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RTOS, Transmission and Tariff Design

 

Designing, Implementing, Joining and Managing the RTO

 

Surviving and Thriving in the RTO Revolution

How to design tariffs to improve reliability and attract merchant generation

Rajat K. Deb, Lie-long Hsue, Alex Ornatsky and Jason E. Christian

 

FERC (Federal Energy Regulatory Commission) in Order 2000 requires Regional Transmission Organizations (RTOs), including for-profit TransCos and non-profit ISOs, must meet a number of criteria in their design, implementation, and management of the transmission grid. But to survive and thrive, they must learn to do much more¾and with better tools attuned to the task.

On the surface, RTOs (regional transmission organizations) perform a concise list of transmission-related functions, assigned to them by the FERC. First, RTOs must to design and collect transmission tariffs. Second, RTOs must manage various parameters of grid operations in the short term, such as congestion, ancillary services, loop flows, and OASIS¾the interface with the grid customer. At the same time, RTOs will take the lead in long-term planning for future grid design and construction. And finally, the RTO acts as the official regional transmission “czar,” coordinating grid operations with other regions and overseeing the efficiency and fairness of regional markets.

Yet the real job of running an RTO may prove more complex. Consider the events of last year in California and the West, and the difficulties experienced by the California Independent System Operator. This experience suggests that the job of the RTO goes far beyond the managing the nominal transmission sector.

Like it or not, RTOs must inevitably engage in integrated resource planning, for both transmission and generation. Of course, it is true that under a market-driven restructuring, the choice and manner of deployment of generation and transmission capacity represent separate business decisions. Yet, these two segments remain interdependent.

Consider generation. The potential investor in additional generation capacity is interested in the future energy prices at a prospective location, which are likely to be influenced by the future state of the transmission grid. The size and location of investments in expanded generation is influenced by expectations regarding the future costs of transmission access and use.  Now consider the grid. The value of a transmission line is increased by congestion. Thus, if anticipated new generation is sited differently than had been expected a line may not receive usage- or congestion-based revenues that were to justify its installation. New generation may decrease congestion in certain parts of the system while increasing it in others, while new transmission facilities may decrease the reliance on transmission facilities already operating below capacity. Investment in either generation or transmission alters the revenue expectations for the other. That is the environment in which the RTO must design and collect tariffs, manage short-term grid operations, and plan for long-term expansion.

To understand the complexity of these market-driven dynamics, compare the RTO’s job with the process of generation and transmission planning and rate setting, as performed historically by the traditional vertically integrated electric utility.

To the traditional utility, the planning of generation and transmission and the setting of rates is a matter of recovering costs of capital investment and operations and of ensuring system reliability as measured against certain probabilities of events. That is much different than operating the grid to increase efficiency and minimize costs, or to locate and build new generating plants only according to economic criteria and profit expectations.

On the wires side, the traditional utility integrated its rates for transmission and distribution services with generation, administrative and other charges. With no real distinction between generation and transmission charges, the actual revenue requirements or costs of transmission and distribution services were transparent. The transmission charge, such as it is, is subsumed into a single, system-wide (postage-stamp) rate for each customer class. Inasmuch as revenue requirements are met by postage-stamp charges, distance and topology have no role in the transmission charge. All potential paths are therefore treated alike, and thus the different values of separate lines are not recognized. The use of a postage-stamp charge for the utilization of transmission that is operating below capacity discourages use of the transmission. It favors generation on the receiving or energy-deficit side of the transmission path over generation on the sending or energy-surplus side of the path. Such usage fees unlinked to transmission constraints are inefficient. Moreover, the pancaking of such fees (the combining of rates for transactions crossing two or more grid systems) discourages efficient inter-regional trading of power.

On the generation side, the traditional utility measured adequacy and reliability by using a static index of Loss of Load Probability (LOLP), which takes into account only generation outages but assumes no uncertainties in demand.  LOLP is a long-term measure often used as a criterion for generation expansion.  However, in the deregulated market, merchant plant additions are based on economic criteria, such as the marginal value of additional generation at a particular grid location, as affected by grid capacity and congestion. For the merchant generator, capacity additions cannot be dictated solely by reliability measures, such as LOLP or reserve margin.

What do these ideas mean for the successful RTO?

Today, in spite of the unbundling of transmission and generation dictated by restructuring, the RRO must plan and operate the backbone transmission grid in an integrated, coordinated way that will also support efficient investment in generation. In a very real sense, the RTO in effect must re-bundle transmission and generation, but in a new way¾one that goes beyond static cost recovery. Instead, the RTO must achieve a dynamic equilibrium that boosts the efficiency of the grid and at the same time raises the transparency of profit opportunities in merchant generation.

To its credit, the FERC acknowledges some of the essence of this revolution, though it recognizes the political realities involved in brining it to fruition. In Order 2000 (and in the Dec. 15 order regarding California power markets), it encourages market pricing to bring into focus the comparative values of transmission facilities, and the impact of congestion under different pricing protocols. Yet it concedes the need to recover sunk costs. The FERC, in its descriptions of the RTO, recognizes that a transition to a more market-based approach is to be accompanied by continued reliance on various fixed-cost recovery measures. It is up to each RTO to determine how either market- or usage-based charges and fixed-cost components can complement one another.

Whether a non-profit ISO or a for-profit GridCo, the RTO combines traditional functions with many that are new to the power industry. Managerial innovation and flexibility are called for in this new organization¾along with some new analytical tools that can help model transmission usage and profit opportunities in generation.

A market simulation tool can provide important information to both the RTO and to users of the RTO-administered grid.  Such a tool must describe the physical and commercial operations of the grid, and be sufficiently flexible to accommodate the various actions that an RTO might take.  A market simulation tool that meets these criteria can both contribute to the RTO’s performance of its functions, and to the profitable interaction of market participants with the power market. To be useful, however, for both the RTO and the RTO’s grid users, the market simulation tool must describe market responses to RTO activities.

In this article, we examine some of the problems involved in three of the eight enumerated functions for RTOs¾tariff design, providing ancillary services, and transmission planning¾and how a dynamic model for market simulation might help form these tasks. (See Technical Appendix.)

Transmission Tariffs: Testing Alternative Designs

The design and administration of an open-access transmission tariff, the collection of prices, terms and conditions that facilitate the use of a transmission grid by generators and loads, is one of the primary duties of the RTO.  The tariff design project is complex.  First, the RTO must satisfy FERC requirements that it offer non-discriminatory access, follow cost-causation principles, and eliminate pancaking. Second, it must deliver the revenue necessary for the physical and financial maintenance of the transmission grid.  Third, the operation of the tariff—the computation and assessment of various rates—interacts with other elements of the RTO design and operation, such as mechanisms for managing congestion, or the grid planning and expansion process.

At the same time, however, the tariff must reflect the needs of the generation sector. The size and location of investments in expanded generation, which contributes to the future market value of transmission, is influenced by generator owners’ expectations regarding the future costs of transmission access and use.  A transmission line’s value is increased by congestion, and thus, if anticipated new generation is sited differently than had been expected, a line may not receive usage- or congestion-based revenues that were to justify its installation.  New generation may decrease congestion in certain parts of the system while increasing it in others, while new transmission facilities may decrease the reliance on transmission facilities already operating below capacity. Investment in either generation or transmission alters the revenue expectations for the other.  A reliability manager who is alert to such possibilities may find pressures on the established balance between usage-based revenues and fixed cost-recovery measures.

How would an RTO employ a market simulation tool to address this problem?

Here, we analyze the likely effects of two potential tariff designs for transmission. The first design is a distance-based, megawatt-mile capacity charge assessed to a bilateral transaction. The second design features a per-megawatt postage-stamp access fee, computed to fill the gap between the grid’s revenue requirements and its earnings from a pure nodal-pricing congestion-management mechanism. Both of these examples are worked through the simplified example of the IEEE 24-bus case.[1]

  •  A Base Case Tariff. First, start with a base-case scenario involving pure nodal locational marginal pricing (LMP) to recover congestion costs, but with no access charge for grid use. This scenario provides a baseline dispatch that minimizes total going-forward cost of delivering energy, subject to the physical constraints of the transmission network.
  • Compare an Alternative. The next step involves choosing a proposed tariff, such as a per-MWh postage-stamp access charge assessed to all loads in the RTO territory, and comparing its effects with the base case. The model simulates the effects of the proposed tariff, including the revenues it will produce, and compares the required physical dispatch to that resulting from the base-case dispatch. Note that the FERC has adopted the same approach to evaluate congestion-management in its recent order on California power markets.[2] In that order the California ISO is instructed to compare the outcomes from its planned zonal locational pricing scheme to the outcomes under full nodal LMP pricing. A demonstration that a proposed postage-stamp access fee is consistent with efficient dispatch of generation is a powerful argument that the tariff meets FERC requirements.
  • Compute Grid Value. Use the estimate of revenues produced by the alternative tariff to calculate a dollar value of the grid implied by such revenues, and compare this grid value against the costs required for ongoing maintenance of the grid, depreciation, capital costs of the grid, administration, and so on.  Grid revenues may depend on many different input variables such as weather, fuel prices, and other more-or-less volatile drivers of supply and demand. The interaction of these drivers produces volatility of grid revenues, which may be evaluated through the use of Monte-Carlo simulations (i.e., a series of separate simulations conducted under specific sets of input variables, drawn at random, according to their respective probabilities.) This exercise produces a range of likely outcomes, allowing a far more useful evaluation of the financial viability of the proposed tariff arrangements.
  • Market Response. Take the evaluation in Step 3 and enhance it further through a long-run evaluation, taking into account both growth of loads and the amount and location of future investments in expanded generation. In other words, update the comparison of grid value and cost by the expansions that respond, in general, to locational price signals that are produced by the proposed tariff. This step recognizes that over a period of time, the owners of generation can choose the size and location of investments in new generating capacity. This type of simulation, with the tariff charge in place, permits a before-the-fact posting of a tariff, rather than a forecast followed by ex-post true-up¾a procedure which is fundamentally inconsistent with market operations in which independent actors respond to existing prices for firm products.

Simulation: Megawatt-Mile Tariff

Megawatt-Mile (MW-M) methods have intuitive appeal for recovery of grid costs. They seem to allocate grid costs based on the way that different customers use the grid. They take account of the strong contribution of distance to grid capital costs (since transmission lines make up a significant element of grid costs).  In addition, transmission losses, which are an element of grid operating costs, are directly related to the distance between generation and load.  On the other hand, in real time, in the absence of congestion, transmission losses are the only distance-related element of the cost a transaction imposes upon the grid. So MW-M charges in excess of that necessary to recover the costs of transmission losses violate cost-causation principles, and provide disincentives to the use of excess grid capacity.

A variety of specifications have been proposed to compute MWM charges, designed to either accommodate or capture “counter-flows” which reduce actual grid use, to ensure full collection of grid costs, to avoid the formation of coalitions to avoid grid charges, and so on.[3] Most of these methods involve an evaluation of the power flows “caused” by a transaction, generally in peak hour.  This can be derived from a simulation that uses a good power-flow model in conjunction with a description of the behavior of generators and loads. 

To conduct the simulations, an incremental MW-M charge using several MW-M methods was computed to allocate grid charges between a flat 10-MW per hour bilateral transaction between specified injection and withdrawal busses. Two simulations are required. For each, the flows on every transmission line are computed. The difference between the no-transaction flows during the peak hour and the flows with the transaction is assigned to the transaction, which is used in the computations whose results are shown in the table.

The annual cost/MW and a rate per MWh are shown in the table below.  For each of the method, the revenue from the 10 MW transaction and the rest of the network are shown. Note that in the power flow method the total revenue is less than the revenue requirement due to under utilization of the lines.  It should be noted that if this fee is charged based on metered grid use, it creates an incentive to not complete the transaction, which can produce under-collection of grid costs.

Table 1. Transmission Charges: MW-M Alternatives

Method

Case

Cost/MW

Cost/MWh

Basic MWM

Base Revenue Recovery

$988,729

 

 

Transaction Revenue

11,271

         $1.29

Power Flow

Base Revenue Recovery

318,836

 

 

Transaction Revenue

5,535

$0.63

Modulus

Base Revenue Recovery

970,536

 

 

Transaction Revenue

29,464

$3.36

Zero Counterflow

Base Revenue Recovery

982,178

 

 

Transaction Revenue

17,822

$2.03

Dominant Flow

Base Revenue Recovery

972,944

 

 

Transaction Revenue

29,015

$3.31

 

Simulation: Congestion Pricing Plus Postage-Stamp Access Fee

Congestion-based charging for use of the transmission grid is attractive on theoretical grounds, and is a principal preoccupation of the FERC in its review of RTO proposals.  The clearing of nodal markets using locational marginal pricing (LMP) produces appropriate signals for both security-constrained dispatch and for investments in generation and transmission, and for the location of energy-intensive loads. In fact, under its recent order regarding California power markets, the FERC is now requiring grid operators to compare their results against an explicit nodal-based LMP approach.

Congestion pricing to recover grid costs has a very considerable practical difficulty: in the absence of congestion there are no grid revenues, either to pay for the energy loss from transmission, or to pay the maintenance, operating, and financial costs of the grid. For these reasons additional charging mechanisms are generally necessary.

In this example, we consider a flat grid access fee, designed as a per-MWh postage-stamp charge, and applied to all loads in a zone corresponding to the service territory of a RTO. Such a tariff design is non-discriminatory among energy suppliers to a zone." However, if exporters apply their “stamps” to wheeling-out transactions, the resulting pancaking of wheeling-out stamps discriminates against wheeled transactions. If the postage-stamp uplift is computed in advance, it may act as a performance-based rate (PBR) to the grid operator: the more transactions are enabled, the greater it's postage-stamp revenues.  

In the IEEE 24-bus case, nodal spot prices are computed were computed for every hour of the year.  All loads are charged, hour by hour, the relevant nodal spot price per MWh of energy consumed at each bus, and all generation is paid, hour by hour, the nodal spot price per MWh of energy injected. The difference between the revenues and the costs is congestion revenue. In the test case, which has a peak load of 2,850 MW and annual energy usage of 15.4 million MWh, congestion revenues were $2.9 Million. If the total annual costs of the grid were $10 Million, the postage stamp would need to collect additional $7.1 million. With load of 15.4 million MWh, the postage stamp rate would be $0.46/MWh ($7.1 million divided by 15.4 MWh).

Such a fee, charged to all loads, would recover the additional revenue required for the financial and physical maintenance of the grid. In the simplified IEEE 24-bus case, there are no wheeling-in transactions, so that the postage stamp would neither alter the hourly dispatch of the energy, nor influence the locational decisions facing load and generation in the future.  If the postage stamp were fixed in advance, with no ex-post true-up, then the charge is a PBR, with an incentive to the grid operator to encourage additional energy transactions, thereby increasing use of the grid.

Pricing Capacity Reserves: Recognizing Opportunity Costs

As the reliability manager, the RTO must provide a design for the delivery of ancillary services. In addition, as the provider of open-access transmission services, the RTO must be the provider of last resort of ancillary services. Several of these ancillary services—the delivery of regulation services, to allow second-by-second maintenance of system balance, and the provision of reserve capacity for possible use in resolving longer-duration imbalances—are closely tied to the energy markets, and may be simulated jointly with those markets. Other ancillary services—the provision of scheduling services, the maintenance of adequate Black Start and voltage-support capability—are either not procured (typically the RTO would provide scheduling services itself) or are provided under long-term contracts.  Our concern here is with the energy-delivering ancillary services.  We use here the specifications and names of the California system, but note that similar services, with different names and different detailed specifications, are found in other systems.  There are four categories of these energy-delivering ancillary services:

  • Regulation.[4]
  • Spinning Reserves.[5]
  • Non-Spinning Reserves.[6]
  • Replacement Reserves.[7]

The design and operation of these markets for capacity reserves can significantly influence the price of energy delivered to a grid’s loads. They do so directly, through the charges for purchased reserves (in systems and situations in which energy transactions are not bundled with self-provided reserves), and indirectly, by contributing an opportunity cost to generating capacity that can bid either energy or ancillary services.  An evaluation of ancillary service costs is directly important to the RTO itself, to current owners of generation, and to public and private utilities considering participation in an existing or proposed RTO. For the RTO, current ancillary service prices are an element of its required market monitoring function, while future prices are part of the signal for the expansion of generation, which is a key element of the RTO’s transmission planning and expansion function.  For the generation owner, both energy bidding strategies, and future plant development process, require a view of current and future ancillary-service pricing.  Finally, the public or private utility contemplating affiliation with an RTO must evaluate the impact of the RTO’s ancillary services costs (as well as other costs of delivering firm energy) on its ratepayers and (for private companies) owners.

Useful and reliable simulation of ancillary services markets requires simultaneous and consistent simulation of the related energy markets. Such a simulation requires a method based on “rational expectations equilibrium pricing.”[8] This point is critical: in the simulated dispatch no supplier of reserves would make more money by providing energy instead, and no supplier of energy would increase his profits by selling more reserves and less energy. In other words, for any generating resource, the marginal cost of operation must reflect this tradeoff of opportunity costs in the competing markets for energy and those ancillary services that also delivery energy.

Consider a thermal unit with an incremental heat rate of 9,000 BTU per kilowatt-hour and paying a fuel cost of $30/MMBTU. For that unit, the marginal cost of providing energy is the greater of (A) $270/MWh and (B) the expected net capacity price for reserves (the reserve price less any expected earnings from the dispatch of energy from those reserves). In similar fashion, the marginal cost of providing reserves is the difference between the expected energy price and the units cost of $270/MWh, less its expected earnings from the dispatch of energy from those reserves.[9]

We conducted a simulation of ancillary services markets in a single hour, using a rational expectation model and the IEEE 24-bus model, as before for transmission pricing. The goal was to find a set of ancillary services and energy prices that are consistent with loads, reliability requirements, transmission constraints and profit-maximizing behavior by competitive price-responding generators. The results are shown in Table 2.

A full nodal-pricing model was used for energy, with locational procurement of reserves where necessary to respect transmission constraints, and a single system-wide market clearing price for each service. For simplicity, it was assumed that the reserves were not expected to earn money from the dispatch of energy.

First, ancillary requirements were set as a percent of load, as shown in the second column of the table. Second, ancillary services and energy markets were simulated simultaneously, allowing energy and reserve prices to interact.  The equilibrium A/S prices are shown in the third column of the table.  Finally, the cost for ancillary services to be assigned to each MW of load transaction is shown in the fourth column of the table.  The total cost of ancillary services, per MW of load served, is $3.54 in the peak hour.  This is about 14 percent of a bundled firm-energy price that includes ancillary services, congestion and recovery of stranded-asset costs.

 

Table 2. Ancillary Services - Capacity Reserves

(Hour 18, Dec 12, 2000  - IEEE 24-Bus Model)

Service

A/S Requirement as a % of Load

Marginal Clearing Price ($/MW)

A/S Cost per MW of Load

Regulation Up

2.8

29.62

0.829

Regulation Down

2.2

28.72

0.632

Spinning Reserves

5.5

29.28

1.610

Non-Spin (ten min)

0.5

32.79

0.164

Replacement

1.0

30.86

0.309

Transmission Planning and Expansion

In the context of the traditional integrated utility, the transmission planning exercise is a straightforward but computationally complicated exercise. It involves involving the selection of transmission and generation expansion plans that minimize the total present-value cost of delivering uninterrupted power to the utility’s customers. At every point on the grid in every hour (and in every reasonable contingency) there needs to be sufficient generating capacity to meet loads. However, when the transmission grid is operated at capacity, there will be some incremental loads that can only be served by increasing the output of local generation. In that circumstance a dynamic, integrated planning process can compare the cost of delivering new generation to the cost of reinforcing the transmission grid to achieve an acceptable level of reliability.[10]

A number of indices may be calculated to measure reliability on a dynamic basis.  For example, the average unserved energy at each demand node measures the demand that would be interrupted due to shortages, transmission constraints or excessive loads. The standard deviation of the unserved energy gives a measure of the volatility of these occurrences. Other measures include the frequency of load interruptions, as well as the variability and the standard deviation of load interruptions. These numbers can be compared for cases with and without particular merchant plant additions, transmission reinforcements or transmission capacity additions. These occurrences are of vital interest to owners and planners of both generation and transmission assets.

To simulate a reliability analysis in this context, an expansion plan for generation is created, showing plant additions through time for which the simulated net present value of the plants is positive, given expected load growth, fuel prices, and the presence of those plants in the simulated future years.  The generation expansion plan is, therefore, a rational-expectations plan, based on future prices rather than on past observations. 

With the generation expansion plan in place, a variety of reliability analyses may be performed, using Monte-Carlo techniques to simulate both market and physical events, such as local variations in loads, and forced outages of generation. One can compute, for example, the probability and expected duration of loss of load events, or the expected annual duration of transmission congestion.

In our simulation we analyzed transmission adequacy using a model of the Eastern Interconnection, with load growth projected over 20 years, and with economic plant expansion over the same horizon. The transmission model for this example was a simplified network, which combines the actual transmission net into a smaller number of interzonal interfaces. This analysis allows one to focus on the main areas of transmission congestion. Of course, investments in generation and transmission to mitigate potential transmission constraints would use more detailed representations of the transmission network.

Table 3 shows an example of a transmission adequacy report, showing both duration and marginal and total annual costs and duration of congestion costs on selected interfaces of the Eastern Interconnect, given the expected generation and loads for 2001.  For operational planning one can obtain the probability of congestion during a particular hour on the main interconnections, as well as the expected congestion prices on those paths and the expected congestion cost. The congestion costs shown here represent the differences between locational prices on either side of the congested interface in those hours in which use of the interface is constrained, minus relevant grid-access or wheeling charges.  It is, therefore, the marginal value of additional generation.  However, if additional transmission capacity is added the congestion will disappear and owners of the new lines have to recover their investment through appropriate transmission charges.

 

Table 3. Projected Transmission Adequacy: Annual Congestion Cost at s
Selected Interfaces for the Year 2001 for the Eastern Interconnect

From Interface

To Interface

Interface Capacity (MW)

Average Marginal Congestion Cost ($/MW)

Total Congestion Cost (000$)

Hours Congested

HydroQuebec

NEPOOL

1,793

16.01

69,972

2,438

HydroQuebec

NYPP

1,400

31.83

181,062

4,063

Ontario Hydro

ECAR-MI

2,170

6.70

11,596

798

PJM

NYPP

4,634

4.36

16,130

798

NYPPzone D

NYPP zone F

2,700

9.19

53,718

2,164

ECAR-Indiana

Main-north

1,310

9.60

38,664

3,072

ConEd

LIPA

975

8.08

42,867

5,445

ECAR-East

PJM-West

2,768

13.50

165,342

4,471

North Illinois

South-Illinois

2000

7.79

20320

1304

 

In addition the RTO will loose the congestion revenue. However the producer and consumers will benefit. Alternatively, installing new generating capacity at the congested side of the interface can relieve the congestion and lower the clearing prices. For example a 500 MW generator installed in PJM west at the ECAR -PJM interface may bring benefit of $13.5*500*4471= 30.2 M$ to the consumers. However, a new generator may not be able to earn the congestion rent that it helped to mitigate.  In a competitive environment the generator has to be viable from the revenue it receives from the nodal spot at the node it is located.  Our simulation run indicated that if this generator is a combined cycle nut located at the node Keystone it is economically viable. 

Dr. Deb is President of LCG Consulting.  Ms. Lie-log Hsue is Director of R&D, Mr. Ornatsky is a Senior Operations Analyst and Dr. Christian is a Senior Economist at LCG. LCG is located in Los Altos, California. The authors thank Jeremy Platt and Andy Van Horn for their comments and suggestions. The authors are particularly thankful to Bruce Radford, the editor of fortnightly for his valuable contribution to focus the main theme of this article. We invite readers to send comments to Dr. Deb: E-mail Deb@energyonline.com, Tel. 650-962-9670. This article reports on work partially supported by the Electric Power Research Institute, Project ID Product ID 041800 / 9488. The views expressed in this article belong to the authors, and do not necessarily represent the opinions of EPRI.

Technical Appendix

Notes on the UPLAN models for grid networks and merchant plants

A market simulation too, such as LCG’s UPLAN system, can both support the activities of the RTO manager and the market participation of generators, loads, and traders.

The UPLAN Network Power Model which integrates a competitive market operations model with an optimal A/C power-flow program, including (where appropriate) generator behavior to take advantage of markets for ancillary services, real-time balancing energy, and congestion management.  The simulations use detailed databases of generation, loads, and transmission, and may be performed in Monte-Carlo mode to simulate network response to contingencies.

The UPLAN Merchant Plant model may be used to forecast economic expansion of supply. The transmission model allows specification of alternative tariff specifications, including postage-stamp and license plate transmission accesses methodologies, other PBR designs, pure congestion-pricing designs, and hybrids.

The use of LCG’s UPLAN market-simulation software, which is built around rational expectations equilibrium pricing methodology, to support these tasks is reviewed in Deb, “Rethinking Asset Valuation in a Competitive Environment,” Public Utilities Fortnightly 138 (February 1, 2000): 48 –55; Deb, “Operating Hydroelectric Plants and Pumped Storage Units,” The Electricity Journal, 13, 3 (April, 2000): 24-32; and Deb, Albert, Hsue, and Brown, “How to Incorporate Volatility and Risk in Electricity Price Forecasting,” The Electricity Journal, 13, 4 (May 2000): 65-75.

Here we summarize the functions and requirements laid out for the RTO in FERC Order 2000, and show how the UPLAN tool addresses these functions, with several examples from recent work.


Figure. UPLAN Treatment of FERC’s RTO Functions

FERC’s RTO Functions

UPLAN Treatment of Mandatory Functions

Tariff design & administration

Directly addressed via evaluation of alternative transmission tariffs and resulting contract flows, producing projected revenues and power transaction volumes. 

Transmission congestion management

Directly determined by optimal AC power flow analysis and UPLAN redispatch costs to relieve congestion. 

Parallel path flow (reduced loop flow)

Directly identified from calculated flows on specified paths and by simulation of measures affecting power flows. 

Ancillary services

Explicitly simulated by applying market design protocols: e.g., UPLAN treats CA, New England & PJM rules in its bidding and ISO dispatch functions for regulation/AGC, spin and non-spin reserves, replacement and real-time imbalance energy. 

Total Transmission Capability (TTC) and Available Transmission Capability (ATC)

TTC is input into the database for each line and interface and may vary by month. ATC is simulated hourly by the AC power flow calculation.

Market monitoring

Direct calculation of multiple measures of generator market power, as well as impact measures of bidding behavior, capacity withdrawal, and the effects of price caps. 

Transmission planning and expansion

Analyzed via alternative scenarios and built-in new entrant model for generation expansion, additions and retirements. Develops measures of transmission system adequacy.

Interregional coordination

UPLAN models each NERC region, as well as the Eastern, Midwestern and Southeastern Interconnected Systems, WSCC & ERCOT, plus flexibly defined RTO regions embedded within each regional grid.

 

A Typical Simulation:

Task I: Develop a Base-Case Scenario.  Develop a detailed model of the grid, incorporating the best available current information regarding market rules, existing and future stock of generation and transmission assets, loads, and so forth. A planning horizon is developed, and the model simulated, generally including Monte-Carlo simulation to take account of the variability of the fundamental volatility drivers. The results may include simulation forecasts of zonal prices and locational marginal prices, as appropriate, generator output, power flows, revenues from congestion and from grid usage fees, and standard grid reliability analyses. 

Task II: Simulate Alternative Scenarios.  Develop a detailed model of the alternative scenario (which might involve different grid-access and wheeling pricing levels or methodologies, zonal configurations, alignment with different RTOs, etc), and simulation under the same load and supply conditions simulated in Task I.

Task III: Asset Evaluation. Produce estimates of the revenues to the owners of transmission assets, from congestion charges, transmission-access and wheeling charges, and other sources, over the planning horizon of the simulation. The present value of these flows of revenues, net of going-forward maintenance expenses, is a simulated value of the assets.  Similar computations can be applied to generation assets, or financial rights to transmission or generation.

Task IV: Market Reaction. Alternative market participant behavior is modeled through modified bidding strategies and/or withholding of generator capacity. Generators make unit-commitment decisions that allow startup and fuel costs to be covered by sales of energy and ancillary services, with generators operating at increased levels whenever incremental revenues at least cover incremental costs (including opportunity costs). These market-power scenarios are analyzed for their impact on the revenues to market participants, including transmission owners, and on other aspects of RTO operations.

 

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