price spike forecasting in a competitive day-ahead energy market

181 Pages·2013·5.43 MB·English

Sergey Voronin
PRICE SPIKE FORECASTING IN A COMPETITIVE
DAY-AHEAD ENERGY MARKET
Thesis for the degree of Doctor of Science (Technology) to be presented with
due permission for public examination and criticism in the Auditorium of the
Student Union House at Lappeenranta University of Technology, Lappeenranta,
Finland on the 1st of November, 2013, at noon.
Acta Universitatis
Lappeenrantaensis 530
Supervisor Professor Jarmo Partanen
Department of Electrical Engineering
Institute of Energy Technology (LUT Energy)
LUT School of Technology
Lappeenranta University of Technology
Finland
Reviewers Professor Risto Lahdelma
Department of Energy Technology
Aalto University
Finland
Professor Ivar Wangensteen
Department of Electric Power Engineering
Norwegian University of Science and Technology
Norway
Opponent Professor Risto Lahdelma
Department of Energy Technology
Aalto University
Finland
ISBN 978-952-265-461-8
ISBN 978-952-265-462-5 (PDF)
ISSN-L 1456-4491
ISSN 1456-4491
Lappeenrannan teknillinen yliopisto
Yliopistopaino 2013
Abstract
Sergey Voronin
Price spike forecasting in a competitive day-ahead energy market
Lappeenranta 2013
177 pages
Acta Universitatis Lappeenrantaensis 530
Diss. Lappeenranta University of Technology
ISBN 978-952-265-461-8, ISBN 978-952-265-462-5 (PDF), ISSN-L 1456-4491,
ISSN 1456-4491
Electricity price forecasting has become an important area of research in the aftermath
of the worldwide deregulation of the power industry that launched competitive
electricity markets now embracing all market participants including generation and
retail companies, transmission network providers, and market managers.
Based on the needs of the market, a variety of approaches forecasting day-ahead
electricity prices have been proposed over the last decades. However, most of the
existing approaches are reasonably effective for normal range prices but disregard price
spike events, which are caused by a number of complex factors and occur during
periods of market stress.
In the early research, price spikes were truncated before application of the forecasting
model to reduce the influence of such observations on the estimation of the model
parameters; otherwise, a very large forecast error would be generated on price spike
occasions. Electricity price spikes, however, are significant for energy market
participants to stay competitive in a market. Accurate price spike forecasting is
important for generation companies to strategically bid into the market and to optimally
manage their assets; for retailer companies, since they cannot pass the spikes onto final
customers, and finally, for market managers to provide better management and planning
for the energy market.
This doctoral thesis aims at deriving a methodology able to accurately predict not only
the day-ahead electricity prices within the normal range but also the price spikes. The
Finnish day-ahead energy market of Nord Pool Spot is selected as the case market, and
its structure is studied in detail.
It is almost universally agreed in the forecasting literature that no single method is best
in every situation. Since the real-world problems are often complex in nature, no single
model is able to capture different patterns equally well. Therefore, a hybrid
methodology that enhances the modeling capabilities appears to be a possibly
productive strategy for practical use when electricity prices are predicted.
The price forecasting methodology is proposed through a hybrid model applied to the
price forecasting in the Finnish day-ahead energy market. The iterative search procedure
employed within the methodology is developed to tune the model parameters and select
the optimal input set of the explanatory variables.
The numerical studies show that the proposed methodology has more accurate behavior
than all other examined methods most recently applied to case studies of energy markets
in different countries. The obtained results can be considered as providing extensive and
useful information for participants of the day-ahead energy market, who have limited
and uncertain information for price prediction to set up an optimal short-term operation
portfolio.
Although the focus of this work is primarily on the Finnish price area of Nord Pool
Spot, given the result of this work, it is very likely that the same methodology will give
good results when forecasting the prices on energy markets of other countries.
Keywords: day-ahead electricity prices, price spikes, feature selection, hybrid
methodology
UDC 621.3:658.8.011.1:338.534:51.001.57:519.2
Acknowledgements
This study was carried out at the Department of Electrical Engineering, Institute of
Energy Technology (LUT Energy) at Lappeenranta University of Technology) between
2009 and 2013.
First of all, I would like to express my deepest gratitude to my supervisor Professor
Jarmo Partanen for his valuable guidance and giving me an opportunity to be his
student.
I thank the preliminary examiners of this doctoral thesis, Professor Risto Lahdelma
from Aalto University and Professor Ivar Wangensteen from Norwegian University of
Science and Technology for examining the manuscript and giving fruitful comments,
which have significantly enhanced the work.
I would express my thanks Dr. Hanna Niemelä and Peter Jones for improving the
language of the thesis and the journal papers.
I would like to thank all the LUT colleagues who have helped me in making this
research a success. Special thanks are due to Dr. Matylda Jab(cid:225)(cid:82)(cid:276)ska and Dmitry
Kuleshov for discussions and valuable advices.
My sincere gratitude to all people who have created a perfect atmosphere during my
studying and living in Lappeenranta.
My special thanks go to my father Vyacheslav and mother Liudmila for their love and
support. This work would not be possible without their trust in me.
Finally, I express my gratitude to Polina for her love, great support and understanding
during the years.
Sergey Voronin
7th September 2013
Lappeenranta, Finland

Dedicated

to my beloved parents

9
Contents
Abstract
Acknowledgements
List of publications supporting the present monograph 13
Abbreviations 15
1 Introduction 17
1.1. Motivation and background .................................................................. 17
1.2. Objectives of the thesis ......................................................................... 19
1.3. Previous work ...................................................................................... 19
1.4. Forecasting time framework ................................................................. 21
1.5. Scientific contribution .......................................................................... 22
1.6. Outline of the thesis .............................................................................. 23
2 Nordic electricity market 24
2.1 Deregulation ......................................................................................... 24
2.2 Electricity as a commodity ................................................................... 25
2.3 Structure of the Nordic electricity market and price formation .............. 25
2.3.1 Elspot market ........................................................................... 26
2.3.2 System price ............................................................................. 26
2.3.3 Area price ................................................................................. 27
2.3.4 Elbas market ............................................................................. 30
2.3.5 Regulation power market .......................................................... 30
2.3.6 Financial market ....................................................................... 31
2.4 Electricity demand................................................................................ 31
2.5 Electricity supply ................................................................................. 34
3 Classical approaches to the modelling and forecasting of electricity
prices 37
3.1 Basic statistics of the Finnish day-ahead electricity prices .................... 37
3.2 Electricity price spikes ......................................................................... 40
3.3 Deterministic factors ............................................................................ 42
3.3.1 Trend and seasonality ............................................................... 42
3.3.2 External factors affecting the electricity prices in the Nordic
region ....................................................................................... 46
3.4 Linear regression .................................................................................. 47
3.4.1 Forecast evaluation methods ..................................................... 48
3.4.2 Regression model building ....................................................... 49
3.4.3 Summary .................................................................................. 51
3.5 The Box-Jenkins methodology ............................................................. 51
3.5.1 ARMA model ........................................................................... 51
10 Contents
3.5.2 Preparing Box-Jenkins models .................................................. 53
3.5.3 ARCH/GARCH modeling ........................................................ 53
3.5.4 Price modeling and forecasting with SARIMA+GARCH.......... 54
3.5.5 Summary .................................................................................. 58
3.6 Stochastic differential equations – Ornstein-Uhlenbeck process ............ 58
3.6.1 Stochastic process..................................................................... 58
3.6.2 Ornstein-Uhlenbeck process ..................................................... 58
3.6.3 Calibration of SDE ................................................................... 59
3.6.4 OU process to simulate electricity prices .................................. 59
3.6.5 OU process with colored noise ................................................. 61
3.6.6 OU process with colored noise to simulate electricity prices ..... 62
3.7 Regime-switching model ...................................................................... 63
3.7.1 Summary .................................................................................. 71
4 Combination of classical and modern forecasting approaches 72
4.1 NN ....................................................................................................... 72
4.2 Hybrid electricity price forecasting model ............................................ 75
4.2.1 Forecasting strategy .................................................................. 75
4.2.2 Normal price module ................................................................ 77
4.2.3 Price spike module ................................................................... 79
4.2.4 Normal range price forecasting results ...................................... 82
4.2.5 Price spike forecasting results ................................................... 84
4.2.6 Overall price prediction ............................................................ 88
4.2.7 Summary .................................................................................. 90
5 Tuning of the forecasting model parameters 91
5.1 Feature selection................................................................................... 91
5.2 Proposed search procedure to tune the model parameters ...................... 93
5.2.1 Tuning NN parameters ............................................................. 95
5.2.2 Linear and nonlinear feature selection techniques ..................... 98
5.3 Simultaneous forecasting electricity prices and demand ...................... 102
5.3.1 Wavelet transform .................................................................. 102
5.3.2 Forecasting time framework ................................................... 105
5.3.3 Forecasting strategy ................................................................ 105
5.3.4 Training phase ........................................................................ 107
5.3.5 Numerical results .................................................................... 110
5.3.6 Summary ................................................................................ 115
6 Iterative day-ahead price prediction with separate normal range
price and price spike forecasting frameworks 116
6.1 Description of the forecasting methodology. ....................................... 116
6.2 Electricity price spike extraction ......................................................... 117
6.3 Compound classifier ........................................................................... 118
6.4 Construction of the candidate input set ............................................... 119
6.4.1 Price spike forecasting: probability of spike occurrence .......... 119