Browsing by Author "Cañizares, Claudio A."

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A Decentralised Transactive Energy Market Considering Physical System Constraints
(Institute of Electrical and Electronics Engineers (IEEE), 2024-05) Pankhurst, Colton; Cañizares, Claudio A.; Bhattacharya, Kankar
Local energy markets leveraging direct Peer-to-Peer (P2P) trading have been proposed as a solution, which can increase prosumer participation in lower cost and more reliable supply of energy to consumers. Many approaches have been proposed to determine the optimal dispatch of distributed resources; however, existing approaches either provide inadequate representation of the physical layer or insufficient handling of infeasibilities for a continuous pricing mechanism. Hence, a gap remains in the research to date on efficiently allowing for prosumer decision autonomy while ensuring that the physical layer of the power system is considered. This paper addresses these issues with a proposed decentralised transactive solution that retains prosumer negotiation and decision autonomy, while using market determined prices to allocate limited system resources for a feasible system state. This is achieved through a transaction fee mechanism for prosumer interactions that considers the network characteristics such as topology and line congestion, and a congestion-clearing process to ensure efficient allocation of network resources. Previously reported distribution networks are used to compare the economic performance and transaction decisions of the proposed solution with existing approaches.
A Review of Modeling and Applications of Energy Storage Systems in Power Grids
(Institute of Electrical and Electronics Engineers (IEEE), 2022-03-25) Calero, Fabian; Cañizares, Claudio A.; Bhattacharya, Kankar; Anierobi, Chioma; Calero, Ivan; Zambroni de Souza, Matheus F.; Farrokhabadi, Mostafa; Guzman, Noela Sofia; Mendieta, William; Peralta, Dario; Solanki, Bharatkumar V.; Padmanabhan, Nitin; Violante, Walter
As the penetration of variable renewable generation increases in power systems, issues, such as grid stiffness, larger frequency deviations, and grid stability, are becoming more relevant, particularly in view of 100% renewable energy networks, which is the future of smart grids. In this context, energy storage systems (ESSs) are proving to be indispensable for facilitating the integration of renewable energy sources (RESs), are being widely deployed in both microgrids and bulk power systems, and thus will be the hallmark of the clean electrical grids of the future. Hence, this article reviews several energy storage technologies that are rapidly evolving to address the RES integration challenge, particularly compressed air energy storage (CAES), flywheels, batteries, and thermal ESSs, and their modeling and applications in power grids. An overview of these ESSs is provided, focusing on new models and applications in microgrids and distribution and transmission grids for grid operation, markets, stability, and control.
Affine Policies and Principal Components Analysis for Self-Scheduling in CAES Facilities
(Institute of Electrical and Electronics Engineers (IEEE), 2022-07-26) Zambroni de Souza, Matheus F.; Cañizares, Claudio A.; Bhattacharya, Kankar; Lorca, Alvaro
This paper presents a novel methodology based on Principal Components Analysis (PCA) and Affine Policies (AP) for self-scheduling of a price-taker Compressed Air Energy Storage (CAES) facility operating under uncertainties. The proposed PCA-AP model is developed from the facility owner's perspective, which partakes in energy, spinning, and idle reserve markets. A methodology is proposed to select the required price uncertainty intervals from actual data based on a Box Cox technique. For a more realistic representation, the detailed thermodynamic characteristics of the CAES facility are considered, taking into account as well modern CAES facilities that may charge and discharge concurrently. To validate the proposed PCA-AP model and approach, the results obtained are compared with an existing Affine Arithmetic (AA) model, which is also based on an affine approach, and Monte Carlo Simulations (MCS), which can be considered as the benchmark for comparison purposes. The input data, forecast prices and intervals of uncertainty, are taken from the Ontario-Canada electricity market for 2015-2019. From the studies presented, it can be observed that the new PCA-AP approach provides less conservative results as compared to the AA approach, and hence can be considered an adequate methodology for day-ahead operations in systems with significant sources of uncertainty.
Aggregate Modeling of Thermostatically Controlled Loads for Microgrid Energy Management Systems
(Institute of Electrical and Electronics Engineers (IEEE), 2023-03-09) Córdova, Samuel; Cañizares, Claudio A.; Lorca, Álvaro; Olivares, Daniel E.
Second-to-second renewable power fluctuations can severely hinder the frequency regulation performance of modern isolated microgrids, as these typically have a low inertia and significant renewable energy integration. In this context, the present paper studies the coordinated control of Thermostatically Controlled Loads (TCLs) for managing short-term power imbalances, and their integration in microgrid operations through the use of aggregate TCL models. In particular, two computationally efficient and accurate aggregate TCL models are developed: a virtual battery model representing the aggregate flexibility of TCLs considering solar irradiance heat gains and wall/floor heat transfers, and a frequency transient model representing the aggregate dynamics of a TCL collection considering communication delays and the presence of model uncertainty and time-variability. The proposed aggregate TCL models are then used to design a practical Energy Management System (EMS) integrating TCL flexibility, and study the impact of TCL integration on microgrid operation and frequency control. Computational experiments using detailed frequency transient and thermal dynamic models are presented, demonstrating the accuracy of the proposed aggregate TCL models, as well as the economic and reliability benefits resulting from using these aggregate models to integrate TCLs in microgrid operations.
An Affine Arithmetic-Based Energy Management System for Cooperative Multi-Microgrid Networks
(Institute of Electrical and Electronics Engineers (IEEE), 2023-08-18) Ceja-Espinosa, Carlos; Pirnia, Mehrdad; Cañizares, Claudio A.
This paper presents an Energy Management System (EMS) for a Multi-Microgrid (MMG) system that considers power exchanges between a set of interconnected microgrids (MGs) in an Active Distribution Network (ADN), taking into account electricity demand and renewable energy generation uncertainties using an Affine Arithmetic (AA) approach. The deterministic EMS model is formulated as a cost minimization problem which includes detailed operational constraints of thermal generators and Energy Storage Systems (ESSs) within each MG, as well as power flow limits at the Point of Common Coupling (PCC), considering all power exchanges among the set of MGs and the ADN. The uncertainties are formulated in the AA domain to obtain an EMS model that is robust for a range of realizations of the uncertain parameters, with no need of statistical assumptions or repeated calculations, which can be solved with relatively low computational burden, as opposed to other approaches such as Monte Carlo Simulation (MCS). The proposed AA model is then tested and validated with data of a set of MGs in an ADN located in São Paulo, Brazil, through comparisons with the deterministic model, MCS, and a Two-Stage Stochastic Programming (TSSP) approach. Results show an execution time improvement in the AA model of approximately 70% when compared to a MCS approach, which is expected to be slower, while considering the same range of uncertainties. Furthermore, the operation cost of the overall system decreases, as expected, by approximately 63% when power exchanges are enabled, as opposed to the individual operation of each MG, demonstrating the economic benefit of MMG systems.
Bifurcation analysis of various power system models
(Elsevier, 1999-02-01) Rosehart, William D.; Cañizares, Claudio A.
This paper presents the bifurcation analysis of a detailed power system model composed of an aggregated induction motor and impedance load supplied by an under-load tap-changer transformer and an equivalent generator and transmission system. Different modeling levels with their respective differential-algebraic equations are studied, to determine the minimum dynamic model of the system that captures the most relevant features needed for bifurcation studies of power systems. An aggregated model of a realistic load is used to illustrate the ideas presented throughout the paper.
Comparing secondary voltage regulation and shunt compensation for improving voltage stability and transfer capability in the Italian power system
(Elsevier, 2004-10-01) Cañizares, Claudio A.; Cavallo, Claudio; Pozzi, Massimo; Corsi, Sandro
This paper concentrates on comparing the advantages and disadvantages, including costs, of using secondary voltage regulation (SVR) versus using shunt-connected controllers, in particular mechanical switched capacitors (MSC), static var compensators (SVC) and static synchronous compensators (STATCOM), to improve voltage stability (VS) and the external transfer capability (TC) of the Italian power network. Basic VS and TC concepts and tools, as well as the models of the various controllers, particularly SVR, used to obtain the results presented are described in detail. The model of the Italian system used and the assumptions made for these studies are also discussed. The paper demonstrates that SVR is an option that should be seriously considered in practice when trying to improve VS and TC of power systems.
Comparison of machine learning and MPC methods for control of home battery storage systems in distribution grids
(Elsevier, 2025-08-02) Mueller, Felicitas; de Jongh, Steven; Cañizares, Claudio A.; Leibfried, Thomas; Bhattacharya, Kankar
Control methods for Home Energy Management Systems implemented with traditional optimization techniques and state-of-the-art Machine Learning methods are presented and compared in this paper in the context of their impact on and interactions with Active Distribution Networks. Thus, model-based methods based on Model Predictive Control algorithms with different prediction qualities are first described and compared against model-free methods based on imitation learning and reinforcement learning. A practical, state-of-the-art, heuristic, rule-based controller is used as the baseline. An in-depth comparison is performed using metrics consisting of objective function values, grid constraint violations, and computational time. The results of applying these Home Energy Management Systems to a realistic German low voltage benchmark grid with 13 connected households, each containing solar generation, a battery storage system, and electrical loads are discussed. It is demonstrated that model-based and model-free methods can achieve improvements over typical rule-based methods, with varying performance in terms of objective function values and grid constraint violations depending on the forecasts, at the cost of higher computational complexity. Furthermore, model-free methods are shown to have in general low computational burden at higher objective function values with more grid constraint violations, with imitation-learning-based techniques proving to be the best compromise for practical applications.
Conditions for saddle-node bifurcations in AC/DC power systems
(Elsevier, 1995-12-27) Cañizares, Claudio A.
Saddle-node bifurcations are dynamic instabilities of differential equation models that have been associated with voltage collapse problems in power systems. This paper presents the conditions needed for detecting these types of bifurcations using power flow equations for a dynamic model of ACIDC systems, represented by differential equations and algebraic constraints. Two methods typically used to detect saddle-node bifurcations, namely, direct and parameterized continuation methods, are briefly analysed from the point of view of numerical robustness.
Data-Driven Topology and Parameter Identification in Distribution Systems With Limited Measurements
(Institute of Electrical and Electronics Engineers (IEEE), 2024-11-05) de Jongh, Steven; Mueller, Felicitas; Osterberg, Fabian; Cañizares, Claudio A.; Leibfried, Thomas; Bhattacharya, Kankar
This manuscript presents novel techniques for identifying the switch states, phase identification, and estimation of equipment parameters in multi-phase low voltage electrical grids, which is a major challenge in long-standing German low voltage grids that lack observability and are heavily impacted by modelling errors. The proposed methods are tailored for systems with a limited number of spatially distributed measuring devices, which measure voltage magnitudes at specific nodes and some line current magnitudes. The overall approach employs a problem decomposition strategy to divide the problem into smaller subproblems, which are addressed independently. The techniques for identifying switch states and system phases are based on heuristics and a binary optimization problem using correlation analysis of the measured time series. The estimation of equipment parameters is achieved through a data-driven regression approach and by an optimization problem, and the identification of cable types is solved using a Mixed-Integer Quadratic Programming solver. To validate the presented methods, a realistic grid is used and the presented techniques are evaluated for their resilience to data quality and time resolution, discussing the limitations of the proposed methods.
Distribution Grid State Estimation With Limited Actual and Pseudo Measurements
(Institute of Electrical and Electronics Engineers (IEEE), 2025-06-25) de Jongh, Steven; Mueller, Felicitas; Cañizares, Claudio A.; Leibfried, Thomas; Bhattacharya, Kankar
Methods for distribution system state estimation in Low Voltage (LV) distribution grids are discussed in this paper, for systems with a high penetration of Distributed Energy Resources (DERs) such as solar generators and heatpumps. The proposed methods are specifically designed for LV grids with sparse measurement availability, such as feeders with measurements only at the distribution transformer, as is typically the case in some European LV grids. For these cases, device locations, temporal data, and weather data are used in the proposed techniques to estimate variables at unmeasured grid nodes. The impact of smart meters is also investigated by simulating the impact of individual smart meter measurements on the estimation results. The proposed methods are based on time series disaggregation of transformer measurements, such as thermoelectrical demand, baseload, and solar generation, enabling improvements over existing Pseudo-Measurement (PM) generation techniques. Furthermore, the paper presents approaches for estimating voltages and currents in the feeder using both actual and PMs, based on classical estimation methods and interval estimation techniques for unmeasured variables. The results for an realistic German LV grid show that the proposed disaggregation step allows to significantly improve the results of the state estimation results over state-of-the-art methods.
Dynamic model of integrated electricity and district heating for remote communities
(Elsevier, 2024-09-03) Abuelhamd, Muhammad; Cañizares, Claudio A.
District heating networks offer promising solutions for remote communities, providing centralized heat supply, improved efficiency, and diverse energy sources, especially with existing diesel generation. Hence, this paper bridges gaps in the existing literature by developing comprehensive dynamic models of combined district heating networks within existing electric power networks in remote communities, which allows identifying challenges and benefits of district heating networks for these communities. It is shown that district heating networks allow utilizing waste energy to enable energy exchanges between the electricity and heating systems, enabling the provision of necessary ancillary services for remote microgrids with renewable energy sources. The presented dynamic district heating network model incorporates particular considerations in remote, northern communities such as soil limitations, extreme cold conditions, and piping insulation to minimize heat loss. It also addresses accurate sizing of heat pumps based on realistic thermal load requirements, weather conditions, and consumer profiles, proposing demand management controls to enhance frequency regulation for the integration of variable renewable energy sources. The main contributions of the paper include detailed dynamic modeling for district heating network operation, heat pump demand response control system design, and a comparative analysis between centralized district heating networks and decentralized electric thermal storage units that have been deployed for thermal supply in remote areas. The presented dynamic models are applied, tested, and validated in an existing electric microgrid at Kasabonika Lake First Nation in Northern Ontario, showcasing the role of a potential district heating network in facilitating renewable energy sources integration in isolated microgrids.
Electricity market price volatility: The case of Ontario
(Elsevier, 2007-05-25) Zareipour, Hamidreza; Bhattacharya, Kankar; Cañizares, Claudio A.
Price volatility analysis has been reported in the literature for most competitive electricity markets around the world. However, no studies have been published yet that quantify price volatility in the Ontario electricity market, which is the focus of the present paper. In this paper, a comparative volatility analysis is conducted for the Ontario market and its neighboring electricity markets. Volatility indices are developed based on historical volatility and price velocity concepts, previously applied to other electricity market prices, and employed in the present work. The analysis is carried out in two scenarios: in the first scenario, the volatility indices are determined for the entire price time series. In the second scenario, the price time series are broken up into 24 time series for each of the 24 h and volatility indices are calculated for each specific hour separately. The volatility indices are also applied to the locational marginal prices of several pricing points in the New England, New York, and PJM electricity markets. The outcomes reveal that price volatility is significantly higher in Ontario than the three studied neighboring electricity markets. Furthermore, comparison of the results of this study with similar findings previously published for 15 other electricity markets demonstrates that the Ontario electricity market is one of the most volatile electricity markets world-wide. This high volatility is argued to be associated with the fact that Ontario is a single-settlement, real-time market.
Hydrogen economy transition in Ontario – Canada considering the electricity grid constraints
(Elsevier, 2009-06-02) Hajimiragha, Amirhossein; Fowler, Michael W.; Cañizares, Claudio A.
This paper investigates the feasibility of electrolytic hydrogen production for the transport sector during off-peak periods in Ontario. This analysis is based on the existing electricity system infrastructure and its planned future development up to 2025. First, a simplified but realistic zonal based model for Ontario's electricity transmission network is developed. Then, based on Ontario's Integrated Power System Plan (IPSP), a zonal pattern of generation capacity procurement in Ontario from 2008 to 2025 is proposed, specifying the total effective generation capacity in each zone that contributes to base-load energy. Finally, an optimization model is developed to find the optimal size of hydrogen production plants to be developed in different zones, as well as optimal hydrogen transportation routes to achieve a feasible hydrogen economy penetration in Ontario up to 2025. The proposed model is shown to be an effective planning tool for electrolysis based hydrogen economy studies. The results of the present study demonstrate that the present and projected electricity grid in Ontario can be optimally exploited for hydrogen production, achieving 1.2–2.8% levels of hydrogen economy penetration by 2025 without any additional grid or power generation investments beyond those currently planned.
Impact of 100‐MW‐scale PV plants with synchronous power controllers on power system stability in northern Chile
(Institution of Engineering and Technology (IET), 2017-08-11) Remon, Daniel; Cañizares, Claudio A.; Rodriguez, Pedro
The impact that renewable energy sources interfaced by power electronics have on power systems becomes more important as their share in the generation mix increases, thus requiring detailed analyses that take into account their dynamics and controllers. In this study, the impact of photovoltaic (PV) power plants on the power system of northern Chile is analysed. The studied plants employ a controller that allows power converters to interact with the grid like virtual synchronous generators, and their model includes the dynamics of the plant and converter controllers, as well as the dc and PV system. The presented analysis, which comprises modal analysis and time-domain simulations of large disturbances, evaluates the impact of these plants with respect to PV plants based on a conventional converter controller. Tests and validations of the proposed models and controllers are carried out for an actual PV plant connected to the power system of northern Chile, and for a higher PV penetration case. The results show the ability of PV plants formed by virtually synchronous power converters to limit frequency excursions induced by large power imbalances, and to mitigate power oscillations of the synchronous machines in the system.
Improved Control and Stability Analysis of a Microgrid Connector Controller Under Unbalanced Network Conditions
(Institute of Electrical and Electronics Engineers (IEEE), 2025-03-19) Gu, Hanwen; Tamimi, Behnam; Cañizares, Claudio A.
The microgrid connector controller has been shown to be a more cost-effective alternative for a microgrid-grid interface than a back-to-back connector. This paper proposes an improved control system for this controller under unbalanced operation, as distribution feeder and microgrid loads are normally unbalanced. The controller regulates the utility interface while minimizing the impact of load unbalancing on both the utility feeder and the microgrid. An improved reference current generation strategy is designed to suppress the fluctuations of the dc link voltage, thereby preventing them from being introduced into the control system and affecting the controller operation. Furthermore, a four-stage start-up strategy is proposed to avoid an external dc source for charging the controller’s dc link capacitor, making the interface more practical and cost-effective. The paper also presents a comprehensive investigation of the effects of the controller parameters and microgrid load unbalance on the small-perturbation stability of grid-connected microgrids. The performance of the presented controller is compared with its original controls, a back-to-back with existing unbalance control techniques, and a switch interface through detailed simulations in a benchmark test system. The results illustrate that the proposed controls can minimize the impact of feeder and microgrid unbalancing by eliminating the negative-sequence components and reducing the fluctuations in the transferred powers and dc link voltage, showing similar overall performance to a back-to-back interface.
Machine Learning-Based Control of Electric Vehicle Charging for Practical Distribution Systems With Solar Generation
(Institute of Electrical and Electronics Engineers (IEEE), 2023-11-16) Calero, Ivan; Cañizares, Claudio A.; Farrokhabadi, Mostafa; Bhattacharya, Kankar
The adoption of Electric Vehicles (EVs) and solar Photovoltaic (PV) generation by households is rapidly and significantly increasing. Utilities are facing the challenge of efficiently managing EV and PV resources to help mitigate the undesirable effects on grid operation. Existing approaches to solve these issues depend on accurate but hard to predict behavior of EVs and PVs, detailed knowledge of customers, and grid infrastructure, all of which complicate the effective deployment of these resources. Motivated by these practical challenges and in collaboration with industry partners working on addressing these issues, this paper proposes a two-level data-driven smart controller for EV charging in distribution systems. The controller is modeled as a Deep Reinforcement Learning (DRL) agent, which coordinates the charging rates of multiple EVs connected to a realistic residential feeder with high penetration of PV generation. The first level coordinates the aggregated EV load at distribution Medium Voltage (MV) level to provide Demand Response (DR) services; at the Low Voltage (LV) level it aims to maximize the EVs’ state of charge at departure while avoiding the overloading of the MV/LV distribution transformers. The controller is verified through simulations on an actual utility grid facing the aforementioned challenges, demonstrating the effectiveness and practicality of the proposed DRL-based smart charging approach.
Marginal Rate of Technical Substitution curves for frequency regulation services
(Elsevier, 2022-07-22) Guzman, Noela Sofia; Cañizares, Claudio A.; Bhattacharya, Kankar; Sohm, Daniel
This paper presents a detailed methodology to develop Marginal Rate of Technical Substitution (MRTS) curves, which can be used to optimally determine the appropriate substitution of traditional regulation signals with fast regulation signals, considering different Energy Storage System (ESS) technologies and discharging times, scenarios, and seasons. The presented work is based on the Ontario Power System (OPS) managed by the Independent Electricity System Operator (IESO) of Ontario, Canada. Different comparisons of the MRTS curves are carried out and the criteria used to obtain 16 average optimized MRTS curves, four per season, are presented. Finally, the 16 MRTS curves obtained for the IESO and their parameters are presented, and the use of these curves is explained through an example.
Optimization- and Rule-based Energy Management Systems at the Canadian Renewable Energy Laboratory microgrid facility
(Elsevier, 2021-03-05) Restrepo, Mauricio; Cañizares, Claudio A.; Simpson-Porco, John W.; Su, Peter; Taruc, John
This paper presents the development, implementation, and commissioning of two different Energy Management Systems (EMSs) for the Canadian Renewable Energy Laboratory (CANREL), a microgrid testbed located in Guelph, ON, Canada, for the existing hardware, software, and communication infrastructure, which constrained the implementation options. A Rule-based EMS (RBEMS), which is typically found in microgrid controllers nowadays, and an implementation of an Optimization-based EMS (OBEMS), which is not usual in today’s controllers, are proposed, tested, and demonstrated in the microgrid testbed. The RBEMS consists of a state machine that represents the commitment of different genset units in the system and the curtailment of load and renewable generation. The OBEMS is based on a unit commitment model for microgrids that minimizes the generation and curtailment costs, while operating the microgrid equipment according to technical limits. Both EMS systems are integrated into a Python application which integrates various open-source packages and solvers, making it affordable, flexible and easy to replicate and upgrade. The successful implementation and performance of the EMS is discussed, showing that the components of the microgrid follow the dispatch commands, with the OBEMS yielding better overall results than the RBEMS, as expected, using the existing communications links and maintaining the stability of the microgrid.
Regulation Signal Design and Fast Frequency Control With Energy Storage Systems
(Institute of Electrical and Electronics Engineers (IEEE), 2021-06-02) Guzman E., Noela Sofia; Arriaga, Mariano; Cañizares, Claudio A.; Simpson-Porco, John W.; Sohm, Daniel; Bhattacharya, Kankar
This paper presents a novel H2 filter design procedure to optimally split the Frequency Regulation (FR) signal between conventional and fast regulating Energy Storage System (ESS) assets, considering typical Communication Delays (CDs). The filter is then integrated into a previously validated FR model of the Ontario Power System (OPS) including Battery and Flywheel ESSs, which is used to analyze the impact of these ESSs, CDs, and limited regulation capacity in the FR process in a real system. The proposed methodology to split the FR signal is also compared with the existing FR process, with the results showing that the proposed H2 filter design and signal splitting strategy can improve the FR process performance significantly, in terms of reducing the Area Control Error (ACE) signal, and thus reduce the need for regulation capacity.