This article proposes a multistage investment planning framework for the fleet transition problem, with a particular focus on the capacity of the electric fleet's aggregated battery to generate revenue for the fleet owner through self-use or third-party provision of electricity energy services. The proposed approach considers the purchase costs, salvage revenues, operational expenses, investments in charging infrastructure, and revenue opportunities derived from the electricity energy services offered by the electric fleet. An illustrative insight into potential revenue opportunities of an electric fleet for behind-the-meter and grid ancillary services is first provided. Subsequently, using a food retailer as a case study, this research evaluates how these auxiliary services can impact the dynamics of fleet transitions. This article also explores the influence of the electricity market on strategic planning and examines the optimal prioritization process between bidirectional chargers and electric commercial vehicle (ECV) investments. Lastly, we highlight how the ancillary energy services can lower the total cost of ownership (TCO) for fleet owners and accelerate the transition to electric fleets.

This paper proposes a load management platform to help industrial and commercial electricity customers assess the feasibility of demand charge management through battery aggregation of commercial and passenger Electric Vehicles (EVs). While electric fleet vehicles primarily serve logistical purposes, they can also contribute to energy services as long as these additional functions do not compromise their primary role. In this paper, as a part of the operation planning model, a new time-indexed vehicle routing formulation compatible with energy management equations is developed to allow fleet owners to simultaneously schedule their logistics and energy management systems. The operation planning model also considers the passenger EVs owned by a load entity and its staff and prepares them for energy services by handling their charge and discharge. Two illustrative case studies are employed to demonstrate the advantage of the proposed operation planning model over an available baseline. Additionally, for larger benchmarks, Column Generation and Branch-and-Price techniques is employed to decompose the large instances into smaller, more manageable problems for efficient solving.

Power system operation and planning decisions for lithium-ion battery energy storage systems are mainly derived using their simplified linear models. While these models are computationally simple, they have limitations in how they estimate battery degradation, either using the energy throughput or the Rainflow method. This article proposes a hybrid approach for lithium-ion battery system modeling suitable for use in power system studies that enhances representation of battery degradation at a reasonable computational cost. The proposed hybrid model combines a physics-based model for improved degradation estimates with a simple and linear energy reservoir model commonly used to represent a battery storage system. The advantage of constructing the battery model with this vision is that it allows its smooth integration into the mixed-integer optimization frameworks. The proposed model is evaluated using both power- and energy-based use cases from the electrical grid. The simulations demonstrate that degradation could be reduced by 45% compared to other modeling strategies while generating the same level of operation profits. The net result of using the proposed hybrid model could be an extended lifespan through better informed planning and operating decisions for energy storage assets.

Recent shifts towards higher renewable penetration in power systems, has resulted in increasing gas-fired generation capacity in systems without hydro or other fast responding alternatives, implying increased electricity and gas infrastructure interdependency. Furthermore, highly efficient combined heat and power (CHP) units lead to heat and electricity interdependency. It is crucial to consider these interdependencies in the expansion planning of energy systems for an effective and reliable investment planning and policy design. In this paper, the problem of integrated expansion planning of electricity, heat, and gas in presence of demand and wind uncertainty is addressed. Representative operating scenario selection with algebraic multi-grid clustering is used to model wind and demand uncertainties, and to address computational complexity of a central planning model. The simulation results on a modified IEEE-118 bus test system with a 14-node gas network shows that the algebraic multi-grid clustering outperforms the classical methods such as kmeans by following the benchmark case more closely.

The exercise of market power through network constraints in electricity markets can lead to high energy prices far from competitive prices. Traditional transmission expansion planning problem formulations do not consider strategic behavior of market agents. Therefore, they cannot capture the potential exercise of market power. In this paper, a predictor-corrector iterative algorithm is proposed to deal with market power mitigation in market-oriented transmission expansion planning problems. The predictor step consists of the solution of an equilibrium market model based on the conjectured supply function. The corrector step is a conventional transmission expansion planning posed as a mixed integer linear programming problem, where the feasible region is dynamically updated taking into account the results from the predictor step. Lerner index and other indices are used to quantify the potential market power. The algorithm finds the minimum cost expansion plan that avoids the exercise of market power through network congestion. The cost of this expansion plan is only slightly greater than the cost of a conventional expansion plan. The approach is illustrated using the 6-Bus Garver and the IEEE-24 RTS test systems.

The penetration of the lithium-ion battery energy storage system (LIBESS) into the power system environment occurs at a colossal rate worldwide. This is mainly because it is considered as one of the major tools to decarbonize, digitalize, and democratize the electricity grid. The economic viability and technical reliability of projects with batteries require appropriate assessment because of high capital expenditures, deterioration in charging/discharging performance and uncertainty with regulatory policies. Most of the power system economic studies employ a simple power-energy representation coupled with an empirical description of degradation to model the lithium-ion battery. This approach to modelling may result in violations of the safe operation and misleading estimates of the economic benefits. Recently, the number of publications on techno-economic analysis of LIBESS with more details on the lithium-ion battery performance has increased. The aim of this review paper is to explore these publications focused on the grid-connected LIBESS applications and to discuss the impacts of using more sophisticated modelling approaches. First, an overview of the three most popular battery models is given, followed by a review of the applications of such models. The possible directions of future research of employing detailed battery models in power systems’ techno-economic studies are then explored.

This paper proposes the linearized physics-based model of a lithium-ion battery that can be incorporated into the optimization framework for power system economic studies. The proposed model is a linear approximation of the single particle model and it allows to characterize dynamics of the physical processes inside the battery that impact the battery operation. There is a need for such model as a simplistic power-energy model that is widely employed in operation and planning studies with the lithium-ion battery energy storage system (LIBESS) results in infeasible operation and misleading economic assessment. The proposed linearized model is computationally beneficial compared with a recently used nonlinear physics-based model. The energy arbitrage application is used to assess the advantages of the proposed model over a simple power-energy model.

The penetration of the lithium-ion battery energy storage system (BESS) into the power system environment occurs at a colossal rate worldwide. This is mainly because it is considered as one of the major tools to decarbonize, digitalize, and democratize the electricity grid. The economic viability and technical reliability of projects with batteries require appropriate assessment because of high capital expenditures, deterioration in charging/discharging performance and uncertainty with regulatory policies. Most of the power system economic studies employ a simple power-energy representation coupled with an empirical description of degradation to model the lithium-ion battery. This approach to modelling may result in violations of the safe operation and misleading estimates of the economic benefits. Recently, the number of publications on techno-economic analysis of BESS with more details on the lithium-ion battery performance has increased. The aim of this review paper is to explore these publications focused on the grid-scale BESS applications and to discuss the impacts of using more sophisticated modelling approaches. First, an overview of the three most popular battery models is given, followed by a review of the applications of such models. The possible directions of future research of employing detailed battery models in power systems' techno-economic studies are then explored.

This paper upgrades the univariate cumulant based approach to solve the probabilistic power flow (PPF) by considering higher-order joint and univariate cumulants in the tensor form. The historical data of wind farms and loads are used to derive their statistical characteristics in the tensor form. The DC formulation of the power flow equations coupled with the principle of maximum entropy are employed to reconstruct the distribution functions of the branch power flow. The robustness of the proposed method is verified comparing with the empirical distribution obtained by the Monte Carlo simulation. The comparison with the traditional method of the univariate cumulants combined with the covariance matrix demonstrated that joint cumulants of order higher than two cannot be neglected when there is a high degree of dependence between random variables or their marginal distributions are far from normal.

This paper develops an investment decision-making tool for a strategic wind producer, who desires to expand its portfolio by investing in battery systems. This enables the wind producer to better manage its production profile, which eventually yields an increased profit. The long-run decisions are strategic siting, sizing, and depth of discharge tuning of the battery system, and the short-run decisions are strategic offers to the market in terms of price and quantity. In this setup, we linearly model the technical characteristics of battery systems, e.g., depth of discharge, and then evaluate its impacts on the battery systems' number of cycles. The wind power uncertainty is modeled by a set of scenarios. The resulting model is a stochastic bi-level problem, which can be recast as a stochastic mixed-integer and linear model. However, this problem is generally hard-to-solve or even computationally intractable if many scenarios are considered. Hence, we use a scenario-based decomposition technique via progressive hedging algorithm to make the model scalable. An upper bound is then derived as a benchmark to assess the quality of results. Two case studies based on a six-bus and the IEEE 24-bus reliability test systems are used to evaluate the performance of the proposed approach.

This paper describes the two test systems for voltage stability studies set up by the IEEE PES Task Force on "Test Systems for Voltage Stability Analysis and Security Assessment" under the auspices of the Power System Stability Subcommittee of the Power System Dynamic Performance Committee. These systems are based on previous test systems, making them more representative of voltage stability constraints. A set of representative results are provided for both systems, with emphasis on dynamic simulation. They illustrate various aspects such as long-term dynamics, voltage security assessment, real-time detection, and corrective control of instabilities. The value for educators, researchers and practitioners are emphasized.

In this paper, representative operating scenario selection for generation expansion planning with demand and wind uncertainty is addressed. Kmeans++ clustering technique is used to generate the operating scenarios and the results are compared with the commonly used duration curve and kmeans clustering techniques in terms of cost and reliability. Furthermore, impact of data correlation on the scenario selection and investment results is investigated. The planning problem is simulated for the IEEE 24-bus test system.

The Engineers Canada Accreditation Board outlines 12 Canadian Engineering Graduate Attributes required for program accreditation. One of these attributes is Individual and Team Work. Since 2016, at the University of Calgary, there has been a voluntary, undergraduate-wide survey administered to the Schulich School of Engineering students every spring via an online platform. The purpose of the survey is to assess students’ perceived development of teamwork skills during their program, and identify avenues to improve program offerings. After four consecutive years of this survey, with sample sizes ranging from 683-973 students, there are three main trends that can be identified: students perceive teamwork skills as highly important for their future careers, there are noticeable differences between male and female students regarding teamwork experiences, and students value teamwork skills training and opportunities for peer feedback. Implications of these findings are that there are gendered teamwork experiences among undergraduate engineering students and more research is needed to understand interventions that can mitigate this.

In this paper, an integrated and multi-area model for expansion planning of electricity, heat, and gas infrastructure is developed with the objective of minimizing cost over the planning horizon. In the proposed model, heat and gas demand are modeled explicitly and the interdependencies between electricity, heat, and gas energy systems are represented. Furthermore, a carbon tax is modeled as an operating cost to analyze its impact on the expansion planning outcome. The introduced centralized model can be used to design incentives and to provide guidelines for different independent energy sector investments. Simulation results on the Alberta energy system indicate that the proposed integrated model outperforms the previously proposed methods in literature, which generally do not explicitly model heat and gas demand, in terms of cost and greenhouse gas emissions simultaneously.

The adequate modeling of power generation is one of the most challenging issues in the field of electricity markets. Practical market clearing requires simple and transparent formulations but some of the financial and technical conditions of power plants are inherently complicated. In Europe, this predicament is usually handled by introducing special order types on day-ahead power exchanges. This approach often leads to algorithmic complications for power exchanges because new order types usually imply specific constraints some of which can affect the behavior of their overall mathematical model.The officially proposed clearing algorithm of the all-European exchange (namely, EUPHEMIA) is supposed to handle so-called complex orders for suppliers. These are composed of a set of traditional hourly step bids with linking conditions i.e. the Minimum Income Condition, the Load Gradient Condition and the auxiliary Scheduled Stop Condition. A new formulation is proposed for the case when all three conditions are attached to the complex order. A single convex Mixed Integer Problem is solved for market clearing which is conceptually similar to the models already widely used in Europe. Numerical case studies are discussed along with practical viability.

In the current research, we report on a peer feedback system for supporting student teamwork skill development during post-secondary education. The peer feedback system is part of a larger suite of assessments at ITPmetrics.com. This is a free assessment-based system that allows team members to provide round-robin ratings of each member's effectiveness in the team on five dimensions (communication; commitment; foundation of knowledge, skills and abilities; emphasising high standards; and focus), as well as provide anonymous written feedback to supplement the numeric scores. Team members have access to a dashboard where they can complete the assessment and store assessment reports generated from their peers' ratings. We summarise data from student learning teams suggesting that peer ratings are reliable (internally consistent, unidimensional and inter-rater correlated; n > 85,000). Recommendations for implementation are provided with respect to pre-briefing, debriefing, and assessment schedules.

We develop a decision-making tool based on a bilevel complementarity model for a merchant price-maker energy storage system to determine the most beneficial trading actions in pool-based markets, including day-ahead (as joint energy and reserve markets) and balancing settlements. The uncertainty of net load deviation in real-time is incorporated into the model using a set of scenarios generated from the available forecast in the day-ahead. The objective of this energy storage system is to maximize its expected profit. The day-ahead products of energy storage system include energy as well as reserve commitment (as one of the ancillary services), whereas its balancing product is the energy deployed from the committed reserve. The proposed model captures the interactions of different markets and their impacts on the functioning of the storage system. It also provides an insight for storage system into clearing process of multiple markets and enables such a facility to possibly affect the outcomes of those markets to its own benefit through strategic price and quantity offers. The validity of the proposed approach is evaluated using a numerical study.

Descriptive stochastic models of the reserve prices are important tools for risk management and derivative pricing in joint energy and ancillary services market. As operating reserve prices present different characteristics from energy prices, such as low price level, high variability, and frequent and significant spikes, appropriate descriptive models need to be developed for these prices. While log prices have been widely used for modeling, this paper further explores models for hourly original reserve prices and compares their performance to log price models. In addition, a new Markov regime-switching model has been proposed to enhance accuracy.

This paper proposes an approach to assist a price-maker merchant energy storage facility in making its optimal operation decisions. The facility operates in a pool-based electricity market, where the ramping capability of other resources is limited. Also, wind power resources exist in the system. The merchant facility seeks to maximize its profit through strategic inter-temporal arbitrage decisions, when taking advantage of those ramp limitations. The market operator, on the other hand, aims at maximizing the social welfare under wind power generation uncertainty. Thus, a stochastic bi-level optimization model is proposed, taking into account the interactions between the storage facility and the market operator, and the existing market opportunities for the storage facility. The proposed bi-level model is then transformed into a mathematical program with equilibrium constraints that can be recast as a mixed-integer linear programming problem. Different case studies are presented and discussed using a six-bus illustrative example and the IEEE one-area reliability test system to evaluate the performance of the proposed approach.