ieee transactions on sustainable energy, vol. 3, no. 3,...

IEEE TRANSACTIONS ON SUSTAINABLE ENERGY, VOL. 3, NO. 3, JULY 2012 455

A Probability-Driven Multilayer Framework forScheduling Intermittent Renewable Energy

Fangxing Li, Senior Member, IEEE, and Yanli Wei, Student Member, IEEE

Abstract—A probability-driven, multilayer framework is pro-posed in this paper for ISOs to schedule intermittent wind powerand other renewables. The fundamental idea is to view the inter-mittent renewable energy as a product with a lower quality (i.e.,the probability of energy availability in real time) than dispatch-able power plants, such as thermal or hydro plants, from the oper-ators’ viewpoint. Multiple layers which consider the probability ofdelivery are proposed such that various loads (critical or non-es-sential controllable loads) may participate in different layers inthe energy market. A layer with a lower expected probability ofenergy availability is generally anticipated to have a lower price.This is similar to having different prices for commodities of varyingqualities. A methodology is proposed to gradually merge the mul-tilayers in the day-ahead market to a single deterministic layer inreal time. The merge is necessary because the market must be de-terministic in real time, whether sources are available or not. Thisis also aligned with the higher accuracy of forecasts when the timeframe moves closer to real time. Further, the proposed schedulingframework is extended to consider the transmission constraintswith a case study based on a modified PJM 5-bus system.

Index Terms—Economic dispatch, day-ahead, hour-ahead, loca-tional marginal pricing (LMP), multilayer framework, one-bucketmarket model, power market, probability, real-time, wind power.

I. INTRODUCTION

A RECENT DOE report in 2008 [1] describes a nationwidegoal of a 20% wind penetration of energy by the year

2030. Fig. 1 shows the latest development of wind power pen-etration in MW capacity by the US National Renewable En-ergy Laboratory (NREL) [2]. Since the 20% goal is energy, theneeded capacity percentage is expected to be even greater be-cause the capacity factor of wind power is typically 0.25–0.40which is lower than the conventional base-load unit. The ben-efit of wind power integration is well understood in society asit has a very low operating cost, reduces the emission of pol-lutants, and relieves dependence on foreign petroleum and gas[1], [3]–[5].Intermittency or uncertainty is a major challenge of wind

power integration from the system operator’s viewpoint be-cause of the nature of wind speed and its associated MW

Manuscript received June 01, 2011; revised December 29, 2011; acceptedMarch 01, 2012. Date of publication May 30, 2012; date of current versionJune 15, 2012.This work was supported by the Stanford GCEP project, NationalScience Foundation (NSF) grant ECCS-1001999, NSF Engineering ResearchCenter program award EEC-1041877 (co-funded by NSF and Department ofEnergy), and the CURENT Industry Partnership Program.F. Li and Y.Wei are with the Department of Electrical Engineering and Com-

puter Science, The University of Tennessee, Knoxville, TN 37996 USA (e-mail:[email protected]).Color versions of one or more of the figures in this paper are available online

at http://ieeexplore.ieee.org.Digital Object Identifier 10.1109/TSTE.2012.2190115

output, which is difficult to be accurately forecasted. Althoughdifferent sources provide different results, the typical windpower forecast error is approximately 10% for hour-aheadforecasting, 15% for 12-hour-ahead forecasting, over 20%for 24-hour-ahead forecasting, and even higher for a longerlook-ahead time [6]–[9]. Although different wind speed fore-casting techniques have been applied to improve the forecastaccuracy [6]–[14], it is likely that the forecast errors willremain relatively high or show only marginal improvement, asthe wind power projection error is closely related to weatherforecasts, which have been subject to the same accuracy issuefor decades. Since the day-ahead energy market trading clearsapproximately 80–90% of the load in real-time, the uncertaintyin wind forecasting may cause a large mismatch in real-timeoperations when wind energy reaches a major portion of 20%penetration by energy. Thus, the intermittency of wind energyrequires a form of backup, such as an additional amount of an-cillary service in real-time, under the present market operationarchitecture.There are other challenges for renewable integration, such as

the negative correlation between wind output and load peaksand insufficient transmission capacity due to the location ofwind-abundant areas which are far away from load pockets. Al-though a traditional transmission-constrainedmodel should pro-vide a mathematical foundation for generation scheduling, themodel should be enhanced to include the uncertain characteris-tics of wind power.Various solutions have recently been presented to address the

different aspects in integrating intermittent wind power, suchas unit commitment [15]–[24], economic dispatch [25]–[27],ancillary service [28]–[30], and other operational impacts[31]–[33]. For instance, a short-term solution may be to usea small portion of wind power for the energy market andthe remaining large portion for ancillary service. However,since the short-run cost for wind power is cheap, we shouldmaximize its production for energy market rather than wastingit or using it conservatively as reserve in the ancillary servicemarket. As a matter of fact, if wind has a 20% penetration ofenergy, the capacity percentage would be even higher than thetypical operating reserve at approximately 10%. Thus, it is noteconomical to use the major portion of wind power for ancillaryservice. Therefore, the goal of this paper is to propose a simpleand effective market-based dispatch framework to encouragewind plant owners to participate into power markets.The remainder of this paper is organized as follows. Section II

discusses the basic strategy of the proposed probability-driven,multilayer framework without transmission model. Section IIIpresents a case study. Section IV proposes the solution with

1949-3029/$31.00 © 2012 IEEE

456 IEEE TRANSACTIONS ON SUSTAINABLE ENERGY, VOL. 3, NO. 3, JULY 2012

Fig. 1. Installed wind capacity in US as of 06/30/2011 [2] (data source: NREL).

transmission constraints considered. Section V presents a casestudy with transmission constraints based on a modified versionof the well-known PJM 5-bus system. Section VI concludes thepaper.

II. BASIC STRATEGY—WITHOUT TRANSMISSION MODEL

A. Basic Concept of the Proposed “Probability-DrivenMultilayer” Market Framework

The proposed solution is termed a “Probability-driven mul-tilayer” framework for power market operations. The basicpremise is to create multiple layers in the power market basedon the probability of availability of renewable resources. Theenabling technology is the controllable loads, including con-ventional loads and plug-in electric vehicles (PHEVs), with theexpected advanced metering and communication technology.To illustrate the proposed concept, we use the conventional

day-ahead (DA) energy market as an example. In the DAmarket, generators, once accepted by ISOs to serve loads,are obligated to serve in the actual real-time (RT) operation.However, generations and loads may have minor modifica-tions, such as re-bidding in the RT market, of their positionsbefore the delivery time. Nevertheless, since the DA market,together with long-term contracts, clears about 80–90% of theactual electricity demand, generators can bid the majority of

its possible MW output into the DA market. If we ignore theforced outage problem that is handled by operating reserves,the delivery is guaranteed because the conventional units likethermal or hydro are dispatchable.In contrast, renewable energy, like wind or solar, depends on

the availability of wind or sunlight. Wind is taken as an examplein the following discussion, but the proposed method can be ap-plied to solar as well. Unlike fuel or hydro power, wind is nota steadily available resource and cannot be stored to generatepower whenever needed. Thus, a mechanism, especially in theDA market, is required to encourage the market participationof uncertain wind power, which may have a probability, for in-stance 75% or 50%, of availability for delivery to buyers in realtime.Due to the uncertainty of wind power output (or at least a

considerable portion), wind power can be viewed as a productsubject to a poorer reliability than conventional thermal orhydro power providers. This means that wind power is apoor-quality product when compared with thermal or hydropower, especially from the viewpoint of buyers who participatein the DA market. In the market of many other commodities,certain buyers are motivated to buy products of low-quality atlower prices, such as in supermarkets; while other buyers arewilling to pay for higher prices for high-quality products suchas in department stores. Therefore, with this basic economicprincipal, a probability-driven, multilayer framework for the

LI AND WEI: A PROBABILITY-DRIVEN MULTILAYER FRAMEWORK FOR SCHEDULING INTERMITTENT RENEWABLE ENERGY 457

Fig. 2. Purchasing commodity of different quality.

electricity market is proposed for sellers and buyers to partic-ipate in where different markets may provide the commodity(electricity) at different qualities (different probabilities ofavailability).For instance, as shown in Fig. 2, we may have two layers,

where each layer targets a different probability level of avail-ability of the product (electricity). The first layer is similar to theconventional deterministic energy market and termed the P100market layer, where P100 means 100% probability of delivery.In this layer, the buyers are critical, essential loads who want tobe guaranteed for service by paying more to dispatchable unitslike thermal or hydro.Then, we may have the layer of P75, which means 75% ex-

pected probability of delivery. The sellers can be renewable en-ergy owners, while the buyers can be non-essential controllableloads including the future PHEVs. Further, an additional layer,like P50, can be added if the complexity can be justified. Notethat within a layer, many bids and offers exist. This is similarto the case where many competing department stores servinghigh quality products, as well as many supermarkets competingfor customers who are willing to buy lower quality products atlower prices.The following is a brief summary of the motivations for

the proposed multilayer market structure. From the viewpointof intermittent generation providers like wind plant owners,a sub-P100 layer (e.g., P75) provides a chance to commit toselling future energy which does not have a 100% assuredavailability in real time. If wind owners act conservatively anddo not bid due to an approximately 25% risk of unavailability,the owners may lose the opportunity to other more expensiveunits. Thus, if the un-dispatched low-cost wind plant becomesavailable in real time, it is not economically efficient and thetotal load payment will be higher. On the other hand, if theplant owner acts too aggressively with overbids and there isinsufficient wind available in real time, the owner will likelyhave to pay a high price in the real-time market to cover thegap, leading to a more volatile market. From the load view-point, if there is cheap power with an acceptable, though not100%, availability rate, the load may be willing to buy. Inthe case when the power is not available, it is also acceptableas some loads are interruptible (e.g., in general up to 25% isinterruptible if participating in the P75 layer).

Fig. 3. Merging different layers to a single deterministic P100 layer.

Note that the proposed different layers are financial; whileenergy trading at different layers is essentially performed viathe same physical transmission network as illustrated in Fig. 2.Additionally, the conventional sense of demand response

(a.k.a., controllable load or responsive load) has been im-plicitly covered in the proposed framework because demandmay participate in sub-P100 layers. For instance, if a load is“non-essential” or “somewhat non-essential”, the load can bidinto the P50 or P75 layers. Therefore, demand response is anintegrated part of the proposed framework.

B. Merging Multilayers in DA Market Into a Single P100Layer in RT Market

Energy trading is usually performed at various times. A typ-ical Day-Ahead (DA) market clears the major needs of the real-time load, while the final balance of generation and load is per-formed in the Real-Time (RT) market. The RT market currentlytends to run in very short durations such as every 5 or 15 min-utes. Many ISOs also have the Hour-Ahead (HA) market, whichwas called the “real-time” market in the past.With the proposed probability-driven multilayer framework,

it is necessary to coordinate the DA, HA, and RT markets. Thereason is that the DA market has some sub-P100 uncertainlayers and the design philosophy allows the change of gen-eration and load (i.e., controllable). However, eventually, allloads in RT must be binary-deterministic: either available ornot. Hence, a method is needed for all sub-P100 layers to coor-dinately and systematically migrate into a single deterministicP100 layer in RT.Fig. 3 illustrates the solution philosophy to solve this chal-

lenge. From DA to HA, we combine P50 into P75 with minorchanges allowed from generators and loads; and similarly, P75will be merged into P100 at RT leading to a single P100 layer inRT. The elimination of the lower layers as time approaches toRT is logical, because participants should be more certain abouttheir generation output or load as the time frame moves closerto real time.Taking the transition from DA to HA as an example, three

steps will be taken by the operator when the time approachesthe HA market, as described below. Note that the case studyshown in Table I in the next section is also mentioned below tomake the understanding of the proposed idea easier.• Step 1—Accepting G/L changes: The ISO accepts the gen-erator/load changes at the previous P50 level. The gener-ators scheduled in the P50 layer need to inform the ISO


TABLE IPROCEDURE OF MOVING FROM DA MARKET TO HA MARKET

whether it can deliver as scheduled. If not, the updatedoutput level should be informed. Meanwhile, the load hasan opportunity to modify its forecast at the P50 layer fromthe DA market. For instance, as shown in the third hori-zontal section in Table I, G3 determines that it can deliveronly 30 MW instead of 50 MW and L determines it needs150MW instead of 100MW for the original P50 layer. TheISO will accept these changes without applying a penaltyto participants like G3 or L, as long as the change is lessthan 50% (i.e., corresponding to P50) of the initially sched-uled amount. Hence, there is a G/L gap of 70 MW. Notethat the original P100 and P75 layers do not need to bechanged at this step. Buyers and sellers only need tomodifytheir positions in the original P50 layer at this point.

• Step 2—Merging P50 into P75 layers: The P75 and P50layers are combined since they are now in the HA market(closer to RT than the original DA market) and everythingshould be “more certain” than a day ago. Results are shownin the fourth horizontal section in Table I. The 70 MW gapof the load from the original P50 layer remains in the newP75 layer.

• Step 3—Incremental re-dispatch for the G/L gap: In thisstep, the ISO dispatches more generation to cover the70 MW G/L gap in the new P75 layer in the HA market.Note that G3 will be excluded from participating in thisincremental dispatch. Otherwise, G3 may intentionallyclaim a reduction of its obligation in the original P50 layer,and then participate in the new incremental dispatch for the70 MW gap to make more profit than as previously agreedto deliver at the uncertain P50 layer. The fifth horizontalsection in Table I shows the final results in which G2 aredispatched for additional 20 MW and G4 for additional50 MW in the P75 layer. It should be noted that if there isno sufficient committed units available for this re-dispatch,load interruption can possibly be applied. Interruption canbe up to 25% without reimbursement from ISO, and theinterruption over 25% can be reimbursed using a penaltycollected from generators that cannot produce (and/orpurchase elsewhere) at least 75%.

Then, when time moves from the HA to the RT market, thesame three steps can be repeated to take G/L changes, to merge

TABLE IIPROCEDURE OF MOVING FROM HA MARKET TO RT MARKET

P75 into P100, and to re-dispatch for the new G/L gap. A casestudy in the next section illustrates the fundamental idea of theproposed approach.

III. CASE STUDY AND DISCUSSION—WITHOUTTRANSMISSION MODEL

A. Case Study

A detailed example is presented in Tables I and II to better il-lustrate the proposed concept. Here we ignore regulation, spin-ning, and non-spinning reserves for simplicity. Assume we havefour generators, G1 to G4, and a load, L. Changes from a pre-vious step are in bold font in the table.As shown in the second horizontal section in Table I, in the

DAMarket we have three layers, P100, P75, and P50. Based onthe forecast, L needs to purchase up to 1200 MW. Among the1200 MW load, 900 MW is a must-supply load that L wants topurchase from the deterministic P100 layer; 200 MW is non-es-sential so L wants to purchase from the uncertain P75 layer; and100 MW is very-non-essential so L wants to purchase from themore uncertain P50 layer. (Note: here we ignore the price-sensi-tive demand elasticity, but do assume 300 MW is interruptible,if really necessary, because of unavailable wind). Correspond-ingly, after running the generation dispatch, the operator decidesto choose 400 MW from G1 and 500 MW from G2 to meet the900 MW load in P100; 100 MW from G3 and G4 each for P75;and 50 MW each from G3 and G4 for P50.The example in Table I shows that the buyer requests an in-

crease of demand, which is the typical case from DA to HA, andthen to RT. It is also possible that the load may request a smallreduction of demand. Thismeans to dispatch generation for low-ering their output. This is also mathematically viable, and thephysical meaning is to pay generators to lower their output toless than scheduled in the DA market.

B. Discussion on the Penalty for Not Meeting Schedules

Producers at the sub-P100 layers should expect to sell theproduct at lower prices, while buyers also expect to buy at lowerprices with a risk of unavailability at real-time. As service is not100% guaranteed in the sub-P100 layers, a mechanism should


be implemented to ensure producers and consumers meet theirschedules as frequently as possible in sub-P100 layers. Mean-while, the mechanism should not discourage participation in thesub-P100 layers. Below is a brief description of one proposedmechanism.1) If a producer claims to produce less than % in the P layerat the time of merging P to its upper layer (e.g., producingless than 50% for the P50 layer when P50 is merged toP75), the producer needs to pay the re-dispatched unit tocover the difference between its actual capability and %as a form of “penalty”. The reason is that delivery of %of the committed amount in the P layer is similar to pro-viding the minimum warrantee of product quality. If thisquality cannot be met, a form of penalty will be applied.

2) If the producer can produce at least % of the committedamount in the P layer but less than its scheduled amountwhen P is merged to its upper layer, no penalty will beapplied. Any gap in G/L will be covered at re-dispatch withthe actual cost to producers and consumers. The reasonfor not applying a penalty in this scenario is that a lowerprobability is expected in P (e.g., P50) by the buyers whenthey participate in the P50 layer. Since the producer meetsthe required reliability (e.g., 50% for P50), the minimumrequirement of product quality is considered met.

3) A producer which cannot meet the scheduled amount andrequest changes in HA or RT, regardless of the amount ofchange, will be excluded from a re-bid to the ISO par-ticipating in the re-dispatch for the G/L gap (Step 3 inSection II.B). This is to prevent a generator, which par-ticipated in a lower layer earlier, to intentionally reduce itsoutput and bid in a higher layer at a later time in HA or RTto manipulate the market price.

It is perhaps easier to understand the first rule that a penalty(i.e., purchasing at the re-dispatch price of the P50 layer whenP50 is being merged to P75) is applied if a producer can pro-duce less than 50%. The second rule that no penalty is appliedif a producer can produce more than 50% but less than initiallydispatched amount in P50, is perhaps different from the conven-tional market operation. The following is an analogy for betterillustration. If a consumer wants to buy a tool from a low-endsupermarket with a limited warrantee of 2 years, he/she shouldnot expect it to last for 10 years because he/she pays much lessfor this tool, as opposed to paying a higher price for a tool, withhigher quality, from a specialized store. That means, if a con-sumer wants to purchase from P50, he/she should expect somelevel of uncertainty while paying lower prices; and similarly,producers should bid at lower prices since the product’s qualityobligation is lower. Otherwise, if the product quality is highlyimportant to a consumer, he/she should not participate in thelower layers. Instead, he/she should focus on trading at the P100layer.Certainly, more refined rules are necessary for whether ad-

ditional penalty costs are appropriate in the P75 or P50 layersfor producers not meeting the 75% or 50% minimum. Also, asystematic rule to develop the sub-P100 layers, such as P75 andP50 versus P80 and P60, is needed in the actual implementationwhich could depend on the characteristics of a specific marketand perhaps the wind uncertainty level.

C. Discussion on the Economic Efficiency

The economic efficiency of the proposed multilayer frame-work is discussed next with a comparison with the existing prac-tice. This is called the “one-bucket” approach here, in which themixed power sources are dispatched, including the uncertain re-newable power and conventional dispatchable power.First, in the one-bucket approach, even though buyers and

sellers may modify their positions using incremental bids in theDA, HA, and RT markets, there is no differentiation betweenhigher-quality and lower-quality products. Particularly, at theDA and HA markets, there are uncertainties for both sellers andbuyers. However, every player is forced to think in a determin-istic way. Probabilistic thinking may be applied, but a player’sfinal decision must be binary, either bid or no-bid. As a com-parison, the proposed framework provides an opportunity forsellers to sell lower-reliability product to buyers who are willingto accept such a product. Since both buyers and sellers under-stand the mutual risk of the lower-reliability product, the priceat the P50 or P75 layer at DA, for example, should be lower thanthe DA price in the one-bucket dispatch in which all productsmust be of high quality.Second, as an alternative viewpoint, the one-bucket approach

has two options, 1 and 0, in DA and HA, while the proposedapproach has four options, P100, P75, P50 and P0 (no-bid). Anyforecasting error should lead to a higher variation in results inthe one-bucket approach, while the proposed approach givesless variation. This is because the proposed approach provides ahigher granularity with four options than the one-bucket binaryapproach.Apparently, more intermediate layers have a higher granu-

larity and more economic efficiency than the binary, one-buckapproach, but the number of layers should be weighted with thecomplexity of multiple layers and is a future research topic.

IV. EXTENSION OF THE PROPOSED SCHEDULING FRAMEWORKTO INCLUDE TRANSMISSION CONSTRAINTS

A. Market Clearing Model With Transmission Model

The basic framework in Section II can be easily extendedto the case where the transmission constraints are considered.Here the popular locational marginal price (LMP) method [34]is used to handle transmission congestion. Also, the DC op-timal power flow (DCOPF) is used for LMP calculation sinceDCOPF is commonly adopted by industrial practices [34]. Theprincipal for considering the transmission constraints is to ini-tially assign a portion of the transmission to a given layer inthe DA market, i.e., to P100, P75 andP50, respectively, for the transmission constraint. Appar-ently, we should have . For example,

, and in DA. Otherweighting approaches, such as based on loads in each layer, canbe applied. The transmission capacity assignment can be ad-justed by the ISOs depending on operating conditions. When amerger is performed, the transmission capacity of a lower layeris added to a higher layer. Also, unused transmission capacityat a particular layer is rolled over to the next layer.


P100 dispatch—DA, HA and RT:

(1)

(2)

(3)

(4)

where the generation bids; generation output withthe assumption of 0 as minimum output for simplicity;generation shift factor; all transmission limits; and

line capacity allocated to the P100 layer (e.g., 0.8 inDA and HA and 1.0 in RT).P75 Dispatch—DA and HA:

(5)

(6)

(7)

where ( minus the actually used capacityin the P100 layer after the P100 dispatch) in DA; and

( minus the actually used capacity in theP100 layer after P100 dispatch) in HA.In the above formulation, different values of are taken

in the DA and HA market. The reason is that more capacity willbe assigned to the P75 layer in the HA market since the P50layer has been merged into the P75 layer.P50 Dispatch—DA:

(9)

(10)

(11)

where the remaining capacity after the P100and P75 dispatches in DA.

B. Incremental Re-Dispatch if a Scheduled Dispatch Is NotMet

For the P75 Layer in the HA Market: As stated in Section IIIfor the proposed framework, no penalty will be applied if thegap of an individual participant is between 0 and 50% in P50.However, as long as there is a generation/load gap from the P50layer when P50 is merged into P75 at HA, an incremental re-dis-patch needs to be performed to cover the gap. With transmissionconsidered, the following model can be applied.

(13)

(14)

(15)

(16)

where is the incremental re-dispatch output;

is the bid to this incremental re-dispatch;

is the G/L gap calculated after the P50 layer ismerged into P75 with the acceptance of G/L changes; and thesuperscript “act” means the actual dispatched amount.For the P100 Layer in the RT Market: Again, no penalty will

be applied if the gap of a participant is between 0 and 25% in theP75 layer when it is merged into P100 at the RT market. How-ever, as long as a generation schedule is not met, an incrementalre-dispatch in the P100-layer RT market needs to be performedusing the following model:

(17)

(18)

(19)

(20)

where is the incremental re-dispatch output;

is the bid to this incremental re-dispatch;

is the G/L gap calculated after the P75 layer ismerged into P100; and the superscript “act” means the actualdispatched amount.


Fig. 4. The flow chart of the proposed algorithm.

TABLE IIIGENERATION BIDS AT DIFFERENT LAYERS

C. Flow Chart

As shown in the above models, the flow chart of the proposedalgorithm is shown in Fig. 4.

V. CASE STUDY WITH TRANSMISSION CONSTRAINTSCONSIDERED

A. Test System

The test system is modified from the PJM 5-bus system [34],[35]. Three wind power plants, W1,W2, andW3, are added intothe system at Buses A, C, and E, while one of the two originalunits at Bus A is removed. The load in the P100, P75, and P50layers is 550 MW, 160 MW, and 30 MW, respectively, and it isequally distributed to the loads on Bus B, C, and D. The systemis depicted in Fig. 5. The generation bids in $ and the dispatchedMW pairs are shown in Table III.

B. Test Results

For better illustration, the loads in this study are assumed toremain unchanged when the time frame moves from DA to RT.

Fig. 5. Illustration of the test system.

Also, for simplicity, it is assumed that the three wind units do notbid at the P100 layer initially while the other conventional unitsbid at P100 only. Test results are presented in Tables IV to IXfor two examples.• Example 1: Tables IV and V show the step-by-step dis-patch results with the assumption that the generators al-ways meet their schedules so there is no G/L gap. Table VIshows the generation production cost calculation.

• Example 2: The step-by-step dispatch results are shownin Tables VII and VIII when the generators may not meettheir schedules. The bid in the incremental re-dispatch tocover the P50 (or P75) layer G/L gap is assumed to takethe original bid from its upper layer, i.e., P75 (or P100), forsimplicity. Table IX shows the total generation productioncost calculation.


TABLE IVEXAMPLE 1: DISPATCHES FROM DA TO HA WITHOUT G/L CHANGES FOR THE

TEST SYSTEM CONSIDERING TRANSMISSION

TABLE VEXAMPLE 1: DISPATCHES FROM HA TO RT WITHOUT G/L CHANGES FOR THE

TEST SYSTEM CONSIDERING TRANSMISSION

TABLE VIEXAMPLE 1: GENERATION PRODUCTION COST FOR THE CASE

WITHOUT G/L CHANGES

While the generation production cost calculation forExample 1 (without the G/L gap) is straightforward, thecalculation for Example 2 (with the G/L gap) is processed asfollows, taking W2 in Table IX as an example:• W2 is not dispatched in the P50 layer bidding price

in the DA market.• W2 is dispatched to produce 3.63 MW during the incre-mental dispatch in the P75 layer in the HA market with thebidding price, as previously assumed, the same as its orig-inal bidding price in P75, $14.

TABLE VIIEXAMPLE 2: DISPATCHES FROM DA TO HAWITH G/L CHANGES FOR THE TEST

SYSTEM CONSIDERING TRANSMISSION

TABLE VIIIEXAMPLE 2: DISPATCHES FROM DA TO HAWITH G/L CHANGES FOR THE TEST

SYSTEM CONSIDERING TRANSMISSION

TABLE IXEXAMPLE 2: GENERATION PRODUCTION COST FOR THE CASE

WITH G/L CHANGES

• W2 fails to meet its 13.63 MW MW MWschedule by 2.63 MW when P75 is merged into the P100layer at the RT market.

Therefore, the final settlement for W2 is calculated asMW MW MW MW. Note the 2.63 MW shortage at RT will not trigger


the penalty since it is less than 25% of scheduled amount of13.63 MW in P75. Similarly, the settlement for W1 is calculatedas , and for W3,

.

C. Discussions

The above comprehensive example shows that the advan-tages of the probability-driven, multilayer framework discussedin Sections II and III are preserved in the model with the trans-mission included. For instance, the framework graduallycombines three layers in DA into a single, deterministic P100layer in RT. This fits the characteristic of the wind forecastwhich is more accurate when the time frame moves closer toreal time. Thus, it encourages the participation of wind in thepower market.As shown in the case study, if a wind plant overestimates its

output, it may fall short when it is closer to RT. Although sometolerance is acceptable (i.e., 50% in P50 and 25% in P75), theseplants will be blocked from participating in the re-dispatch whenit is closer to RT. Then, the deficiency due to the shortage ofthis plant will be opportunities for other power plants to gen-erate more revenue. For instance, as shown in Table VII, due toits underproduction (or overestimation at the very beginning),W1 produces 10.39MW less than scheduled. Then, W2 andW3are dispatched to produce more MW after the P75 incrementalre-dispatch, i.e., W2 from 10 MW to 13.63 MW and W3 from107.61 MW to 114.37 MW. Also, the rule prevents the possi-bility that a wind plant owner intentionally produces less thanscheduled in P50 in DA and then re-bids in P75 in HA. On theother hand, if a wind plant underestimates its output while othershave done better forecasting, it may lose the opportunity to sellthe extra power because all loads are met. Therefore, the pro-posed model encourages wind owners to improve the accuracyof their wind power output forecasting.Since the framework encourages participation, a more com-

petitive market can be achieved even with intermittent windpower. Yet, it is still very necessary to carry out future researchconsidering an oligopoly system with a few large wind powerowners, especially in the case of high-penetration of wind.It should be mentioned that the wind power plants, which can

produce more than dispatched, will also be available to partici-pate in the reserve market. A full model with the ancillary ser-vice market included can be a future topic.

VI. CONCLUSION

In this paper, a probability-driven, multilayer framework toschedule the intermittent wind energy under high penetration isproposed. The key idea is to consider generation sources withdifferent reliability, or probability of availability, as different“quality” of services. Multiple layers are established for buyersand sellers to trade electricity of different “quality” in the DA orHA market when the availability of future generation is subjectto different probability. An analogy of the proposed framework,i.e., trading at different layers for different “quality” levels ofelectricity supply, is the shopping at different stores such as de-partment stores and supermarkets for commodities of differentquality. Within a layer, we still have multiple generators to com-pete for the same group of loads. This is similar to the case that

there are many different department stores competing for con-sumers willing to paymore for higher quality products, while wealso have many supermarkets targeting lower end consumers.The proposed framework is naturally and implicitly inte-

grated with the ongoing efforts of demand response (a.k.a.controllable loads or responsive load) and smart grid. Thisprovides both buyers and sellers the opportunity to tradeelectricity at different levels of “quality”. A simple case studywithout transmission networks is presented to illustrate thebasic concept.In addition, the probability-driven, multilayer framework has

been extended to include the model of transmission constraints.Case studies are also performed in amodified version of the PJM5-bus system to verify feasibility and efficiency.Future works may include the consideration of unit commit-

ment, ancillary service, and market power under the proposedframework. Also, detailed designs such as the choice of layersin different markets and possible penalty rules may be studied.

REFERENCES[1] “20% Wind Energy by 2030: Increasing Wind Energy’s Contribution

to US Electricity Supply,” US DOE Report, Jul. 2008 [Online]. Avail-able: http://www1.eere.energy.gov/wind/pdfs/41869.pdf, accessed inOct. 2011.

[2] US National Renewable Energy Laboratory (NREL). [Online]. Avail-able: http://www.windpoweringamerica.gov/wind_installed_capacity.asp, accessed in Oct. 2011.

[3] R. Thresher, M. Robinson, and P. Veers, “To capture the wind,” IEEEPower and Energy Mag., vol. 5, no. 6, pp. 34–46, Nov.–Dec. 2007.

[4] J. Blatchford, “Participating Intermittent Resource Program (PIRP)101,” Casio, Jan. 2009 [Online]. Available: http://www.caiso.com/2343/2343d5d01ee50.pdf, California ISO, accessed Oct. 2011

[5] B. Parsons, M. Milligan, B. Zavadil, D. Brooks, B. Kirby, K. Dragoon,and J. Caldwell, “Grid Impacts of Wind Power: A Summary of Re-cent Studies in the United States,” NREL Report, Jun. 2003 [Online].Available: http://www.nrel.gov/docs/fy03osti/34318.pdf, accessed inOct. 2011.

[6] S. J. Watson, L. Landberg, and J. A. Halliday, “Application of windspeed forecasting to the integration of wind energy into a large scalepower system,” in Proc. IEE Generation, Transmission and Distribu-tion, Jul. 1994, vol. 141, no. 4, pp. 357–362.

[7] A. Fabbri, T. G. S. Roman, J. R. Abbad, and V. H. M. Quezada, “As-sessment of the cost associated with wind generation prediction errorsin a liberalized electricity market,” IEEE Trans. Power Syst., vol. 20,no. 3, pp. 1440–1446, Aug. 2005.

[8] Ahlstrom, L. Jones, R. Zavadil, and W. Grant, “The future of windforecasting and utility operations,” IEEE Power Energy Mag., vol. 3,no. 6, pp. 57–64, Nov.–Dec. 2005.

[9] C. W. Potter and M. Negnevitsky, “Very short-term wind forecastingfor Tasmanian power generation,” IEEE Trans. Power Syst., vol. 21,no. 2, pp. 965–972, May 2006.

[10] T. G. Barbounis, J. B. Theocharis, M. C. Alexiadis, and P. S.Dokopoulos, “Long-term wind speed and power forecasting usinglocal recurrent neural network models,” IEEE Trans. Energy Conv.,vol. 21, no. 1, pp. 273–284, Mar. 2006.

[11] T. H. M. EI-Fouly, E. F. EI-Saadany, and M. M. A. Salama, “Grey pre-dictor for wind energy conversion systems output power prediction,”IEEE Trans. Power Syst., vol. 21, no. 3, pp. 1450–1452, Aug. 2006.

[12] G. Sideratos and N. D. Hatziargyriou, “An advanced statistical methodfor wind power forecasting,” IEEE Trans. Power Syst., vol. 22, no. 1,pp. 258–265, Feb. 2007.

[13] H. Bludszuweit, J. A. Dominguez-Navarro, and A. Llombart, “Statis-tical analysis of wind power forecast error,” IEEE Trans. Power Syst.,vol. 23, no. 3, pp. 983–991, Aug. 2008.

[14] S. Fan, J. R. Liao, R. Yokoyama, L. Chen, and W.-J. Lee, “Forecastingthewind generation using a two-stage network based onmeteorologicalinformation,” IEEE Trans. Energy Conv., vol. 24, no. 2, pp. 474–482,Jun. 2009.

[15] B. C. Ummels et al., “Impacts of wind power on thermal generationunit commitment and dispatch,” IEEE Trans. Energy Conv., vol. 22,no. 1, pp. 44–51, Mar. 2007.


[16] C.-L. Chen, “Simulated annealing-based optimal wind-thermal coor-dination scheduling,” IET Generation, Transmission and Distribution,vol. 1, no. 3, pp. 447–455, May 2007.

[17] W. Lei, M. Shahidehpour, and T. Li, “Stochastic security-constrainedunit commitment,” IEEE Trans. Power Syst., vol. 22, no. 2, pp.800–811, May 2007.

[18] K. Methaprayoon, C. Yingvivatanapong, W.-J. Lee, and J. R. Liao,“The ANN integration of ANN wind power estimation into unitcommitment considering the forecasting uncertainty,” IEEE Trans.Ind. Appl., vol. 43, no. 6, pp. 1441–1448, Nov.–Dec. 2007.

[19] C.-L. Chen, “Optimal wind-thermal generating unit commitment,”IEEE Trans. Energy Conv., vol. 23, no. 1, pp. 273–280, Mar. 2008.

[20] F. Bouffard and F. D. Galiana, “Stochastic security for operationsplanning with significant wind power generation,” IEEE Trans. PowerSyst., vol. 23, no. 2, pp. 306–316, May 2008.

[21] J. Wang, M. Shahidehpour, and Z. Li, “Security-constrained unit com-mitment with volatile wind power generation,” IEEE Trans. PowerSyst., vol. 23, no. 3, pp. 1319–1327, Aug. 2008.

[22] L. Wu, M. Shahidehpour, and T. Li, “Cost of reliability analysis basedon stochastic unit commitment,” IEEE Trans. Power Syst., vol. 23, no.3, pp. 1364–1374, Aug. 2008.

[23] B. Venkatesh, P. Yu, H. B. Gooi, and D. Choling, “Fuzzy MILP unitcommitment incorporating wind generators,” IEEE Trans. Power Syst.,vol. 23, no. 4, pp. 1738–1746, Nov. 2008.

[24] A. Tuohy, P. Meiborn, E. Denny, and M. O’Malley, “Unit commitmentfor systems with significant wind penetration,” IEEE Trans. PowerSyst., vol. 24, no. 2, pp. 592–601, May 2009.

[25] V.Miranda and P. S. Hang, “Economic dispatchmodel with fuzzywindconstraints and attitudes of dispatchers,” IEEE Trans. Power Syst., vol.20, no. 4, pp. 2143–2145, Nov. 2005.

[26] J. Hetzer, D. C. Yu, and K. Bhattarai, “An economic dispatch modelincorporating wind power,” IEEE Trans. Energy Conv., vol. 23, no. 2,pp. 603–611, Jun. 2008.

[27] R. A. Jabr and B. C. Pal, “Intermittent wind generation in optimalpower flow dispatching,” IET Generation, Transmission and Distribu-tion, vol. 3, no. 1, pp. 66–74, Jan. 2009.

[28] N. R. Ullah, K. Bhattacharya, and T. Thiringer, “Wind farms as reactivepower ancillary service providers—Technical and economic issues,”IEEE Trans. Energy Conv., 2003.

[29] T.-Y. Lee, “Optimal spinning reserve for a wind-thermal power systemusing EIPSO,” IEEE Trans. Power Syst., vol. 22, no. 4, pp. 1612–1621,Nov. 2007.

[30] M. A. Ortega-Vazquez and D. S. Kirschen, “Estimating the spinning re-serve requirements in systems with significant wind power generationpenetration,” IEEE Trans. Power Syst., vol. 24, no. 1, pp. 114–124,Feb. 2009.

[31] J. Matevosyan and L. Soder, “Minimization of imbalance cost tradingwind power on the short-term power market,” IEEE Trans. Power Syst.,vol. 21, no. 3, pp. 1396–1404, Aug. 2006.

[32] T. H. M. Ei-Fouly, H. H. Zeineldin, E. F. EI-Saadany, and M. M.A. Salama, “Impact of wind generation control strategies, penetrationlevel and installation location on electricity market prices,” IET Re-newable Power Generation, vol. 2, no. 3, pp. 162–169, Sep. 2008.

[33] Y. V. Makarov, C. Loutan, J. Ma, and P. de Mello, “Operational im-pacts of wind generation on California power systems,” IEEE Trans.Power Syst., vol. 24, no. 2, pp. 1039–1050, May 2009.

[34] F. Li and R. Bo, “DCOPF-based LMP simulation: Algorithm, compar-ison with ACOPF, and sensitivity,” IEEE Trans. Power Syst., vol. 22,no. 4, pp. 1475–1485, Nov. 2007.

[35] PJM Training Materials—LMP101. [Online]. Available: http://www.pjm.com/training/training-material.aspx accessed in Oct. 2011

Fangxing (Fran) Li (S’98–M’01–SM’05) receivedthe B.S. andM.S. degrees from Southeast University,Nanjing, China, in 1994 and 1997, respectively, andthe Ph.D. degree from Virginia Tech, Blacksburg, in2001.He is presently an Associate Professor at The Uni-

versity of Tennessee at Knoxville (UTK). He was asenior, and then a principal engineer, at ABB Elec-trical System Consulting (ESC) in Raleigh, NC, from2001 to 2005, prior to joining UTK in August 2005.His current interests include renewable energy inte-

gration, power markets, distributed energy resources, power system computing,and smart grid.Dr. Li is a registered Professional Engineer (P.E.) in the state of North Car-

olina and a Fellow of IET.

Yanli Wei (S’09) received the B.S. and M.S. degreesin electric power engineering from Southeast Univer-sity, China, in 2006 and 2008, respectively. He startedhis Ph.D. study at The University of Tennessee atKnoxville in January 2009.His interests include power system operation and

planning, power system economics, and market sim-ulation.

ieee transactions on sustainable energy, vol. 3, no. 3,...

Documents