Impact of wind power integration on operating reserves

BOUZIDI Lotfi University of Tunis El Manar-ENIT- LSE

Tunis, Tunisia Lotfi.bouzidi2008@yahoo.fr

BELLAAJ MRABET Najiba University of Tunis El Manar-ISI

Tunis, Tunisia najiba_bm@yahoo.fr

Mohamed EL EUCH University of Tunis El Manar-ENIT- LSE

Tunis, Tunisia melleuch2005@yahoo.fr

Abstract— Wind power integration leads to new challenges for power system operator arising from the variability and the forecasting uncertainty. Many studies have treated technical and economic impacts occurred at high level of wind penetration. Operating reserves determination taking account the variability and the uncertainty of wind generation is considered as a crucial impact to the power system operator. In this paper, different rules are used to calculate the operating reserves in the Tunisian power system and the impact of wind power penetration is studied.

Keywords: wind power integration, spinning reserves impact

I. INTRODUCTION

A yearly evolution of 25% at world scale of wind power capacity is noted from 2002 year to 2010 year; so actually, it attempts 193 Gw [01], [02], [03]. The fast growth of wind power capacity required a high level penetration to the power system. It has four main steps to integrate the wind generation into the grid:

- A low penetration : the power system operator ignore the wind generation and it considered as fluctuating load

- An average penetration: in this case, the power system operations are not changed but the operator is aware of the wind integration impact.

- Evolution from average to high penetration: new solutions are adopted to adapt the intermittent generation to vertical operation of the power system (update GCR Rules, creation of international commission…)

- A large scale penetration: real time operations are considered. This step presented by electrical spot markets such as EPEX created by a European countries group.

A multitude of studies had treated the high penetration impact on the operations of power system management [04], [05], [06]. This impact can be considered as the results of the power system structure evolution from a vertical type (the energy is transported from conventional resources to the consumption through a grid system) to a new paradigm characterized by bidirectional circulation of the energy due to

the non conventional productions integration and decentralized resources connected to the distribution grid.

With this new paradigm, the actual power system operator evolves to a wind power system operator which is able to take account the impacts of the variability and forecasting uncertainty on the unit commitment (resources programming for horizon of 24 hours) and the real time unit commitment (resources programming for horizons of 6 hours and 2 hours).

Operating reserves are taken account in the both process (unit commitment and real time unit commitment) especially in systems reserves and also the (n-1) rule. In many studies, it was shown that when the level of wind power production increases, the need for operating reserves increases which translated into higher operation cost [07].

Operating reserves can be classified into several types and characterized as a spinning or non spinning reserves.

Spinning reserves are a portion of operating reserves and defined as an online reserves and apt to maintain immediately the balance load-production at any time under an instruction from the power system operator [08], [09], [10].

Non-spinning reserves or contingency reserves are considered as off-line and non-synchronous reserves. This type of operating reserves is not needed to respond to frequency deviation autonomously and immediately but it considered as a quick start reserves

In this paper, many approaches for spinning reserves determination are identified and the calculation of spinning reserves used by the Tunisian power system operator is done. Finally, a study of the variability and the uncertainty prediction impact of wind power on spinning reserves is detailed.

A. Systems services : Frequency control levels

Operating reserves are necessary to assure the security operation of power system by three frequency control levels (primary, secondary and tertiary reserves); in fact, operating reserves can be used in real time to react to any gap between load and production. Besides, there are used to cover the loss of any generation unit due to a breakdown or any other cause.

Systems service are defined according to a broad guidelines based on the Grid Connection requirement (GCR) which are changed from a country to another. In Europe for example, guidelines are given by the Union for Coordination of Transmission of Electricity (UCTE); but in North America,

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it is the North American Electric Reliability Corporation (NERC). The purpose of primary control is to limit the frequency deviation in system power. In Europe, the primary control is used when the system frequency deviate by a 20 mHz from a set point value which is 50Hz.

The secondary control is used to restore the frequency to its nominal value and to reduce the area control error. This type of control is based on generation units controlled by automatic generation control (AGC) or others characterized by a fast starting. In this case, reserves are activated 30 seconds after a contingency event and must be fully operational within 15 minutes.

The tertiary control is used to restore an important imbalance and to solve congestion problems. Tertiary reserves are activated after primary and secondary reserves activation and remained until problems resolution.

B. Types of operating reserves

In literature [11], [12], [13], [14], operating reserves can be classified into five types:

- Regulating reserves,

- Load following reserves,

- Frequency responsive reserves,

- Supplemental reserves,

- Ramping reserves.

Regulating and load following reserves are used for frequency control during normal conditions (non-event); load following reserves are slower movement than regulation. Two types are subsets of primary and secondary operating reserves.

Frequency responsive reserves are used in the case of a major disturbance and there are considered as the first autonomous response. An operating reserves type that is a subset of primary reserves.

Supplemental reserves are defined as the tertiary reserves that replace primary and secondary reserves. Ramping reserves are used during failures and events that required a long time frame.

Each type of operating reserves can be characterized as a spinning or non spinning reserves; but in some cases, an operating reserves type can be divided into spinning and non spinning reserves.

Operating reserves can be characterized as:

- Spinning reserves,

- Non- spinning reserves

In the following figure, different types of operating reserves and their characterization of spinning and non spinning reserves are summarized.

Figure 1. Operating reserves diagram [9],[16]

II. DETERMINISTIC APPROACHES FOR SPINNING RESERVES

CALACULTION

A. spinning reserves determination

• 1st rule

It used in Australia and New zealand

��� = max {���� ��} (1)

i: index of online production unit,

SRt: spinning reserves at the instant t

�� ��: Maximum power of the unit i

�� : Statute of the production unit at the instant t

• 2nd rule

It used in Manitoba Hydro.

SR� = 80% max(u��P����) + 20%(∑ P����)"# (2)

• 3rd rule

It used in Hydro Québec.

��� =90% max(���� ��) +10%(∑ �� ��) &'( )*+,-. < 5 -(12,3.4# (3)

��� =90% max(���� ��) + 10%(∑ �� ��) &'( )*+,-. >4#6 -(12,3. (4)

• 4th rule

It used in Germany.

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��� =1% × (ℎ'(*9 *'+:) +;',-2,<3,;9 .)2,,2,< (3.3(=3. (5)

B. Tunisian power system application

Due to the dataset availability of the Tunisian power system operator function, we are tried to calculate the spinning reserves for some days using the four rules explained previously for three types of days:

- Ordinary day (an average load day),

- Peak load day (19-08-2004),

- Minimum load day (14-11-2004).

A Comparison between the results of each rule and spinning reserves allocated by the Tunisian power system operator is done for the three types of days.

The following figure presented the wind power production curve of the first wind farm section of Sidi Daoud with an installed capacity of 10MW. This curve is used during the following work; besides, it must be noted that for a wind capacity C the same curve is used but it is multiplied by an integer n such as C=n×10.

Figure 2. Wind power production curve (wind capacity=10MW)

In the case of the minimum load day (figure 3), we note that the quantity of spinning reserves allocated by the Tunisian power system operator is different than which is calculated by the four rules. During minimum load period, spinning reserves allocated by the Tunisian power system operator is equalized to the load. During maximum load period, spinning reserves allocated are the same than which are calculated by the 1st rule.

Figure 3. Spinning reserves determination

Figure 4. Spinning reserves determination

In figure 4, during the maximum load day the quantity of spinning reserves allocated by Tunisian power system operator is near of the quantities calculated by the 1st, 2nd and the 3 rd rule.

For a case of an ordinary day, it showed in figure 5 that spinning reserves allocated is lower than which are calculated by the four rules especially during the maximum load period.

Figure 5. Spinning reserves determination

Differences between quantities of spinning reserves calculated by four rules used in different countries and which are allocated by the Tunisian power system operator are noted. These differences explained by the distinction between the practical and the theoretical function of the power system operator; in fact, practical one is characterized by some problems or events meeting that not taken account by the rules. In section III, we tried to detail the intermittency and the forecasting uncertainty impact on the spinning reserves determination.

III. IMPACT OF WIND POWER PENETRATION ON SPINNING

RESERVES

In order to determine the impact of wind power variability and its forecasting uncertainty, we are based on a rule used by

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 241

1.5

2

2.5

3

3.5

4

4.5

Hours

Pw

ind(

MW

)

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German power system operator taken account the wind power integration.

�� = 3 × ?(#%×@A�BCDEFGHI )J + (KL�)J + 1× KMN+ 50% of

1.5× �OP (6)

SR: spinning reserves,

σR�: Standard deviation of wind power forecasting error for a horizon of 10 minutes in function of the mean of wind power during one hour.

σST : Standard deviation of wind power forecasting error during a horizon of one hour.

�OP : Plant having a greatest installed capacity

A. wind power variability impact

In order to evaluate the wind power variability impact, it necessary to calculate the total standard deviation which is a sum of the load standard deviation and the wind power production standard deviation.

KUA��@=V( K@A�B)J + (KW�XB)J

TABLE I. WIND POWER VARIABILITY

Installed wind

power capacity (MW)

Standard deviation

YZ[\](^_) Y`ab](^_) Yc[d\Z(^_)

10 214 0.69 214

50 214 3.45 214.12

200 214 13.8 214.53

500 214 34.5 216.85

800 214 55.2 221

We note that the impact of wind power variability is considered as negligible; in fact, from the table an evolution of wind power capacity from 10MW to 800MW leads to a standard deviation increase from 214MW to 221MW (that’s 7MW).

B. Wind power forecasting uncertainty

The rule detailed in the equation 6 is applied on the data of the Tunisian power system operator dataset. In figure 5, spinning reserves are determined for different installed capacities of wind power but for a prediction horizon of 24 hours.

It noted that for a wind power capacity of 500MW the calculated quantity of spinning reserves attempt a value of 700MW; so, an addition of a quantity of 400MW moreover of the initial quantity of spinning reserves (without wind power). As a conclusion, for a prediction horizon of 24 hours the integration of 500MW of wind power into the Tunisian power system required a spinning reserves quantity of 400MW.

Figure 6. Spinning reserves determination (prediction horizon of 24 hours)

In many studies [15], [16]; it had showed that wind power forecasting error decrease when the prediction horizon chosen very short.

Basing on this idea, the spinning reserves was determined for the same different wind power capacities but for a prediction horizon of 2 hours.

Figure 7. Spinning reserves determination (prediction horizon of 2 hours)

In figure 7, it showed the spinning reserves quantities determined for different wind power capacities and for a prediction horizon of 2 hours. So, until an integration of 200MW it remains the same quantity of spinning reserves determined in the case without wind power. For an integration of 500MW, we note a light increase in spinning reserves quantity which is evaluated by a maximum of 10MW.

Comparing between the figures 6 and 7, it concluded that the wind power forecasting for a short prediction horizon (such as 2 hours) is necessary to integrate the wind power production into the power system; consequently, the real time unit commitment based on real time wind power forecasting are required for the new paradigm in the power systems.

IV. CONCLUSION

In this work, several rules used to spinning reserves calculation are identified and compared. Basing on the dataset of the Tunisian power system operator, spinning reserves allocated by the operator are determined and compared with

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the quantities calculated by theoretical rules. In the last section, the impact of the wind power variability and its uncertainty prediction on spinning reserves determination is showed.

ACKNOWLEDGMENT

This work was supported by the Tunisian Ministry of High Education, Research.

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