INTERTEMPORAL CONSIDERATIONS IN SUPPLY
OFFER DEVELOPMENT IN THE WHOLESALE
ELECTRICITY MARKET
Paul Stewart
Energy Modelling Research Group, Department of Management, University of Canterbury,
Private Bag 4800, Christchurch, New Zealand.
paul.stewart@canterbury.ac.nz
ABSTRACT
Traditionally, electricity markets around the world have been operated centrally by an
agent of the local government, in order to minimise the cost of supplying electricity to all
users. However, over the last 20 years, these markets have gradually been deregulated,
producing wholesale markets in which generating companies compete for the right to
supply electricity, through an offering system. These major structural changes to the
operation of the electricity systems have significantly affected the behavioural incentives
of the players in the market, and have thus presented a whole new realm of problems for
researchers to address. Specifically, generating companies now operate in order to
maximise their own profits, subject to various behavioural constraints and other
considerations. In particular, these considerations include intertemporal constraints and
issues that the generator may face over time, such as fuel limitations and thus
conservation, market uncertainty and correlation, uncertain fuel inflows, unit operational
rules, and potential reactions of rival market participants to the generator’s own
behaviours.
In the New Zealand context, deregulation began in 1996 with ownership of generation
capabilities being split between multiple state-owned enterprises (SOEs) and private
companies. In every half-hour period of the day, each of the generators provides a set of
offer stacks and each of the demand-side participants provides a set of bid stacks to the
central market coordinator, for each of many coming periods. From a generator’s point of
view, for each of these periods they are allowed to provide a five-stepped offer stack for
every generation unit that they operate, or alternatively, they can aggregate their offers to
a station–level, or even a block-level. Because the market coordinator optimises the
market for each period independently, any internal constraints faced by the generators
must be accounted for within their own offers.
It is the optimisation of this offering process, subject to intertemporal constraints and
issues that we address primarily in this thesis. Some of these constraints have been
considered previously in the literature, but they have never been dealt with
simultaneously, or in a computationally efficient manner. This thesis presents two new
stochastic dynamic programming algorithms that optimise the supply behaviour over a
short-term planning horizon of a generator with limited fuel, where the stochastic rest-ofmarket behaviour and pricing outcomes is correlated over time. The difference between
the two algorithms is in the way that they consider correlation between market states in
consecutive periods.
The first algorithm considers a single Markov chain for each period, which provides the
probability of observing a particular residual demand curve in this period, given that we
know the residual demand curve that occurred in the previous period. The main
contribution of this algorithm is in the application of a technique known as marginal cost
patching. This technique enables us to separate the relatively straightforward, but highly
inefficient two-level dynamic programming algorithm into a significantly more efficient
two-phase dynamic program which has computational times that are practical with
respect to real-life application.
The second algorithm that we develop combines the methods of dynamic programming
and decision analysis in a new approach with a wide range of potential applications,
beyond the offering problem faced by generators that is primarily addressed in this thesis.
The idea of the technique is to partition the state of the system of interest at each stage
into discrete groups of possible system states, each associated with a particular macrostate. These macro-states represent overall, high-level states of the system at the given
stage, where transitions between these macro-states are either probabilistic or based on an
internal decision by the decision maker. With respect to the application of this technique
to the electricity offering problem, sets of residual demand curves that could occur under
a particular macro-state are grouped together. These macro-states could be based on
external market scenarios (such as a significant interconnector being binding or nonbinding) or on internal operating decisions that the generator has made (offering
aggressively or defensively, for example). Under the external scenarios, water value
curves for the alternative future macro-states are combined probabilistically at the point
in time where the uncertainty is to be resolved, while under internal decisions, water
value curves are combined depending on the optimal decision from each reservoir level at
the point in time where the decision is to be made. Dividing the overall set of possible
residual demand curves in the method proposed by this second model has many benefits,
most notably that of an improved representation of the uncertainty and decision structures
observed in the real-world.
In summary, the key contributions of this thesis are:
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The development of a marginal cost based offer patching theory, which can
significantly reduce the computational complexity of the offer strategy
development problem. Based on this theory, we develop and implement a twophase dynamic programming algorithm, which separates the construction of offers
into separate Pre-Processing and Real-Time phases, in a manner not previously
suggested in the literature.
We determine theoretical extensions to this algorithm to deal with a combination
of intertemporal constraints and considerations, including market correlation and
uncertainty, fuel limitations, and unit rules (including ramp rates) that have not
previously been considered together, while still maintaining feasible
computational times, with respect to realistic marketplace requirements. These
extensions are then implemented.
The development of a new technique that combines the concepts of dynamic
programming and decision analysis by building a branching structure into the
overall state of the generator and the market at any point in time. As well as being
able to better represent a real-world scenario that has this type of structure, this
branching will significantly reduce the computational complexity of the
algorithm.