Challenges in Assessing the Behaviour of Nodal Electricity Prices in Insular Electricity Markets: The Case of New Zealand
Abstract
:1. Introduction
2. Nodal, Zonal, and Uniform Pricing Systems: A Short Critical Literature Survey
3. Challenges to Assessing the Behaviour of Electricity Prices in NZ
3.1. Brief Overview of the Energy Mix in NZ
3.2. Challenges in Processing Big Data on Nodal Electricity Markets: The Suitability of Econometric Models
4. Discussion
5. Final Remarks
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
1 | The Python packages used to download, filter, and store data are the following: pandas, numpy, and datetime. |
2 | To obtain the database, each .csv file was downloaded (i.e., each day was individually downloaded), and then all days were compacted and stored in Python. |
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Zonal Electricity Pricing Mechanism | ||||
Authors | Period Analysed | Electricity Market | Method | Main Findings |
Macedo et al. (2021) | Daily data from January 2016 to April 2020 | Nord Pool Electricity Market (Sweden) | Seasonally Adjusted Autoregressive Moving Average (SARMA)/Generalized Autoregressive Conditional Heteroskedasticity (GARCH) | Wind power ↓ electricity prices, and this impact is similar in magnitude in the 24 h of the day. |
Maciejowska (2020) | Daily data from January 2015 to January 2018 | German | Quantile Regression Model | Solar and wind power ↓ electricity prices; their prominent impact occurs during peak prices. |
Sapio (2019) | Daily data from September 2006 to July 2015 | Italian | Quantile Regression Model | Solar and wind power electricity prices; solar power is likely to price volatility, more than wind; the establishment of a new cable for electricity transmission price volatility. |
Benhmad and Percebois (2018) | Hourly data from January 2012 to December 2015 | German | Seemingly unrelated regression | Solar PV and wind power ↓ electricity prices. The impact is likely to vary throughout the 24 h of the day. |
Papaioannou et al. (2018) | Daily data from January 2004 to December 2014 | Greek | SARMAX/(E)GARCH | This electricity market does not exhibit asymmetries (i.e., leverage or inverse leverage effect) in volatility of electricity prices. |
Gürtler and Paulsen (2018) | Hourly data from 2010 to 2016 | German | Fixed Effects Regression with Driscoll-Kraay estimator | Solar PV and wind power electricity prices; their impact is less prominent between 2013 and 2016, due to a in fuel prices. Reductions in forecast errors on electricity generation from wind and solar PV would decrease price volatility. |
de Lagarde and Lantz (2018) | Hourly data from 2014 to 2016 | German | Markov Switching Model | Solar PV and wind power electricity prices in both regimes, i.e., low and high prices; this negative impact is more pronounced in regimes of high electricity prices. |
Rintamäki et al. (2017) | Houry data from January 2010 to December 2014 (Denmark); Hourly data from January 2012 to December 2014 (Germany) | German and Danish (Nord Pool Electricity Market) | SARMA | Wind power ↓ electricity prices both in Germany and Denmark. In Denmark, price volatility is lower when wind production increases, while high in Germany; these contrasting results are related to flexible power generation capacity in each country. |
Nodal Electricity Pricing Mechanism | ||||
Authors | Period | Electricity Market | Method | Main Findings |
Csereklyei et al. (2019) | 30-min and daily data from November 2010 to June 2018 | Australian | Autoregressive distributed lag regression model | Wind power and solar PV electricity prices-min estimations); increased electricity dispatched by wind power is associated with a lower magnitude of the MOE from solar PV. |
Paul et al. (2017) | N/A | Australian | Simulation/sensitivity analysis using ANEM model | Wind power wholesale electricity prices, albeit retail electricity prices; underinvestment in interconnection capacity is the main argument for wind power not causing a further in electricity prices. Reliable capacity of interconnections is highly required in nodal pricing systems. |
Woo et al. (2016) | Hourly data from December 2012 to April 2015 | Californian Independent System Operator | Iterated Seemingly Unrelated Regression | Real-time and day-ahead electricity prices seems to not converge mainly due to day-ahead forecast errors. |
Forrest and Macgill (2013) | 30-min data from March 2009 to February 2011. | Australian | Tobit Model | Wind power electricity prices; larger levels of electricity produced from wind power will likely intensify the downward pressure in electricity prices and reduce the incentives for new wind power players to enter the market. |
Uniform Electricity Pricing Mechanism | ||||
Authors | Period | Electricity Market | Method | Main Findings |
Macedo et al. (2022) | Daily data from May 2015 to December 2020. | Iberian (Spain) | SARMAX/GARCH | Wind power and solar PV electricity prices at most times of the day; the magnitude of these impacts varies substantially for each of the 24 h. |
Macedo et al. (2020) | Daily data from January 2011 to September 2019. | Iberian (Portugal) | SARMAX/GARCH | Wind power and solar PV electricity prices, while increasing its volatility. A leverage effect was confirmed. |
NI | SI | |
---|---|---|
Variables | Mean Equation | |
LCONS | −0.3199 *** | 1.1861 *** |
LWIND | −0.0965 *** | −0.0065 *** |
LHYDRO | 0.5901 *** | −0.1928 *** |
ω | 4.1054 *** | −3.5858 *** |
AR(1) | 0.9753 *** | 0.9770 *** |
SAR(24) | 0.0865 *** | 0.0883 *** |
MA(1) | −0.4089 *** | −0.4286 *** |
SMA(1) | 0.3422 *** | 0.3601 *** |
Variance Equation | ||
C | −0.0610 *** | −0.0495 ** |
α | 1.1627 *** | 1.1764 *** |
β | 0.3882 *** | 0.4388 *** |
LWIND | −0.0006 | 0.0004 * |
LCONS | 0.0162 *** | 0.0101 *** |
LHYDRO | −0.0092 *** | −0.0027 |
Diagnostic Tests | ||
R2 | 0.8443 | 0.8524 |
AIC | −0.5504 | −0.5580 |
SIC | −0.5455 | −0.5545 |
QLB(24) | 451.61 [0.000] | 652.89 [0.000] |
QLB2(24) | 117.35 [0.000] | 212.18 [0.000] |
ARCH(24) | 4.9840 [0.000] | 9.0092 [0.000] |
Inverted AR Roots | Stationary | Stationary |
Inverted MA Roots | Stationary | Stationary |
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Macedo, D.P.; Marques, A.C.; Damette, O. Challenges in Assessing the Behaviour of Nodal Electricity Prices in Insular Electricity Markets: The Case of New Zealand. Economies 2023, 11, 159. https://doi.org/10.3390/economies11060159
Macedo DP, Marques AC, Damette O. Challenges in Assessing the Behaviour of Nodal Electricity Prices in Insular Electricity Markets: The Case of New Zealand. Economies. 2023; 11(6):159. https://doi.org/10.3390/economies11060159
Chicago/Turabian StyleMacedo, Daniela Pereira, António Cardoso Marques, and Olivier Damette. 2023. "Challenges in Assessing the Behaviour of Nodal Electricity Prices in Insular Electricity Markets: The Case of New Zealand" Economies 11, no. 6: 159. https://doi.org/10.3390/economies11060159