Alberta Electricity Research Articles

BPET: A Unified Blockchain-Based Framework for Peer-to-Peer Energy Trading

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.

The mid-transition in the electricity sector: impacts of growing wind and solar electricity on generation costs and natural gas generation in Alberta

In response to climate change, electricity grids are decreasing their carbon intensity with the addition of wind and solar variable renewable energy generation (VREN). This leads to a mid-transition period, where renewable energy is unable to satisfy electricity demand without contributions from other fossil sources such as natural gas, but also generates sufficiently to constrain conventional generation—changing their operating and market conditions. We use a simplified copper plate model, which scales up and down historical wind and solar generation, to examine how and when the patterns and generation costs for fossil fuel power could change by the increasing capacities of VREN on the relatively isolated Alberta electricity grid. We find that beginning at 20% VREN an increasingly diverse range and reduced hours of dispatched capacity is necessitated from the existing generation. However, even as capacity factors for fossil fuel generation decrease their costs remain reasonable and we found this to be a low-cost pathway for achieving moderate to deep emission reduction goals. A full 86% of demand could be met with VREN before generation costs exceeded 100$/MWh, allowing for an emissions reduction of 28.4–9 million tonnes yr−1 of CO2eq, on a lifecycle basis. In order to integrate the renewable generation, new and existing fossil fuel units will require market rules that incentivise flexibility and ensure they remain in place throughout the transitionary period as they are crucial to balance variable renewable generation.

A Hybrid Model for Multi-Day-Ahead Electricity Price Forecasting considering Price Spikes

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.

Calibration of Grid Models for Analyzing Energy Policies

Intermittent forms of renewable energy destabilize electricity grids unless adequate reliable generating capacity and storage are available, while instability of hybrid electricity grids and cost fluctuations in fossil fuel prices pose further challenges for policymakers. We examine the interaction between renewable and traditional fossil-fuel energy sources in the context of the Alberta electricity grid, where policymakers seek to eliminate coal and reduce reliance on natural gas. We develop a policy model of the Alberta grid and, unlike earlier models, calibrate the cost functions of thermal generation using positive mathematical programming. Rather than employing constant average and marginal costs, calibration determines upward sloping supply (marginal cost) functions. The calibrated model is then used to determine an optimal generation mix under different assumptions regarding carbon prices and policies to eliminate coal-fired capacity. Results indicate that significant wind capacity can enter the Alberta grid if carbon prices are high, but that it remains difficult to eliminate reliable baseload capacity. Adequate baseload coal and/or natural gas capacity is required, which is the case even if battery storage is allowed into the system. Further, significant peak-load gas capacity will also be required to backstop intermittent renewables.

Alberta's Renewable Energy Market: Developing Contract Issues

Governments and corporations around the world have increasingly demonstrated leadership in tackling climate change, resulting in new decarbonization policies and initiatives and new corporate environmental, social, and governance mandates. At the same time, wind and solar capital costs have decreased and new technologies, such as battery storage, are becoming more prevalent, thereby increasing the flexibility and utility of renewable energy generation. Developers and investors have already expended significant capital to further the development of renewable generation with, according to the International Energy Agency, renewable energy investment reaching its highest level ever in 2021 — $367 billion internationally. This trend is only expected to increase if the global decarbonization goals are to be realized. Within Canada, Alberta is at the forefront of renewable energy development due in part to its unique power industry structure — a deregulated, competitive, wholesale power market with non-discriminatory system access — which has attracted corporate off-takers and buoyed investment in renewable energy projects in the province, supported by corporate power purchase agreements. The pace of development is striking, and Alberta has rapidly become a preferred destination for renewable energy investment. The Alberta Electric System Operator says that 22 percent of the energy generated in Alberta in 2021 came from renewable energy sources. This is predictedto rise sharply — for instance, there are 61 solar projects currently under development in Alberta that could be completed by the middle of the decade. This article examines several emerging contracting and development challenges currently facing renewable energy projects in Alberta, and aims to provide concrete, practical advice on several discrete contractual issues. Topics covered include, among others: (1) force majeure issues faced by project developers; (2) diverse carbon offset/renewable attributes programs and implications for power purchase arrangements; (3) developments pertaining to ethical procurement, including forced labour and the use of tracing protocols; and (4) the need for increased flexibility in contractual arrangements to accommodate the pairing of wind and solar with storage resources. Although this article is based primarily on experiences gained while developing renewable energy projects in Alberta, the analyses and solutions presented can be applied in many jurisdictions.

Prediction of electricity prices for non‐regulated markets based on a power transformed mean reverting process

AbstractThe electricity price time series for non‐regulated markets presents two basic properties: (a) a mean reversion trend around a constant or deterministic function and (b) a price process with high volatility and marginal, conditional, and asymptotic distributions skewed to the right. We propose a microeconomic‐based model for the dynamics of electricity prices that provides a satisfactory explanation of the stylized features observed in non‐regulated electricity markets. The suggested model is based on a power transformation of the Ornstein–Uhlenbeck process, which accounts for the unobserved demand process. This means that the model requires only the spot price of the electricity price to be available. Parameter estimates were obtained using the maximum likelihood method. This approach was implemented for data from the Alberta electricity market in Canada, with satisfactory results in terms of residual analysis and forecasting. The success rate of predicting the price jump sign (upward and downward) was approximately . Moreover, the statistical properties for the maximum likelihood parameter estimators are shown via a Monte Carlo simulation of the electricity price time series.

Pricing Patterns in Wholesale Electricity Markets: Unilateral Market Power or Coordinated Behavior?*

AbstractWe examine allegations that firms in Alberta's electricity industry manipulated public information to coordinate in the wholesale market. We investigate whether bids by firms who employed unique pricing patterns were consistent with unilateral expected profit maximization. Our results suggest that these firms could have increased expected profits through unilateral deviations. For one firm, the potential to increase profits is greater on days when certain offer patterns are observed, providing support for the claim that such patterns may have assisted coordination on high‐priced outcomes. These results suggest that regulators should exercise caution when designing information disclosure policies in concentrated electricity markets.

Energy Storage: The Regulatory Landscape in Alberta

Energy storage technologies are increasingly being deployed in Alberta. In the recent past, costs were the largest hurdle to widespread energy storage deployment. But this is changing given falling battery prices. Indeed, the Alberta Electric System Operator (AESO) and the Alberta Utilities Commission (AUC) processes are increasingly considering energy storage development and potential but within the scope of existing legislation and its policy framework. Alberta’s traditional model of electricity regulation is based on generators supplying electricity to load customers for consumption and does not directly contemplate the unique attributes of energy storage. These attributes include the flexibility of customers to switch between supply and load, such as where a customer discharges a battery into the grid during peak hours and charges the battery during off-peak hours. Energy market participants and policy-makers need to consider the use of flexible resources in an evolving electricity industry where distributed and intermittent power sources are increasingly prominent. Energy storage is playing a key role in this ongoing evolution. To that end, this article seeks to provide practitioners and industry stakeholders guidance on the current state of the Alberta regulatory landscape applicable to energy storage and anticipated changes. Specifically, this article sets out the regulatory framework applicable to, and policy issues raised by, energy storage, including tariffs and competitive market issues, the concept of “hybrid sites” and self-supply and export issues, and AUC decisions approving the deployment of energy storage. As to how the landscape may change, this article looks at recent policy statements by the AUC and the AESO describing potential changes on the horizon.

LS-LSTM-AE: Power load forecasting via Long-Short series features and LSTM-Autoencoder

Aiming at weak representation ability and severe loss of time series features in the traditional methods when facing large-scale and complex power load forecasting tasks, an LSTM-Autoencoder model that integrates long-term and short-term features of the samples is proposed for load forecasting. The encoder part simultaneously receives long time series and short time series as input to extract time series features of different levels and generate related latent vectors. The decoder tries to reconstruct the input sequence while outputting the prediction results to ensure that the latent vectors are meaningful. In addition, the model also uses a mixture of supervised and unsupervised training methods. Experiments based on a publicly available dataset from Alberta Electric System Operator showed that the method presented in this research is superior to many existing mainstream methods, with a mean absolute error of less than 52MW between the prediction results and the actual load values.

Developing a framework to assess the long-term adoption of renewable energy technologies in the electric power sector: The effects of carbon price and economic incentives

Greenhouse gas emissions from the burning of fossil fuels are one of the main causes of anthropogenic climate change. Large-scale deployment of renewable energy can play an immense role in transforming the global energy system and mitigating the emissions. This paper describes the development of a novel framework called MArket Penetration ModeLing of Renewable Energy Technologies in Electric Power Sector (MAPLET-PS). MAPLET-PS assesses the impacts of policy measures such as carbon price and financial incentives on the adoption of renewable energy technologies. The framework was used to develop a case study for the electric power sector of Alberta, a fossil-dominated province in Western Canada. The results show that implementing a carbon price on fossil fuel electric power sources and incentives for renewable energy, along with the phase-out of coal-fired electricity generation, can mitigate 29% of Alberta's electricity sector 2020 GHG emissions by 2050 and reduce GHG emissions from 46.5 Mt of CO2 eq. in 2020 to 23.6 and 29.1 Mt of CO2 eq. per year in 2030 and 2050, respectively, in Alberta. Moreover, these changes can increase the share of renewable energies from 12.5% in 2018 to 30% in 2050. These rates can be achieved by implementing a carbon price along with a 1000 $ incentive per kW new capacity development and 70 $ incentive per MWh electric power generation from renewable sources, from 2021 to 2025, primarily from wind turbines.

Offer Price Information and the Exercise of Market Power: The Effect of the Publication of the Historical Trading Report on Competition in the Alberta Electricity Market

This paper considers the effect of the publication of offer price information on unilateral market power in Alberta’s electricity market. This market is an hourly auction characterized by repeated interaction among a small number of producers, common knowledge of costs and production capabilities, and price inelastic demand. For the period July 13, 2000 to May 18, 2017, offer prices for each hour were published by the market operator, the Alberta Electric System Operator (AESO), in the Historical Trading Report (HTR) after the end of the hour. Using counter-factual analysis from 2010 to 2015 (52,584 hours), the paper finds that the effect of offer price changes after the HTR publication was to raise the average hourly price for electricity in Alberta by $2.48/MWh or about 4.2%, which raised the cost of electricity for Alberta consumers during the six-year period by approximately $1.14 billion. Based on an earlier version of this paper, the AESO was instructed by Alberta’s utilities regulator to cease publication of the HTR.

The Economics of the Site C Hydroelectric Project in British Columbia

The Site C hydroelectric project is an 1100-Megawatt hydroelectric facility under development on the Peace River in British Columbia. Construction costs for the project are currently estimated to be $10.7 billion. The project has been criticized for its high cost and the impacts it will have on the river system, including the flooding of land to establish the reservoir. Project proponents argue that Site C will provide low-carbon electricity that will help BC and Canada reach their greenhouse gas emission reduction goals. In this paper, we assess the cost-effectiveness of the Site C project using a detailed linear programming capacity expansion and dispatch model of the British Columbia and Alberta electricity systems. Our model includes hourly electricity demand data for sixteen sub-regions within BC and Alberta, hourly wind resource data for 681 grid cells in the two provinces, hourly solar irradiation data from 112 weather stations to characterize the solar resource, and a detailed hydroelectric model that accounts for the chaining of hydroelectric facilities and reservoirs within the Peace River, Columbia River, Kootenay River and Bridge River watersheds. We assess the value of the Site C project by calculating the optimal electricity system for supplying BC and Alberta electricity demand in 2030 both with and without the presence of the Site C project. Our model simulations reveal that under current electricity sector policies, the Site C project is uneconomic. We find that the value created by Site C only exceeds its costs in scenarios where BC and Alberta coordinate electricity markets, build additional transmission capacity, and work to reduce greenhouse gas emissions significantly below baseline levels.

Benefits of Strategically Sizing Wind-Integrated Energy Storage and Transmission

We examine the behavior of a strategic firm that invests-in and operates wind, energy storage, and transmission. The capacity of the energy storage and transmission are co-optimized with the firm's wind-supply and energy-storage offers into a centrally dispatched electricity market. We employ a bi-level stochastic optimization model. The upper level determines the capacities and offering strategies to maximize the firm's expected profits. Multiple lower-level problems represent market clearing under different operating conditions, which capture uncertainties. The resulting large-scale optimization model is solved using multi-cut Benders's decomposition. The model is applied to a case study that is based on Alberta's electricity market.

Offer Price Information and the Exercise of Market Power: The Effect of the Publication of the Historical Trading Report on Competition in the Alberta Electricity Market

This paper considers the effect of the publication of offer price information on unilateral market power in Alberta’s electricity market. This market is an hourly auction characterized by repeated interaction among a small number of producers, common knowledge of costs and production capabilities, and price inelastic demand. For the period July 13, 2000 to May 18, 2017, offer prices for each hour were published by the market operator, the Alberta Electric System Operator (AESO), in the Historical Trading Report (HTR) after the end of the hour. Using counter-factual analysis from 2010 to 2015 (52,584 hours), the paper finds that the effect of offer price changes after the HTR publication was to raise the average hourly price for electricity in Alberta by $2.48/MWh or about 4.2%, which raised the cost of electricity for Alberta consumers during the six-year period by approximately $1.14 billion. Based on an earlier version of this paper, the AESO was instructed by Alberta’s utilities regulator to cease publication of the HTR.

Evaluation of energy and GHG emissions’ footprints of bitumen extraction using Enhanced Solvent Extraction Incorporating Electromagnetic Heating technology

Enhanced Solvent Extraction Incorporating Electromagnetic Heating is an emerging technique for in situ oil sands extraction. A bottom-up method based on first engineering principles was conducted to determine the greenhouse gas emissions and energy requirements of the process. Two pathways in which to reduce solvent loss and energy use by the process were investigated. In Pathway I, the produced gases are captured to minimize solvent losses, while Pathway II considers their use as fuel. Energy scenarios were developed to analyze the overall impacts of the electricity sources. A case for Alberta, Canada, was conducted to study the impacts of electricity consumption on emission performance. The current Alberta electricity mix, future Alberta electricity mix (to 2030), 100% renewable electricity (from biomass), and cogeneration were the energy scenarios considered. The results show that energy consumption and greenhouse gas emissions for Pathways I and II are 487.2 and 375.5 MJ/bbl, and 77.7 and 59.77 kg CO2 eq/bbl of bitumen, respectively. Electricity use by the antenna for heating is the key contributor. The electricity-from-biomass scenario offers the highest emission reduction, 83.0% below Alberta's current electricity mix. Improvement in the antenna performance and cleaner electricity generation would reduce environmental impacts.

How big a battery?

Countries are investing in renewable energy such as wind or solar to meet electricity demand and minimize CO2 emissions. Due to the intermittent nature of these energy sources, back-up generating assets or adequate storage is needed to meet peak demand. In this study, we evaluate the extent to which an electricity grid can rely on intermittent renewable energy (wind and solar) if a ‘black box’ battery is used for storage. We examine the potential for 100% generation from wind and solar, as well as scenarios that are consistent with a policy to eliminate coal generation. A constrained optimization model is used that minimizes thermal generating capacity and battery size subject to load and battery constraints. Our application is to the fossil-fuel dependent Alberta electricity system. Our results suggest that it might be difficult and costly to supply the grid with 100% renewable energy. Replacing coal with renewable energy and using natural gas and a battery as back-up would be feasible, but would require a very large battery and result in high costs. Other storage methods should be considered to facilitate increased generation from renewable sources.

Optimal investment in electric generating capacity under climate policy

Mitigating climate change will require reduced use of fossil fuels to generate electricity. To do so while eschewing nuclear power, countries have turned to wind and solar energy. In this paper, load duration and screening curves are used to investigate the extent to which a jurisdiction should invest in intermittent (solar and wind) sources of energy, gas and nuclear power. The application is to the Alberta electricity grid because it is nearly 90 percent reliant on fossil fuels, particularly coal, and recent policy intends to eliminate coal generating capacity by 2030 and replace two-thirds of the lost capacity with renewables. Results suggest that solar and wind are unable to replace two-thirds of coal generation, and that larger than anticipated investments in natural gas capacity will be required. However, if Alberta is serious about reducing emissions, it will need to rely on nuclear energy; at high carbon prices, nuclear power could reduce CO2 emissions by some 95% compared to only 55% when relying totally on solar and wind.

A higher-order Markov chain-modulated model for electricity spot-price dynamics

Over the last three decades, the electricity sector worldwide underwent massive deregulation. Power market participants have encountered a growing number of challenges due to competition and other pertinent factors. For most markets at present, electricity is a non-storable commodity and its price is extremely sensitive to changes in supply and demand. The evolution of electricity prices exhibits pronounced mean reversion and cyclical patterns, possesses extreme volatility and relatively frequently occurring spikes, and manifests presence of memory property. These observed features necessitate the development of models aimed to simultaneously capture such price characteristics for forecasting, risk management, and valuation of electricity-driven derivatives. Majority of current research studies examining electricity spot prices are based on one-state models. Such models though may not accurately capture the stylised behaviours of price evolution, especially during periods of sudden spikes driven by the abrupt changes of market sentiments. This paper tackles the modelling and estimation problems under a new paradigm that integrates the deterministic calendar seasons and stochastic factors governing electricity prices with a regime-switching feature. The de-seasonalised component of our proposed model has both the jump and mean-reverting properties to account for spikes and periodic cycles alternating between lower price returns and compensating periods of higher price returns. The parameters of the de-seasonalised model components are also modulated by a higher-order hidden Markov chain (HOHMC) in discrete time. This provides a mechanism to extract latent information from historical data. The HOHMC’s state is interpreted as the “state of the world” resulting from the interaction of various forces impacting the electricity market. Filters are developed to generate optimal estimates of HOHMC-relevant quantities using the observation process, and these provide online estimates of model parameters. Empirical demonstrations using the daily electricity spot prices, compiled by the Alberta Electric System Operator (AESO), show that our HOHMM approach has considerable merits in terms of price data fitting and forecasting metrics. Our proposed framework driven by a hidden Markov chain with a state memory-capturing mechanism provides a powerful alternative to support the pricing of contracts in the electricity markets.

Impacts of transmission tariff on price arbitrage operation of energy storage system in Alberta electricity market

This paper investigates the application of existing tariff structures to understand how they impact the economic operation of energy storage system (ESS) for arbitrage. The scope of this research covers impacts on profitability, operating cost, energy-traded volume, and price volatility. Two facilities of different scales are considered: an ESS unit small enough to have no impact on price, and an ESS unit, large enough to have a quantifiable impact on pool price. The hourly impact of ESS operations on the pool price is estimated by modeling the price sensitivity quota curve from actual hourly market data.

Carbon pricing with an output subsidy under imperfect competition: The case of Alberta's restructured electricity market

In this paper, we examine the use of carbon pricing and an output-based subsidy in a market with imperfect competition. We consider a carbon pricing policy in Alberta's electricity market as a case study. This policy consists of two phases. In the first phase, the carbon price is increased with the output subsidy being based on a fraction of facility-level emission intensity. In the second phase, the output subsidy is altered to be uniform across assets and based on the emissions intensity of an efficient natural gas asset. Using a model of oligopoly competition, we simulate the short-run impacts of the two phases on electricity prices, emissions, and unit and firm-level profitability. We find that the mechanisms by which electricity prices and emissions change in response to carbon pricing differ depending on whether the market is perfectly competitive or oligopolistic. We demonstrate that by differentiating the effective carbon price across technologies, changing the basis of the output subsidy has substantially larger price and emissions effects than increasing the carbon price for all generators. The estimated effects of carbon pricing vary as the firms’ generation portfolios change.