IEEE Power & Energy Magazine - November/December 2017 - 79

In ERCOT, policy-maker and stakeholder discussions
over the years have, to date, resulted in a continuation of
the energy-only market design. During discussions in 2012,
the use of an operating reserve demand curve (ORDC) was
proposed as a necessary intermediate step before contemplating major changes. An ORDC helps value all available
operating reserve capacity in the short-run time horizon
based on the value of lost load (VOLL) and the loss-of-load
probability (LOLP). The incremental value of operatingreserve capacity would be based on the product of LOLP
times VOLL. A constant minimum price on the ORDC is
set at the minimum contingency reserve requirement. The
intent here is that, when reserves fall below the minimum
contingency reserve, the ORDC sets the price for marginal
capacity at the maximum. Another important aspect of the
ORDC and how it differs from shortage pricing in other
markets is that the demand curve extends (albeit at a very
low price) well beyond the normal reserve requirements,
such that there is always some adder to the energy price.
Upon approval by ERCOT stakeholders, a version of
ORDC was implemented in 2014 for the real-time market.
ERCOT's implementation of ORDC is a post-processing step
in which the value of system-wide operating reserve capacity
is determined by looking up the price on the ORDC for that
level of reserves. There are two ORDCs: one represents the
marginal value of total available operating reserves (online
plus offline) over the next hour; the other represents the marginal value of available online operating reserves over the
next 30 min. The final value determined for online operating reserves is the sum of the marginal values of reserves
from both ORDCs. This value is added to the energy prices.
Qualified online operational reserves are credited the value
of the online operating reserves, while the qualified offline
operating reserves are credited the value of offline operating
reserves. Figure 7(a) and (b) represents the two ORDCs used,
with 2,000 MW of minimum contingency reserve.
Another notable trend observed with increased VER is
traditional fossil fuel plants running for fewer hours but also
cycling on and off more often. A number of these resources
are dispatched at their minimum generation limits as the
system operator may need only a fraction of their energy or
because they have minimum run times but were needed for
only a portion of that run time. Generally, these resources
are not eligible for setting prices under a traditional marginal
cost definition because they do not represent the resource
used to meet the next increment of demand. Furthermore,
additional costs, such as minimum generation costs or startup costs, often are not reflected in the marginal cost-pricing
paradigm either. Out-of-market payments have to be used,
which undermines the price transparency needed to provide
incentives for market participants to make efficient decisions
and investments. As a result, increasing interest in price formation enhancements has continued among RTOs/ISOs.
The extended locational marginal price (ELMP) at MISO
addresses these issues by allowing committed resources to meet
november/december 2017

demand (but at minimum generation to set prices) and include
their commitment costs. The method is based on a mathematical concept of convex hull. MISO implemented ELMP through
a staged approach. Phase 1 went live in the MISO markets in
March 2015. Modest results were produced by the conservative implementation that validated the design objectives, as
summarized in Table 2. Figure 8 shows that important price
signals were produced during morning ramp-up or peaking periods when fast-start resources were committed to help
meet demands. Given the validated production results, in
May 2017 MISO launched ELMP phase 2, which expands the
ELMP logic to apply to a larger pool of resources and capture
broader benefits.
New technologies also bring new challenges to grid
operations and price formation. For example, if not appropriately modeled, when an RTO deploys demand response
under emergency capacity shortage conditions, the demand
response may appear as load reduction or "free supply,"
which could lead to suppressed prices, contrary to the signals
needed during these operating conditions. As supply margins

1
LOLPs

2,000
Online Reserves (Rs)
(a)

LOLPsns

2,000
Online +
Offline Reserves (Rsns)
(b)

Source: ERCOT

figure 7. ERCOT's ORDC (a) over the next 30 min and
(b) over the next hour.

table 2. The production results of
MISO ELMP phase 1.
Design Objectives

Production Results

More fully reflect in
prices the cost of online
fast-start resources used
to meet demand

~US$1/MWh average increase
over relevant real-time intervals

Reduce uplift costs

~1% uplift reduction during
expected periods

More accurately price
shortage or transmission
violation when MISO
has offline fast-start
resources available to
address the conditions

~US$15/MWh average decrease
during relevant real-time intervals

Reduce price volatility
and improve dayahead/real-time price
convergence

Day-ahead/real-time price
deviation reduced by 2.25%

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Table of Contents for the Digital Edition of IEEE Power & Energy Magazine - November/December 2017