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In this paper, we make the case for an increased focus on teaching an understanding of societal context and the integral role it plays in energy infrastructure design. Power system design education is inadequate in terms of holistic understanding of the non-technical aspects of a client-society in the development of energy infrastructure solutions. These are reflected in many failed designs, primarily designed by student engineers as part of capstone projects, senior design projects, or extra-curricular work through humanitarian-oriented programs administered by NGOs. We are developing coursework that introduces students to the complexities involved in the practice of engineering in rural communities at the international level. [ABSTRACT FROM AUTHOR]

This article presents a library of second-order models for synchronous machines that can be utilized in power system dynamic performance analysis and control design. The models have a similar structure to that of the so-called classical model in that they consist of two dynamic states, the power angle and the angular speed. However, unlike the classical model, they find applications beyond first swing stability analysis, i.e., they can be utilized for multi-swing transient stability analysis. The models are developed through a systematic reduction of a nineteenth-order model using singular perturbation techniques. They are validated by comparing their response with that of the high-order model from which they were derived, as well as that of other synchronous machine models existing in the literature, such as the so-called two-axis model and the so-called one-axis model. [ABSTRACT FROM AUTHOR]

This paper provides information on the issues that impact the adoption of Small Modular Reactors (SMR) in microgrids as additional resources. It summarizes the technical and economic characteristics and financial model cases. It also discusses technical issues related to SMR installations and their operation and control in load following and frequency regulation. [ABSTRACT FROM AUTHOR]

In this paper, we address the problem of setting the tap positions of load tap changers (LTCs) for voltage regulation in power distribution systems. The objective is to find a policy that maps measurements of voltage magnitudes and topology information to LTC tap ratio changes so as to minimize the voltage deviation across the system. We formulate this problem as a Markov decision process (MDP), and propose a data and computationally efficient batch reinforcement learning (RL) algorithm to solve it. To circumvent the “curse of dimensionality” resulting from the large state and action spaces, we propose a sequential learning algorithm to learn an action-value function for each LTC, based on which the optimal tap positions can be directly determined. By taking advantage of a linearized power flow model, we propose an algorithm to estimate the voltage magnitudes under different tap settings, which allows the RL algorithm to explore the state and action spaces freely offline without impacting the system operation. The effectiveness of the proposed algorithm is validated via numerical simulations on the IEEE 13-bus and 123-bus distribution test feeders. [ABSTRACT FROM AUTHOR]

In this paper, we develop a data-driven framework for controlling distributed energy resources (DERs) in a balanced radial power distribution system with the objective of regulating voltages across the whole system. The objective is to determine optimal DER power injections that minimize the voltage excursions outside a desirable voltage range without knowing a complete model of the power distribution system. To this end, we approximate the nonlinear relationship between the voltage magnitudes and the power injections by a linear model. The parameters of this linear model—referred to as the voltage sensitivities—can be computed using information on the network topology and the line parameters, the values of which will be estimated. Assuming the knowledge of feasible network topology configurations and distribution line resistance-to-reactance ratios, we propose a framework for identifying the true network topology configuration and the corresponding line parameters using only a few measurements of voltage magnitudes and power injections. Utilizing the estimated voltage sensitivities, the optimal DER power injections can be readily determined by solving a convex optimization problem. Due to its data-driven nature, the proposed framework is intrinsically adaptive to changes in system conditions such as unknown topology reconfiguration. The effectiveness of the proposed framework is validated via numerical simulations on the IEEE 123-bus distribution test feeder. [ABSTRACT FROM AUTHOR]

In this paper, we propose a framework for coordinating distributed energy resources (DERs) connected to a power distribution system, the model of which is not completely known, so that they collectively provide a specified amount of active power to the bulk power system, while respecting distribution line capacity limits. The proposed framework consists of a linear time-varying input-output (IO) system model that represents the relation between the DER active power injections (inputs), and the total active power exchanged between the distribution and bulk power systems (output); an estimator that aims to estimate the IO model parameters; and a controller that determines the optimal DER active power injections so that the power exchanged between both systems equals to the specified amount at a minimum generating cost. We formulate the estimation problem as a box-constrained quadratic program and solve it using the projected gradient descent algorithm. To resolve the potential issue of collinearity in the measurements, we introduce random perturbations in the DER active power injections during the estimation process. Using the estimated IO model, the optimal DER coordination problem to be solved by the controller can be formulated as a convex optimization problem, which can be solved easily. The effectiveness of the framework is validated via numerical simulations using the IEEE 123-bus distribution test feeder. [ABSTRACT FROM AUTHOR]

In this paper, we analyze control-related attacks in supervisory control and data acquisition systems for power grids. This class of attacks introduces a serious threat to power systems, because attackers can directly change the system’s physical configuration using malicious control commands crafted in a legitimate format. To detect such attacks, we propose a semantic analysis framework that integrates network intrusion detection systems with a power flow analysis capable of estimating the execution consequences of control commands. To balance detection accuracy and latency, the parameters of the power flow analysis algorithm are dynamically adapted according to real-time system dynamics. Our experiments on IEEE 24-bus, 30-bus, and 39-bus systems and a 2736-bus system demonstrate that by opening three transmission lines, an attacker can put the tested system into an insecure state, and the semantic analysis can complete detection in 200 ms for the large-scale 2736-bus system with about 0.78% false positives and 0.01% false negatives, which allow for timely responses to intrusions. [ABSTRACT FROM PUBLISHER]

Generalized injection shift factors (ISFs) are the sensitivities of active-power line flows to active-power bus injections. They are computed without designating an artifactual slack bus in the power network; therefore, conceptually, they reflect sensitivities of power flows more accurately than conventional ISFs, the values of which depend on the choice of the slack bus. This paper derives analytical closed-form expressions for generalized ISFs from a perturbative analysis of the ac circuit equations. In addition, they are computed from a system of linear equations that arise from high-frequency synchronized measurements collected from phasor measurement units. As an application, generalized ISFs are used to predict active-power line flows during the transient period following a contingency by leveraging inertial and governor power flows over appropriate time horizons. [ABSTRACT FROM PUBLISHER]

In This article, We Relate events leading up to the creation of the Power Systems Engineering Research Center (PSERC ) and the problems associated with convincing people that a group of universities can function as a single center. The keys to this achievement are industrial support, university collaboration, and effective management. The article continues with a discussion of how PSERC has evolved over the past 20 years. [ABSTRACT FROM PUBLISHER]

In this paper, we propose a measurement-based method to compute the power flow Jacobian matrix, from which we can infer pertinent information about the system topology in near real-time. A salient feature of our approach is that it readily adapts to changes in system operating point and topology; this is desirable as it provides power system operators with a way to update, as the system evolves, the models used in many reliability analysis tools. The method uses high-speed synchronized voltage and current phasor data collected from phasor measurement units to estimate entries of the Jacobian matrix through linear total least-squares (TLS) estimation. In addition to centralized TLS-based algorithms, we provide distributed alternatives aimed at reducing computational burden. Through numerical case studies, we illustrate the effectiveness of our proposed Jacobian-matrix estimation approach as compared with the conventional model-based one. [ABSTRACT FROM PUBLISHER]

This paper proposes a framework to quantify the impact of uncertainty that arises from load variations, renewable-based generation, and noise in communication channels on the automatic generation control (AGC) system. To this end, we rely on a model of the power system that includes the synchronous generator dynamics, the network, and the AGC system dynamics, as well as the effect of various sources of uncertainty. Then, we develop a method to analytically propagate the uncertainty from the aforementioned sources to the system frequency and area control error (ACE), and obtain expressions that approximate their probability distribution functions. We make use of this framework to obtain probabilistic expressions for the frequency performance criteria developed by the North American Electric Reliability Corporation (NERC); such expressions may be used to determine the limiting values of uncertainty that the system may withstand. The proposed ideas are illustrated through the Western Electricity Coordination Council (WECC) 9-bus 3-machine system and a 140-bus 48-machine system. [ABSTRACT FROM PUBLISHER]