Electric Power Optimization Centre
Our research uses mathematical modelling, optimization and statistical tools to comprehend modern electricity markets such as the NZEM, and to analyze and develop methods for efficient generation schemes and demand-side participation.
EPOC Summer Workshop
A special EPOC Summer Workshop was held on Tuesday, November 13,
at the Engineering School at the University of Auckland, 70 Symonds Street.
The talks are now available for downloading in Workshops below.
EPOC offers congratulations to co-director Golbon Zakeri who has
just been promoted to Associate Professor at the University of Auckland.
EPOC Winter Workshop (Updated Programme) (pdf)
The 15th Annual EPOC Winter Workshop was held on Wednesday, September 7,
at the Engineering School at the University of Auckland, 20 Symonds Street.
The programme is here and talks are now available for downloading in Workshops below.
On payment mechanisms for electricity markets with uncertain supply
(pdf) Forward commodity trading with private information
(pdf) On the marginal value of water for hydroelectricity
(pdf) Non-invasive test scheduling of the grid over live electricity markets (pdf)Interfaces (DOI 10.1287/inte.2016.0865)
In 2013 Transpower NZ commissioned a new HVDC link to transfer electrical
power between the North and South Islands of New Zealand. This was a substantial
and prolonged undertaking, requiring ca. 400 in-situ capability tests.
Transpower elected to perform these tests ``live",
without suspending the normal operation of the wholesale electricity market.
Instead, Transpower's trading team attempted to create suitable flow
conditions for each test by entering into innovative financial derivative
contracts with power generation firms. We created a stochastic
dynamic programming model to handle the contingent scheduling of the tests;
its most important random variable was the state of water storage available
to hydropower plants.
Arbitrage-free Pricing of Contingent Claims in Incomplete Markets via Moment
This talk covers pricing of contingent claims, such as financial options, using
discrete time stochastic programming under multi-period scenario trees. It is well
known that the absence of arbitrage profits in the state space is equivalent to the
existence of a martingale probability measure when there are no transactions costs.
When such trees are utilized in stochastic programming in pricing contingent claims
defined on the state space, the problem reduces to one of solving a generalized
moment problem at each node. Appealing to well-known bounding results from the
literature, I propose a simplicial upper bounding procedure based on recursive
backward dynamic programming. This serves to formalize the known results as well as
to provide extensions. The results are also generalized to the case of when
proportional transactions costs are present for trading. With increased transactions
costs, it is shown why arbitrage-free multi-period (state price) scenario trees are
easier to generate. Geometric interpretations and preliminary computational results
will be presented.
Living in a Carbon-based World: CO2 and its impact on the EU Power
The EU ETS, launched in 2005, is the world's first
international cap-and trade system governing CO2 emissions. The
consideration of carbon, its pricing and business implications has
become key for all actors in the European power sector, from board
level to the trading floor. As well as providing a brief background and
overview of the EU ETS and European power market(s), this talk will
provide a personal experience of the changing impact of the ETS on the
pricing of power, investment decisions, trading, hedging and analysis.
We will also touch briefly on some key trends driving developments
within the EU energy complex, of which CO2 forms an integral part.
Predictive and prescriptive analytics of grid defection
There are many rapidly emerging technologies becoming available to electricity consumers around the world in economically viable form. These technologies allow for self-generation of electricity using green and renewable means (such as PV panels), storage (household batteries such as the Tesla Power Wall, or electric vehicles), demand response (smart meters and smart appliances) and/or combined heat and power units.
The common theme of these technologies is that unlike large hydro, wind, or thermal power plants, they do not benefit from economies of scale and they are economically viable for individual households. As electricity consumers move towards self-generation and storage, utilization of the electricity grid will decrease. This project is aimed at constructing a suite of analytics tools that can be easily utilized to assess the impact of various scenarios of uptake of technology and demand response on the grid, transmission pricing and any natural consequences of under utilization of the grid.
Investment Models for Electricity Systems under Uncertainty
Investment in electricity generation and transmission is subject to considerable uncertainty. In the short term, wind intermittency means that the investment mix must include enough fast ramping and peaking plant to cover shortfalls in capacity when the wind does not blow. In the medium-term security of energy supply in dry winters needs to be included in provision and use of energy on an annual basis. Over the long term, variations in the rate and location of demand growth will necessitate robust investment plans to avoid poor choices of generator location and technology. The future of prices on carbon emissions also affects this choice. Finally, there is considerably regulatory uncertainty that affects electricity prices, and hence the income that generators earn from their investments. This PhD project will entail the development of a suite of stochastic integer programming models that integrate these sources of uncertainty into a system that can be used to study the long-term evolution of the New Zealand electricity market. These will build on and complement the GEMSTONE model developed by Giradeau and Philpott. This research is also linked to strategic models of investment.
Consultation on Transmission Pricing Methodology Second Issues Paper
(pdf) Consultation on Transmission Pricing Methodology Review TPM Options
(pdf) Decomposition in multistage stochastic integer programming (pdf)
NTNU Winter School, Passo Tonale, Italy
Mixed Integer Dynamic Approximation Scheme (pdf)
INFORMS Meeting, Nashville