Browsing by Author "Bhattacharya, Kankar"

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A Comprehensive Process for Addressing Market Power in Decentralized ADN Electricity Markets
(University of Waterloo, 2025-03-05) AboAhmed, Yara; Salama, Magdy; Bhattacharya, Kankar
Electric power systems have transformed globally, with distribution grids evolving into active distribution networks (ADNs), altering their characteristics and operations. Traditional centralized market structures have become inadequate for the complexities of the ADNs, leading to inefficiencies and challenges in reliable operation and energy pricing. ADN electricity markets offer a solution by leveraging smart grid features to integrate distributed energy resources (DERs), allowing non-utility entities, such as producers, consumers and prosumers, to participate directly, enhancing market efficiency, reducing monopoly power, and limiting utility control over prices. However, with the increasing penetration of DERs, there is a growing risk of market concentration and manipulation by entities owning large shares of DERs in ADN electricity markets. This poses a potential threat to market fairness, as some participants may exploit market power, leading to an uneven playing field, reducing the integrity and efficiency of ADN electricity markets. From this standpoint, this thesis investigates and adapts the concept of market power within ADN electricity markets, considering the unique characteristics of the market and the system. The investigation is structured around six central questions: (1) Can non-utility entities exercise market power in ADN electricity markets? (2) Is there a comprehensive framework for accurately monitoring, evaluating, and mitigating market power in decentralized ADN markets? (3) If such a framework exists, can it manage the complexity of monitoring the large number of ADN market participants? (4) If market power manipulation exists, are current investigations adequate, considering the decentralized market structure, the physical characteristics of the system, DER operational constraints, and the interplay between active and reactive power markets? (5) What types of decentralized market structures and frameworks—such as fully decentralized, community-based, or network-based peer-to-peer (P2P)—are appropriate for addressing market power in ADN electricity markets? (6) Are traditional market power mitigation methods applicable and effective in the context of ADN electricity markets considering the decentralized nature of the ADN and the dispersed DERs?. The primary objective of this thesis is to develop a fair and decentralized energy trading platform that limits monopoly power and mitigates market power abuse in ADN electricity markets. To achieve this goal, the thesis proposes an innovative comprehensive process for monitoring, evaluating, and mitigating market power, specially designed for the decentralized structure of ADNs and their market frameworks. This process considers the shifts in network configuration as well as the physical and operational characteristics of ADNs and their components. The process begins by monitoring market power of dominant market participants through introducing the zoning concept. These operational zones narrow down the number of market participants within each zone, addressing the challenge of monitoring a large number of market participants with widely distributed DERs and improving the identification and control of potential market power exercisers, thus minimizing their potential market power. These operational zones serve as decentralized interfaces between the zonal market participants and their corresponding zonal market operators, establishing a decentralized platform for energy trading. The second stage of the process focuses on evaluating market power through investigating and analyzing the strategic offering behavior of the potential market power exercisers identified in stage one. This analysis is conducted within the framework of a community-based P2P decentralized ADN electricity market, considering the physical and operational characteristics of both the system and DERs, along with the coupled active and reactive power markets. A comparative evaluation of market outcomes under competitive and strategic conditions is performed to identify strategic manipulators. In this context, the study also examines the applicability and effectiveness of conventional market power mitigation techniques used for the centralized market and assesses their impact on the strategic offering behavior of identified manipulators. While some traditional market power mitigation techniques may demonstrate efficiency, a new approach is necessary to address the unique decentralization characteristic of ADN electricity markets. A novel market power mitigation technique is proposed in the third stage of the process, targeting the root cause of market power: market concentration. This approach introduces an innovative market zoning concept, dynamically partitioning the system into "Market-Zones" to reduce market concentration while adapting to different system operational conditions, considering the uncertainties in system demand and generation, thereby aligning with the decentralized nature of ADNs and their markets. The proposed innovative zoning approach offers a robust solution for mitigating market power in decentralized ADN electricity markets. Within these Market-Zones, each player can actively engage and participate in the market and obtain the benefit without being overtaken by entities with large market shares. Consequently, the market power of the dominant players is subsided and diluted by utilizing the proposed Market-Zones, establishing a fair energy trading platform.
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 Novel Affine Arithmetic Method to Solve Optimal Power Flow Problems With Uncertainties
(Institute of Electrical and Electronics Engineers (IEEE), 2014-05-02) Pirnia, Mehrdad; Canizares, Claudio A.; Bhattacharya, Kankar; Vaccaro, Alfredo
An affine arithmetic (AA) method is proposed in this paper to solve the optimal power flow (OPF) problem with uncertain generation sources. In the AA-based OPF problem, all the state and control variables are treated in affine form, comprising a center value and the corresponding noise magnitudes, to represent forecast, model error, and other sources of uncertainty without the need to assume a probability density function (pdf). The proposed AA-based OPF problem is used to determine the operating margins of the thermal generators in systems with uncertain wind and solar generation dispatch. The AA-based approach is benchmarked against Monte Carlo simulation (MCS) intervals in order to determine its effectiveness. The proposed technique is tested and demonstrated on the IEEE 30-bus system and also a real 1211-bus European system.
A Procurement Market Model for Reactive Power Services Considering System Security
(Institute of Electrical and Electronics Engineers (IEEE), 2008-01-21) El-Samahy, Ismael; Bhattacharya, Kankar; Canizares, Claudio; Anjos, Miguel F.; Pan, Jiuping
This paper proposes a two-level framework for the operation of a competitive market for reactive power ancillary services. It is argued that the first-level, i.e., reactive power procurement, be on a seasonal basis while the second-level, i.e., reactive power dispatch, be close to real-time operation. To this effect, a reactive power procurement market model is proposed here taking into consideration system security aspects. This procurement procedure is based on a two-step optimization model. First, the marginal benefits of reactive power supply from each provider with respect to system security are obtained by solving an optimal power flow (OPF) that maximizes system loadability subject to transmission security constraints imposed by voltage limits, thermal limits, and stability limits. Second, the selected set of generators is then determined by solving an OPF-based auction to maximize a societal advantage function comprising generators' offers and their corresponding marginal benefits with respect to system security, considering all transmission system constraints. The proposed procedure yields the selected set of generators and zonal price components, which would form the basis for seasonal contracts between the system operator and the selected reactive power service providers.
A Range Arithmetic-Based Optimization Model for Power Flow Analysis Under Interval Uncertainty
(Institute of Electrical and Electronics Engineers (IEEE), 2012-09-18) Vaccaro, Alfredo; Canizares, Claudio A.; Bhattacharya, Kankar
This paper presents a novel framework based on range arithmetic for solving power flow problems whose input data are specified within real compact intervals. Reliable interval bounds are computed for the power flow problem, which is represented as an optimization model with complementary constraints to properly represent generator bus voltage controls, including reactive power limits and voltage recovery processes. It is demonstrated that the lower and upper bounds of the power flow solutions can be obtained by solving two determinate optimization problems. Several numerical results are presented and discussed, demonstrating the effectiveness of the proposed methodology and comparing it to a previously proposed affine arithmetic based solution approach.
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.
A Sustainable Energy Management System for Isolated Microgrids
(Institute of Electrical and Electronics Engineers (IEEE), 2017-04-12) Solanki, Bharatkumar V.; Bhattacharya, Kankar; Canizares, Claudio A.
In this paper, the equivalent CO2 emission models for fossil-fuel-based distributed generator units are developed considering their individual emission characteristic and fuel consumption. These models are then integrated within a microgrid energy management system (EMS) model. Constant energy, demand shifting load models are further integrated in the EMS to examine the possible impact of demand response (DR) on the total system emissions and economics of a microgrid. Thus, the impacts of including the developed emission models on the operation of an isolated microgrid, equivalent CO2 emissions, and costs are examined considering five different operating strategies. The proposed operating strategies are validated on a modified CIGRE medium voltage benchmark system. The results obtained highlight the effectiveness of the proposed EMS and also demonstrate the impact of DR on emissions and costs.
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.
Aggregated BESS Dynamic Models for Active Distribution Network Studies
(Institute of Electrical and Electronics Engineers (IEEE), 2020-12-31) Calero, Fabian; Canizares, Claudio A.; Bhattacharya, Kankar
This article proposes a transmission-system-level aggregated model of Battery Energy Storage Systems (BESSs) distributed through Active Distribution Networks (ADNs), to study the dynamic performance and services provided by these systems to power grids. ADNs comprise intelligent loads, local generation, particularly solar PV, and BESSs, which can provide different services to transmission grids, including voltage control, oscillation damping, frequency regulation, and active and reactive power injections. Proper equivalent models of the ADN components allow to evaluate the impact and integration of these networks on power grids. In this article, ADN's measurements of the aggregated response of the BESSs at the boundary bus with the transmission system are used to develop an aggregated black-box model based on two Neural Networks (NNs), one for active power and another for reactive power, with their optimal topology obtained using a Genetic Algorithm (GA). Detailed simulations are performed, using a commercial-grade software for power system analysis, of multiple BESSs connected to a CIGRE benchmark ADN connected to a bus of the 9-bus WSCC benchmark transmission network; the test ADN is then replaced by the proposed black-box model, with aggregated models of the loads and PV generation, demonstrating that the proposed model can accurately reproduce the results obtained.
An Affine Arithmetic-Based Method for Voltage Stability Assessment of Power Systems With Intermittent Generation Sources
(Institute of Electrical and Electronics Engineers (IEEE), 2013-08-21) Munoz, Juan; Canizares, Claudio; Bhattacharya, Kankar; Vaccaro, Alfredo
This paper presents a novel method based on affine arithmetic (AA) for voltage stability assessment of power systems considering uncertainties associated with operating conditions, which may be attributed to intermittent generation sources, such as wind and solar. The proposed AA-based method reduces the computational burden as compared to Monte Carlo (MC) simulations, and also improves the accuracy as compared to some analytical approaches. The proposed method is tested using two study cases: first, a 5-bus test system is used to illustrate the proposed technique in detail, and thereafter a 2383-bus test system to demonstrate its practical application. The results are compared with those obtained using MC simulations to verify the accuracy and computational burden of the proposed AA-based method, and also with respect to a previously proposed technique to estimate parameter sensitivities in voltage stability assessment.
Battery Energy Storage System Models for Microgrid Stability Analysis and Dynamic Simulation
(Institute of Electrical and Electronics Engineers (IEEE), 2017-08-14) Farrokhabadi, Mostafa; Konig, Sebastian; Canizares, Claudio A.; Bhattacharya, Kankar; Leibfried, Thomas
With the increasing importance of battery energy storage systems (BESS) in microgrids, accurate modeling plays a key role in understanding their behavior. This paper investigates and compares the performance of BESS models with different depths of detail. Specifically, several models are examined: an average model represented by voltage sources; an ideal dc source behind a voltage source converter; a back-to-back buck/boost and bidirectional three-phase converter, with all models sharing the same control system and parameters; and two additional proposed models where the switches are replaced by dependent sources to help analyze the differences observed in the performance of the models. All these models are developed in PSCAD and their performances are simulated and compared considering various issues such as voltage and frequency stability and total harmonic distortion in a benchmark test microgrid. It is shown through simulation results and eigenvalue studies that the proposed models can exhibit a different performance, especially when the system is heavily loaded, highlighting the need for more accurate modeling under certain microgrid conditions.
Behind-the-Meter Compressed Air Energy Storage Feasibility and Applications
(University of Waterloo, 2019-05-30) Anierobi, Chioma Christiana; Canizares, Claudio; Bhattacharya, Kankar
In many jurisdictions, commercial and industrial (C&I) customers are charged for their energy consumption as well as the power drawn from the grid at peak load hours. In Ontario, the demand-based charge component of the electricity cost has been skyrocketing, and this cost often accounts for a significant portion of the overall operating cost of large customers. The Ontario Government in 2010 launched the Industrial Conservation Initiative (ICI) program which requires large customers (Class A) to pay a Global Adjustment (GA) charge, based on their percentage contribution in load during the top five system peak load hours over a one-year base period. This offers enormous savings opportunity to many industrial customers by using strategies to reduce or offset their load during these system peak load hours. However, managing demand can be challenging when faced with production constraints in areas of high-energy sensitive production lines where short interruptions are not permitted. Energy Storage System (ESS) offers the customer the capability to carry out its usual operations while simultaneously saving on the electricity bill through demand reduction. ESS can provide electricity to the facility during system peak periods to reduce the power drawn from the grid, while during non-peak price periods, the ESS is recharged by harnessing the low-cost power. In this work, a detailed operations model of behind-the-meter Small Scale Compressed Air Energy Storage (SS-CAES) is developed for an industrial customer, with an existing well/cavern that can be re-purposed for air storage. The developed optimization model manages the operation of the CAES facility to minimize electricity costs, determining the storage energy output and the corresponding charging and discharging decisions of the SS-CAES system. Furthermore, a detailed economic analysis is carried out to examine financial viability of a practical behind-the-meter SS-CAES project. Some key parameters such as life cycle, CAES capacity and capital cost, and electricity price are considered for carrying out a sensitivity analysis, and the results suggest that SS-CAES is economically viable in the current Ontario rate structure. It is shown that the cost of an SS-CAES project and GA charges are the key determining factors for economic deployment of SS-CAES in Ontario.
Behind-the-meter compressed air energy storage feasibility and applications
(Elsevier, 2020-08-08) Anierobi, Chioma C.; Bhattacharya, Kankar; Canizares, Claudio A.
In this paper, the operations model of a behind-the-meter Small Scale Compressed Air Energy Storage (SS-CAES) facility is developed for an industrial customer with existing wells/caverns that can be re-purposed for air storage. The operations model seeks to minimize the electricity costs of the industrial customer, while determining the energy output and the corresponding charging and discharging decisions of the SS-CAES system. In order to examine the financial viability of a practical behind-the-meter SS-CAES facility, an economic analysis is carried out using real data of an industrial customer based in Ontario, Canada. Key parameters such as life cycle, CAES capacity and capital cost, and electricity price are considered for carrying out a sensitivity analysis, with the results showing that SS-CAES is economically viable for the current Ontario electricity tariff rate structure. The low capital cost of a SS-CAES project with a re-purposed storage cavern, and the high Global Adjustment charges levied in Ontario are shown to be a key determining factors for the economic feasibility of deployment of SS-CAES in Ontario.
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.
Compressed Air Energy Storage System Modeling for Power System Studies
(Institute of Electrical and Electronics Engineers (IEEE), 2019-02-25) Calero, Ivan; Canizares, Claudio A.; Bhattacharya, Kankar
In this paper, a detailed mathematical model of the diabatic compressed air energy storage (CAES) system and a simplified version are proposed, considering independent generators/motors as interfaces with the grid. The models can be used for power system steady-state and dynamic analyses. The models include those of the compressor, synchronous motor, cavern, turbine, synchronous generator, and associated controls. The configuration and parameters of the proposed models are based on the existing bulk CAES facilities of Huntorf, Germany. The models and performance of the CAES system are first evaluated with step responses, and then examined when providing frequency regulation in a test power system with high penetration of wind generation, comparing them with existing models of CAES systems. The simulation results confirm that the dynamic responses of the detailed and simplified CAES models are similar, and demonstrate that the simultaneous charging and discharging can significantly contribute to reduce the frequency deviation of the system from the variability of the wind farm power.
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.
A Decentralised Transactive Energy Market Considering Physical System Constraints
(University of Waterloo, 2022-08-29) Pankhurst, Colton; Bhattacharya, Kankar; Canizares, Claudio
Increasing levels of Distributed Energy Resources (DERs) are expected to play a key role in achieving global electricity decarbonisation goals, providing both a challenge and an opportunity for the electricity industry. Conventional approaches such as Net Energy Metering (NEM) have been questioned regarding their effectiveness in properly rewarding DERs, and larger efforts around the integration of DERs into wholesale markets do not address potential value streams at the distribution system level. 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, a gap remains in the research to date on how to efficiently allow for prosumer decision autonomy while ensuring that the physical layer of the power system is considered. This thesis proposes a decentralised transactive solution that retains prosumer negotiation and decision autonomy, while using network operator and market determined prices to allocate limited system resources for a feasible, locally optimal system state. Peer-to-Utility (P2U) transactions are added to existing P2P energy frameworks to obtain transactive local peer decision criteria considering Peer-Centric (PC) and System-Centric (SC) objectives. Peers are able to interact with wholesale electricity market derived prices through P2U transactions, allowing for consideration of net export value in welfare maximising decisions. The proposed approach includes a split transaction fee pricing mechanism for virtual prosumer interactions that considers the networks characteristics such as topology and operational constraints to ensure consideration of the physical layer in peer decision making. In addition to pricing mechanisms for coupling the virtual and physical layers, a congestion clearing process is proposed, which coordinates with the decentralised transaction matching process and the Network Usage Charges (NUCs) to ensure efficient allocation of network capacity. Previously reported distribution networks are used to compare the transaction decisions, economic performance, and system performance of the proposed solution with existing approaches. The results demonstrate the effectiveness of the proposed method in ensuring system feasible, locally optimal transaction sets with prioritisation of local peers.
Demand Response and Battery Energy Storage Systems in Electricity Markets: Frameworks & Models
(University of Waterloo, 2019-08-09) Padmanabhan, Nitin; Bhattacharya, Kankar; Ahmed, Mohamed
Ensuring a balance between the generation and demand is one of the most challenging tasks in power systems because of contingencies, sudden load changes, forecasting errors and other disturbances, occurring from time to time. The peak demand, which occurs only for a short duration, has always been a concern for independent system operators (ISOs), as it leads to high market prices and reliability concerns. Furthermore, in recent years there have been significant increase in the penetration of renewable energy sources (RES) to address the challenge of significantly reducing carbon dioxide (CO2) and other greenhouse gas (GHG) emissions and the system's dependence on fossil fuels based generation resources. However, the high penetration of RES, because of their intermittency and uncertainty, poses operational and reliability issues and thus necessitates an increase in the procurement and deployment of primary and secondary regulation reserves, as well as spinning and non-spinning reserves. In recent years, demand response (DR) and battery energy storage systems (BESS), because of their characteristic features such as fast response time, high ramp rate, and the ability to provide flexible upward and downward response as compared to conventional generators, have been considered as promising and viable options by the ISO to reduce the peak demand, facilitate RES integration and for the provision of ancillary services, such as regulation and spinning reserves. Despite the benefits and the growth opportunities of DR and BESS, there are still many challenges associated with their market participation. To address the challenges pertaining to DR and BESS participation in electricity markets, this thesis proposes appropriate models and frameworks, which can efficiently integrate these resources into the day-ahead and real-time electricity markets, and at the same time effectively address the aforementioned challenges of ISOs. This thesis first presents a new bid/offer structure for DR provisions, simultaneously through price responsive demand (PRD) based bids and load curtailment based DR offers from customers. Thereafter, incorporating the DR offer structure, a novel day-ahead, co-optimizing market auction framework and mathematical model for DR-energy-spinning reserve market, based on LMPs, which includes transmission loss representation within the dc power flow constraints is proposed. The impact of DR on both energy and spinning reserve market prices, market dispatch, line congestions, and other economic indicators, is studied using the IEEE Reliability Test System (RTS), by considering various scenarios and cases. In the next stage, the thesis considers the BESS participation in the day-ahead markets. First, a novel BESS cost function model, considering Degradation Cost, based on depth of discharge (DOD) and discharge rate, and Flexibility Cost, in terms of the battery power-to-energy (P/E) ratio, is presented. A detailed bid/offer structure based on the proposed cost functions is formulated. Thereafter, a new framework and mathematical model for BESS participation in an LMP-based, co-optimized, day-ahead energy and spinning reserve market, have been developed. Three case studies are presented to investigate the impact of BESS participation on system operation and market settlement. The proposed model is validated on the IEEE RTS to demonstrate its functionalities. Finally, the thesis considers BESS participation in the real-time operations. Firstly, a novel framework for simultaneously procuring primary and secondary regulation reserves alongside energy, in a BESS integrated electricity market, by taking into account probabilistic scenarios of contingencies, is proposed. Thereafter, an appropriate mathematical model is developed considering BESS alongside conventional generators to determine the optimal real-time primary and secondary regulation reserves and energy market clearing, in a co-optimized, LMP based market, taking into consideration the a priori cleared day- ahead market schedules. Lastly, the impact of participation of BESS in day-ahead and real-time energy and reserve markets on prices, market clearing dispatch, and other economic indicators are investigated using the IEEE RTS, for various scenarios and cases.
Distributed Computing Architecture for Optimal Control of Distribution Feeders With Smart Loads
(Institute of Electrical and Electronics Engineers (IEEE), 2016-09-28) Mosaddegh, Abolfazl; Canizares, Claudio A.; Bhattacharya, Kankar; Fan, Hongbing
This paper presents a distributed computing architecture for solving a distribution optimal power flow (DOPF) model based on a smart grid communication middleware (SGCM) system. The system is modeled as an unbalanced three-phase distribution system, which includes different kind of loads and various components of distribution systems. In this paper, fixed loads are modeled as constant impedance, current and power loads, and neural network models of controllable smart loads are integrated into the DOPF model. A genetic algorithm is used to determine the optimal solutions for controllable devices, in particular load tap changers, switched capacitors, and smart loads in the context of an energy management system for practical feeders, accounting for the fact that smart loads consumption should not be significantly affected by network constraints. Since the number of control variables in a realistic distribution power system is large, solving the DOPF for real-time applications is computationally expensive. Hence, to reduce computational times, a decentralized system with parallel computing nodes based on an SGCM system is proposed. Using a “MapReduce” model, the SGCM system runs the DOPF model, communicates between master and worker computing nodes, and sends/receives data among different parts of parallel computing system. Compared to a centralized approach, the proposed architecture is shown to yield better optimal solutions in terms of reducing energy losses and/or energy drawn from the substation within adequate practical run-times for a realistic test feeder.
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.