generation scheduling of thermal units with considering startup and shutdown ramp limits IEEE International Conference on Computational Intelligence and Computing Research, ICCIC-2014. [11] Selvakumar.K, Vignesh.R.S and Vijayabalan.R, shuffled frog leaping algor ithm for solving Profit Based Unit Commitment problem, in proc. IEEE ICCCA, pp.1-6, 2012. [12] Selvakumar.K, Venkatesan.T and Sanavullah.M.Y., Price Based Unit Commitment problem solution using shuffled frog leaping algorithm, in proc. IEEE ICAESM, pp.794-799, 2012. [13] K.Selvakumar, K.Vijayakumar, R.Palanisamy, D.Karthikeyan and G.Santhoshkumar, SFLA to solve Short term Thermal Unit Commitment Problem with Startup and Shutdown Ramp limits International Review on Modelling& Simulations (IREMOS) Vol.8 Issue 6, p670-678, Dec2015. [14] Shantanu Chakraborty, Takayuki Ito and Tomonobu Senjyu, Fuzzy logic-based thermal generation scheduling strategy with solar-battery system using advanced quantum evolutionary method IET Generation, Transmission & Distribution, Volume 8, Issue 3, 2014. [15] R-H. Liang and J-H Liao, "A fuzzy-optimization approach for generation scheduling with wind and solar energy systems" IEEE Trans. on Power Syst., vol. 22, no. 4, pp. 1665-1674, November 2007. 6858

[16] C. K. Pang, G. B. Sheble, F. Albuyeh, Evaluation of Dynamic Programming Based Methods and Multiple area Representation for Thermal Unit Commitments, IEEE Transactions on Power Apparatus and Systems, Volume PAS-100, Issue 3, pp. 1212-1218, March 1981. [17] W. J. Hobbs, G. Hermon, S. Warner, and G.B. Sheble An enhanced dynamic programming approach for unit commitment, IEEE Transactions on Power Systems, No.3 pp. 1201-1205, August 1988. [18] Z. Ouyang, S. M. Shahidehpour, An intelligent dynamic programming for unit commitment application IEEE Transactions on Power Apparatus and Systems, Volume 6, Issue 3, pp. 1203-1209, Aug. 1991. [19] K selvakumar, Easwar.R and Santhoshkumar.G, Pumped Hydrothermal Unit Commitment Problem Solution Using Dynamic Programming Method, International Journal of Advanced Engineering Research (IJEAR), (9)10,pp.9137-9143, 2015. Appendix Table 1: Unit characteristics and coefficients of a IEEE 39-bus 10-unit base problem Unit 1 Unit 2 Unit 3 Unit 4 Unit 5 P i max 455 455 130 130 162 P i min 150 150 20 20 25 A i 1000 970 700 680 450 B i 16.19 17.26 16.60 16.50 19.70 C i 0.00048 0.00031 0.00200 0.00211 0.00398 MU i 8 8 5 5 6 MD i 8 8 5 5 6 H i 4500 5000 550 560 900 C i 9000 10000 1100 1120 1800 Chour i 5 5 4 4 4 I.state 8 8-5 -5-6 Unit 6 Unit 7 Unit 8 Unit 9 Unit 10 P i max 80 85 55 55 55 P i min 20 25 10 10 10 A i 370 480 660 665 670 B i 22.26 27.74 25.92 27.27 27.79 C i 0.00712 0.00079 0.00413 0.00222 0.00173 MU i 3 3 1 1 1 MD i 3 3 1 1 1 H i 170 260 30 30 30 C i 340 520 60 60 60 Chour i 2 2 0 0 0 I.state -3-3 -1-1 -1 Table 2: Load Demand of the IEEE 39-bus 10-unit base problem Hours Power Demand (MW) Hours Power Demand (MW) 1 700 13 1400 2 750 14 1300 3 850 15 1200 4 950 16 1050 5 1000 17 1000 6 1100 18 1100 7 1150 19 1200 8 1200 20 1400 9 1300 21 1300 10 1400 22 1100 11 1450 23 900 12 1500 24 800 Table 3: Solar Irradiance data for 150 MW plant (Chennai) Hours Solar Irradiance (W/m^2) Hours Solar Irradiance (W/m^2) 1 0 13 594 2 0 14 613 3 0 15 620 4 0 16 465 5 0 17 189 6 0 18 0 7 0 19 0 8 125 20 0 9 445 21 0 10 612 22 0 11 604 23 0 12 538 24 0 Table 4: IEEE 30 bus Thermal generating unit scheduling output Hour Demand State Max. Min. Start up number MW MW Fuel Total production 1 700 615 300 910 0 13683 13683 2 750 615 300 910 0 14554 28238 3 800 615 300 910 0 15427 43665 4 950 838 345 1202 1460 19146 64271 5 1000 838 345 1202 0 20020 84291 6 1100 924 365 1332 1100 22387 107778 7 1150 924 365 1332 0 23262 131040 8 1200 924 365 1332 0 24150 155190 9 1300 1006 410 1497 860 27251 183301 10 1400 1018 420 1552 60 30058 213419 11 1450 1023 430 1607 60 31916 245395 12 1500 1024 440 1662 60 33890 279345 13 1400 1018 420 1552 0 30058 309403 14 1300 1006 410 1497 0 27251 336654 15 1200 924 365 1332 0 24150 360804 16 1050 924 365 1332 0 21514 382318 17 1000 924 365 1332 0 20642 402959 18 1100 924 365 1332 0 22387 425346 19 1200 924 365 1332 0 24150 449497 20 1400 1018 420 1552 920 30058 480474 21 1300 1006 410 1497 0 27251 507725 22 1100 868 370 1237 0 22736 530461 23 900 701 320 990 0 17645 548106 24 800 615 300 910 0 15427 563534 6859

Table 5: IEEE 30 bus Thermal generating unit with 150 MW solar scheduling output Hour Demand Solar State Max. Min. Start up Output number MW MW Fuel Total production 1 700 0 615 300 910 0 13683 13683 2 750 0 615 300 910 0 14554 28238 3 800 0 615 300 910 0 15427 43665 4 950 0 838 345 1202 1460 19146 64271 5 1000 0 838 345 1202 0 20020 84291 6 1100 0 924 365 1332 1100 22387 107778 7 1150 0 924 365 1332 0 23262 131040 8 1184 16 924 365 1332 0 23858 154898 9 1251 49 973 385 1412 340 25590 180828 10 1325 75 1006 410 1497 520 27763 209111 11 1361 89 1018 420 1552 60 29220 238391 12 1408 92 1018 420 1552 0 30240 268631 13 1304 96 1006 410 1497 0 27333 295964 14 1208 92 924 365 1332 0 24310 320274 15 1120 80 924 365 1332 0 22737 343011 16 994 56 924 365 1332 0 20537 363548 17 975 25 924 365 1332 0 20206 383754 18 1098 2 924 365 1332 0 22352 406106 19 1200 0 924 365 1332 0 24150 430257 20 1400 0 1018 420 1552 920 30058 461234 21 1300 0 1006 410 1497 0 27251 488485 22 1100 0 868 370 1237 0 22736 511221 23 900 0 701 320 990 0 17645 528866 24 800 0 615 300 910 0 15427 544294 6860