# INVESTIGATION OF GENERATION AND LOAD UNCERTAINTIES ON ELECTRICITY MARKETS.

Byline: Saeed Molaei Abolfazl Jalilvan Mehdi Mokhtarifard Hadi MokhtarifardABSTRACT: Nowadays the increase of demand for electric power systems has increased voltage and power levels. On the other hand governments tend to personalize their power systems thus; the impact of a new device which is installed in power system must be studied. Owners of power plants like to know about their profits in electricity market. Also to make decision for electricity market the operators need the accurate amount of generation and demand. Although several advances have been occurred in recent decades there are several sources of errors. In this paper the effects of generation uncertainties and load uncertainties on electricity market are studied. Using a Direct Current Optimal Power Flow in an 8 bus bar system the value of LMPs is calculated. Total cost Generation of the power plants and their benefits are considered under uncertainties and their variations in different conditions are analyzed.

Key Words: Electricity market LMP Uncertainty Total cost Benefit DCOPF

1.INTRODUCTION

In recent years due to technological advances a large numbers of governments incline to personalize their power systems; as a consequence the power plant's owner and electricity market operators like to know about affects of any

variation or installation of a new device in power systems. In other words owners are going to know about the variation of their benefits.

Any variation in generations and loads can affect the electricity market; as a result the accurate amount of errors and uncertainties in generations and demands must be calculated.

In recent decades scientists all around the world have studied more about the uncertainties [1-3]. In order to calculate uncertainty several approaches are introduced:

Monte Carlo Simulation method (MCS) Beta method point Estimation [4-5] and Quantile Regression [6] are some of them.

In several studies the impacts of these uncertainties on [7] studied the impacts of various distributed generations (DGs) on whole market price. In [8] the England Electricity market's unbalanced price has been investigated. Botterud et. al. in [9] have studied the wind energy trading in real time and day ahead electricity market. They investigated the balance between risk value and returned costs under locational marginal price (LMP) then provided a method for optimum bidding strategy. In [10] based on Kernel density estimation (KDE) method the probable

distribution of unbalanced prices has been calculated. Also the uncertainties of unbalanced prices the strategies for optimal bidding have been introduced. In this paper the impacts of generation and load uncertainties on LMP are analyzed. In section 2 the LMP's formulation is introduced. Afterwards in section 3 the impact of uncertainties on LMPs and benefits is explained.

2. LMP FORMULATION

There are two common pricing methods: Marginal Clearing Price (MCP) and LMP. In a large number of studies the second method has been considered. Indeed LMP is the cost of increased generation at a special bus when the generator at the mentioned bus increases its production 1MW. LMP is affected by three components: Congestions Losses and LMP at the cheapest bus. Based on afore-mentioned points ISO which is independent unit is paid attention by

both of sellers and buyers. ISO by solving an optimization problem helps producers consumers and LMPs to make a decision. To clarify ISO tries to maximize the social welfare which is described below: Equation

B(P) and C(P) are consumers' benefit and cost of generation respectively. B(P) has complex equation and usually is neglected; as a result the ISO tries to minimize the above equation:Equation

SIMULATION AND RESULTSThe 8 bus system which is shown in figure 1 is used to show the impact of uncertainties. Information about the generators and system data can be found in table I and table II. From equation (15) and running a DCOPF the LMPs are calculated. Figure 2 shows the LMP at buses. It can be easily understood that the bus 2 3 and 8 are more expensive than the others. For more information owners benefit can be calculated by the following equations:Equation

As shown in figure 3 just 2 Gencos (2 and 6) benefit and the others have roughly zero or negative profit. It's recommended that the Gencos with the low profits don't contribute at the electricity market on that time. If these Gencos have to stay at the system due to limits of generators in turn off and start up or system stability they tolerate the losses.

TABLE I INFORMATION OF GENERATORS

Bus a($/MWh) b($/MWh) C($/h) P min(MW) P max (MW) Pd(Mw)

1###0###35###0###89.62###14.37181 0.0048193

2###0.0245283###37.60189###17.64###0###20###11

3###0.0730337###26.34562###31.60###0###32###15

4###0###0###0###0###0###15

5###0.05###25.47###24.05###0###12###0

6###0###0###0###0###0###15

TABLE II

INFORMATION OF TRANSMISSION LINES

Line###From Bus###To Bus###Reactance (p.u.)###Limit (MW)

1###1###2###0.03###9

2###1###4###0.03###15

3###1###5###0.0065###20

5###2###3###0.011###10

6###3###4###0.03###10

7###4###5###0.03###20

8###5###6###0.02###10

9###6###1###0.025###19

10###7###4###0.015###19

11###7###8###0.022###20

12###8###3###0.018###15

By assumption of uncertainty of generation at bus 2 7 and the load at bus 2 the impact of uncertainties will b investigated. In terms of generation Gencos adjusts their productions in several situations. As shown in figure 4 when G2 has an error in its generation (10%) the amount of produced energy at G4 and G5 decreases. In contrary G3 increases its production. Similar attitudes occur when thereis 10% error at G6. Also lack of demand at bus 2 causes todecrease the production in all Gencos. When L1 has positive error all generation except G4 remain constant

Total cost used to produce the energy in several uncertainties has been shown in figure 5. Lack of energy at bus 2 or 6 causes the increase of the amount of total cost. Indeed uncertainty in generation increases the LMP of the buses and decreases the amount of profits. G2 is more expensive than G6; thus the increase of the total cost in case 2 is more than case 3. +10% error of load L1 has the biggest effect on the total cost.

These differences between total costs can be found in figure 5. Cost variation must be provided with the Gencos and consumers. In some cases the uncertainty causes to win some Gencos and lose other ones. Consumers who contribute in electricity market tend to buy the energy with the less cost as much as possible. Being these fluctuations causes some consumers to hesitate to buy the energy and encourage some others to buy the energy in low cost

4. CONCLUSION

To sum up in this paper the impact of uncertainties on electricity market has been studied. The studies show that uncertainties at generation cause the increase of the total cost of generation and decrease the social welfare. Also uncertainties affect the generations of Gencos and some of them tolerate a big loss. Uncertainty at the loads has the biggest effects on LMPs and total cost. Uncertainties lead to increase the profits of some unit and consumers and on the other hand decrease the profits of other groups; as a consequence every owner or consumer who contributes in electricity market has to have a special strategy which considers the uncertainty to maximize their profits. This can be the subject of the future works.

REFERENCES

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[2] Han Shuang Yang Yongping Liu Yongqian. "Study of Short-Term Wind Power Prediction Method". PHD Thesis of Engineering in Power Engineering in North China Electric Power University 2008.

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[4] Su C.-L. "Probabilistic Load-Flow Computation Using Point Estimate Method". Power Systems IEEE Transactions on 20(4) pp. 1843-1851 2005.

[5] Mokhtari G.; Rahiminezhad A.; Behnood A.; Ebrahimi J. ; Gharehpetian G. B. "Probabilistic Dc Load-Flow Based on Two-Point Estimation (T-Pe) Method" in Power Engineering and Optimization Conference (PEOCO) 2010 4th International pp. 1-5 2010.

[6] Liu Y.; Yan J.; Han S. ; Peng Y. "Uncertainty Analysis of Wind Power Prediction Based on Quantile Regression" in Power and Energy Engineering Conference (APPEEC) 2012 Asia-Pacific pp.1-4 2012.

[7] Zeineldin H. H.; El-Fouly T. H. M.; El-Saadany E. F. ; Salama M. M. A. "Impact of Wind Farm Integration on ElectricityMarketPrices".RenewablePower Generation IET 3 (1) pp. 84-95 2009.

[8] Bremnes J. B. "Probabilistic Wind Power Forecasts Using Local Quantile Regression". Wind Energy 7(1) 2004 pp. 47-54.

[9] Botterud A.; Zhou Z.; Wang J.; Bessa R. J.; Keko H.; Sumaili J. ; Miranda V. "Wind Power Trading under Uncertainty in Lmp Markets". Power Systems IEEE Transactions on 27 (2) pp. 894-903 2012. [10] Bourry F.; Juban J.; Costa L. ; Kariniotakis G. "Advanced Strategies for Wind Power Trading in ShortTerm Electricity Markets" in Proceedings European Wind Energy Conference and Exhibition EWEC 2008 2008.

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Publication: | Science International |
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Article Type: | Report |

Date: | Sep 30, 2014 |

Words: | 1555 |

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