Alberta Electric System Operator 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.

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.

Short-term Wind Power Prediction Based on Soft Margin Multiple Kernel Learning Method

For short-term wind power prediction, a soft margin multiple kernel learning (MKL) method is proposed. In order to improve the predictive effect of the MKL method for wind power, a kernel slack variable is introduced into each base kernel to solve the objective function. Two kinds of soft margin MKL methods based on hinge loss function and square hinge loss function can be obtained when hinge loss functions and square hinge loss functions are selected. The improved methods demonstrate good robustness and avoid the disadvantage of the hard margin MKL method which only selects a few base kernels and discards other useful kernels when solving the objective function, thereby achieving an effective yet sparse solution for the MKL method. In order to verify the effectiveness of the proposed method, the soft margin MKL method was applied to the second wind farm of Tianfeng from Xinjiang for short-term wind power single-step prediction, and the single-step and multi-step predictions of short-term wind power was also carried out using measured data provided by alberta electric system operator (AESO). Compared with the support vector machine (SVM), extreme learning machine (ELM), kernel based extreme learning machine (KELM) methods as well as the SimpleMKL method under the same conditions, the experimental results demonstrate that the soft margin MKL method with different loss functions can efficiently achieve higher prediction accuracy and good generalization performance for short-term wind power prediction, which confirms the effectiveness of the method.

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.

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.

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.

Predicting Electricity Pool Prices Using Hidden Markov Models

In this paper, Alberta electricity spot market or Power Pool pricing is studied and the pool price is modeled through a hidden Markov model and multiple local ARX models. By selecting and preprocessing the exogenous factors (e.g. the price forecast from Alberta Electric System Operator (AESO), demand forecast and so forth), a one-hour ahead prediction model for pool price is formulated with parameters being estimated from the real data. Validation results show that this approach can improve the price forecasting and in particular, for high pool prices.

Control Automático de Voltaje en Sistemas Eléctricos de Potencia basado en Sistemas Expertos

This paper presents the design of an expert controller applied to voltage level control in a power system. The use of pilot node concept allows to keep stable the list of controllers against topological changes in the network. To achieve the optimal voltage profile, the Alberta Electric System Operator regulations are used. The controller validation is performed by simulating a case library and the solution is compared with results provided by an optimal power flow and a genetic based algorithm. The main advantage of the propose model is the unified treatment of the voltage profile and the reactive dispatch without the need of a two-step algorithm. The proposed controller is suitable for online real-time applications and it is flexible when dealing with problems that present a complex analytical formulation.

Transmission Policy in Alberta and Bill 50

Various plans developed by the Alberta Electric System Operator (AESO) have indicated for some time a need for expansion of the Alberta electrical transmission system. This was also recommended in Alberta’s Provincial Energy Strategy released in December of 2008 and it seems generally accepted that some expansion is appropriate. However, there are many complex issues involved in determining the exact nature and extent of this expansion. These issues have traditionally been sorted out through the work of the AESO, followed by a formal application and open hearings by the regulator, the Alberta Utilities Commission (AUC). In June of 2009 the Alberta government proposed new amendments (Bill 50) to the Electric Utilities Act, the Hydro and Electric Energy Act, and the Alberta Utilities Commission Act that significantly alter the regulatory process for the approval of new high-voltage transmission lines. Bill 50 introduces the concept of Critical Transmission Infrastructure (CTI), defined as transmission and related facilities determined by the government to be in the public interest. Under this Bill, CTI is subject to substantially reduced regulatory oversight by the Alberta Utilities Commission (AUC). It suspends the regulatory framework established under the Electric Utilities Act requiring the Alberta Electric System Operator (AESO) to demonstrate a “need” for the facilities and a finding by the AUC that the facilities are in the public interest. Bill 50 also identifies the following four transmission projects as critical infrastructure: • Two High Voltage Direct Current (HVDC) transmission facilities in the Edmonton-Calgary corridor, each with a minimum capacity of 2,000 megawatts (MW). • A double circuit 500 kV alternating current line between Edmonton and the GibbonsRedwater region. • A new 240 kV substation in southeast Calgary. • Two single-circuit 500 kV alternating current lines between Edmonton and Fort McMurray. The estimated cost for these four projects is approximately $5.7 billion. The cost for the two HVDC lines in the Edmonton-Calgary corridor is more than $3.1 billion. The four projects in Bill 50 are a subset of the $14.5 billion for transmission projects identified in the Long-term Transmission System Plan filed by the AESO in June of 2009.

Prospective Reconciliation of Transmission Loss Cost in Alberta

One of the challenges in Alberta's deregulated electricity market is to recover transmission loss costs in a fair and equitable manner. The independent system operator (ISO), the Alberta Electric System Operator or AESO in Alberta, Canada, has introduced a new prospective methodology to reconcile the transmission loss revenue to loss cost on an annual basis since January 1, 2006. In the reconciliation process, a quarterly calibration factor (CF) is calculated to true up the difference between loss revenue and loss cost. The CF is defined as the percentage of the combined actual and forecasted difference in loss revenue and loss cost divided by the loss factor customer volumes and pool price. The focus behind this chosen methodology is to have a small CF in each quarter, a stable reconciliation pattern, and a small year-end difference. The new methodology has met these three goals in Alberta's deregulated electricity market.

Calling for backup

In this paper, the importance of, and key design issues for, backup control centers (BCC) in maintaining power system reliability are discussed. It also highlights the importance of the key factors incorporated into the initial design studies of the BCC of the Alberta Electric System Operator (AESO) and may help other utilities that might want to implement a similar backup facility at their premises.