Impact of wind farm integration on electricity market prices

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Published in IET Renewable Power GenerationReceived on 7th March 2008Revised on 29th May 2008doi: 10.1049/iet-rpg:20080026

ISSN 1752-1416

Impact of wind farm integration on electricitymarket pricesH.H. Zeineldin1 T.H.M. El-Fouly2 E.F. El-Saadany2

M.M.A. Salama2

1Masdar Institute of Science and Technology, PO Box 45005, Abu Dhabi, UAE2Department of Electrical and Computer Engineering, University of Waterloo, 200 University Avenue West,Waterloo, ON, CanadaE-mail: [email protected]

Abstract: Wind generation is considered one of the most rapidly increasing resources among other distributedgeneration technologies. Recently, wind farms with considerable output power rating are installed. Thevariability of the wind output power, and the forecast inaccuracy could have an impact on electricity marketprices. These issues have been addressed by developing a single auction market model to determine the closeto real-time electricity market prices. The market-clearing price was determined by formulating an optimalpower flow problem while considering different operational strategies. Inaccurate power prediction can resultin either underestimated or overestimated market prices, which would lead to either savings to customers oradditional revenue for generator suppliers.

1 IntroductionCurrently, utilities are increasingly relying on wind energy tomeet their growing demand. It is predicted that by 2020, thetotal wind energy generation worldwide will be �1261 GW,which is expected to supply �12% of the total worldelectricity demands [1]. The Canadian Wind EnergyAssociation is working towards generating 10 GW of windenergy by 2010 (10 � 10 Canada Wind Vision Program)[2]. This will be sufficient to supply �6% of the Canadianelectricity demand.

Wind resources provide a cheap and clean source ofenergy. However, unlike dispatchable central stations,the generated power depends on the available wind speeds.Therefore, there is a high uncertainty and variability insuch type of generated power. Integrating wind facilitiesto power system networks presents a major challenge topower system operators. Such integration affects severalpower system related issues, including optimum powerflow, transmission congestion, power quality, systemstability, system economics (including market clearingprices) and load dispatch [3]. The impacts of wind speed

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variability could be addressed by developing accurate windspeed models and prediction techniques [4, 5].

Information regarding future wind power generationdepends mainly on the accuracy of the implementedprediction technique. During the last decades, severaltechniques have been utilised and developed for wind speedforecasting and wind power prediction. Among thedeveloped models, auto regressive models, auto regressivemoving average models and auto regression integratedmoving average models [4, 6–8]. Artificial neural network-based models have also been involved in hourly averagedwind speed forecasting such as Elman recurrent network,adaptive network based fuzzy inference system, radial basisfunction network and neural logic networks [8–12]. Thesemodels have been proved effective for short-term prediction(few hours ahead), but they require large set of historicaldata for their parameter estimation and model training (upto weeks of recorded data).

Techniques, based on utilising the wind speed data fromthe neighbouring sites, have been proposed in [13–16].These techniques require the usage of large sets of data

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from more than one site to achieve reasonable accuracy ofprediction. The most advanced developed techniques arethe physical models that use weather data withsophisticated meteorological for wind power predictions[17–19]. However, these models are very complicated andexpensive. In addition, these models have been reported tobe inefficient for short-term prediction (few hours ahead,up to 6 h). Accuracy of wind forecasting techniques can beevaluated using several parameters. The most commonlyused parameter is the mean absolute error (MAE) [20].

Recently, several studies have been conducted toinvestigate the impacts of wind power integration onelectric power system operating costs. In [21], one of theissues raised, due to the impact of wind power variability, isload forecasting accuracy and its impact on costs. The studyshows that the cost incurred because of rescheduling unitsas a result of wind power predication inaccuracy increasesas the inaccuracy of the forecast increases. Forecastingerrors lead to increased risk through imbalance costs in caseof advance contracting [22]. A method was developed toreduce the imbalance costs by determining the optimumlevel of contract energy to be sold on the advance markets[22]. In [23], a probabilistic approach is used to determinethe energy costs because of prediction errors. It wasassumed that the hourly energy production errors arecompensated by using supplementary reserve energy andthat the wind power generators should pay for the resultingenergy deviations because of prediction errors. Theprediction errors were modelled through a probabilitydensity function and it was concluded that the predictioncosts could be reduced by decreasing the time horizon,making the prediction closer to the real-time market, andby improving the accuracy of the forecast model.

This paper investigates the impact of wind powerintegration to power system on the total generation costsand close to real-time electricity market prices. Differentcase studies are demonstrated to study the effect of windpower variability and prediction accuracy. A short-termpredication horizon was chosen and was set to 1 h, thussimulating an hour-ahead electricity market. The paper isorganised as follows: Section 2 presents the problemformulation for the single auction market model. This isfollowed by presenting the mathematical formulation forthe prediction technique involved in this study in Section3. The network configuration under investigation ispresented in Section 4. In Section 5, simulation results arediscussed. Finally, discussions and conclusions arepresented in Sections 6 and 7, respectively.

2 Problem formulationAs stated earlier, the integration of wind power generationcould have an impact on electricity market prices. Ingeneral, there are two basic structures for electricitymarkets: the single auction market and the double auctionmarket [24]. This paper will focus on the single auction

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market structure where the generators provide their bidsand the market price is determined based on the totaldemand. The standard objective for single auction marketsettlement is the maximisation of a social welfare. In singleauction markets, maximisation of the social welfare isequivalent to the minimisation of the total generation cost,which has been considered as the objective in this marketsolution [24, 25]. The optimisation problem is

J ¼XN

i¼1

Ci(Pi) (1)

where C(Pi) ¼ aP2i þ bPi þ c, J is the total cost of

generation, Ci(Pi) the generator’s cost function, Pi thepower output of generator i and N the number ofgenerators. The function is minimised subject to thefollowing constraints.

2.1 Power flow equations

The two basic equations governing the flow of power can bewritten as follows

Pi � PDi ¼X

j

Vi

�� Vj

�� Yi, j cos (ui, j þ dj � di) (2)

Qi �QDi ¼ �X

j

Vi

�� Vj

�� Yi, j sin (ui,j þ dj � di) (3)

where Vi is the voltage of bus i, di the power angle, Yi, j theelement of the bus admittance matrix, u the angleassociated with Yi,j, P and Q the real and reactive powergeneration, while PD and QD are the real and reactivepower demand, respectively.

2.2 Generator limits

The following equations provide bounds on the active andreactive power generation

PMini � Pi � PMax

i (4)

QMini � Qi � QMax

i (5)

where PMin, PMax, QMin and QMax are the minimum and themaximum limits on the active and reactive power generatedby each generator, respectively.

2.3 Voltage limits

In addition, voltage limits are included to assure that thevoltage at load buses are within acceptable levels. Forgenerator buses, the voltage is set fixed as shown in (7)

V Mini � Vi � V Max

i 8i [ 1, . . . , NL (6)

Vi

�� ¼ constant 8i [ 1, . . . , NG (7)

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where V Min and V Max are the limits on bus voltage, NL is thenumber of load buses and NG the number of generator buses.

2.4 Uniform market price formulation

There are different pricing mechanisms to determine theelectricity market price namely the uniform, local andzonal-based pricing [24]. In this work, the uniform marketprice mechanism was adopted in which the highest value ofthe bus incremental cost, obtained by solving the abovemodel, is set as the market price. Thus

r � li 8i [ 1, . . . , N (8)

where r represents the uniform electricity market price and li

the local marginal cost at a bus i.

The wind farm (WF) generated output power P and Q arefunctions of the wind speed. Hence, we have to study theprediction model for wind power. This will be explained inthe next section.

3 Wind power predictionTwo time series-based prediction models are beingconsidered in this study to investigate the impact of windpower prediction accuracy on electricity market prices. Thefirst model is the persistent model, which states that the

future predicted value (X^

i þ 1ð Þ) is the current observation(X (i)) and it can be expressed by

X^

i þ 1ð Þ ¼ X (i) (9)

The second model is the Grey predictor model GM(1,1)[26]. In developing such model, the data series is firstaccumulated using the following formula

X 1ð Þ kð Þ ¼Xk

i¼1

X 0ð Þ(i) 8k ¼ 1, . . . , n (10)

where X (0) represents the original series, X (1) theaccumulated series, n the sample data length and k and ithe step for the accumulated and the original series,respectively.

This is followed by predicting the future value of theaccumulated series using the GM(1,1) model expressedmathematically by

X^ (1)

i þ 1ð Þ ¼ X (0) 1ð Þ �b0

a0

� �e�aiþ

b0

a0(11)

where X^ (1)

represents the predicted value for the accumulatedseries, X (0) the first data in the original series and a0 and b0 arethe GM(1,1) model parameters that can be estimated using

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the least square method as follows

A ¼a0

b0

� �¼ bT

� bh i�1

� bTY (12)

where

b ¼

�Z 1ð Þ 2ð Þ 1�Z 1ð Þ 3ð Þ 1

..

. ...

�Z 1ð Þ nð Þ 1

266664

377775 (13)

Y ¼

X 0ð Þ 2ð ÞX 0ð Þ 3ð Þ

..

.

X 0ð Þ nð Þ

266664

377775 (14)

and

Z 1ð Þ(i) ¼X 1ð Þ i � 1ð Þ þ X 1ð Þ(i)

2(15)

Finally, the predicted accumulated time series is inversed totransform the forecasted set of data back to the originalseries. This is carried out using the following equations

X^ (0)

1ð Þ ¼ X^ (1)

1ð Þ (16)

X^ (0)

i þ 1ð Þ ¼ X^ (1)

i þ 1ð Þ � X^ (1)

(i) 8i ¼ 1, 2, 3, . . .

(17)

4 Network configuration understudyThe network configuration under study consists of a 6-bustransmission system and a 3-bus distribution system asshown in Fig. 1 [25]. Two central generation stations areassumed connected to Bus 1 and Bus 3 with a totalcapacity of 250 and 500 MW, respectively. A WF with atotal generation capacity of 59.4 MW (WF consisting of36 units, each rated at 1.65 MW) is connected at Bus8. The wind generation units are assumed to be operatingunder the same wind speed conditions. The distributionsystem is connected to the transmission system via a100 MVA transformer.

Table 1 presents the generation and demand data at eachbus. The values for the generation cost function parametersfor Bus 8 will be discussed in details latter. Thetransmission and distribution network parameters are givenin Table 2. The load connected to each bus is assumedtime varying [3] and this was done by introducing a scalingfactor at each interval given in Fig. 2. Therefore, the net


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load at each bus at a given time interval is given by

Pd, net i, tð Þ ¼ Pd(i)� Scaling Factor(t) (18)

where Pd, net (i, t) is the net demand at bus i at time interval t,Pd (i) the demand given in Table 1 at Bus i and ScalingFactor (t) the loading level at time interval t.

Wind power prediction can be carried out either bypredicting the wind power directly from previously recordedhistorical wind production data or by forecasting windspeed at wind turbines locations and then uses windturbine power curves to predict wind power production.The second approach is considered the most commonapproach. Thus, the predicted wind speed time series isused as an input to the manufacturer power curve of the

Figure 1 Network configuration under investigation

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VESTAS V66-1.65 MW wind turbine, given in Fig. 3, topredict the output wind power production of each unitwithin the farm.

5 Simulation resultsThe market model discussed in Section 2 was implementedon the General Algebraic Modeling System (GAMS). Theproblem was formulated as a nonlinear programmingproblem and was solved using the MINOS solver inGAMS. In addition, the wind power predication modelswere implemented on MATLAB. In this work, several casestudies were designed to highlight the effect of wind powervariability and prediction accuracy on electricity marketprices. The simulation results of each individual case studywill be investigated in the next subsections.

5.1 Case study 1: WF against dieseldistributed generation impacts

The main aim of this case study is to analyse the impact ofWF generation on the electricity market-clearing price.The market price for a system with existing windgeneration is compared with the case where there is a dieseldistributed generation (DG) (capable of continuous activepower generation). These aforementioned results were thencompared with the market price in the absence of a dieselDG and WF on Bus 8. In addition, two different actualwind power generation patterns (shown in Fig. 4), hereafterreferred to as Wind 1 and Wind 2, are considered. Thesepatterns possess different characteristics, including periodswith no production such as the 9 h period in Wind 2;shifting in the peak production period where Wind 2pattern has its peak production period coincide with thedemand peak period, while Wind 1 pattern has its peakproduction period shifted away from the peak demandperiod. Sample ‘Wind 2’ is characterised by morerandomness in wind power production than ‘Wind 1’.Moreover, two different cost functions, given in Table 3,for the diesel DG are included in this case, and are referred

Table 1 Generation and load data

Bus a, $/MWh2 b, $/MWh c, $/h Pmax, pu Pmin, pu Qmax, pu Qmin, pu Pd, pu Qd, pu

1 0.01 25.5 9 2.5 0.5 1.5 20.2 0.92 0.29

2 0 0 0 0 0 0 0 0.78 0.39

3 0.05 8.5 5 5 1 3 20.2 0.73 0.19

4 0 0 0 0 0 1 0 0.67 0.24

5 0 0 0 0 0 1 0 1.12 0.31

6 0 0 0 0 0 0 0 0.26 0.12

7 0 0 0 0 0 0 0 0.1 0.02

8 a b c 0.594 0 0 0 0.15 0.05

9 0 0 0 0 0 0 0 0.1 0.03

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to as Low Cost DG (LCDG) and High Cost DG (HCDG).All DGs and the WF are assumed to operate at unity powerfactor.

Figs. 4–6 show the active power generation at Bus 8, themarket clearing price and the total generation cost,respectively. Referring to Table 3 and Fig. 4, it can beshown that as the diesel DG cost increases, the DG activepower decreases. The main system market price is affectedby the presence of both the wind farm and diesel DG asshown in Fig. 5. In general, the presence of any of thepresented types of DGs decreases the electricity marketprice. Comparing the market price in case of WF with thecase of diesel generation, it can be seen that the loads onthe system might experience a market spike especially atthe instants where there is low wind power generation. Thehigh fluctuation in market prices with a wind farm isbecause of the variable nature of the wind generated.However, the market price is still lower than the case wherethere is no existing generation on Bus 8. With respect togeneration costs (refer to Fig. 6), the total generation costdecreases in the presence of either the WF or DG. It has

Table 2 Network parameters

Buses Reactance,pu

Resistance,pu

Line charging,pu

1–2 0.1097 0.021 0.004

1–6 0.2732 0.0824 0.004

2–5 0.3185 0.107 0.005

3–4 0.2987 0.0945 0.005

3–5 0.1804 0.0662 0.003

4–5 0.1792 0.0639 0.001

4–6 0.098 0.034 0.004

6–7 0.1 0 0

7–8 0.082 0.054 0

8–9 0.082 0.054 0

Figure 2 Load scaling factor variation curve with time

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been observed that the generation cost could be reducedwhen the wind power generation is closest to its rated value.

5.2 Case study 2: impact of wind powerprediction

This case is dedicated to examine the impacts of the accuracyof the WF prediction tools, presented in Section 3, onelectricity market-clearing price. Similarly, two differentwind power samples are considered. The predicationhorizon is chosen to be equal to 1 h, thus simulating anhour-ahead electricity market. Fig. 7 shows the outputpower predicted using both the GM (1,1) and thepersistent and compared to the actual power. Fig. 8 showsthe 1 h ahead and spot market price. The MAE for themarket prices is determined as follows

MAE ¼

PSPactual � SPpredicted

�� Length of Sampled Data

(19)

where SPactual refers to the expected spot market pricecalculated on the basis of actual wind power generation andSPpredicted refers to the spot market price using predictedwind power generated (this is the real-spot market price).

It should be noted that MAE is a symmetric measure ofaccuracy that assumes that the cost of error is independenton the direction of error. Future work could consider

Figure 3 VESTAS V66-1.65 MW wind turbine power curve

Figure 4 Active power generation at Bus 8


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measures for the costs associated with asymmetrical error. Forsample data 1 ‘Wind 1’, the MAE of the predicated power forthe Grey predictor and the persistent are 5.34 and 5.62 MW,respectively. The error in the predicted values will in turnaffect the electricity market price. Fig. 9 shows the error inspot market prices. The MAE of spot market price for theGrey predictor and persistent is 1.25 and 1.37 $/MWH,respectively. It can be seen that for both predictors, thereare cases where SPactual is higher than SPpredicted whileother cases show lower SPactual. Thus, in some cases, thecustomers could benefit from this error by buying electricityat lower rates than actual prices. On the other hand, thereare intervals where the opposite is true, i.e. customers willpay more than the actual electricity price. Fig. 10 shows thedifference in electricity costs because of market price error

Table 3 Generation cost function parameters and reactivepower limits for bus 8

Connected DG to bus 8 a, $/MWh2 b, $/MWh c, $/h

WF 0 0 0

LCDG 0.004 12 5

HCDG 0.25 12 5

Figure 5 Market-clearing price

Figure 6 Total generation cost

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for both predictors. This was determined using thefollowing equation

Cd ¼ (SPactual � SPpredicted)XNGþNL

i¼1

Pi (20)

where Cd is the gain in the electricity costs because of marketprice error. The daily average electricity cost difference is

Figure 7 WF generated power (Wind 1 Sample)

Figure 8 One hour ahead and spot market price (Wind 1Sample)

Figure 9 Market price error (Wind 1 Sample)

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determined and found to be equal to 310 and 155 $/h for theGrey predictor and persistent, respectively. The positive dailyaverage indicates a gain in the customer savings in electricitybills or a loss in generator revenue. Considering that thisnumber to be the average cost difference over the wholeyear, it will be expected that the total annual savings/losswill be equal to $2 715 600 and $1 314 000.

Figs. 11–14 show the WF generation, electricity marketprice, error in market price and difference in electricitycosts because of market price error for both predictors, forthe second sample data ‘Wind 2’, respectively. For thissample data, the MAE of the predicated power for theGrey predictor and the persistent are 3.28 and 4.74 MW,respectively. Accordingly, the corresponding MAE for thespot market price are 0.48 and 0.81 $/MWH, respectively.From Fig. 14, the daily average electricity cost difference isfound to be equal to 43 and 45 $/h for the Grey predictorand persistent, respectively. Similarly, the total annualsavings/loss expected will equal $376 680 and $394 200.

The analysis was further applied to 50 additional windsamples. Tables 4 and 5 present the MAE in power andelectricity market prices for both the persistent and greyprediction method. The gain/loss in electricity costs is also

Figure 10 Gain in electricity costs due to market price error(Wind 1 Sample)

Figure 11 WF generated power (Wind 2 Sample)


calculated. For the 52 samples, presented in this paper, apositive percentage error is obtained indicating thesuperiority of the grey prediction method over thepersistent method in providing accurate prediction ofthe wind power generated. By referring to Table 5, thevalues for the MAE in electricity market prices indicatethat for some samples, the persistent is more accurate thanthe grey method and for other samples vice versa. It can be

Figure 12 One hour ahead and spot market price (Wind 2Sample)

Figure 13 Market price error (Wind 2 Sample)

Figure 14 Gain in electricity costs due to market price error(Wind 2 Sample)


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Table 4 Wind farm predicted power

Sample No. MAE

Wind power, MW

Persistent Grey % Improvement

1 2.73 2.57 5.75

2 6.48 5.85 9.62

3 8.74 8.45 3.37

4 2.65 2.31 12.78

5 7.53 6.06 19.52

6 3.34 3.13 6.29

7 3.19 2.92 8.21

8 4.53 4.45 1.72

9 8.57 7.60 11.39

10 3.02 2.23 26.07

11 2.64 2.54 3.87

12 8.10 7.38 8.88

13 8.39 6.21 26.06

14 4.23 3.28 22.35

15 5.27 4.35 17.39

16 4.53 3.79 16.29

17 4.17 3.78 9.38

18 7.35 5.83 20.63

19 2.79 1.66 40.47

20 6.67 5.51 17.41

21 7.20 6.20 13.94

22 4.24 4.15 2.08

23 3.24 3.16 2.64

24 2.67 1.76 34.02

25 6.83 5.60 18.02

26 7.86 6.53 16.84

27 4.09 2.39 41.64

28 4.27 3.34 21.66

29 6.66 6.31 5.18

30 6.92 6.11 11.80

31 5.04 4.28 15.07

32 6.13 5.07 17.22

33 2.50 2.09 16.16

Continued

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concluded that a better MAE in power prediction does notnecessarily result in a better MAE in electricity marketprices. In addition, the results show that the error in theprediction will result in either a gain for the customer(positive values) or a gain to the generation supplier(negative values). The results show that it is essential totake into account the prediction errors when settlingelectricity market prices.

6 DiscussionAn extensive research has been developed in the area of windpower prediction. This work tackles and addresses animportant and vital issue, which is the impact of predictionaccuracy on electricity markets. The main aim of this paperwas to highlight that close to real market price couldsignificantly be affected by, generally, the prediction errorand specifically, by prediction technique. Despite focusingon two prediction methods, it is worthy to mention thatthe findings in this paper are valid for other predictionapproaches. Possible solution that could minimise or even

Table 4 Continued

Sample No. MAE

Wind power, MW

Persistent Grey % Improvement

34 5.92 5.11 13.69

35 3.73 3.60 3.47

36 1.16 1.09 5.85

37 2.50 2.38 5.04

38 1.76 1.44 18.27

39 2.31 2.21 4.35

40 5.80 5.11 12.00

41 4.62 3.88 16.18

42 5.67 4.55 19.68

43 6.36 5.11 19.61

44 5.79 4.97 14.24

45 9.20 8.84 4.00

46 5.68 5.30 6.77

47 3.37 2.71 19.41

48 5.68 4.84 14.80

49 3.53 2.93 16.95

50 5.56 5.24 5.84

51 4.74 3.28 30.80

52 5.62 5.34 4.98

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Table 5 MAE in electricity market prices

Sample No. MAE Gain (saving) inelectricity costs, $/h

Market price, $/MWh

Persistent Grey % Improvement Persistent Grey

1 0.47 0.41 13.35 32.46 98.92

2 0.97 0.87 10.52 2138.85 2150.85

3 0.97 1.02 25.26 218.42 238.19

4 0.23 0.31 233.33 34.54 214.74

5 0.78 0.64 17.89 121.38 70.32

6 0.41 0.44 26.85 251.77 216.56

7 0.32 0.36 212.19 254.50 276.17

8 0.52 0.56 26.32 56.43 2105.46

9 1.68 1.53 8.95 2238.63 70.84

10 0.31 0.20 36.27 2107.67 255.47

11 0.22 0.20 9.13 80.46 36.32

12 1.47 1.14 22.25 2239.93 206.83

13 1.28 0.98 23.26 225.18 2142.70

14 0.76 0.69 9.75 113.34 292.88

15 0.93 0.85 9.00 257.84 205.40

16 0.72 0.66 7.13 228.13 142.04

17 0.66 0.69 25.49 246.21 293.44

18 1.43 1.16 18.74 2180.12 6.32

19 0.42 0.22 46.39 201.59 76.18

20 0.78 0.63 19.39 60.29 213.57

21 1.26 0.90 28.20 143.98 143.06

22 0.76 0.90 218.68 54.03 29.20

23 0.45 0.42 6.07 210.40 193.56

24 0.62 0.35 43.34 312.75 101.53

25 1.75 1.73 1.15 2733.71 2781.45

26 1.43 1.08 24.60 282.04 17.06

27 0.75 0.60 20.56 276.47 54.42

28 0.84 0.66 21.39 114.30 287.11

29 1.12 0.88 21.08 67.45 198.61

30 1.01 0.99 2.27 2197.45 256.29

31 0.76 0.68 9.89 2113.34 43.79

32 1.12 0.94 16.11 102.96 7.80

33 0.478 0.43 26.38 211.79 98.80

Continued

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Table 5 Continued

Sample No. MAE Gain (saving) inelectricity costs, $/h

Market price, $/MWh

Persistent Grey % Improvement Persistent Grey

34 0.90 0.947 25.47 222.21 117.69

35 0.76 0.69 9.58 168.37 161.02

36 0.24 0.18 23.33 60.29 71.73

37 0.41 0.42 23.70 247.79 48.65

38 0.15 0.18 214.29 60.15 80.25

39 0.45 0.44 1.56 2104.84 1.12

40 0.85 0.77 9.61 116.79 87.22

41 1.11 0.83 25.54 224.89 173.56

42 1.07 1.01 5.71 191.28 105.28

43 1.26 0.86 31.96 264.27 271.78

44 1.25 1.29 23.29 66.83 52.40

45 1.28 1.23 4.44 218.29 221.96

46 0.94 0.91 2.25 2159.37 81.04

47 0.46 0.45 3.02 64.61 29.01

48 1.03 0.91 12.28 2115.08 101.08

49 0.84 0.75 10.14 227.37 72.74

50 1.21 1.16 4.78 107.80 346.74

51 0.81 0.48 40.74 45.00 43.00

52 1.37 1.25 8.76 155.00 310.00

eliminate the market price errors, resulting from predictioninaccuracy, is to equip the WF with energy storage devices[27]. However, it should be noted that for large scale WFs,this solution could be very challenging.

The paper addressed one type of WF operation, which isthe unity power factor case. Possible WF operationalstrategies include power factor regulation and voltageregulation. In these cases, the reactive power generated/absorbed by the WF will alter the network power lossesand thus will have an impact on the electricity marketprices. This issue has not been addressed in this paper andrequires further investigation.

In addition, another approach that could take into accountuncertainties in wind power production is to use stochasticanalysis tools. The optimal power flow algorithm presentedin this paper is a deterministic tool that would need to berun several times to take into account uncertainties in windpower production [28]. Future work would be to considerthe application of probabilistic optimal power flow to

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determine the uncertainties in electricity market prices as aresult of the uncertainties in wind power production.

7 ConclusionsThe work presented in this paper highlights the impact ofwind power prediction and variability on close to realmarket prices. Wind power prediction has an impact onelectricity market prices. Inaccurate power prediction caneither result in underestimated or overestimated marketprices, which would lead to either savings to customers oradditional revenue for generator suppliers. There areinstants where the customer buys electricity at lower ratesthan the actual prices and vice versa. This depends to agreat extent on the wind power sample as well as theprediction method used. As highlighted in the simulationresults, a prediction method with a better MAE does notnecessarily result in more accurate market prices.

By comparing the market price in case of WF with the caseof diesel generation, it can be seen that the loads on the

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system might experience a market spike, especially at theinstants where there is low wind power generation. Thehigh fluctuation in market prices with a WF is because ofthe variable nature of the wind generated and due to thereliance on more expensive thermal units to supplythe power. However, the market price is still lower than thecase where there is no existing generation. The more thepower generated by the WF, the lower the electricitymarket prices. The inaccuracy in market prices as a resultof the errors associated in prediction tools should be takeninto account when settling market prices.

8 References

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