Unit Commitment in Smart Grid Considering Demand Response and Stochastic Wind Generation

Transcription

1 J. Energy Power Source Vol. 1, No. 6, 2014, pp Received: September 8, 2014, Publihed: December 30, 2014 Journal of Energy and Power Source Unit Commitment in Smart Grid Conidering Demand Repone and Stochatic Wind Generation Hoein Haroonabadi Department of Electrical Engineering, Ilamic Azad Univerity-Ilamhahr Branch, Tehran, Iran Correponding author: Hoein Haroonabadi Abtract: Integration of wind unit into power ytem introduce variou ource of technical and economical challenge to operate power ytem due to it inherent uncertainty and variability. In operation of power ytem, tochatic wind generation can affect the ytem ecurity. In thi paper, optimal Security-Contrained Unit Commitment (SCUC) in preence of Demand Repone (DR) program baed on tochatic wind generation i propoed a a novel method of SCUC to addre the mentioned concern. In order to ecure ytem againt tochatic wind generation, the SCUC reult have to be valid for mot probable wind generation cenario. The problem i formulated a a Mixed-Integer Programming (MIP) and olved uing Bender decompoition which i implemented uing GAMS oftware (CPLEX olver). The reult of applying propoed method on IEEE ix-bu tet ytem how that the propoed method can uccefully find the optimal SCUC which atified all contraint while i ecure againt variation in wind generation. Keyword: Security-contraint unit commitment, tochatic wind generation, demand repone, bender decompoition. Nomenclature: i, j Index for bu K Index for time S Index for wind power generation cenario G Index for generator NK,, Number of time period, bue and generation unit of ytem Revenue from contant load and C(Pdf ),C(Pdr ) reponive load, repectively StCt, SdCt Start up and hut down cot of unit g at time k A Supply bidding price of unit g at time k Cmin No load operation cot Pdf Non-reponive demand at bu i at time k pwind Wind farm generation at bu i at time k Pdrmax Submitted reponive load at bu i at time k Pdxmin Minimum curtailable load at bu i at time k X ik, X ik ON and OFF time of load at bu i at time t RU i, RD i Pick up and drop rate of load at bu i Emax i Maximum daily curtailable load at bu i SR k Required pinning reerve at time t NSR k Required non-pinning reerve at time k plmax Maximum power tranfer between bu i and bu j at time k T g Minimum OFF time of unit g on T g Minimum ON time of unit g RU g, RD g X on X Pmax g, Pmin g pwind Pwind QSC MSR Bline ij delta g Pdr U1 V1 PG PSR PNSR pline W M Ramp-up rate and ramp-down limit of unit g OFF time of unit at time k ON time of unit at time k Upper and lower limit of real power generation of unit g Forecated generation of wind power unit g at time k Simulated generation of wind power unit g at time k in Scenario Quick tart capability (QSC) of unit g Maximum utained rate (MSR) of unit g Suceptance of line ij Phae angle of bu i at time k Permiible real power adjutment of unit g Reponive load at bu i at time t Start up tate of unit i at time t Shut down tate of unit i at time t Active generation of unit g at time t Provided pinning reerved by unit i at time t Provided non-pinning reerved by unit i at time t active power flow between bu i and bu j at time t Binary variable that how the tate of generation unit of bu i at time t Binary variable for DR which how the tate of load of bu i at time t

2 Unit Commitment in Smart Grid Conidering Demand Repone and Stochatic Wind Generation Introduction In retructured power ytem, Security Contrained Unit Commitment (SCUC) deal with generation chedule to atify the hourly ytem load while maintaining ytem ecurity at the maximum ocial welfare. In recent year, intalled wind capacity ha been rapidly increaed due to it clean and indigenou nature. However, thi capacity i not readily dipatched due to it intermittent nature. The variation and uncertainty of wind power generation impact power ytem characteritic uch a frequency and generation adequacy which can reduce the reliability of power ytem. In Ref. [1] a SCUC algorithm for conidering the uncertainty of wind power generation i preented. The UC problem i olved in the mater problem with the forecated wind power generation. Then initial dipatch i checked in the ub-problem conidering imulated poible cenario for repreenting the wind power volatility. Ref. [2] invetigate the uncertainty in the prediction of wind unit in the UC problem and economic dipatch. It how that repreenting uncertainty in the wind power forecating with wind power cenario that rely on tochatic UC ha advantage over determinitic approache that mimic the claical model. The cenario-baed approach and the interval optimization approach for the Stochatic SCUC olution with the conideration of uncertain wind power generation are invetigated in Ref. [3]. Uncertainty of wind unit uing thee two method ha been modeled and reult obtained from thee two method are compared. A SCUC approach with uncertain wind power generation i preented in Ref. [4] which the computational burden aociated with the calculation of the reerve deployment for each cenario i reduced remarkably. A two-tage tochatic SCUC with a cenario election algorithm for chooing and weighing wind power generation cenario and compoite component failure i preented in Ref. [5]. A two-tage adaptive robut SCUC model with uncertain demand and wind power generation at the individual nodal level i propoed in Ref. [6]. A combination of Bender decompoition type algorithm and the outer approximation technique are ued for olving the problem. With the recent progre in power ytem, different approache uch a compreed air energy torage [7], hybrid vehicle [8], hydrogen torage [9], and Demand Repone (DR) [10-11] have been propoed to manage intermittency and volatility of wind power generation and reduce the operation cot. In thi paper, DR i conidered a a potential olution for the reliable integration of wind generation reource into power ytem operation. Load erving entity act a an cutomer load aggregator and provide the load data to the Independent Sytem Operator (ISO). The ISO run SCUC baed on the available data generating unit and tranmiion line information a well a wind power forecat and poible wind power cenario incorporate DR into the market clearing proce to obtain the efficient market. The ret of thi paper i organized a follow: Section 2 preent SCUC model with DR and wind power generation and formulate the problem. The imulation reult are provided in Section 3 and the paper i concluded in Section SCUC with Hourly DR Conidering Stochatic Wind Power Generation The propoed SCUC with hourly DR i preented in thi ection. 2.1 Market Clearing Proce The general model of the target market i a follow: wind farm owner ubmit their hourly wind power forecat for 24 hour day ahead to the ISO, and the operating cot of wind unit are aumed to be zero. The ISO receive tranmiion line information from tranmiion companie, and tranmiion contraint are conidered in the bae cae and contingencie. It i

3 316 Unit Commitment in Smart Grid Conidering Demand Repone and Stochatic Wind Generation aumed that both generation companie and load erving entitie could ubmit complex er and bid to the ISO. The data from generation companie include hourly quantity and price of power, unit ramping up limit, unit ramping down limit, unit minimum ON time limit, unit minimum OFF time limit, and unit generation limit. Load erving entitie bid conit of fixed and reponive load bid. Fixed load are expected to be fully met and will be treated in accordance with the market-clearing price. Reponive load bid include hourly quantity and price of load along with minimum ON/OFF time limit, recovery/lo rate, minimum hourly curtailment, and maximum daily curtailment are ubmitted to the ISO. Then The SCUC chedule for complex bid and wind generation cenario would demontrate the optimal commitment and dipatch of generating unit and the hourly DR baed on ubmitted er and bid. 2.2 Wind Power and Demand Scenario Generation and Reduction To model the uncertainty of wind power and demand, in thi paper wind power and demand are aumed a normal ditribution, and we ue the concept of net demand. The Net Demand (ND) i the difference between the demand and the wind power forecat [11-12]: ND f = demand wind power forecat (1) = ND mean + net demand forecat error In thi cae, auming no correlation between demand and wind forecat error, the tandard deviation of the net load i given a follow: 2 2 NL L N (2) Here, Monte Carlo method i ued to generate a large number of cenario ubject to a normal ditribution [1]. However, we hould ue cenario reduction method due to time limitation and low probability of mot of the cenario. Uing thi method, the problem would be olved only for cae that have more probability to happen in normal operation of power ytem. 2.3 Formulation of SCUC with DR The objective of the SCUC problem i to determine the day-ahead chedule of generating unit and load with the aim of maximizing the ytem ocial welfare while repecting the unit, load and ytem contraint. The objective hown in Eq. (3) i the conumption benefit minu the generation cot, and tartup and hutdown cot of individual unit over the cheduling horizon. NK NL (3) K1 i1 NK 1 1 k g Max [ C(Pdf Pdr )] [( StCt * U1 ) ( SdCt * V1 ) ( C min* W ) ( A* PG )] The hourly SCUC i ubjected to the following contraint: Eq. (4) depict the ytem energy balance for each bu at all time. Pdr i the reponive load which can be changed in order to decreae the ytem cot or balancing the generation veru load. The reponive load cannot be negative and the amount of curtailed load cannot be le than it minimum curtailment rate. Thee fact are modeled in Eq. (5) and (6). Eq. (7) will hift the load when load hould be curtailed and/or when the energy price i high to another time horizon. Contraint Eq. (8) and (9) would limit hourly load pickup and pick drop, repectively. Eq. (10) and (11) impoe the minimum number of time which load cannot be curtailed or retored. In addition, the amount of active power which can be curtailed cannot be higher than it limit which i impoed by Eq. (12). g1 NW * g 1 PG W pwind ( Pdf ) 0 i 1 Pdr, k 1,..., NK [ Pdr max Pdx min Pdr ] M 0, i 1,..., ; k 1,..., NK (4) (5) Pdr M 0, i 1,..., ; k 1,..., NK (6) [ Pdr Pdr max ][1 M ] 0, i 1,..., ; k 1,..., NK ( k 1) i i (7) Pdr Pdr RU, i1,..., ; k 1,..., NK (8)

4 Unit Commitment in Smart Grid Conidering Demand Repone and Stochatic Wind Generation 317 Pdr( k1) i Pdr RDi, i 1,..., ; k 1,..., NK on ik ( 1) i ik ( 1) ik [ X UT ][ M M ] 0, i 1,..., ; k 1,..., NK ik ( 1) k ik i( k 1), [ X DT ][ M M ] 0 i1,..., ; k 1,..., NK (9) (10) (11) NK ( Pdr max ) max k 1 Pdr E i, i 1,..., (12) PSR * W SRk, k 1,..., NK (13) g 1 PSR 10 * MSR * W (14) Sytem operating reerve requirement: PNSR * W NSRk, k 1,..., NK (15) g1 PNSR QSC (16) The Maximum Sutained Rate (MSR) and the Quick Start Capability (QSC) are ued to limit the pinning and operating reerve of the unit, repectively. Unit' operating reerve i the ame a pinning reerve, when a unit i ON. When a unit i OFF, it operating reerve i equal to it QSC. Unit ramping up limit: PG PG( k1) g [1 W (1 W( k1) g )] RUg (17) W (1 W ) Pmin, g 1,..., ; k 1,..., NK ( k1) g g Unit ramping down limit: PG PG [1 W (1 W )] RD ( k1) g ( k1) g g W( k1) g(1 W) Pmin g, g 1,..., ; k 1,..., NK Unit minimum ON time limit: on on [ X T ]( W W ) 0, ( k1) g g ( k1) g g 1,..., ; k 1,..., NK Unit minimum OFF time limit: ( k 1) g g ( k 1) g [ X T ]( W W ) 0, g 1,..., ; k 1,..., NK Unit generation limit: Pmin* W PG Pmax* W, g g g 1,..., ; k 1,..., NK Network contraint: j1 (18) (19) (20) (21) PG pwind Pdf Pdr pline (22) pline Blineij *( delta deltakj ) (23) pline pl max (24) The cenario contraint Eq. (25)-(36) repreent the ytem power balance Eq. (25), ytem pinning reerve Eq. (26) and (27), ytem operating reerve Eq. (28) and (29), permiible adjutment of real power generation Eq. (30), unit generation Eq. (31), network contraint Eq. (31)-(34), and reponive load contraint Eq. (35) and (36). PG * W Pdnet Pdr g1 i1 Pdnet Pdf Pwind PG g 1 k 1,..., NK (25) PSR * W SRk, k 1,..., NK (26) g1 PSR 10* MSR * W (27) PNSR * W NSRk, k 1,..., NK (28) PNSR PG g QSC (29), g 1,..., ; k 1,..., NK (30) P min * W PG Pmax * W, g g g 1,..., ; k 1,..., NK j1 (31) PG Pdnet Pdr pline (32) ij kj pline Bline *( delta delta ) (33) pline pl max (34) Pdr ( Pdr max Pdx min )* M 2.4 Propoed Method Pdr max *(1 M ) (35) Pdr Pdr max * (1 M ) (36) Table 1 how the propoed method algorithm. Firt, the optimization problem will be run uing Eq. (3). The reult of thi optimization hould be checked for different wind generation and load cenario. Therefore, the reult will be conducted to the next optimization problem which i called ub-problem. In

5 318 Unit Commitment in Smart Grid Conidering Demand Repone and Stochatic Wind Generation Table 1 Algorithm of propoed method uing bender decompoition. Solving Problem Algorithm Solve Mater Problem (Conventional UC Problem 1 With DR and Wind generation) 2 If Threhold > ε 3 Solve Sub-problem for different Wind generation cenario 4 Calculate feaibility Cot at each hour for each 5 Create Feaibility Cot Cut and add to Mater problem 6 Repeat thi procedure until Threhold < ε 7 Print the UC and DR reult thi part, the aigned UC, PSR, NSR and DR program will be checked in preence of different wind generation and load cenario. Baed on Bender decompoition approach, if one or more contraint being violated during optimization, the Bender cut aociated with the bu and the time which contraint i violated will be created and added to the mater problem. Technically, thi cut will add feaibility cot to the original objective function. The procedure will be repeated until the feaibility cot (threhold) being le than feaibility minimum rate ɛ. 3. Numerical Studie In thi ection, the reult of applying propoed method on IEEE ix-bu tet ytem i dicued. The data for thi ytem are given in Ref. [13]. Three reponive load are located in the ytem and will participate in DR program. In addition the wind farm i connected to the Bu 4 and provide power for ytem. Twenty percent of the total load in thee bue are conidered a reponive while the ret i fixed. Characteritic of reponive load are preented in Table 2. In order to how the importance of DR program and it role to increae the ytem efficiency, the reult are dicued in two part. In the firt part, SCUC i done when the wind farm provided to the ytem but DR program i not conidered. In the econd part, SCUS i done when both DR program and wind power generation are conidered and utilized in the ytem. GAMS oftware (CPLEX olver) i employed to olve the problem [14]. Table 2 Characteritic of reponive load. Bu No. B ($/MWh) MUT (h) MDT (h) Ramp Up (MW/h) Ramp Dn (MW/h) Table 3 Generation dipatch without DR. Hour PG1 U1 PG2 U2 PG3 U3 WF SCUC without DR Program In thi cae, we aume that the load i fixed (DR i not conidered) in SCUC. The reult of UC are hown in Table 3. Sytem operation cot for 24 hour would be $. 3.2 SCUC with DR Program In thi part, the reult of the optimization conidering both demand repone and wind generation are dicued. Table 4 how the SCUC reult and generation unit tatu for next 24 hour. It can be found that generation unit 1 i the mot economical generator in the ytem, and o thi unit

6 Unit Commitment in Smart Grid Conidering Demand Repone and Stochatic Wind Generation 319 will provide power for ytem mot of the time. On the other hand, Generation unit 3 i the mot expenive unit in the ytem, o thi unit motly provide non-pinning reerve for the ytem.the operation cot for thi optimization i $ which ha decreaed by 14.4 % compared to previou cae. Fig. 1 compare ytem load prolife of 2 cae tudie. A it can be een, peak demand i reduced by either curtailing reponive load or hifting reponive load Table 4 Generation dipatch conidering DR. Hour PG1 U1 PG2 U2 PG3 U3 WF to -peak hour. Thi peak load reduction would alleviate price pike and enhance flexibility and efficiency of market operation. 4. Concluion In thi paper, a SCUC algorithm wa propoed to model DR in clearing of electricity market baed on tochatic wind power generation. Phyical contraint of reponive load in addition to generation unit and tranmiion line were conidered in the bae cae and different wind generation and load cenario. The problem i formulated a an MIP problem and olved uing Bender decompoition. The reult of imulation on the IEEE ix-bu tet ytem are: The propoed method er a flat load and LMP profile; The method lead to lower ytem operation cot and higher market efficiency; Providing a robut unit commitment by tang into account the intermittency and volatility of wind power generation a well a reducing ON/OFF commitment of generating unit. Suggetion for future reearch reulting from the propoed model are lited below: (1) Demand repone reource are technically capable of providing operating reerve, which can be formulated in the propoed method with few a change; (2) The propoed model can alo be extended to tochatic day-ahead cheduling with plug-in vehicle in which uncertaintie aociated with renewable energy are conidered. Reference Fig. 1 Comparion between load profile while there i no DR and DR i utilized. [1] J. Wang, M. Shahidehpour, Z. Li, Security-contrained unit commitment with volatile wind power generation, IEEE Tranaction on Power Sytem 23 (3) (2008) [2] J. Wang, A. Botterud, R. Bea, H. Keko, L. Carvalho, D. Iicaba, J. Sumaili, V. Miranda, Wind power forecating uncertainty and unit commitment, Applied Energy 88 (11) (2011) [3] W. Lei, M. Shahidehpour, Z. Li, Comparion of cenario-baed and interval optimization approache to tochatic SCUC, IEEE Tranaction on Power Sytem

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