Integrating Non-dispatchable Producers In

Electricity Markets Antonio J. Conejo, Fellow, IEEE, Juan M. Morales, Student Member, IEEE

Abstract-This short paper analyzes some of the issues arising from a large-scale integration of non-dispatchable producers into an electricity market. On the one hand, market-related operations problems and their solutions are reviewed. On the other hand, market-related structural problems are examined.

Index Terms-Electricity market, Non-dispatchable producers.

I. ANALYSIS

This short paper analyzes some of the issues arising from the

large-scale integration of non-dispatchable power producers

into a pool-based electricity market.

The analysis is twofold. On the one hand, market-related

operations problems and their solutions are reviewed. These

problems include reserve management and valuation, offering

strategies by non-dispatchable producers, etc.

On the other hand, market-related structural problems are

examined. These problems comprise the re-design of market­

clearing tools, the temporal arrangements of the adjustment

markets, the mechanism for pricing energy imbalances, etc.

These issues are considered in the subsections below.

A. Futures market

Non-dispatchable producers have no incentive to participate

in futures markets (or bilateral contracting) due to their non­

dispatchability, i.e., to their inability to guarantee the supply

of a pre-specified amount of power during a future period.

Hence, no issues pertaining to futures markets or bilat­

eral contracting arise from a large-scale integration of non­

dispatchable producers.

B. Pool

For power trading, we consider that the pool includes:

1) A reserve market, to provide reserve power for next-day

operation.

2) A regulation market, to allocate enough regulating power

to maintain the frequency within appropriate bounds

during the next day.

Non-dispatchable producers do not participate in power

markets (reserve and regulation), because they cannot control

A. J. Conejo and J. M. Morales are partly supported by the Junta de Comunidades de Castilla - La Mancha through project PCI-08-0102, and by the Ministry of Science and Technology of Spain through CICYT Project DPI2009-09573.

A. J. Conejo and J. M. Morales are with Univ. Castilla-La Mancha, Ciudad Real, Spain (e-mails: Antonio.Conejo@uclm.es. Juan­Miguel.Moraies@uclm.es).

their output power. Thus, the large-scale integration of non­

dispatchable producers is expected to increase the need for

reserve, but not to impact the spectrum of reserve providers.

For energy trading, we consider that the pool includes:

1) A day-ahead market, cleared about twelve hours before

power delivery begins. This market accommodates most

of the volume of trading and as a result, is usually used

as the reference market to establish, e.g., the mechanism

for pricing energy imbalances.

2) A number of adjustment markets, sequentially arranged

before and throughout the delivery horizon. Thus, these

markets are cleared several hours previous to actual

power delivery.

3) A balancing market, cleared each hour and minutes

before the actual power delivery. This market is aimed

to restore the system balance on a real-time basis. Bal­

ancing prices should, therefore, represent the cost of the

energy required to counteract the net system imbalance.

The mechanism for balancing price formation should be

fair, non-discriminatory, and transparent, while pursuing

economic efficiency. No economic penalty other than

that derived from the market natural laws should be

imposed on deviated market agents.

Non-dispatchable producers sell their energy through the

day-ahead market and the adjustment markets.

Since adjustment markets are comparatively closer in time

to power delivery, they are more advantageous for non­

dispatchable producers because the level of uncertainty on

their energy production diminishes.

Non-dispatchable producers need to participate in the bal­

ancing market to balance (i) the power level (to be delivered)

agreed in the day-ahead plus the adjustment markets and (ii)

the actual power that is eventually delivered.

To adapt the pool to the behavior of non-dispatchable pro­

ducers, the following redesign actions need to be considered:

1) Include network constraints in clearing procedures as

high wind power production may result in network

congestion. Transmission issues prove to be of especial

relevance in wind integration studies because, unfortu­

nately, wind blows stronger in certain specific regions,

usually far away from large energy consumption areas.

In this sense, representing the network has the advantage

of implicit congestion management. Moreover, it natu­

rally leads to the notion of loeational marginal prices (LMPs).

2) Include inter-temporal constraints in the clearing pro­

cedures to facilitate the deployment of reserves. Wind

978-1-4244-6840-9/10/$26.00 © 2010 IEEE

generation is highly uncertain and variable, and con­

sequently, the proper management of such variability

requires a flexible operation of power systems. This can

be optimally fulfilled if the inter-temporal limitations of

production units are explicitly taken into account in the

market-clearing procedures.

3) Give relevance to adjustment markets at the cost of

the day-ahead market. Adjustments markets allows non­

dispatchable producers reducing their uncertainty on

power production.

Additionally, it seems appropriate to redesign the reserve

market as follows:

1) Clear simultaneously reserve and energy so that a high

coordination reserve-energy is achieved. This is im­

portant since a higher penetration of non-dispatchable

producers entails higher reserve need, and eventually,

deployment complications. Further details can be found

in [ 1], [2], [3].

2) Use stochastic procedures to select the required reserve

level and to estimate its cost. This is the most econom­

ical manner to proceed.

C. Offering strategy

The offering strategy of a non-dispatchable power producer

should be based on a description of the wind level distribution,

not on its expected value. In addition, it should be profit

effective, while reducing the risk of profit variability as much

as possible. Further details can be found in [4], [5], [6], [7].

If more than one farm is involved, the correlation among the

stochatic processes describing wind production at the different

locations should be properly modeled. Further details can be

found in [8].

Offering strategies reducing the risk of profit volatility

by slightly reducing its expected value are possible. Further

details are available in [7].

II. CONCLUSIONS

A pool based market with a significant number of non­

dispatchable producers benefits from incorporating the follow­

ing features:

1) A clearing mechanism simultaneously involving both

energy and reserve and using a stochastic criteria (with­

out imposing a pre-specified reserve level). This allows

economic coordination among energy and reserve, and

results in a comparatively higher economic efficiency.

2) A representation of the network in the clearing model

resulting in LMPs. This solves implicity network con­

gestion issues and transmits appropriate price signals.

3) Embedding of inter-temporal constraints in the clearing

model. This facilitates and simplifies reserve deployment

actions.

4) Trading floors should be close to real time operation,

hours ahead, not day-ahead. This reduce the uncertainty

plaguing decision making by non-dispatchable produc­

ers, and allows a more efficient trading.

2

REFERENCES

[1] F. D. Galiana, F. Bouffard, J. M. Arroyo and 1. F. Restrepo, "Scheduling and Pricing of Coupled Energy and Primary, Secondary, and Tertiary Reserves," Proceedings of the IEEE, vol. 93, no. 11, pp. 1970-1983, November 2005.

[2] F. Bouffard and F. D. Galiana, "Stochastic Security for Operations Planning with Significant Wind Power Generation," IEEE Trans. Power Syst., vol. 23, no. 2, pp. 306-316, May 2008.

[3] J. M. Morales, A. 1. Conejo and J. Perez-Ruiz, "Economic Valuation of Reserves in Power Systems with High Penetration of Wind Power;' IEEE Trans. Power Syst., vol. 24, no. 2, pp. 900-910, May 2009.

[4] G. N. Bathurst, J. Weatherill and G. Strbac, "Trading Wind Generation in Short Term Energy Markets;' IEEE Trans. Power Syst., vol. 17, no. 3, pp. 782-789, Augnst 2002.

[5] P. Pinson, C. Chevallier and G. N. Kariniotakis, "Trading Wind Gener­ation From Short-Term Probabilistic Forecasts of Wind Power," IEEE Trans. Power Syst., vol. 22, no. 3, pp. 1148-1156, August 2007.

[6] J. Matevosyan and L. SOder, "Minimization of Imbalance Cost Trading Wind Power on the Short-Term Power Market," IEEE Trans. Power Syst., vol. 21, no. 3, pp. 1396-1404, August 2006.

[7] J. M. Morales, A. J. Conejo and J. Perez-Ruiz, "Short-term trading for a wind power producer," IEEE Trans. Power Syst., vol. 25, no. 1, pp. 554-564, February 2010.

[8] J. M. Morales, R. Mfnguez and A. J. Conejo, "A methodology to generate statistically dependent wind speed scenarios," Appl. Energy., vol. 87, no. 3, pp. 843-855, March 2010.

Antonio J. Conejo (F'04) received the M.S. de­gree from MIT, Cambridge, MA, in 1987, and a Ph.D. degree from the Royal Institute of Technol­ogy, Stockholm, Sweden in 1990. He is currently a full Professor at the Universidad de Castilla - La Mancha, Ciudad Real, Spain.

His research interests include control, operations, planning and economics of electric energy systems, as well as statistics and optimization theory and its applications.

Juan M. Morales (S'07) received the Ingeniero Industrial degree from the Universidad de M:ilaga, Spain, in 2006. He is currently working toward the Ph.D. degree at the Universidad de Castilla-La Mancha.

His research interests are in the fields of power systems economics, reliability, stochastic program­ming and electricity markets.