Edinburgh Research Archive >
Engineering, School of >
Engineering, School of >
Engineering thesis and dissertation collection >
Please use this identifier to cite or link to this item:
|Title: ||Dynamic modelling of generation capacity investment in electricity markets with high wind penetration|
|Authors: ||Eager, Daniel|
|Supervisor(s): ||Harrison, Gareth|
|Issue Date: ||25-Jun-2012|
|Publisher: ||The University of Edinburgh|
|Abstract: ||The ability of liberalised electricity markets to trigger investment in the generation capacity required
to maintain an acceptable level of security of supply risk has been - and will continue to
be - a topic of much debate. Like many capital intensive industries, generation investment suffers
from long lead and construction times, lumpiness of capacity change and high uncertainty.
As a result, the ‘boom-and-bust’ investment cycle phenomenon, characterised by overcapacity
and low prices, followed by power shortages and high prices, is a prominent feature in the debate.
Modelling the dynamics of generation investment in market environments can provide
insights into the complexities involved and address the challenges of market design.
Further, many governments who preside over liberalised energy markets are developing policies
aimed at promoting investment in renewable generation. Of particular interest is the mix and
amount of generation investment over time in response to policies promoting high penetrations
of variable output renewable power such as wind. Consequently, improved methods to calculate
expected output, costs and revenue of thermal generation subject to varying load and random
independent thermal outages in a power system with a high wind penetration are needed.
In this interdisciplinary project engineering tools are applied to an economic problem together
with knowledge from numerous other disciplines. A dynamic simulation model of the aggregated
Great Britain (GB) generation investment market has been developed. Investment is
viewed as a negative feedback control mechanism with current and future energy prices acting
as the feedback signal. Other disciplines called upon include the use of stochastic processes
to address uncertainties such as future fuel prices, and economic theory to gain insights into
investor behaviour. An ‘energy-only’ market setting is used where generation companies use
a classical NPV approach together with the Value at Risk criterion for investment decisions.
Market price mark-ups due to market power are also accounted for.
The model’s ability to simulate the market trends witnessed in GB since early 2001 is scrutinised
with encouraging findings reported. A reasonably good agreement of the model with
reality, gives a degree of confidence in the realism of future projections. An advancement to
the dynamic model to account for expected high wind penetrations is also included. Building
on the initial model iteration, the short-term energy market is simulated using probabilistic
production costing based on theMix of Normals distribution technique with a residual load calculation
(load net of wind output). Wind speed measurement data is combined with the outputs
of atmospheric models to assess the availability of the GB wind resource and its relationship
with aggregate load.
Simulation results for 2010-40 suggest that the GB system may experience increased generation
adequacy risk during the mid to late the 2020s. In addition, many new investments are unable to
recover their fixed costs. This triggered an investigation into the design of a capacity mechanism
within the context of the modelling environment. In light of the ongoing GB market electricity
market reform debate, two mechanisms are tested; a strategic reserve tender and a marketwide
capacity market. The goal of these mechanisms is to mitigate generation adequacy risk
concerns by achieving a target winter peak de-rated capacity margin.|
|Sponsor(s): ||Natural Environment Research Council (NERC)|
|Keywords: ||power generation economics|
Mix of Normals distribution
thermal power generation
wind power generation
|Appears in Collections:||Engineering thesis and dissertation collection|
This item is licensed under a Creative Commons License
Items in ERA are protected by copyright, with all rights reserved, unless otherwise indicated.