EFFECTIVE UTILIZATION OF FLYWHEEL ENERGY STORAGE (FES) FOR FREQUENCY REGULATION SERVICE PROVISION MIRAT TOKOMBAYEV THESIS

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1 EFFECTIVE UTILIZATION OF FLYWHEEL ENERGY STORAGE (FES) FOR FREQUENCY REGULATION SERVICE PROVISION BY MIRAT TOKOMBAYEV THESIS Submitted in partial fulfillment of te requirements for te degree of Master of Science in Electrical and Computer Engineering in te Graduate College of te University of Illinois at Urbana-Campaign, 2014 Urbana, Illinois Adviser: Professor George Gross

2 ABSTRACT Te deeper penetration of variable energy resources (VERs) in te form of wind farms and solar potovoltaic systems as impacted power system operations and planning in many ways. Today, renewable energy resources constitute a significant portion of new power generation capacity additions. As te outputs of renewable generation resources are function of te climatic conditions at teir geograpic locations and over wic te resource operators ave no control and te current renewable tecnologies provide only limited controllability, te renewable outputs are subject to, by and large, uncontrollable, rapid and uncertain canges. Suc volatility in te renewable outputs, tat may also include intermittent beavior, can affect power systems operations significantly. Indeed, te continual variability impacts introduce new complications due to teir interactions wit te impacts of te continuously canging loads in te system. In particular, suc interactions may exacerbate te callenge to provide te critical function to maintain te supply-demand balance around te cloc. Given te limited controllability over te renewable resources, system operators ave no coice but to impose additional burdens on te controllable conventional generation resources. Many of tese resources, owever, ave rater slow response times and limited ramping capabilities resulting in less tan ideal performance in te provision of te second-by-second supply-demand balance te so-called frequency regulation service. Tis service, also nown by te tecnical term of automatic generation control (AGC), is absolutely essential to maintain te frequency of te power system at its nominal value, wic is 60 Hz in te United States and 50 or 60 Hz in oter countries. Te recent advancements on te storage tecnology front indicate great potential in terms of applications to power systems for frequency regulation service. A particularly exciting development is te integration into te power system of storage devices nown as flyweel ii

3 energy storage (FES). In tis tesis, we investigate te effective utilization of FES for frequency regulation service provision via te competitive marets for suc service. Te FES utilization in frequency regulation service provision presents a number of callenges. A FES unit as a relatively low energy-to-power ratio, a situation tat implies a clear limitation to its ability to provide frequency regulation over longer periods of time in only a single direction. Suc a limitation, consequently, constrains te amount of service te FES unit can offer. As te frequency regulation service is procured in te competitive environment troug maret mecanisms, te FES pysical limitations and te uncertainty associated wit te second-by-second requirements of te system mae te formulation of appropriate offer strategies for a FES unit a callenging tas. To address all tese callenges, we ave developed a compreensive approac to effectively utilize te FES to provide guaranteed frequency regulation service to te grid. We prepared tis tesis to discuss te development of te approac and describe its application to various studies. We demonstrate te capability of te developed approac and quantify te improved performance over existing tecniques troug various case studies using actual 2011 AGC signal and price data from two large systems te CAISO and te PJM. Te representative results clearly indicate tat te proposed approac generates offer strategies tat result in better utilization of FES to provide guaranteed frequency regulation service. iii

4 To my family iv

5 ACKNOWLEDGMENTS I would lie to tan my adviser, Professor George Gross, for te guidance and support e gave me trougout my studies at te University of Illinois at Urbana-Campaign. His willingness to provide elp and a critical eye ave taugt me to strive for continuous improvements. I also tan my colleagues and friends: Kai, Yannic, Isaac, Ti, Ada, and Marco wit wom I spent a lot of time during te past two years and wose lively discussions ave made a valuable contribution into my researc. I would also lie to tan all te students, faculty and staff in te Power and Energy Group for teir constant support and encouragement. Finally, noting would ave been possible witout te love of my family, wo as always supported me during my stay in te United States. v

6 TABLE OF CONTENTS CHAPTER 1 INTRODUCTION Motivation and Bacground Te Salient Aspects of AGC Service Provision by Storage Tecnology Review of te State of te Art in Storage Utilization for Frequency Regulation Scope and Contribution of te Tesis Outline of te Tesis CHAPTER 2 MODELING OF FREQUENCY REGULATION SERVICE AND CONSTRUCTION OF ANALYTIC FRAMEWORK Te Tree-Layer Framewor of te Regulation Service Provision Te FES Modeling for te Frequency Regulation Service Provision Layer for Offer Formulation into te Hourly DAMs Layer for Additional Offer Formulation into RTMs Summary CHAPTER 3 THE FES OFFER STRATEGY FORMULATION Offer Regulation Service Strategy Formulation into te DAMs DAMs Regulation Offer Strategy Formulated as a Linear Program Offer Regulation Service Strategy Formulation into te RTMs Summary CHAPTER 4 CASE STUDIES Scope and Nature of te Simulation Case Studies Results and Sensitivity Analysis Summary CHAPTER 5 CONCLUSIONS Summary Directions for Future Wor APPENDIX A. NOTATION USED IN THE THESIS APPENDIX B. THE PROCEDURE TO CALCULATE THE PAYMENT FOR FES REGULATION SERVICE PROVISION APPENDIX C. OVERVIEW OF THE SIMULATION APPROACH REFERENCES vi

7 CHAPTER 1 INTRODUCTION Tis tesis deals wit te utilization of flyweel energy storage for frequency regulation service provision. In tis capter, we start by discussing te motivation and bacground of our researc to set te stage for te wor presented in te tesis. We provide a brief summary of te frequency regulation service provision by storage tecnology and review te prior wor in tis area. Ten, we present te scope and te contribution of te wor. We end by outlining te contents of te capters tat follow. 1.1 Motivation and Bacground Te deeper penetration of variable energy resources (VERs), suc as wind and solar potovoltaic, as significantly impacted power systems operations and planning. Today te renewable resources already constitute a significant portion of new power generation capacity additions. As te outputs of renewable generation resources are determined by climatic conditions at teir locations and over wic te resource operators ave no control, and te current renewable tecnologies provide only limited controllability, te renewable outputs are 1

8 subject to rapid and uncertain canges. Suc volatility in te renewable outputs, tat may also include intermittent beavior, can affect power systems operations significantly and introduce additional complications to te impacts of te continuously canging loads in te system. In order to maintain te supply-demand balance around te cloc, system operators must impose additional burdens on te controllable generation resources. Many of tese resources, owever, ave rater slow response times and limited ramping capabilities. Te recent advancements on te storage tecnology front ave great potential in terms of applications to power systems for te second-by-second supply-demand balance te so-called frequency regulation service. Tis service, also nown by te tecnical term of automatic generation control (AGC) is absolutely essential to maintain te frequency of te power system at its nominal value. A particularly exciting development is te integration into te power system of storage devices nown as flyweel energy storage (FES). Tese are compact structures tat can range in output from 250 W to 20 MW wit a storage capability of up to 5 MW. In tis tesis, we investigate te effective utilization of FES for AGC service provision in te context of te competitive marets for suc service. Te recent studies [1]-[4] investigated te benefit of fast storage for frequency regulation by performing te economic analysis and dynamic simulation of ancillary service marets and system operations. Te studies indicate tat fast storage is economically viable for frequency regulation. In addition, te grid benefits from te ig ramping capabilities and te sort response times of te regulation providers. In order to speed up te pace of storage adoption for te purposes of frequency regulation, te Department of Energy as funded several demonstration projects under te American Recovery and Reinvestment Act (ARRA). For example, Beacon Power wit $24 million in funding from ARRA and $48 million of te total 2

9 project value, as designed and built te 20 MW and 5 MW flyweel frequency regulation plant. Te project is intended to put tis tecnology on equal footing wit conventional regulation providers by demonstrating te pysical and economical feasibility of te flyweel tecnology [5]. Te plant was commissioned in 2011 in te area of te NewYor ISO and so far demonstrates good economic and tecnical performance. To consolidate tis acievement, in te beginning of 2013 Beacon Power started to build te flyweel regulation plant wit te same parameters on te PJM footprint. Te flyweel tecnology is acnowledged for its caracteristics tat mae it attractive for te provision of frequency regulation service. Specifically, tis tecnology can witstand ars cyclic requirements of regulation. We can also identify following beneficial aspects of flyweels: fast response capability (te regulation storage resource based on flyweel tecnology is able to respond to any AGC signal command witin its capacity limits), long life time, ig round-trip efficiency of 95-98%, low maintenance costs and ease of siting. Te recent pus for demonstration projects as been accompanied by a number of policy regulations, wic goal is to furter promote te use of storage tecnologies in frequency regulation provision. All wolesale electricity marets in te United States are overseen by te Federal Energy Regulatory Commission (FERC), an agency tat ensures competitiveness and fairness for all parties, operating in te marets. Te integration of new tecnologies into frequency regulation provision as been te subject of two FERC orders recently. Te major stimulus for greater use of te storage came wit te FERC Order No.755 released on October 20, 2011 [6]. Under tis order, frequency regulation service providers must be compensated using a two-part tariff. Te first part is a payment for capacity to operate under AGC signal, i.e., a unit must reserve a part of te output for regulation provision. Te second part of frequency 3

10 regulation compensation is te performance payment tat reflects te quantity of te up and down movements in response to te system operator's AGC dispatc signal. Tis part accounts for te fact tat a fast-response resource wit a iger ramping rate is more effective in providing regulation service tan a slow-response resource wit a low ramping capability and must be paid appropriately. Hence, FERC ensures tat Order No. 755 results in more regulation being provided by tecnologies tat are faster-responding to te AGC signal and more suitable to provide regulation service, wile fewer conventional resources will participate in te maret. Since resources tat provide greater benefits to te grid are compensated appropriately, te implementation of Order No. 755 will lead to better maret efficiency troug alignment of performance and incentives. Te subsequent regulatory action indirectly affecting te integration of storage into frequency regulation is FERC Order No. 764 released on June 22, 2012 [7]. Te order mandates to partition te day-aead maret (DAM) sceduling period into sorter-duration subperiods to facilitate te integration of VERs and energy-limited resources into energy and ancillary services marets. For te storage-based regulation suppliers tis order is beneficial since it provides more flexibility in allocation of te stored energy in a most effective way. Te demonstration projects and policy initiatives ad demonstrated tat FES is a viable tecnology, wic can provide regulation service into te grid. Suc encouraging results raise a question of wat is an effective deployment of FES tecnology for tis purpose. Tis is a precise issue we address in te tesis. But, as a first step we discuss te salient aspects of te AGC service provision and review te current state of te art in te area of te frequency regulation service provision by storage devices, in general, and by FES, in particular. We ten follow wit a 4

11 description of te contribution made by te wor reported ere. Section 1.5 outlines te contents of tis tesis. 1.2 Te Salient Aspects of AGC Service Provision by Storage Tecnology AGC service is implemented to control te output of several generators to maintain frequency witin acceptable bounds and regulate te power intercange between control areas. AGC service is one of te ancillary services tat are traded in electricity marets running by independent system operators (ISOs) and regional transmission organizations (RTOs). Te ISOs and RTOs, referred in te tesis as independent grid operators (IGOs), purcase te frequency regulation service in te DAMs and real-time marets (RTMs) from suppliers wic are able to respond to te AGC signal by varying teir output according to te signal commands. By selling certain capacity bandwidt on regulation maret, te unit operator implies te responsibility to vary te unit s output under AGC commands witin tis bandwidt. Te sceduled capacity output tat resulted from te outcome of energy DAMs clearing establises te baseline rate of te unit providing AGC service. If te resource following te AGC commands raises its output above te baseline rate, it provides regulation up service. In contrast, if te unit lowers its output below baseline rate, it provides regulation down. Te IGOs ave different approaces in procurement of te regulation service. For example, CAISO procures up and down regulation as two separate products. On te oter and, PJM considers up and down regulation as a single product. In order to facilitate our discussion, we consider regulation in up and down directions as two separate products, wic are traded separately. Under current policies, all IGOs proibit te energy-limited resources from selling energy. Terefore, storage devices operate wit zero 5

12 baseline rate, wic implies tat teir generation mode is for te regulation up service provision and teir load mode is for regulation down service. 1.3 Review of te State of te Art in Storage Utilization for Frequency Regulation Te question of ow to utilize te storage for te frequency regulation service provision as been te subject of several papers. In tis section we give a brief summary of te literature related to te utilization of storage resources in te frequency regulation service provision. A very useful and compreensive survey paper assessing te storage utilization in te AGC service provision is by Ibraeem et al. [8]. Te emergence of new storage tecnologies over te last two decades pused te researcers to explore te tecnologies compatibility for frequency regulation service provision. A number of papers and reports ave outlined conceptually wat storage tecnologies fit te requirements of AGC service [9]-[13]. For example, Gross and Guille ave presented te conceptual framewor of veicle-to-grid (V2G) implementation and demonstrated its feasibility for regulation service provision [11]. Te utilization of superconducting magnetic energy storage (SMES) for AGC is introduced in [10]. Te recent advancements of battery tecnology enabled te conceptual development of large-scale storage facilities for AGC service provision, wic as been analyzed in [9], [12], [13]. Te large majority of te papers related to te AGC service provision by storage tecnologies discuss te modifications in control algoritms tat allow accommodation of te limited capability of storage tecnologies [14]-[18]. Te decentralized AGC concept deals wit 6

13 storage facilities spread over distantly connected geograpical territories [14]. Te multilevel control concept is used to build a ierarcical structure in te usage of conventional and storage AGC providers [15]. Tere is a long istory of wor on using te fast-acting storage in conjunction wit VER generation [19]-[24]. Tatte et al. proposed te sceme to coordinate wind generation and FES for a provision of grid frequency regulation service [19]. Te sceme enables bot wind generation and FES to collectively respond to te system frequency deviation. It is sown tat tis combination can effectively provide AGC service, owever, under certain conditions te valuable wind generation can be spilled out due to te limited storage capability of te FES. Te effect is also observed for aggregation wit potovoltaic energy resources [20]. After restructuring, te storage facilities are operated as private entities by offering regulation capacities in IGO-run ancillary service marets. A number of wors as focused on strategies to maximize profits from participating in te maret [25]-[27]. Researcers from te Pacific Nortwest National Laboratory ave developed a metod to generate te DAM offer scedule for a ybrid storage system, wic includes fast-response FES and slow response ydro unit [25]. Te strategy aims to maximize profit from energy and frequency regulation provision in te wolesale maret. Te Donadee and Ilic approac to generate te offers into te DAMs for frequency regulation and energy bids for carging te aggregated fleet of electric veicles involves te stocastic co-optimization of tese services [26]. Teir sceme allows te provision of bot up and down regulation services during te low-load conditions as tey assume a nonzero baseline rate of carging. Hence, if aggregation carges wit capacity lower tan te baseline rate, ten it provides regulation down service; in contrast, if aggregation carges wit capacity above te baseline rate, it provides te regulation up service. Bot [25] and [26] are 7

14 focused on offering services in ourly DAMs, but do not recognize te ample potential from participating in RTMs. Te deployment and successful operation of te FES in te NYISO marets provides researcers valuable data on FES performance in te frequency regulation provision. References [28]-[30] report te current practice of FES utilization, particularly by analyzing te ey callenges and disadvantages associated wit te FES tecnology. Engineers are careful to note tat FES as a relatively low energy-to-power ratio, a situation tat implies a clear restriction on its ability to provide frequency regulation over longer periods of time and one type of regulation needs. Figure 1.1 represents te regulation AGC signal requirements for a typical summer day of June 14, 2011 in PJM and te corresponding FES status during tis day. Te 20 MW and 5 MW FES participates on ourly DAMs by offering its full capacity in up and down directions and is able to provide regulation only for 62% of te total 24 ours. Te remaining time te FES was unable to respond to te AGC commands because it ad it te upper or te lower pysical limit of te energy level. Vu, Masiello, et al. report an unavailability rate as ig as 41% [28]. Te effect is especially unfavorable because te unit cannot contribute regulation during ig load conditions. Te IGO as no coice but to procure regulation services from oter resources to meet regulation requirements. In order to effectively accommodate te energy-limited regulation resources, IGOs are looing for oter solutions. Tere are several attempts to deal wit te described limitation on FES performance. For example, CAISO is implementing te regulation energy management (REM) system for nongenerating resources [31] participating in te regulation maret. Te REM is based on a smart grid system, wic controls not only te capacity output of te unit, but also monitors te energy level of te storage device. Using te regulation requirements and energy level of eac resource 8

15 tat provides regulation service, IGO determines te appropriate AGC signal for eac player and ensures nonviolation of energy limits. Tis approac allows for te improvement of te regulating units performance, but some critics express concerns of grid reliability once te number of energy-limited regulation suppliers attains critical mass [32]. Until now tere as been no wor wic investigates te advantageous caracteristics of te real-time AGC service maret and teir possible utilization to improve te performance of regulation suppliers. Since te regulation service is a commodity now, deeper understanding of ow to effectively exploit te maret environment is needed. Figure 1.1: PJM regulation requirements and FES status on June 14,

16 Te regulation service is crucial for grid reliability, so tat te issue of FES effective utilization is certainly of interest in te frequency regulation realm. We address in tis tesis te solution approac of effective utilization of te FES in maret environment. In Section 1.4 we summarize our proposed approac of effective utilization of te FES in te regulation service provision. 1.4 Scope and Contribution of te Tesis In tis tesis, we provide a compreensive approac for te formulation of offers into te DAMs and RTMs wit te express objective to effectively utilize FES for te provision of regulation service. In our studies we consider a single FES operated by an entity to offer regulation services into te IGO-run ancillary service marets. We ave analyzed and employed te procedures establised for storage resources participating in te ancillary service maret to provide frequency regulation. We explicitly tae into account te requirements of te recent FERC Orders No. 755 and No Our wor maes several contributions to te state-of-te-art. Te developed approac of FES utilization for te frequency regulation provision allows te FES to provide reliable service into te grid. At te eart of our approac is te formulation of strategies wit te objective to produce te offers into te ourly DAMs and te RTMs around te cloc. Te strategies are formulated as optimization programs wit explicit representation of FES limited storage capability, response time and capacity limitations. Since te pysical considerations are fully taen into account and te up-to-date information of FES time-varying variables is available for 10

17 te RTMs, te robust solution provides a reliable regulation service provision and can andle te inerent uncertainties in te AGC signals. We demonstrate te capability of te developed approac troug a number of case studies using AGC signal and year 2011price data from te CAISO and PJM. Te test results indicate tat te strategies formulated by te FES for te DAMs and RTMs are effective in ensuring full compliance wit AGC signals sent by te IGO. Moreover, te effective utilization of te FES results in commensurate increase of FES montly payment for frequency regulation service provision. In our sensitivity studies we investigate te impact of canging te duration of te cyclic offer pattern in DAMs and te impact of deployment of te ris-taing offer strategy on RTM. Anoter application of te proposed approac is to te analysis of te impacts of te policy canges promulgated by FERC. Specifically, we investigate te impact of te FERC Order No. 764 implementation, wic mandates to partition ourly DAM periods into sorter subperiods. Te proposed approac for te formulation of offers into te DAMs and RTMs enables te FES operator to effectively utilize te regulation unit by providing reliable service in te grid. Furtermore, te implemented case studies can elp te FES operator to quantify te range of benefits and limitations of IGO s ancillary service maret rules and frequency regulation service provision procedures. 1.5 Outline of te Tesis Te tesis consists of four additional capters and tree appendices. In Capter 2 we provide a detailed description of te frequency regulation provision framewor, wic is 11

18 represented as a tree-layer structure of te regulation service provision in te day-aead and real-time marets, and te layer of pysical operations under AGC. In Capter 3, we use te proposed framewor to construct te FES strategies to offer in ourly DAMs and RTMs around te cloc. First we provide a general matematical statement of te FES offer strategy in ourly DAMs maing use of te DAM maret rules and timeline. We, next, exploit te regulation provision cyclic beavior to formulate te DAM strategy as a linear optimization program. After setting up te necessary assumptions, we construct te FES offer strategy into RTMs. We detail all te constraints, imposed as a result of te accepted offers into ourly DAMs and te preceding same our RTMs, and by te limited pysical capability of te FES. In Capter 4, we describe te results from representative case studies we ave carried out using te proposed approac and actual 2011 data from te CAISO and PJM. In our studies, we investigate te impact of canging te duration of te cyclic offer pattern in DAMs and te impact of deployment of te ris-taing offer strategy on RTM. In tis capter, we also analyze te impacts of te policy canges promulgated by te FERC. Specifically, we investigate te impact of FERC Order No. 764 implementation, wic mandates partitioning ourly DAM periods into sorter subperiods. In Capter 5, we summarize te results of our studies and point out directions for future wor. Appendix A provides a summary of notations used in te tesis. In Appendix B, we give te procedure to calculate te FES payment for te regulation service provision, taing into account te requirements of FERC Order No In Appendix C, we provide a description of te AGC model, incorporated into te tree-layer structure, and present an overview of te simulation approac. 12

19 CHAPTER 2 MODELING OF FREQUENCY REGULATION SERVICE AND CONSTRUCTION OF ANALYTIC FRAMEWORK In tis capter we describe te development of te analytic framewor, wic we construct for te formulation of te strategies for FES participation in te two sets of marets DAMs and RTMs. We start from te FES modeling to appropriately represent te pysical caracteristics of te unit, te impacts of te maret rules and te unit response to te AGC signal under actual operating conditions. We continue wit an overview of te framewor structure wic includes tree layers and provide a detailed description of eac layer. 2.1 Te Tree-Layer Framewor of te Regulation Service Provision Te ey objective of tis capter is to develop a framewor capable to deal wit all aspects of te regulation service provision by FES suc as obligations imposed by participation in DAMs and RTMs and obligations to respond to AGC signals under real-time pysical operations. In order to meet tese objectives, a detailed representation of marets and AGC control is required. However, tere is no single standard maret design across te United States 13

20 and implementation of AGC control also varies. Terefore, in order to accommodate tese differences, te framewor must be stated on te most general basis. Te developed framewor structure, as illustrated in Figure 2.1, as tree layers a layer for te offer formulation into te ourly DAMs (DAM layer), anoter layer for te additional offer formulation into RTMs (RTM layer) and a simulation layer of te FES operations in response to te AGC signals sent by te IGO to wose system te FES is interconnected (AGC pysical operations layer). We interconnect tese tree layers by introducing te information flows to represent te interactions between te marets and actual operations under AGC. In order to appropriately represent tese information flows in Section 2.2 we provide a detailed description of te FES model for te frequency regulation service provision. Figure 2.1: Tree-layer structure of te framewor 2.2 Te FES Modeling for te Frequency Regulation Service Provision We consider te parameters of te FES for te frequency regulation service provision. We denote by r M te maximal ramping rate of te unit. Te ramping rate is a parameter provided 14

21 by temanufacturer to indicate te capacity increase/decrease per minute capability of te regulation unit. Te ramping rate of FES is very ig and can attain ±300 MW/min. We next consider te storage capability of te storage regulation unit. We denote by E M te maximum storage capability of te FES in MW. Based on te manufacturer s FES performance analysis, discarge of te FES to zero stored energy is not recommended, since tis can exacerbate te unit s degradation. Terefore, we denote by E m te minimal level of stored energy te unit can be discarged to during te regulation service provision. Te p M, designates a maximum capacity of regulation up service and p M, is a maximum capacity for regulation down service. Due to lac of operational experience wit limited energy storage resources, te existing IGO maret rules put certain restrictions on te ability of FES to offer energy into te DAM and RTM. As a result, te output base point of te FES for every time period is zero. Te zero base point implies tat te FES operates in generation mode for regulation up service provision and in load mode for regulation down service provision. A convenient starting point for te FES modeling description is te AGC pysical operations layer in wic we embed te representation of FES operations in response to te AGC signals sent by te IGO. Figure 2.2 depicts te functional diagram of te AGC system. Frequency f is measured and compared wit te reference frequency to generate a signal proportional to te frequency deviation f. A weigted value of tis signal is added to te net tie-line f ref intercange error P tie to produce te area control error (ACE) tat corresponds to te power by wic total generation must be canged in order to maintain frequency and te tie-line power intercange at te sceduled values [33]-[38]. Te ACE value and DAMs and RTMs results from corresponding layers are input data to te energy management system (EMS) wic defines te contribution of FES to te total frequency control service provision based on RTMs and DAMs clearing results and FES pysical caracteristics. Te control algoritm of EMS broadcasts every n interval te specific AGC instruction wit a command to raise or lower its power output. Analyzing te regulation service provision by FES, we consider te interval as a smallest indecomposable unit of time, and no penomena of sorter duration is considered in our study. n 15

22 Figure 2.2: Functional diagram of te AGC system We use r, n [ ] to denote te AGC signal instruction. By te adopted convention, r, n [ ] is positive wen te regulation unit is instructed to increase generation and negative wen it is instructed to lower generation. We also use c, n [ ] to denote te actual power n output of te FES at interval of subperiod in our. By te same convention, te c, [ n] 0, wen te unit injects energy into te grid (generator mode) and c, [ n] 0, wen te unit witdraws energy (load mode). Terefore, te output c [ ] in interval is related to c [ ] in interval by:, n 1 n1, n n 16

23 c [ ] c [ ] c, n 1, (2.1) n c r [ ] if r [ ],, 0 n n r [ ] if r [ ] 0, n, n Figure 2.3 depicts te functional diagram of te FES response to AGC signal instructions. We use [ ] to denote te energy carge level at te end of interval of subperiod in our, n n. Te output [ ] at interval is correlated wit at as follows:, n n, [ 1 ] n n1 c [ ], n [ ] [ ], n1, n NK (2.2) Equations (2.1) and (2.2) establis te equations of motion of te FES unit under AGC control. Figure 2.3: Te FES response to AGC signal instructions 17

24 As discussed in Section 1.2, we consider regulation in te up and down directions as two separate products. Figure 2.4 represents an example of te FES providing regulation service for two ours by illustrating te capacity range over wic te unit as to vary its output under te given AGC signal. Te unit participates in regulation service provision and scedules to provide regulation up and down in our and regulation up and down in our In oter words, by providing regulation up service te seller assumes an obligation to vary its capacity output under te AGC signal between te zero base point and, and for regulation down between 0 and. A similar statement is applicable to our 1. It is evident, tat te M, M, FES is constrained by maximum capacity limits in generation p and load p modes. Figure 2.4: Regulation up and down service provision After te electric power industry restructuring, frequency regulation became one of te ancillary services tat must be procured on a competitive basis. Hence, te fleet of units participating in regulation is selected up on te clearing of te DAMs and RTMs using maret mecanisms. In Sections 2.3 and 2.4 we analyze te frequency regulation provision in a maret 18

25 environment by considering te DAM and RTM layers of te framewor and focusing on offer formulation and dependence between two marets. 2.3 Layer for Offer Formulation into te Hourly DAMs Te regulation DAM is a capacity maret operated by te IGO. Every AGC service seller indicates willingness to sell te service by submitting an offer to te IGO. Offers specify te sale quantities and prices. Unlie an energy maret, were te demand is determined by equilibrium of demand and supply curves, te total regulation requirements for every ourly DAM are determined by IGO as a percentage of te total load forecast [39]-[40]. Figure 2.5 depicts te timeline of te regulation DAM. All sellers, willing to participate on DAMs of day d, must submit teir offers for eac our in day d by 10 a.m. of day d 1. Under existing maret rules, te IGO determines te outcome of te DAM by co-optimizing energy and ancillary services marets so tat energy, regulation and spinning reserve requirements are met in te most economic manner. At 12 p.m. of day d 1, te IGO informs all regulation sellers cleared to provide service about te uniform clearing prices and te awarded capacity. Te participants cleared on te DAM ave an obligation for provision of regulation between 12 a.m. and 11:59:59 p.m. of day d. Figure 2.5: Regulation DAM timeline 19

26 By 10 a.m. on day d 1 te regulation service seller submits te offer decision ˆ d for every our in. In order to comply wit FERC Order No. 755, wic requires te IGO to account for te performance of te regulation provision, te offer ˆ d must include te following components and can be represented as d ˆ ˆ, ˆ, ˆ, ˆ, ˆ (2.3) ˆ ˆ We denote by ( ) te offer capacity price for regulation up (down) on DAM at our, and by ˆ te offer mileage price on DAM at our. As a result of maret clearing, every frequency regulation service provider receives bac information regarding te capacities it is cleared to provide and capacity, and mileage clearing prices, wic are uniform for all cleared participants. IGO can clear not all te capacity offered for regulation but only part of it, so tat following inequalities old: ˆ ˆ Based on te maret outcome information te seller is obligated to follow AGC instructions witin an establised capacity range, bounded by and, and receive payment based on uniform clearing prices 28 days after te regulation service provision. Te settlement sceme and equations to quantify te seller s payment are given in Appendix B. 2.4 Layer for Additional Offer Formulation into RTMs Regulation RTM is a rolling maret, run by IGO every subperiod. Te objective of regulation RTM is te procurement of additional regulation requirements arising in nearer-toreal- time operations. In tis section we discuss te mecanics of regulation RTM we exploit in developing te formulation of te RTM offer strategy. 20

27 On a regulation RTM we define a subperiod to be a smallest indecomposable unit of time. We use a subscript (, ) after a variable to represent te subperiodic RTM quantities. According to eac IGO s rules, regulation service sellers indicate teir willingness to participate on RTM by submitting te offer, offer into a DAM in (2.3), but apply to only interval : ˆ, wose components are similar to te components of te Te offer ˆ must be submitted during te subperiod, as depicted on te RTM timeline sown in Figure 2.6. After RTM clearing, IGO publises te information regarding clearing prices,, and FES regulation capacity rewards,. ˆ ˆ, ˆ, ˆ, ˆ, ˆ,,,,,,, 2,,,,, Figure 2.6: Regulation RTM timeline Te combined effect of cleared DAM and RTM results necessitates te addition of regulation capacities from bot marets. We denote by, (, ) te combined regulation up,, (down) capacity on subperiod in our. Since te combined regulation capacity for eac 21

28 individual regulation unit results from participating in bot te DAM and te RTM, we define te combined regulation up capacity as,, and te combined regulation down capacity as,, To explain te concept, we provide a grapical diagram on Figure 2.7 to indicate te impact of te regulation up and down provision on bot DAMs and RTMs. Regulation up and down capacities cleared on DAMs form te our-long capacity range around a zero base point to operate under AGC signal. Additionally, every RTM subperiod te regulation resource is obligated to widen te capacity range for regulation up by and down by resulting from,, te accepted RTM offer. According to te IGO s maret rules, te maximal combined offer is constrained by maximal capacity output of te unit in te generation and te load modes: M, M, ˆ ; ˆ p p M, p ; M, p,, On te oter and, te minimal capacity offer for te regulation maret is constrained by te m, m, minimal capacity offer in up p and down p directions, specified in IGO s maret rules: m, ˆ p ; m, ˆ p ˆ, p ˆ m, ; m, p, For example, most IGOs do not accept regulation offers in eiter direction below 0.5 MW. 22

29 Figure 2.7: Combined DAM and RTM regulation capacity for 10 RTM subperiods of our 2.5 Summary In tis capter we ave developed te compreensive framewor for te offer strategies formulation in te two sets of marets DAMs and RTMs. Te framewor incorporates te regulatory, financial and pysical considerations of frequency regulation service provision by a FES unit, as well as teir interactions, togeter wit te analytical basis for te formulation of offers in compliance wit te specified maret rules. Te developed framewor structure as tree layers a layer for te offer formulation into te ourly DAMs, anoter layer for te additional offer formulation into te RTMs and a simulation layer of FES operations in response to te AGC signals sent by te system operator into wose system te FES is integrated. We ave also discussed te FES modeling to appropriately represent te pysical caracteristics of te 23

30 unit, te impacts of te maret rules and te unit response to te AGC signal under actual operating conditions. 24

31 CHAPTER 3 THE FES OFFER STRATEGY FORMULATION In tis capter we apply te developed framewor to construct te FES offer strategies formulation into te DAMs and RTMs. Tese two marets operate on different timescales, involve different levels of granularity, and differ in te time at wic te maret decisions are taen. Under tese conditions, we may decouple te offer problem into two subproblems of offer regulation service on DAMs and RTMs independently. As a FES unit is a profit maximizing entity, te goal of te FES unit operator is to maximize te quantity of service provision so as to assure a steady stream of return on its investment. In order to meet objectives of FES participation in te frequency regulation maret, te offer strategy formulations are stated as constraint optimization problems wit a representation of te inter-temporal evolution of te storage in te FES. Te capter contains tree sections. In Section 3.1 we develop te offer strategy on ourly DAMs in most general terms and state it as a bilinear optimization problem. Next, in Section 3.2 we mae use of periodicity of te frequency regulation provision cycle, so as te offer strategy is stated as a linear program. Section 3.3 presents te offer strategy on RTM. 25

32 3.1 Offer Regulation Service Strategy Formulation into te DAMs In tis section we provide a matematical statement of te FES offer strategy formulation into te ourly DAMs maing use of te DAM structure wit te timeline as specified in Capter 2. By 10 a.m. of day d 1 te FES operator must mae offer decision ˆ. Te determination of set ˆ, consists of following steps: for every our in 1. determination of te ours wen te FES as willingness to provide regulation service 2. determination of te type of regulation (up, down or bot) to provide if te FES is willing to offer te service in tat our 3. determination of te capacity in MW for eac regulation service in our 4. determination of capacity and mileage price offers for eac regulation service in our On a DAM, we consider an our to be a smallest indecomposable unit of time. As suc, if FES cooses to provide regulation in our, ten it as to operate under te AGC signal during te entire our. We denote by provide any regulation in our : u te binary variable, wic indicates te willingness to u 1 if FES provides regulation service in our 0 oterwise Next, we denote by up (down) in our : u ( u ) te binary variable, indicating te willingness to provide regulation u 1 if FES provides regulation up service in our 0 oterwise u 1 if FES provides regulation down service in our 0 oterwise 26

33 We can establis te following relationsips between tese binary variables: u 1 u u 1 It indicates tat if FES offers frequency regulation service at least in one direction ten it is under AGC for te entire our. On te oter and, if FES does not offer any regulation, ten it is not under AGC and can use tis our to offer te service into te RTMs and move its storage energy carge to a pre-specified value. According to te IGO s maret rules, if a regulation provider offers AGC service into te DAM ten te capacity must be greater tan te minimum capacity offer in te up down m, p and m, p directions, wic quantities are stipulated by IGO, and less tan te FES pysical upper capacity limit in carging M, p and discarging p M, modes. Hence, for tose ours wen FES offers any type of regulation on DAM ( u 1), we state te following capacity offer constraints and relate tem to corresponding binary variables: p ˆ p u 1 (3.1) m, M, p ˆ p u 1 (3.2) m, M, For an our in wic FES does not offer up (down) regulation service, te corresponding binary variable u ( u ) is set equal to zero. Te Figure 3.1 depicts te offer decision diagram. Next, we discuss te energy constraints, imposed by te FES limited energy capability. Since te AGC signal indicates te demand-supply imbalance in real time, te actual sape of te AGC curve is not nown aead of its determination. Terefore, te FES operator does not ave information of te exact value of stored energy at te end of an our wen te unit offers regulation service. Te energy constraints are specified in terms of te upper and lower bounds of te storage unit capability. We denote for te end of te our te upper and lower bounds of stored energy by e and e, respectively. Moreover, if te unit does not offer te regulation 0 service in our DAM, it uses tis our to recarge te FES to pre-specified carge rate e. 27

34 Figure 3.1: Offer decision-maing diagram To establis inter-our relationsips of FES energy carge, we need to consider all possible realizations of te AGC signal over te period of te offer. Tis tas involves an enormous amount of wor. In order to simplify te problem, we may only consider tose AGC signal realizations wic move te FES carge toward boundary conditions. Since te main feature of te proposed offer strategy is a FES guaranteed capability to provide regulation service under every possible realization of AGC signal, te boundary conditions of AGC can establis te FES energy carge bounds, ence, significantly simplifying te problem. Figure 3.2 depicts a possible AGC realization and te associated energy carge in te FES tat results. Te upper (lower) energy bound at te end of our is calculated based on assumption tat unit was carging (discarging) wit a capacity ( ) during te entire our. 28

35 At te end of te our, during wic te FES offers regulation capacity, te upper bound on its stored energy is determined under te assumption tat te AGC signal commands te FES to provide regulation down service wit capacity ˆ trougout te our: e e ˆ (3.3) 1 Te lower bound of stored energy is determined under te analogous assumption tat te AGC signal commands te FES to provide regulation up service wit capacity ˆ trougout te our: e e ˆ (3.4) 1 Figure 3.2: A possible AGC signal wit its impact on te associated FES energy carge and te worst-case boundary limits 29

36 Te equations (3.3) and (3.4) are applicable for te our, during wic te FES provides regulation. For te case tat te FES does not provide regulation service in our, te FES moves its stored energy to a pre-specified value binary variable in te ourly DAMs: 0 e. To capture bot tese possibilities, we use u in te energy constraint formulation, wic introduces cross-ourly coupling 0 ˆ 1 1 e e u e u 0 ˆ 1 1 e e u e u Te ourly energy bounds are also constrained by te FES s upper and lower pysical energy storage limits: e e E E M m Te objective of te offer problem is to maximize te FES revenues for te regulation service provision. As suc, we formulate te objective function as follows: H 1 f( ˆ, ˆ ) = w ˆ w ˆ were w and w are te weigting coefficients tat reflect te ourly capacity prices of te day d, based on price forecasts or past istorical data. In summary, we denote te inter-ourly energy constrained DAM regulation service offer problem by and state it as follows: ˆ max ˆ,, u, u, 1 u, e, e H ˆ w w ˆ subject to 30

37 p ˆ p u 1 m, M, p ˆ p u 1 e e e e m, M, e ˆ 1 if u 1 0 e if u 0 e ˆ 1 if u 1 0 e if u 0 E E M m e e e Te optimal solution to is specified by te set u,, wic indicates te ours in wic to regulation service is provided ( u 1) and te ours in wic te FES does not participate in te regulation service provision ( u 0 ) so as to allow it to ave te stored energy at te pre-specified value at te end of te our, te sets u wic indicate te types of regulation service provided in eac our in, and u,, in wic te FES offers regulation service and te values of ˆ and ˆ, wic determine te offer amount in our. Note tat all tese quantities are te components of te set ˆ,. Wit regard to offer prices, we assume tat eac FES is a price taer and so submits offers wit zero prices ˆ ˆ 0 and ˆ 0 and e, for in. In addition, tere are te associated sets e, of upper and lower energy bounds for every our in. We use tese sets to impose te energy constraints in te RTM offer problem formulation. Te problem statement above is in its general form and cannot be solved analytically as it involves te solution of a bilinear mixed-integer optimization problem and is intractable for H 24. In Section 3.2 we mae use of te salient caracteristics of energy limited frequency regulation units and features of frequency regulation DAM to formulate a linear optimization problem and state te conditions to determine its optimal solution. 31

38 3.2 DAMs Regulation Offer Strategy Formulated as a Linear Program In order to simplify te DAM offer problem, we wis to exploit te fact tat te alternation of ours wen te unit provides regulation service in bot te up and te down directions ( u 1, 1, 2,..., ) and our 1 wen te unit does not participate in regulation provision ( u 1 0) constitutes a cycle. We provide in Figure 3.3 te grapical illustration of regulation service provision patterns for 1, 2,..., 5 and te assumption tat H 24 is an our in wic te FES does not participate in te regulation service provision. Te ours in wic te FES provides regulation service are crossatced. Eac 1 our cycle scedule as FES participation in te DAMs for te first ours and no participation in our 1. Moreover, for te transition to te next-day DAMs, te FES does not participate in te our 24 DAM. Tis latter constraint may sorten te last cycle duration. For example, for 5 te FES participates at te ours 1-5, 7-11, 13-17, Te FES does not participate in te ours 6, 12, 18 and 24. Figure 3.3 Regulation service provision patterns for 1, 2,..., 5 wit H 24 designated as an our for no participation in te regulation service 32

39 Based on initial carge 0 e 1 at te beginning of te cycle and caracteristics of FES, we can quantify te cycle of ours wic guarantees at least minimum capacity offers m, p and for every our. In oter words, we need to coose a value for wic ensures tat te FES is capable of providing bot types of regulation for te entire ours. p m, We use te following procedure to cec te feasibility of for a given 1. Specify te upper and lower bounds of stored energy at te end of ours. If tere are no oter restrictions, we coose tem to be an upper regulation unit. M E and lower e 0 : 1 m E energy limits of te M 0 0 m E e e E If or, ten is not feasible and any ' is also not feasible. m, m, p p M E e 3. If m, p o 1 o e E 1 and m, p m, ten is feasible. Once is determined, we next define te set of te ours in wic te FES participates in te offer of regulation service in te DAM: 1, 2,...,, 2,..., 2 1, 2, 2 2,..., H 1 Now we can proceed to te statement of te optimization problem wit te objective to maximize te capacity revenues over te set. We denote te inter-ourly energy constrained offer problem by and state it as follows: subject to 33

40 p p e e e e m, m, ˆ ˆ ˆ d, 1 ˆ d, 1 e e E E M m e e e o Te optimal solution to is specified by te set of te values of and, wic determine te offer amount into te DAM for, and te sets and e, of upper and lower energy bounds for every in. Te problem formulated as a linear program and, ence, can be easily solved by well-nown metods. is 3.3 Offer Regulation Service Strategy Formulation into te RTMs In tis section we develop te formulation of te FES offer strategy for regulation service in te RTMs associated wit te our DAM. We employ te outcomes of te our DAM as te basis to determine te real-time offers into an associated RTM. Specifically, te DAM results establis constraints on te FES offers into an associated RTM. Given tat te DAM capacity awards and, we identify two distinct cases depending on te and values. Te first case covers tose ours in wic te FES offer for regulation service is accepted in wole or in part. Te second case is for te set ',, in wic te FES does not offer regulation service into te DAM and moves its carge to a specified value. 34

41 Figure 3.4: Energy constraints tat must be satisfied in te formulation of te RTM offer We consider first te formulation of te RTM offer strategy for ours and so te unit as te obligation to provide regulation service into te DAM. In ligt of te analysis of te IGO rules discussed in Section 2.4, we adopt te following offer protocols. Te FES operator maes te offer decision for te subperiod of our, wic must be submitted at te end of subperiod 2, as sown in Figure 2.6. Similar to te offer strategy into te DAMs, we assume te regulation seller is a pricetaer in te RTMs and so submits te capacity and mileage prices to be 35

42 In order to be able to submit te offer by te deadline, te FES operator starts to formulate its offer at te end of subperiod 3, as indicated in Figure 2.6. At tat time, te FES seller as te following information: energy carges at te end of subperiod, 3 in our 3 te capacity awards after clearing te our DAM, specifically and, wic are constant over te entire our RTM awards and, 2, 2 for subperiod 2 FES operator s capacity offers and for subperiod 1 te lower bound e and te upper bound e of te stored energy at te end of our Tis information serves to formulate te following constraints (Figure 3.4): te allowed capacity range to participate in response to te AGC signals sent in te subperiod 2 limits te combined DAM and RTM awards of eiter service,, 2 2,, 2 2 at te end of subperiod 3 te RTM for subperiod 1 is unnown but te allowed capacity range ;, 1, 1 to participate in response to te AGC signals during te subperiod 1 limits te combined DAM award and te RTM offer amount in period 1: 36

43 te lower and upper bounds of te stored energy in te subperiod 2 in terms of te carge s and te combined capacity awards for up service and down service 3,, 2 regulation in subperiod 2 :, 2 s,,, 2 2 2, 2 s K,, 2 3, 2 s K,, 2 3 te lower and upper bounds of te stored energy in te subperiod 1 in terms of te period 2 bounds s, te combined DAM award and period,,, 1 accepted RTM offer: s,,, 1 1 1, 1,, 1 2 K, 1,, 1 2 K te lower and upper bounds of te stored energy in te subperiod in terms of te allocated energy range restricted by e and e in our and te obligations entailed by te our DAM clearing results taing into account tat tere are K remaining subperiods in our : s,,, ( K ) K e, ( K ) K e, 37

44 te constraints imposed by te upper and te lower stored energy bounds in te subperiods and 1 on te real-time offers and : te impacts of te constraints of te FES maximum capacity output in te carging and discarging modes p M, and p M,, respectively, and tose of te minimum capacity offer m, p and m, p : We denote te formulation of te DAM outcome constrained RTM offer problem for subperiod in our as ( ) and state it as follows: subject to 38

45 ,,,,,,,,,,,,,,, ( ) ( ) s K s K K K K e K K e K 1 1,,,,,,,,,,,,, ˆ ˆ ˆ ˆ ˆ ˆ M M m m K K p p p p Te optimal solution to ( ) is specified by te set of te values of, ˆ and, ˆ, wic determine te offer amount into te RTM for subperiod of our. We next consider te RTM offer strategy for te ours ', in wic te unit does not participate in te offer of regulation service into te DAMs. Rater, in suc ours, te FES moves its carge to be at a pre-specified value of 0 e. Te RTM regulation timeline is te same as depicted in Figure 2.6. A significant advantage of flyweel tecnology is te relatively ig ramping rate and so te FES can carge or discarge to attain a pre-specified value 0 e witin no more tan a few real-time subperiods. We denote by te number of real-time subperiods, used by te FES to carge or discarge. For te remaining K subperiods of our 1 in te cycle, te FES may participate in regulation service provision and so offers its capacity into tose RTMs witout being constrained by te our 1 DAM clearing outcomes. Moreover, te

46 FES regulation RTM offer for subperiod is constrained only by te obligations to provide service in te preceding subperiods 1 and 2. Tese facts allow us to formulate te following constraints: te lower and upper bounds of te stored energy in te subperiod 2 in terms of te carge s and te real-time capacity awards for up service and down service 3,, 2 regulation in subperiod 2 :, 2, 2 s K,, 2 3, 2 s K,, 2 3 te lower and upper bounds of te stored energy in te subperiod 1 in terms of te period 2 bounds s and submitted RTM offer in subperiod,,, : ˆ, 1,, 1 2 K ˆ, 1,, 1 2 K te lower and upper bounds of te stored energy in te subperiod in terms of te pysical energy limits of te FES taing into account tat tere is no DAM clearing in our 1: s,,, m E, M E, te constraints imposed by te upper and te lower stored energy bounds in te subperiods and 1 on te real-time offers and : 40

47 41 1,,, ˆ K 1,,, ˆ K We denote te formulation of te RTM offer problem for subperiod in our ' as ' ( ) and state it as follows:,,,, 2 2,,,, 1 1,, ˆ ˆ,,,,,,, ˆ ˆ max subject to ,,,,,,,,,,,,,,,,,,,, ˆ ˆ ˆ ˆ m M s K s K K K K K E E Te optimal solution to ' ( ) is specified by te set of te values of, ˆ and, ˆ, wic determine te offer amount into te RTM for subperiod in ours '.

48 3.4 Summary In tis capter we ave introduced te formulation of te offer strategies for te regulation service provision into te DAMs and te RTMs. Te statement of te formulation may be expressed as a linear program wit te explicit representation of te inter-temporal evolution of te storage in te FES. Te solution approac for tis optimization problem maes extensive use of robust optimization concepts to determine te offer amounts of up and down regulation to service in eac maret for its corresponding period. Tese solutions provide conservative results under wic bot up and down regulation can be provided wit 100% guarantee, independent of wat te AGC signal turns out to be and taing into account te most up-to-date information on unit status up to te time an offer must be submitted. In Capter 4, we apply tis approac to obtain via simulation te results of representative studies in order to quantify te economics of te FES participation in te regulation service provision. 42

49 CHAPTER 4 CASE STUDIES We devote tis capter to demonstrate te capability of te DAM and RTM offer strategy formulation approac and quantify te improved performance over current tecniques troug various case studies using actual 2011 AGC signal and price data from two large systems te CAISO and PJM. Our studies include te investigations of te impacts of canging te duration of te cyclic offer pattern into te DAMs. We also study te impacts of te deployment of ristaing offer strategies into te RTMs. A very insigtful application of te proposed approac is te analysis of policy issue impacts. We illustrate suc an application troug a study of te canges promulgated by FERC in its Order No. 764 to mandate te partition of eac ourly DAM period into sorter subperiods. We begin te capter by describing te scope and nature of te simulations carried out for te set of representative studies discussed in tis capter. We ten proceed to present our results and findings obtained from te case studies. We conclude te capter wit a summary of te ey results. 43

50 4.1 Scope and Nature of te Simulation In tis section, we present a brief description of te test system and provide an overview of te various applications of te proposed metodology presented in Capter 3 to construct DAM and RTM offer strategies for frequency service provision. Te representative case studies presented serve to illustrate te capabilities of te metodology. Te results we present in te capter are drawn from te case studies performed using PJM and CAISO control signal data and price information wit te provision of regulation service by a 20 MW FES wit 5 MW storage capability. Since te regulation service is procured by te IGO on a zonal basis, we do not consider te topology of te grid and assume tat all te energy produced by te FES can be absorbed by te grid and te grid as te capability to supply te energy carged by te FES in te provision of down regulation. Tis assumption is reasonable given a large system and a limited capacity/capability FES. All our studies are performed under te assumption of perfect nowledge of AGC signal and are totally deterministic in nature. Te deepening penetration of VERs integrated into te grid as driven federal policy, wic aims to encourage te furter implementation of VERs in te most effective way [41]-[42]. Recently FERC issued Order No. 764 wit te mandate to require IGOs to implement intra-our sceduling canges, i.e., to partition ourly DAM periods into smaller subperiods. Te sorter DAM subperiods introduce certain benefits from te utilization of additional meteorological data for wind and solar generation forecasting due to te iger time resolution. For energy-limited storage resources providing frequency regulation service, te introduction of sub-ourly DAM subperiods allows te more cost-effective allocation of te unit s limited energy capability. Te simulation approac is constructed by maing use of te simulation layer of te framewor and as te capability to perform te FES economic studies by varying te duration of te DAM period. Tese studies allow te identification of te optimal duration of te DAM period for te effective utilization of te FES unit in frequency service provision. Te FES operator s offer decision into te DAMs impacts te RTM offers for te entire day d. Te alternative to te offer of more capacity into te DAMs is te additional offer of regulation capacity into te RTMs and te reverse. In order to determine te optimal combination of day-aead and real-time offers, we need to perform an economic assessment of various DAM 44

51 offer strategies wit different durations of te cycle offer pattern. Suc studies can elp to identify te most effective regulation offer strategy into te DAMs. Te offer strategies into te ourly DAMs and associated RTMs formulated in Capter 3 are made under te worst-case scenario assumption for te provision of a single type of regulation service over te entire duration of te period covered by a submitted offer. Tese strategies provide conservative results under wic bot up and down regulation can be provided wit 100% assurance independent of wat te AGC signal turns out to be. Tis very conservative approac does not tae into account te inerent uncertain caracteristics of te AGC signals. Te nature of frequency regulation service as been te subject of several studies. Te study done by Oa Ridge National Laboratory investigates te mean value of AGC requirements over different time durations [1]. It reports tat, in most cases, te long-term mean value of te AGC curve is in te zero value neigborood. In ligt of tis finding, we may use a more risy approac in te formulation of te RTM capacity offers. In oter words, we first scale up te regulation capacity offers, calculated as a result of te proposed approac to determine te realtime offer capacity values: q + +,, q,, were q is an augmentation factor > 1. We next examine te economic impacts of suc augmented RTM capacity offers. 4.2 Case Studies Results and Sensitivity Analysis In tis section, we discuss te case study results wit te two test data sets from CAISO and PJM. In order to analyze te differences across te seasons, we disaggregate te FES revenues for frequency regulation service provision on a seasonal basis wit te monts of June, July and August as te summer period, te monts of September, October and November as te fall period, te monts of December, January and February as te winter period and te monts of Marc, April and May as te spring period. As a reference case for eac system we tae te existing offer strategy wen te FES participates only in ourly DAMs in wic it offers its full capacity to provide up and down regulation services. 45

52 Figure 4.1 depicts te average total montly revenues for capacity in te frequency regulation service provided in eac season wit different DAM offer patterns. Specifically, we cange te duration of te cycle offer parameter from 1 to 5. Every montly payment is represented as a sum of te DAM and te RTM revenues. We note, tat if te FES provides service using conventional offer strategy (te reference case), ten all payments are calculated based on day-aead regulation prices. In contrast, if te unit offers service only into RTMs ( RTM only case), ten te revenues are calculated based on RTM prices. For all oter cases considered in te study, te total revenues are te sum of te RTM and te DAM revenues. From Figure 4.1, we see tat for all four seasons te average montly capacity payment decreases monotonically as te offer cycle increases. Te igest increase in montly capacity revenue is for te offer strategy wit 1 in all four seasons. Compared to te base case tis strategy provides up to a 4.6% of increase in te montly revenues. Any increase of obtains no improvements in eiter te CAISO test system or in te PJM test system. Te RTM only offer strategy provides revenue growt for te winter monts of 2.2%, but in te summer, fall, and spring monts, we observe revenue reductions of 2.7%, 0.8%, and 2.4%, respectively. In Figure 4.2, we plot te montly mileage payments for different DAM strategies in te four seasons. We observe increases in te montly mileage payments under te offer strategy wit 1 in te summer and te winter seasons of 4.2 and 6.3%, respectively, wit respect to te reference case. On te oter and, for te RTM only strategy, tere is a mileage payment increase for all seasons. Te deployment of tis strategy provides revenue growt of 4.4, 2.8, 5 and 6.1% for te summer, fall, winter, and spring seasons, respectively, compared wit te reference case values. In general, te capacity revenues and mileage payments beave in a similar manner due to te fact tat capacity revenues compensate capacity bandwidt of te regulation unit dedicated to provide regulation, i.e., to follow AGC signal instructions. As more capacity is provided by te FES, te larger te sifts are tat te unit needs to perform. Hence, te mileage payment increases is a consequence. Next, we explore te seasonality effect on te FES total montly revenues. Te igest payment for regulation service provision occurs in te summer season. Te reason for tis is tat te summer load is considerably iger tan tat in te oter tree seasons. Increases in load cause more volatility in te load-generation balance, consequently te FES is instructed to 46

53 provide more regulation resulting in additional revenues. Tis finding olds for bot te CAISO and te PJM test systems. Figure 4.1: Te average total montly revenues for capacity of te delivered frequency regulation service for te CAISO test system 47

54 Figure 4.2: Te average montly mileage payments from regulation service in te DAMs and te RTMs in te CAISO test system 48

55 In Figure 4.3 we depict te FES total montly revenues for tose offer strategies in wic we observe an increase in revenues. Te total montly revenues consist of capacity revenues, mileage payments and payments or carges for supplied or consumed energy. In tese studies, we do not include te energy payments or carges because teir fraction in total payment never exceeds 0.2%. From Figure 4.3 we see tat an offer strategy wit 1 results in increases in FES montly revenues on 3.3, 3.2, 4.9 and 2.7% for te summer, fall, winter, and spring seasons, respectively, wit reference to te conventional approac. Moreover, for te winter season, we observe te increase in te FES revenues of 2.1 and 2.8% for te DAM strategy wit 2 and RTM only offer strategies, respectively, wit reference to te conventional case. Figure 4.3: Te montly total revenues for regulation service in te CAISO test system In Section 4.1, we discussed te FERC Order No. 764 obligation on te IGOs to partition te ourly DAM periods into sorter intervals. Tis order impacts FES regulation service 49

56 providers wit benefits since wit te sorter duration subperiods, te provider manages its stored energy more effectively. However, tere is te lingering question as to te most beneficial duration of te DAM period for te FES frequency regulation service provision. In order to answer tis question we performed an extensive sensitivity study on te DAM period duration. To illustrate te impact of a DAM period reduction, we depict in Figures te total montly regulation service provider revenues wit DAM period durations of 15, 20, and 30 minutes under different service provision cycles. Te conventional offer strategy serves as te reference for te analysis of simulation results. For a 15-minutes DAM period, te pea montly revenues are obtained wit te cycle 3. Smaller values result in lower revenues because of less effective FES stored energy utilization. On te oter and, for 3 te limited FES storage capability limits te unit ability to offer more capacity on regulation into te DAMs. Terefore, te montly revenues decrease. For 20- and 30-minute DAM periods, te montly revenues are iger for 1, as illustrated in Figures 4.5 and 4.6. Tese results are commensurate wit tose obtained for case studies wit ourly periods, but since we ave te finer granularity for regulation provided in te DAMs, te montly revenues increase above te reference case levels obtained wit one-our DAM periods. Figure 4.4: Total montly FES revenues under 15-minute DAM periods 50

57 Figure 4.5: Total montly FES revenues under 20-minute DAM periods Figure 4.6: Total montly FES revenues under 30-minute DAM periods From tis discussion we conclude tat te FES can benefit from te reduced duration DAM period. Te DAM period duration of 15 minutes combined wit appropriate DAM offer strategy 51

58 3 provides te largest increase in montly revenues. For te 20- and 30-minute DAM periods, te revenue increases are smaller, but are still above tose in te reference case. We next investigate te impacts on FES montly revenues of te deployment of ris-taing offer strategies into te RTMs. In our studies we parametrized te augmentation factor q to calculate FES total revenues for a given value of. Figure 4.7 depicts te plot of te FES total montly revenues for 1 and 2. Te plots for 3, 4 and 5 are not of interest, since te total montly revenues are below tose in te reference case. From Figure 4.7 we see tat for 1 wit 100% q 160%, as q increases te montly total revenues increase. Tis means tat te benefits from te additional capacity of te offer exceed te revenue losses from te inability to respond to AGC signals due to itting a storage capability limit. If we increase q above 160% we obtain no revenue increases because of te reduction in te number of intervals wen te FES is responsive to AGC signals. Suc a reduction causes te decrease in capacity revenues and mileage payments, and, terefore, in te total revenues. We see tat te coice of q involves a trade-off: q needs to be large enoug to ave an impact on capacity revenues and mileage payments, but also needs to be sufficiently small to avoid itting a storage capability limit during regulation service provision. We obtain a similar plot for 2 wit pea montly revenues wit q 170%. In order to obtain a better representation of te relationasip between q and montly revenues, we display te q sensitivity for 150% q 170% under 1 in Figure 4.8. We see tat te pea montly revenues of $353,403 corresponding to te RTM capacity offer augmentation by 63%. In tis case, we ave an increase in montly revenues of $30,867, wic is a meaningful amount for a FES service provider. 52

59 Figure 4.7: Total FES revenues for RTM capacity offer augmentation over te 110% q 200% wit 1 and 2 Figure 4.8: Total FES revenues for RTM capacity offer augmentation over te 150% q 170% wit 1 53

60 4.3 Summary In tis capter we demonstrated te ability of te proposed formulation for te offer strategies into DAMs and teir associated RTMs troug representative case studies using te actual 2011 year AGC signals and te price data from two large systems te CAISO and te PJM. Studies sow tat te offer strategies formulated for te FES participation in te DAMs and te RTMs result in te provision of guaranteed service fully responsive wit te AGC signals sent by te IGO. We also presented te studies to investigate te impacts of canging te duration of te cycle offer pattern parameter for te DAMs so as to bring about te most effective utilization of te FES unit. Wit a 20 MW FES wit 5 MW storage capability in te 2011 CAISO data case, suc utilization results from participation in every oter ourly DAM i.e., a cycle of one our participation followed by one our of non-participation to ave 12 consecutive suc cycles eac day. Te simulation study results for tis specific case indicate tat tis cycle increases te FES annual revenues by 3.3% over tose wit te current FES offer strategy at full capacity for every ourly DAM. We also studied te impacts of te deployment of ris-taing offer strategies into te RTMs. We find tat suc offer strategies into te RTMs result in increases in FES montly revenues. Specifically, te increase of te RTM capacity in te up and down regulation service offers by 63% increases te montly revenue by nearly 10% over te ris-free conservative strategy. A very insigtful application of te proposed approac is to te analysis of policy issue impacts. Our studies indicate tat te mandate of te FERC Order No. 764 to introduce sorter DAM subperiods result in improved utilization of storage and in increases in revenues for te frequency regulation service provision. 54

61 CHAPTER 5 CONCLUSIONS In Section 5.1, we provide a brief synopsis of te wor presented in tis tesis. In Section 5.2, we detail directions for future wor for te utilization of FES resources in frequency regulation service provision. 5.1 Summary In tis tesis we ave developed a compreensive approac to effectively utilize FES resources to provide guaranteed AGC service to te grid. Te approac maes detailed use of te analytic framewor we ave developed for bot analysis and simulation purposes. Te framewor incorporates te regulatory, financial and pysical considerations in frequency regulation service provision by a FES unit and constructs te analytical basis for te formulation of offers into te two sets of marets te DAMs and te RTMs. Te framewor as a treelayer structure a layer for offer formulation into te ourly DAMs, anoter layer for additional offer formulation into te RTMs and a simulation layer of FES operations in response to te AGC signals sent by te system operator into wose system te FES is integrated. Te analytic basis in te framewor allows te determination of te appropriate constraints for use in te formulation of te offers for eac set of marets. Tis basis incorporates te modeling of te FES unit 55

62 developed to appropriately represent its salient pysical caracteristics wit different levels of granularity in te tree layers. In addition, te representations of te maret rules are embedded in eac layer. Te models in te DAM and te RTM layers are able to provide te respective boundary limits under worst-case scenario of one type regulation service over te entire duration of te period covered by a submitted offer. As a result, te offer is formulated wit te full assurance tat te frequency regulation service can be provided over te entire period associated wit te offer so as to satisfy all te pysical and te regulatory constraints. Te application of te framewor to te formulation of offer strategies maes use of robust optimization concepts in te solution of te optimization problem to determine te amount of te up and down regulation to offer in eac maret for eac period/subperiod. Tese solutions provide conservative results under wic bot up and down regulation can be provided wit 100% guarantee independent of wat te AGC signal turns out to be and taing into account te most up-to-date information on te unit status at te time te offer is submitted. To obtain tese results, we tae full advantage of te distinct considerations in te ourly DAM and in its associated RTMs. Te offers for eac maret are made at different times and we use participation in te RTM to adaptively correct te accepted offer into its associated DAM by detailed use of te updated information on te FES stored energy level. We also investigated te modification of te conservative solutions to construct more risy RTM offers and evaluate teir performance over a range of values of te capacities for regulation up and down service provision. We ave demonstrated te capability of te developed approac and quantified te improved performance over existing tecniques troug various case studies using te istorical AGC signal and price data from two large systems te CAISO and te PJM. Te representative studies presented in tis tesis sow tat te offer strategies formulated for te FES participation 56

63 into te DAMs and te RTMs provide guaranteed service fully responsive to te IGO sent AGC signals. Te specific studies tat investigated te impacts of canging te duration of te cyclic offer pattern in te DAMs to bring about te best utilization of te FES unit provide important insigts into te cycle coice. For te 2011 CAISO data case, te best utilization results from participation in every oter our s DAM i.e., a cycle of one our participation followed by one our of non-participation to ave 12 consecutive suc cycles per day. Indeed, te results indicate tat tis cycle increases te FES annual revenues by 3.3% over tose wit te current FES offer strategy. We also studied te impacts of te deployment of risy offer strategies into te RTMs. We find tat suc offer strategies in te RTMs results in increased FES montly revenues. A very insigtful application of te proposed approac is to te analysis of policy issues. We illustrate suc an application troug an analysis of te canges promulgated by FERC in its Order No. 764 to mandate te partitioning of eac ourly DAM period into four 15-minute subperiods. Our studies indicate tat te sorter subperiods result in improved utilization of te storage capability and in increases in revenues for te frequency regulation service over tose for te ourly periods. Tese representative results clearly indicate tat te proposed approac generates offer strategies tat result in te better utilization of te FES to provide guaranteed frequency regulation service. 5.2 Directions for Future Wor Te tesis reported constitutes a good starting point for te future study of additional issues related to frequency regulation provision by storage tecnologies. Te ongoing advancements in 57

64 flyweel tecnology motivate te parametric study of te sensitivity of te regulation service provision wit respect to improvements obtainable from capacity and storage capability increases of FES units. Suc studies can also sed ligt on te willingness to pay for suc improvements by te FES owners. In ligt of recent experiences in te deployment of storage devices for frequency regulation service provision an interesting extension of te wor is te consideration of optimal deployment of aggregations of multiple FES units wit bot uniform and non-uniform caracteristics. In considering te aggregation of multiple FES units, te important question tat is raised, is weter it maes sense to cluster te FES units into various subgroups so as to allow teir more effective utilization for frequency regulation service provision. Suc a question can be answered as part of a broader problem concerning te effective utilization of a fleet of different storage tecnologies, say, ydro, FES and large-scale batteries. Te formulation of an answer to tis question requires te extension of te framewor to include te representation of all te tecnologies of interest. Anoter issue tat requires future wor is te deployment of te extended framewor to formulate te appropriate incentives to stimulate te energy storage suppliers to provide guaranteed frequency regulation service. Suc an investigation needs to formulate appropriate payments to te storage resources so as to veer tem away from te submission of risy offers to provide guaranteed service. 58

65 APPENDIX A NOTATION USED IN THE THESIS For te DAM layer we adapt te convention tat te our starts at (-1:00:00) + and ends at :00:00 and so te our excludes te point (-1:00:00) and includes :00:00. Hence, te our is represented by semi-open time interval, as sown in Figure A.1. Figure А.1: Hourly intervals for te DAM Eac DAM as an our as te smallest indecomposable unit of time and no penomena of sorter duration may be represented in a DAM. We represent te system by its snapsot wose values are assumed to old for te entire our. We define te set of H ours to denote te collection of te ours for wic te DAMs are cleared and te outcomes are determined. Under te selection of an our as a smallest indecomposable unit of time, we are unable to represent any penomenon of any duration under an our. Terefore, we ave adopted te following protocol: all values on DAM are establised at te end of te our and tey are assumed to old over te entire our (Figure A.1). For example, DAM capacity award ( ) 59

66 for regulation down (up) is constant for te entire our, energy carge is measured at te single point in time :00:00 and assumed to old over te entire our. Te similar protocol is applicable for te RTM layer, wen we consider a subperiod as te smallest indecomposable unit of time. Te variables associated wit eac RTM are assumed to be constant for te entire subperiod and subscript (, ) of a variable represents te RTM variables. We define te subset of K equal duration subperiods,,..., 1 2 K one for eac RTM wit duration 60 K in our, minutes in tat our. We adopt te protocol tat te 60( 1) subperiod of our starts at ( 1 : : 00 ) + and so te subperiod K excludes te 60( 1) 60 point ( 1 : : 00) and includes ( 1 : : 00 ). Hence, eac subperiod K K, 1,2,..., K, is represented by te semi-open time interval tat covers te range from 60( 1) ( 1 : : 00) + 60 to ( 1 : : 00 ), as we indicate for te semi-open intervals in K K Figure A.2. Figure А.2: Te subperiods for eac RTM are semi-open intervals All values on RTM are establised at te end of te subperiod and tey are assumed to old over te entire subperiod. Tus, energy carge s is measured at te single point in, 60

67 60 time ( 1 : : 00 ) and assumed to old over te entire subperiod K of our, RTM capacity award our. (,, ) for regulation up (down) is constant for te entire subperiod of We next examine te AGC pysical operations layer to effectively represent te AGC intervals. Te values during real-time operation under AGC are assumed to be constant for te entire interval n. We use te variable n in square bracets to denote te variables relating to pysical operation under AGC. We define a subset of N equal duration intervals,,..., in subperiod of our, one for eac AGC signal wit duration 3600, 1 2 N KN seconds. We adopt te protocol tat te interval n of subperiod of our starts at 60( 1) 3600( n 1) ( 1 : : ) + 60( 1) 3600n and ends at ( 1 : : K KN K KN ) and so te interval n 60( 1) 3600( n 1) 60( 1) 3600n excludes te point ( 1 : : ) and includes ( 1 : : K KN K KN ). Hence, eac interval, n 1,2,..., N, is represented by te semi-open time interval tat n 60( 1) 3600( n 1) covers te range from ( 1 : : ) + 60( 1) 3600n to ( 1 : : K KN K KN ). Similarly to RTM and DAM layers, all values relating to pysical operations under AGC are establised at te end of te interval n and tey are assumed to old over te entire interval. n For example, capacity output c [ ] is assumed to be constant for te entire interval, n, n energy carge [ ] is measured at te single point in time ( 60( 1) 3600n 1 : :, n K KN ) and assumed to old over te entire interval n. Figure A.3 depicts te semi-open intervals of n pysical operations under AGC. 61

68 Figure A.3: Te intervals n of pysical operations under AGC layers. Figure A.4 represents te time segments of DAM, RTM and pysical operations under AGC Figure A.4: Time frame for te AGC service in te maret environment Key aspects include te following: Te elements of eac offer ave te ˆnotation All regulation up-related variables/parameters ave te superscript + All regulation down-related variables/parameters ave te superscript We define for regulation up service for regulation down service RTM-related notation (units): : RTM regulation capacity for subperiod, in our (MW) RTM regulation capacity price for te subperiod in our ($/MW/), : 62

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