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Keywords = alberta energy markets

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28 pages, 4199 KiB  
Article
Toward Sustainable Electricity Markets: Merit-Order Dynamics on Photovoltaic Energy Price Duck Curve and Emissions Displacement
by Gloria Durán-Castillo, Tim Weis, Andrew Leach and Brian A. Fleck
Sustainability 2025, 17(10), 4618; https://doi.org/10.3390/su17104618 - 18 May 2025
Viewed by 861
Abstract
This paper examines how the slope of the merit-order curve and the share of non-zero-dollar dispatched energy affect photovoltaic (PV) price cannibalization and the declining market value of all generation types. Using historical merit-order data from Alberta, Canada—during its coal-to-gas transition—we simulated the [...] Read more.
This paper examines how the slope of the merit-order curve and the share of non-zero-dollar dispatched energy affect photovoltaic (PV) price cannibalization and the declining market value of all generation types. Using historical merit-order data from Alberta, Canada—during its coal-to-gas transition—we simulated the introduction of zero-marginal-cost PV offers. The increased PV penetration rapidly suppresses midday electricity prices, forming a “duck curve” that challenges solar project economics. Emission reductions improve with rising carbon prices, indicating environmental benefits despite declining market revenues. Years with steeper merit-order slopes and lower non-zero-dollar dispatch shares show intensified price cannibalization and a reduced PV market value. The integration of battery storage alongside PV significantly flattened daily price profiles—raising the trough prices during charging and lowering the highest prices during discharging. While this reduces price volatility, it also diminishes the market value of all generation types, as batteries discharge at zero marginal cost during high-price hours. Battery arbitrage remains limited in low- and moderate-price regimes but becomes more profitable under high-price regimes. Overall, these dynamics underscore the challenges of integrating large-scale PV in energy-only markets, where price cannibalization erodes long-term investment signals for clean energy technologies. These insights inform sustainable energy policy design aimed at supporting decarbonization, and investment viability in liberalized electricity markets. Full article
(This article belongs to the Special Issue Sustainable Development of Renewable Energy Resources)
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20 pages, 3186 KiB  
Article
A Wind Offset Paradox: Alberta’s Wind Fleet Displacing Greenhouse Gas Emissions and Depressing Future Offset Values
by Faith Nobert, Tim Weis, Andrew Leach and Sergi Arús García
Wind 2025, 5(1), 2; https://doi.org/10.3390/wind5010002 - 24 Jan 2025
Viewed by 1110
Abstract
The introduction of a significant industrial carbon price in Alberta, Canada, has precipitated major changes in its electricity market, both for fossil fuel generators, which has resulted in a rapid transition from coal to natural gas, as well as for renewable energy projects, [...] Read more.
The introduction of a significant industrial carbon price in Alberta, Canada, has precipitated major changes in its electricity market, both for fossil fuel generators, which has resulted in a rapid transition from coal to natural gas, as well as for renewable energy projects, which can monetize emission offset credits. Coal, which generated close to half of the electricity in the province in 2016 before the major changes were introduced, had fallen to less than 8 percent by the end of 2023 and was completely phased out by June 2024. Conversely, wind energy grew from 6 to 12 percent of the annual supply, in part due to the increasing value of the carbon credits whose value is connected to the deemed greenhouse emissions they are displacing. As wind energy increased in penetration, it lowered its own market price, which was discounted from the average market price by 10–43 percent, but in turn increased the relative importance of its offset. This paper examines the evolution of emissions displaced by wind energy in Alberta by considering 10 years of historical merit order data and creating a counterfactual scenario where historical wind generation is replaced by next-in-merit units. On average, coal made up 84 percent of the marginal energy and 93 percent of the marginal emissions in 2018. As the coal capacity declined, natural gas units replaced coal on the margins, jumping from 21 percent of next-in-merit generation in 2020 to 84 percent in 2023. Alberta uses a deemed emissions displacement factor, which is a combination of historical build and operating margins that declined from 0.65 tCO2e/MWh in 2010 to 0.52 tCO2e/MWh in 2023. Using the counterfactual scenario, an alternative offset value is considered, which had a maximum difference of 57 percent (9 CAD/MWh) of increased value over the actual historical offset. However, the counterfactual rate of emission offsets fell to near parity with the deemed grid displacement factor by 2022 as natural gas became increasingly dominant in the market. As the carbon price is scheduled to increase from 65 CAD/tCO2e in 2023 to 170 CAD/tCO2e by 2030, the provincial offset could reach a maximum value of 53 CAD/MWh in 2030 but begin to decline thereafter as the carbon price drives decarbonization, thereby lowering displaced emissions in either method of calculation. The introduction of significant carbon pricing into a thermally dominated electricity market resulted in more emissions being displaced by renewable energy than they were credited for in the short term, but the resultant decarbonization of the grid decreases the long-term value of emission offsets. Full article
(This article belongs to the Topic Market Integration of Renewable Generation)
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19 pages, 474 KiB  
Article
BPET: A Unified Blockchain-Based Framework for Peer-to-Peer Energy Trading
by Caixiang Fan, Hamzeh Khazaei and Petr Musilek
Future Internet 2024, 16(5), 162; https://doi.org/10.3390/fi16050162 - 7 May 2024
Cited by 8 | Viewed by 1957
Abstract
Recent years have witnessed a significant dispersion of renewable energy and the emergence of blockchain-enabled transactive energy systems. These systems facilitate direct energy trading among participants, cutting transmission losses, improving energy efficiency, and fostering renewable energy adoption. However, developing such a system is [...] Read more.
Recent years have witnessed a significant dispersion of renewable energy and the emergence of blockchain-enabled transactive energy systems. These systems facilitate direct energy trading among participants, cutting transmission losses, improving energy efficiency, and fostering renewable energy adoption. However, developing such a system is usually challenging and time-consuming due to the diversity of energy markets. The lack of a market-agnostic design hampers the widespread adoption of blockchain-based peer-to-peer energy trading globally. In this paper, we propose and develop a novel unified blockchain-based peer-to-peer energy trading framework, called BPET. This framework incorporates microservices and blockchain as the infrastructures and adopts a highly modular smart contract design so that developers can easily extend it by plugging in localized energy market rules and rapidly developing a customized blockchain-based peer-to-peer energy trading system. Additionally, we have developed the price formation mechanisms, e.g., the system marginal price calculation algorithm and the pool price calculation algorithm, to demonstrate the extensibility of the BPET framework. To validate the proposed solution, we have conducted a comprehensive case study using real trading data from the Alberta Electric System Operator. The experimental results confirm the system’s capability of processing energy trading transactions efficiently and effectively within the Alberta electricity wholesale market. Full article
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26 pages, 1180 KiB  
Article
Stochastic Modeling of Wind Derivatives with Application to the Alberta Energy Market
by Sudeesha Warunasinghe and Anatoliy Swishchuk
Risks 2024, 12(2), 18; https://doi.org/10.3390/risks12020018 - 23 Jan 2024
Cited by 1 | Viewed by 2415
Abstract
Wind-power generators around the world face two risks, one due to changes in wind intensity impacting energy production, and the second due to changes in electricity retail prices. To hedge these risks simultaneously, the quanto option is an ideal financial tool. The natural [...] Read more.
Wind-power generators around the world face two risks, one due to changes in wind intensity impacting energy production, and the second due to changes in electricity retail prices. To hedge these risks simultaneously, the quanto option is an ideal financial tool. The natural logarithm of electricity prices of the study will be modeled with a variance gamma (VG) and normal inverse Gaussian (NIG) processes, while wind speed and power series will be modeled with an Ornstein–Uhlenbeck (OU) process. Since the risk from changing wind-power production and spot prices is highly correlated, we must model this correlation as well. This is reproduced by replacing the small jumps of the Lévy process with a Brownian component and correlating it with wind power and speed OU processes. Then, we will study the income of the wind-energy company from a stochastic point of view, and finally, we will price the quanto option of European put style for the wind-energy producer. We will compare quanto option prices obtained from the VG process and NIG process. The novelty brought into this study is the use of a new dataset in a new geographic location and a new Lévy process, VG, apart from NIG. Full article
(This article belongs to the Special Issue Risks: Feature Papers 2023)
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30 pages, 7109 KiB  
Review
Overview of Some Recent Results of Energy Market Modeling and Clean Energy Vision in Canada
by Anatoliy Swishchuk
Risks 2023, 11(8), 150; https://doi.org/10.3390/risks11080150 - 14 Aug 2023
Viewed by 5627
Abstract
This paper overviews our recent results of energy market modeling, including The option pricing formula for a mean-reversion asset, variance and volatility swaps on energy markets, applications of weather derivatives on energy markets, pricing crude oil options using the Lévy processes, energy contracts [...] Read more.
This paper overviews our recent results of energy market modeling, including The option pricing formula for a mean-reversion asset, variance and volatility swaps on energy markets, applications of weather derivatives on energy markets, pricing crude oil options using the Lévy processes, energy contracts modeling with delayed and jumped volatilities, applications of mean-reverting processes on Alberta energy markets, and alternatives to the Black-76 model for options valuation of futures contracts. We will also consider the clean renewable energy prospective in Canada, and, in particular, in Alberta and Calgary. Full article
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23 pages, 603 KiB  
Article
A Hybrid Model for Multi-Day-Ahead Electricity Price Forecasting considering Price Spikes
by Daniel Manfre Jaimes, Manuel Zamudio López, Hamidreza Zareipour and Mike Quashie
Forecasting 2023, 5(3), 499-521; https://doi.org/10.3390/forecast5030028 - 19 Jul 2023
Cited by 13 | Viewed by 6449
Abstract
This paper proposes a new hybrid model to forecast electricity market prices up to four days ahead. The components of the proposed model are combined in two dimensions. First, on the “vertical” dimension, long short-term memory (LSTM) neural networks and extreme gradient boosting [...] Read more.
This paper proposes a new hybrid model to forecast electricity market prices up to four days ahead. The components of the proposed model are combined in two dimensions. First, on the “vertical” dimension, long short-term memory (LSTM) neural networks and extreme gradient boosting (XGBoost) models are stacked up to produce supplementary price forecasts. The final forecasts are then picked depending on how the predictions compare to a price spike threshold. On the “horizontal” dimension, five models are designed to extend the forecasting horizon to four days. This is an important requirement to make forecasts useful for market participants who trade energy and ancillary services multiple days ahead. The horizontally cascaded models take advantage of the availability of specific public data for each forecasting horizon. To enhance the forecasting capability of the model in dealing with price spikes, we deploy a previously unexplored input in the proposed methodology. That is, to use the recent variations in the output power of thermal units as an indicator of unplanned outages or shift in the supply stack. The proposed method is tested using data from Alberta’s electricity market, which is known for its volatility and price spikes. An economic application of the developed forecasting model is also carried out to demonstrate how several market players in the Alberta electricity market can benefit from the proposed multi-day ahead price forecasting model. The numerical results demonstrate that the proposed methodology is effective in enhancing forecasting accuracy and price spike detection. Full article
(This article belongs to the Collection Energy Forecasting)
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24 pages, 1534 KiB  
Article
Modelling of Fuel- and Energy-Switching Prices by Mean-Reverting Processes and Their Applications to Alberta Energy Markets
by Weiliang Lu, Alexis Arrigoni, Anatoliy Swishchuk and Stéphane Goutte
Mathematics 2021, 9(7), 709; https://doi.org/10.3390/math9070709 - 25 Mar 2021
Cited by 5 | Viewed by 2591
Abstract
This paper introduces a fuel-switching price to the Alberta market, which is designed for encouraging power plant companies to switch from coal to natural gas when they produce electricity; this has been successfully applied to the European market. Moreover, we consider an energy-switching [...] Read more.
This paper introduces a fuel-switching price to the Alberta market, which is designed for encouraging power plant companies to switch from coal to natural gas when they produce electricity; this has been successfully applied to the European market. Moreover, we consider an energy-switching price which considers power switch from natural gas to wind. We modeled these two prices using five mean reverting processes including a Regime-switching processes, Lévy-driven Ornstein–Uhlenbeck process and Inhomogeneous Geometric Brownian Motion, and estimate them based on multiple procedures such as Maximum likelihood estimation and Expectation-Maximization algorithm. Finally, this paper proves previous results applied to the Albertan Market where the jump modeling technique is needed when modeling fuel-switching data. In addition, it not only gives promising conclusions on the necessity of introducing Regime-switching models to the fuel-switching data, but also shows that the Regime-switching model is better fitted to the data. Full article
(This article belongs to the Special Issue New Trends in Random Evolutions and Their Applications)
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