Publications by Colleges and Departments (MSU - Bozeman)
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Item Distribution System State Estimation Using Hybrid Traditional and Advanced Measurements for Grid Modernization(MDPI AG, 2023-06) Radhoush, Sepideh; Vannoy, Trevor; Liyanage, Kaveen; Whitaker, Bradley M.; Nehrir, HashemDistribution System State Estimation (DSSE) techniques have been introduced to monitor and control Active Distribution Networks (ADNs). DSSE calculations are commonly performed using both conventional measurements and pseudo-measurements. Conventional measurements are typically asynchronous and have low update rates, thus leading to inaccurate DSSE results for dynamically changing ADNs. Because of this, smart measurement devices, which are synchronous at high frame rates, have recently been introduced to enhance the monitoring and control of ADNs in modern power networks. However, replacing all traditional measurement devices with smart measurements is not feasible over a short time. Thus, an essential part of the grid modernization process is to use both traditional and advanced measurements to improve DSSE results. In this paper, a new method is proposed to hybridize traditional and advanced measurements using an online machine learning model. In this work, we assume that an ADN has been monitored using traditional measurements and the Weighted Least Square (WLS) method to obtain DSSE results, and the voltage magnitude and phase angle at each bus are considered as state vectors. After a period of time, a network is modified by the installation of advanced measurement devices, such as Phasor Measurement Units (PMUs), to facilitate ADN monitoring and control with a desired performance. Our work proposes a method for taking advantage of all available measurements to improve DSSE results. First, a machine-learning-based regression model was trained from DSSE results obtained using only the traditional measurements available before the installation of smart measurement devices. After smart measurement devices were added to the network, the model predicted traditional measurements when those measurements were not available to enable synchronization between the traditional and smart sensors, despite their different refresh rates. We show that the regression model had improved performance under the condition that it continued to be updated regularly as more data were collected from the measurement devices. In this way, the training model became robust and improved the DSSE performance, even in the presence of more Distributed Generations (DGs). The results of the proposed method were compared to traditional measurements incorporated into the DSSE calculation using a sample-and-hold technique. We present the DSSE results in terms of Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) values for all approaches. The effectiveness of the proposed method was validated using two case studies in the presence of DGs: one using a modified IEEE 33-bus distribution system that considered loads and DGs based on a Monte Carlo simulation and the other using a modified IEEE 69-bus system that considered actual data for loads and DGs. The DSSE results illustrate that the proposed method is better than the sample-and-hold method.Item Distribution System State Estimation and False Data Injection Attack Detection with a Multi-Output Deep Neural Network(MDPI AG, 2023-02) Radhoush, Sepideh; Vannoy, Trevor; Liyanage, Kaveen; Whitaker, Bradley M.; Nehrir, HashemDistribution system state estimation (DSSE) has been introduced to monitor distribution grids; however, due to the incorporation of distributed generations (DGs), traditional DSSE methods are not able to reveal the operational conditions of active distribution networks (ADNs). DSSE calculation depends heavily on real measurements from measurement devices in distribution networks. However, the accuracy of real measurements and DSSE results can be significantly affected by false data injection attacks (FDIAs). Conventional FDIA detection techniques are often unable to identify FDIAs into measurement data. In this study, a novel deep neural network approach is proposed to simultaneously perform DSSE calculation (i.e., regression) and FDIA detection (i.e., binary classification) using real measurements. In the proposed work, the classification nodes in the DNN allow us to identify which measurements on which phasor measurement unit (PMU), if any, were affected. In the proposed approach, we aim to show that the proposed method can perform DSSE calculation and identify FDIAs from the available measurements simultaneously with high accuracy. We compare our proposed method to the traditional approach of detecting FDIAs and performing SE calculations separately; moreover, DSSE results are compared with the weighted least square (WLS) algorithm, which is a common model-based method. The proposed method achieves better DSSE performance than the WLS method and the separate DSSE/FDIA method in presence of erroneous measurements; our method also executes faster than the other methods. The effectiveness of the proposed method is validated using two FDIA schemes in two case studies: one using a modified IEEE 33-bus distribution system without DGs, and the other using a modified IEEE 69-bus system with DGs. The results illustrated that the accuracy and F1-score of the proposed method are better than when performing binary classification only. The proposed method successfully detected the FDIAs on each PMU measurement. Moreover, the results of DSSE calculation from the proposed method has a better performance compared to the regression-only method, and the WLS methods in the presence of bad data.Item Distribution System State Estimation and False Data Injection Attack Detection with a Multi-Output Deep Neural Network(MDPI AG, 2023-02) Radhoush, Sepideh; Vannoy, Trevor; Liyanage, Kaveen; Whitaker, Bradley M.; Nehrir, HashemDistribution system state estimation (DSSE) has been introduced to monitor distribution grids; however, due to the incorporation of distributed generations (DGs), traditional DSSE methods are not able to reveal the operational conditions of active distribution networks (ADNs). DSSE calculation depends heavily on real measurements from measurement devices in distribution networks. However, the accuracy of real measurements and DSSE results can be significantly affected by false data injection attacks (FDIAs). Conventional FDIA detection techniques are often unable to identify FDIAs into measurement data. In this study, a novel deep neural network approach is proposed to simultaneously perform DSSE calculation (i.e., regression) and FDIA detection (i.e., binary classification) using real measurements. In the proposed work, the classification nodes in the DNN allow us to identify which measurements on which phasor measurement unit (PMU), if any, were affected. In the proposed approach, we aim to show that the proposed method can perform DSSE calculation and identify FDIAs from the available measurements simultaneously with high accuracy. We compare our proposed method to the traditional approach of detecting FDIAs and performing SE calculations separately; moreover, DSSE results are compared with the weighted least square (WLS) algorithm, which is a common model-based method. The proposed method achieves better DSSE performance than the WLS method and the separate DSSE/FDIA method in presence of erroneous measurements; our method also executes faster than the other methods. The effectiveness of the proposed method is validated using two FDIA schemes in two case studies: one using a modified IEEE 33-bus distribution system without DGs, and the other using a modified IEEE 69-bus system with DGs. The results illustrated that the accuracy and F1-score of the proposed method are better than when performing binary classification only. The proposed method successfully detected the FDIAs on each PMU measurement. Moreover, the results of DSSE calculation from the proposed method has a better performance compared to the regression-only method, and the WLS methods in the presence of bad data.Item Distribution System State Estimation and False Data Injection Attack Detection with a Multi-Output Deep Neural Network(MDPI AG, 2023-02) Radhoush, Sepideh; Vannoy, Trevor; Liyanage, Kaveen; Whitaker, Bradley M.; Nehrir, HashemDistribution system state estimation (DSSE) has been introduced to monitor distribution grids; however, due to the incorporation of distributed generations (DGs), traditional DSSE methods are not able to reveal the operational conditions of active distribution networks (ADNs). DSSE calculation depends heavily on real measurements from measurement devices in distribution networks. However, the accuracy of real measurements and DSSE results can be significantly affected by false data injection attacks (FDIAs). Conventional FDIA detection techniques are often unable to identify FDIAs into measurement data. In this study, a novel deep neural network approach is proposed to simultaneously perform DSSE calculation (i.e., regression) and FDIA detection (i.e., binary classification) using real measurements. In the proposed work, the classification nodes in the DNN allow us to identify which measurements on which phasor measurement unit (PMU), if any, were affected. In the proposed approach, we aim to show that the proposed method can perform DSSE calculation and identify FDIAs from the available measurements simultaneously with high accuracy. We compare our proposed method to the traditional approach of detecting FDIAs and performing SE calculations separately; moreover, DSSE results are compared with the weighted least square (WLS) algorithm, which is a common model-based method. The proposed method achieves better DSSE performance than the WLS method and the separate DSSE/FDIA method in presence of erroneous measurements; our method also executes faster than the other methods. The effectiveness of the proposed method is validated using two FDIA schemes in two case studies: one using a modified IEEE 33-bus distribution system without DGs, and the other using a modified IEEE 69-bus system with DGs. The results illustrated that the accuracy and F1-score of the proposed method are better than when performing binary classification only. The proposed method successfully detected the FDIAs on each PMU measurement. Moreover, the results of DSSE calculation from the proposed method has a better performance compared to the regression-only method, and the WLS methods in the presence of bad data.Item A Review on State Estimation Techniques in Active Distribution Networks: Existing Practices and Their Challenges(MDPI, 2022-02) Radhoush, Sepideh; Bahramipanah, Maryam; Nehrir, Hashem; Shahooei, ZagrosThis paper provides a comprehensive review of distribution system state estimation in terms of basic definition, different methods, and their application. In the last few years, the operation of distribution networks has been influenced by the installation of distributed generations. In order to control and manage an active distribution network’s performance, distribution system state estimation methods are introduced. A transmission system state estimation cannot be used directly in distribution networks since transmission and distribution networks are different due to topology configuration, the number of buses, line parameters, and the number of measurement instruments. So, the proper state estimation algorithms should be proposed according to the main distribution network features. Accuracy, computational efficiency, and practical implications should be considered in the designing of distribution state estimation techniques since technical issues and wrong decisions could emerge in the control center by inaccurate distribution state estimation results. In this study, conventional techniques are reviewed and compared with data-driven methods in order to highlight the pros and cons of different techniques. Furthermore, the integrated distribution state estimation methods are compared with the distributed approaches, and the different criteria, including the level of area overlapping execution time and computing architecture, are elaborated. Moreover, mathematical problem formulation and different measuring methods are discussed.Item A Novel Agent-Based Power Management Scheme for Smart Multiple-Microgrid Distribution Systems(MDPI AG, 2022-02) Shahooei, Zagros; Martin, Lane; Nehrir, Hashem; Bahramipanah, MaryamIn this work, a novel agent-based day-ahead power management scheme is proposed for multiple-microgrid distribution systems with the intent of reducing operational costs and improving system resilience. The proposed power sharing algorithm executes within each microgrid (MG) locally, and the neighboring MGs cooperate via a multi-agent system cooperation scheme, established to model the communication among the agents. The power management for each agent is modeled as a multi-objective optimization problem (MOP) including two objectives: maximizing load coverage and minimizing the operating costs. The proposed MOP is solved using the Nondominated Sorting Genetic Algorithm (NSGA-II), where a set of Pareto optimal solutions is obtained for each agent through the NSGA-II. The final solution is obtained using an Analytical Hierarchical Process. The effectiveness of the proposed scheme is evaluated using a benchmark 4-MG distribution system. It is shown that the proposed power management scheme and the cooperation of agents lead to a higher overall system resilience and lower operation costs during extreme events.Item A Survey on Smart Agent-Based Microgrids for Resilient/Self-Healing Grids(2017-05-01) Dehghanpour, Kaveh; Colson, Christopher; Nehrir, HashemThis paper presents an overview of our body of work on the application of smart control techniques for the control and management of microgrids (MGs). The main focus here is on the application of distributed multi-agent system (MAS) theory in multi-objective (MO) power management of MGs to find the Pareto-front of the MO power management problem. In addition, the paper presents the application of Nash bargaining solution (NBS) and the MAS theory to directly obtain the NBS on the Pareto-front. The paper also discusses the progress reported on the above issues from the literature. We also present a MG-based power system architecture for enhancing the resilience and self-healing of the system.Item Agent-Based Modeling of Retail Electrical Energy Markets with Demand Response(2018-08-01) Nehrir, Hashem; Dehghanpour, Kaveh; Sheppard, John W.; Kelly, NathanIn this paper, we study the behavior of a Day-Ahead (DA) retail electrical energy market with price-based Demand Response (DR) from Air Conditioning (AC) loads through a hierarchical multiagent framework, employing a machine learning approach. At the top level of the hierarchy, a retailer agent buys energy from the DA wholesale market and sells it to the consumers. The goal of the retailer agent is to maximize its profit by setting the optimal retail prices, considering the response of the price-sensitive loads. Upon receiving the retail prices, at the lower level of the hierarchy, the AC agents employ a Q-learning algorithm to optimize their consumption patterns through modifying the temperature set-points of the devices, considering both consumption costs and users' comfort preferences. Since the retailer agent does not have direct access to the AC loads' underlying dynamics and decision process (i.e., incomplete information) the data privacy of the consumers becomes a source of uncertainty in the retailer's decision model. The retailer relies on techniques from the field of machine learning to develop a reliable model of the aggregate behavior of the price-sensitive loads to reduce the uncertainty of the decision-making process. Hence, a multiagent framework based on machine learning enables us to address issues such as interoperability and decision-making under incomplete information in a system that maintains the data privacy of the consumers. We will show that using the proposed model, all the agents are able to optimize their behavior simultaneously. Simulation results show that the proposed approach leads to a reduction in overall power consumption cost as the system converges to its equilibrium. This also coincides with maximization in the retailer's profit. We will also show that the same decision architecture can be used to reduce peak load to defer/avoid distribution system upgrades under high penetration of Photo-Voltaic (PV) power in the distribution feeder.Item Real-Time Multiobjective Microgrid Power Management Using Distributed Optimization in an Agent-Based Bargaining Framework(2018-11-01) Dehghanpour, Kaveh; Nehrir, HashemIn this paper, we present a market-based resilient power management procedure for electrical distribution systems consisting of multiple cooperative MiroGrids (MGs). Distributed optimization is used to find the optimal resource allocation for the multiple MG system, while maintaining the local and global constraints, including keeping the voltage levels of the micro-sources within bounds. The proposed method is based on probabilistic reasoning in order to consider the uncertainty of the decision model in preparation for expected extreme events and in case of unit failure, to improve the resiliency of the system. Basically, the power management problem formulation is a multiobjective optimization problem, which is solved using the concept of Nash Bargaining Solution (NBS). The simulation results show that the proposed method is able to improve the resiliency of the system and prepare it for extreme events and unit failure, by increasing power reserve and modifying the operating point of the system to maintain voltage and power constraints across the MGs.Item A Market-Based Resilient Power Management Technique for Distribution Systems with Multiple Microgrids Using a Multi-Agent System Approach.(2019-01-24) Dehghanpour, Kaveh; Nehrir, HashemIn this paper, we present a market-based resilient power management procedure for electrical distribution systems consisting of multiple cooperative MiroGrids (MGs). Distributed optimization is used to find the optimal resource allocation for the multiple MG system, while maintaining the local and global constraints, including keeping the voltage levels of the micro-sources within bounds. The proposed method is based on probabilistic reasoning in order to consider the uncertainty of the decision model in preparation for expected extreme events and in case of unit failure, to improve the resiliency of the system. Basically, the power management problem formulation is a multiobjective optimization problem, which is solved using the concept of Nash Bargaining Solution (NBS). The simulation results show that the proposed method is able to improve the resiliency of the system and prepare it for extreme events and unit failure, by increasing power reserve and modifying the operating point of the system to maintain voltage and power constraints across the MGs.