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Evaluating Impact of Wind Power Hourly Variability On Day-ahead Electricity Markets Rui Bo, Senior Member, IEEE, Jordan Bakke 

Abstract — Wind power is undergoing a rapid development in many countries and has gained greater attention as the penetration of wind power continues to grow. To ensure reliable and efficient operation and planning of large amount of wind power resources, the impact of wind power on the power systems need to be carefully examined. This paper attempts to evaluate the impact of wind power variability on the efficient operation of day-ahead electricity markets. An approach to adjust wind hourly variability is presented, and full-year simulations have been carried out on much of the US Eastern Interconnection power systems. The simulation includes six wind variability levels in conjunction with three wind penetration levels to reveal a complete picture of the impact of wind variability. Detailed results quantifying the impact are presented including production cost, load cost, LMP, thermal generation and wind curtailment. Index Terms—electricity markets, wind power, variability, day-ahead market.

I. INTRODUCTION

A

S a renewable resource, wind power is undergoing an incredible development around the world. With growing penetration of wind power resources, its impact on system operation and planning has become an increasingly important topic. Many studies have been performed to evaluate various aspects of the impact of wind power resources being integrated into the power grids, including technical challenges of integration, operational impact, computation models, etc [14]. This paper will focus on the hourly variability of wind power and its impact on the operation of day-ahead electricity markets. The intent is to understand the cost of wind variability in terms of system production cost, and its relation with wind penetration. The paper is organized as follows. Section II introduces the study approach proposed for generating wind power profiles with different level of variability, as well as the wind penetration levels. In Section III, the essential components relevant to simulating day-ahead electricity markets are briefly reviewed. In Section IV, the analysis is performed on much of the US Eastern Interconnection system, and detailed results are presented including production cost, LMP, generation and wind curtailment. Discussion and conclusions are presented in Section V.

Rui Bo and Jordan Bakke are with Mid-continent Independent Transmission System Operator (MISO), St. Paul, MN 55108, USA. Contact: [email protected], +1-651-632-8447 (R. Bo).

II. WIND POWER HOURLY VARIABILITY STUDY APPROACH A. Wind Power Variability In a study report conducted by National Renewable Energy Laboratory (NREL) [5], coefficients of variation (COV) is used for measuring wind power variability. COV is defined as the ratio of standard deviation value to the mean value of wind power. Certainly, different COVs can be used to randomly generate wind power profiles with different levels of variability. However, it is evident that different wind power profiles with the same COV may have different impact on the system. Therefore, to decouple the impact of wind power variability from that of randomness of wind power profiles, we keep the hourly pattern in wind power profiles by adjusting the hourly wind power proportionally to its original value. We define ̅ ̅ (1) where is the hourly wind power of wind unit w at hour t in the original wind power profile; ̅ is the daily average of wind unit w for day d; is the wind variability factor, and ; is the new hourly wind power of wind unit w at hour t after applying wind variability factor . When realizes value 0, the new wind power profile has no intra-day variation and holds a constant output for the entire day, presenting no intra-day hourly variability. When realizes value 1, the new wind power profile will be exactly the same as the original wind power profile. For , the new wind profile will have half of the deviations from daily average value when compared to the original profile. For , the new wind power profile will be in between the constant-value profile ( ) and the original profile ( ). The smaller the wind variability factor is, the less hourly variability the wind power profile has. Fig.1 depicts the 24-hour hourly profile for six different levels of wind power variability, with wind variability factor ranging from 0 to 1. The hourly profile is obtained using annual average of the same hour. It should be noted that, under the above approach of adjusting wind power profiles, the total energy associated with the wind profiles is held constant. No energy gain or loss among different wind variability levels, and therefore it is easier to make fair comparisons.

2 rv=0 rv=0.4 rv=0.8

Wind Power (MW)

10 9

rv=0.2 rv=0.6 rv=1

wind profiles, corresponding to wind variability factor . Hypothetical wind profiles with less variability are obtained using the approach proposed in subsection II-A. B. Study Footprint

8 7 6 5 1

3

5

7

9 11 13 15 17 19 21 23 Hour

Fig. 1. Illustration of wind power profile with six different wind variability factor (i.e., 0, 0.2, 0.4, 0.6, 0.8, 1)

This analysis is performed the US Eastern Interconnection (EI) wide power systems, as shown in Fig. 2. The study footprint includes MISO (which consists of Midwest and MISO South), PJM, NYISO, TVA, SERC, SPP and MRO. Among them, MISO, PJM and NYISO are Independent System Operators (ISOs) and run electricity markets with centralized unit commitment and economic dispatch. SPP will be launching its “Day 2” market in 2014. Therefore, in this analysis, each of the seven regional entities in the study footprint is simulated as a centralized day-ahead market.

B. Wind Power Penetration The impact from wind power variability may not be significant when wind power accounts for only a small portion of the total energy produced, and will likely be much greater with stronger presence of wind power. With the Renewable Portfolio Standard mandates and goals enacted in many US states [6], wind power penetration is expected to continue to rise in the years to come. Therefore, three different wind penetration scenarios will be analyzed in this work. Scenario 1 represents a reference case scenario where the wind energy penetration level is 7.41% in 2018. Scenarios 2 and 3 represent doubled and tripled wind capacity and energy respectively and subsequently the penetration levels become two and three times of Scenario 1 respectively. These three scenarios, as shown in Table 1, will be analyzed together with aforementioned the six different wind power variability levels to fully reveal the impact of wind power variability under various conditions. TABLE 1 THREE STUDY SCENARIOS

Scenario 1

Total Wind Energy MWH (2018) 207,154,729

Scenario 2

414,309,459

14.82%

Scenario 3

621,464,188

22.23%

Scenarios

Wind Energy Penetration Level 7.41%

III. US DAY-AHEAD MARKET SIMULATION A. Wind Power Profile Wind power profiles used in this analysis are taken from NREL [7]. NREL eastern wind dataset contains wind power data in 10-minute resolution for onshore and offshore sites of the entire US Eastern Interconnection (EI) power systems for three years (2004, 2005 and 2006). Since this paper aims at analyzing the impact of different wind power variability on day-ahead electricity markets, aggregated hourly wind power profiles for year 2006 are employed in this analysis. The NREL wind power profiles are taken as the original

Fig. 2. Study Footprint

C. Simulation Tool Commercial software PROMOD® is utilized for this analysis. It features hourly chronological security constrained unit commitment and economic dispatch (SCUC/SCED), and is capable of simulating the entire Eastern Interconnection system which includes thousands of generating units, tens of thousands of electrical buses and transmission facilities. Transmission constraints including system-in-tact, N-1 and N-2 contingencies are included in the simulation to guarantee reliability of the system.

IV. SIMULATION RESULTS In this section, numerical simulations have been performed on much of the EI system using the MISO Transmission Expansion Planning (MTEP) 2018 out-year models under Business As Usual (BAU) future scenario. Therefore, all numbers shown in this section represent the forecasted results for 2018 unless noted otherwise. A. Production Cost Fig.3 shows the total production cost under different wind variability factors and scenarios. Production cost increases

3 Increased Load Cost (billion$)

3.00

2.50 2.00 1.50 1.00 0.50 0

0.2

0.4

0.6

0.8

1

Wind Variability Factor

Fig. 5. Increase in load cost under Scenario 3

72.00 70.00 68.00 66.00 64.00 62.00 60.00 58.00

39.50 Load LMP ($/MWH)

Total Production Cost (billion$)

steadily with increased wind power variability for each of the three scenarios. Fig. 4 shows the increased production cost from wind variability factor 0 (i.e., no hourly wind power variability), which reveals the additional cost associated with the wind variability. The increase in production cost is up to $277 million in Scenario 1, and much higher in high wind penetration scenarios, up to $1.2 billion in Scenario 3. In other words, if the wind variability in the original wind profiles can eliminated (such as through energy storage system), a production cost savings between $277 million and $1.2 billion may be achieved.

Scenario 1 Scenario 2 Scenario 3

0

0.2

0.4

0.6

0.8

1

39.00 38.50 38.00 37.50

Wind Variability Factor

0

0.2

0.4

0.6

0.8

1

Wind Variability Factor Fig. 3. Production cost under different wind variability factors and scenarios

Increased Production Cost (million$)

Fig. 6. Load LMP under Scenario 3

1,400 1,200 1,000 800 600 400 200 -

Scenario 1 Scenario 2

C. Thermal Generation

Scenario 3

The changes in production cost, load cost and LMP are all related to generation changes. Taking Scenario 2 as an example, the generation difference between wind variability 0 and 1.0 is listed in Table 2. It indicated with higher wind variability, CC and CT Gas runs more to accommodate the variability of wind power. As a consequence, in the case of transmission congestions, wind power may be curtailed more.

0

0.2

0.4

0.6

0.8

1

Wind Variability Factor

TABLE 2 THERMAL GENERATION IN SCENARIO 2

Generation Difference (MWH)

Gen Differe nce in Percent age

356,616,028

(12,434,495)

-3.49%

18,588,102

20,467,876

(1,879,774)

-9.18%

ST Coal

1,222,322,627

1,221,034,530

1,288,097

ST Gas

16,216,389

17,308,160

(1,091,771)

Generation (MWH)

Fig. 4. Increased production cost under different wind variability factors and scenarios

B. Load Cost and LMP Fig.5 shows the increase in load cost in Scenario 3 when wind variability increases. Load cost increases by up to $2.79 billion, much more than the increase in production cost. The increase in load cost is a result of increase in LMP. Fig.6 shows load LMP for the scenario. It can be seen the load LMP gradually increases with greater wind variability.

Generator Type

Wind Variability Factor=0

Wind Variability Factor=1

CC

344,181,533

CT Gas

0.11% -6.31%

D. Wind Power As the wind variability increase, wind power curtailment increases as well, as shown in Fig. 7. It shows under reference Scenario 1, the wind curtailment is very minimal because of

4

Wind Curtailment Percentage

the transmission system can support the delivery of the amount of wind power modeled in Scenario 1. However, curtailment level will grow drastically for Scenario 2 and 3, as shown in Fig. 8. It shows that, for the same original wind power profiles, wind curtailment jumps from 0.80% in Scenario 1 to 23.82% in Scenario 3. It implies transmission system upgrade is needed to enable significant amount of additional wind power. The increase curtailment is also partly due to the assumption made in this analysis that the increase in wind power is applied to all wind units in proportion to the original wind capacity. If only wind units with strong transmission support are selected for distributing the additional wind capacity and energy, the wind curtailment will be as significant.

VI. REFERENCES [1]

[2]

[3]

[4]

1.00% [5]

0.80% 0.60%

[6]

0.40%

[7]

0.20% 0.00% 0

0.2

0.4

0.6

0.8

1

Wind Variability Factor

Fig. 7. Wind curtailment percentage under Scenario 1

30.00% Wind Curtailment Percentage

Future work should include evaluation of wind variability impact on the real-time markets with the consideration of intra-hour wind variability.

25.00% 20.00%

15.00% 10.00% 5.00% 0.00% Scenaro 1

Scenaro 2

Scenaro 3

Fig. 8. Wind curtailment percentage under different scenarios for wind variability factor 1.0

V. DISCUSSIONS AND CONCLUSIONS This paper evaluates the impact of wind variability on dayahead electricity markets in the US. Different wind power variability level is generated using proposed wind variability factor. Six wind variability levels are studied in conjunction with three scenarios of different wind penetration level are simulated. Results show that the wind variability is associated with up to $1.2 billion increase in production cost and up to $2.79 billion increase in load cost.

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