Search

This paper presents a novel modular voltage control algorithm for optimal scheduling of a distribution system's load tap changers to minimize the number of tap changes while maintaining a voltage deviation (VD) around a desired target. To this end, a bi-objective optimal voltage regulation (OVR) problem is addressed in two distinct stages. First, the operational constraint on the load tap changer is removed to form a single-objective OVR problem relating to the voltage. The solution obtained in this stage is ultimately utilized to determine the penalty value assigned to the distance from the optimal (solely in terms of voltage) control value. In the second stage, the optimal scheduling problem is formulated as a minimum-cost-path problem, which can be efficiently solved via dynamic programming. This approach allows the identification of optimal scheduling that considers both the voltage-related objective as well as the number of load tap changer switching operations with no added computational burden beyond that of a simple voltage optimization problem. The method imposes no restriction on the load tap changer's operation and is tested under two different target functions on the standard IEEE-123 test case. The first attains a nominal voltage with a 0.056 p.u. voltage deviation and the second is the well-known conservation voltage reduction (CVR) case with a 0.17 p.u. voltage deviation. The method is compared to an evolutionary-based algorithm and shows significant improvement in the voltage deviation by a factor of 3.5 as well as a computation time acceleration of two orders of magnitude. The paper demonstrates the effectiveness and potential of the proposed method as a key feature in future cutting-edge OVR methods. [ABSTRACT FROM AUTHOR]

In this paper, the modeling of storage elements and a unified theory liberated of the I–V characteristics of a specific technology is developed. First, the definition of an ideal storage element with limited energy capacity is presented, then the semiideal storage properties are discussed. The addition of power conservative two-port network denoted POPI (Pout = Pin) is added in order to define a flexible storage element in terms of characteristics. The paper demonstrates the theory by examples of storage elements integrated in power electronics systems. The ideas and methodology presented in this work can be applied to power electronics applications as well as power systems, smart grids, and microgrids, where storage is an important factor. [ABSTRACT FROM AUTHOR]

A non-intrusive load monitoring (NILM) process is intended to allow for the separation of individual appliances from an aggregated energy reading in order to estimate the operation of individual loads. In the past, electricity meters specified only active power readings, for billing purposes, thus limiting NILM capabilities. Recent progress in smart metering technology has introduced cost-effective, household-consumer-grade metering products, which can produce multiple features with high accuracy. In this paper, a new method is proposed for applying a BIRCH (balanced iterative reducing and clustering using hierarchies) algorithm as part of a multi-dimensional load disaggregation solution based on the extraction of multiple features from a smart meter. The method uses low-frequency meter reading and constructs a multi-dimensional feature space with adaption to smart meter parameters and is useful for type I as well as type II loads with the addition of timers. This new method is described as energy disaggregation in NILM by means of multi-dimensional BIRCH clustering (DNB). It is simple, fast, uses raw meter sampling, and does not require preliminary training or powerful hardware. The algorithm is tested using a private dataset and a public dataset. [ABSTRACT FROM AUTHOR]

The realization of capacitive converters based on a modular approach is presented and analyzed in this paper. The converters are based on topologies with identical capacitors. First, the switching network (SN) of a basic series–parallel topology is presented. The analysis reveals the assembly and growth rules for expanding the topology to any number of capacitors. Subsequently, the SN for the more versatile and complex general transposed series–parallel (GTSP) topology is presented. The topology is expanded by defining a basic cell comprising a capacitor and five peripheral switches. The expansion of this converter improves its performance by ameliorating accuracy and regulation in the dc/dc voltage ratio. The control schemes of these converters are also presented with emphasis on the GTSP topology, whose switches are not assigned in advance. The control scheme for achieving the desired voltage ratio is described. A five-capacitor converter with its control is built to illustrate the theory in open-loop mode, which provides experimental results that are in full agreement with the analytical predictions. [ABSTRACT FROM PUBLISHER]

Recent research has enabled the integration of traditional Volt-VAr Control (VVC) resources, such as capacitor banks and transformer tap changers, with Distributed Energy Resources (DERs), such as photo-voltaic sources and energy storage, in order to achieve various Volt-VAr Optimization (VVO) targets, such as Conservation Voltage Reduction (CVR), minimizing VAr flow at the transformer, minimizing grid losses, minimizing asset operations and more. When more than one target function can be optimized, the question of multi-objective optimization is raised. In this work, a general formulation of the multi-objective Volt-VAr Optimization problem is proposed. The applicability of various multi-optimization techniques is considered and the operational interpretation of these solutions is discussed. The methods are demonstrated using a simulation on a test feeder. [ABSTRACT FROM AUTHOR]

Recent research has enabled the integration of traditional Volt-VAr Control (VVC) resources, such as capacitor banks and transformer tap changers, with Distributed Energy Resources (DERs), such as photo-voltaic sources and energy storage, in order to achieve various Volt-VAr Optimization (VVO) targets, such as Conservation Voltage Reduction (CVR), minimizing VAr flow at the transformer, minimizing grid losses, minimizing asset operations and more. When more than one target function can be optimized, the question of multi-objective optimization is raised. In this work, a general formulation of the multi-objective Volt-VAr Optimization problem is proposed. The applicability of various multi-optimization techniques is considered and the operational interpretation of these solutions is discussed. The methods are demonstrated using a simulation on a test feeder. [ABSTRACT FROM AUTHOR]

Electric load identification and classification for smart grid environment can improve the power service for both consumers and producers. The main concept of electric load identification and classification is to disaggregate various loads and categorize them. In this paper, a new practical method for electric load identification and classification is presented. The method is based on using a power monitor to analyze a real measured current waveform of a grid-connected device. A set number of features is extracted using the currents' physical components-based power theory decomposition. Using currents' physical components ensures a constant number of features, which maintains the signal's characteristics regardless of the harmonic content. These features are used to train a supervised classifier based on two techniques: artificial neural network and nearest neighbor search. The theory is outlined, and experimental results are shown. This paper demonstrates high accuracy performance in identifying an electric load from a designated database. Furthermore, the results show a definite classification of an untrained operation state of a device to the closest trained operation state, for example, the excitation angle of a dimmer. In a comparative study, the method is shown to outperform other state-of-the-art techniques, which are based on harmonic components. [ABSTRACT FROM AUTHOR]

Non-intrusive load monitoring (NILM) techniques estimate the consumption of individual appliances in a household or facility, based on readings of a centralized meter. In this work a new method for multi-dimensional NILM signals is proposed—the Multi-objective NILM (MO-NILM). While classical NILM algorithms are based on a single objective function, MO-NILM classifies NILM events by solving a multi-objective optimization problem. The main idea is to model each NILM feature as an objective function, and to mutually minimize these objectives based on the Non-dominated Sorting Genetic Algorithm II (NSGA-II). The presented algorithms can operate in real time using low sampling rates (0.25 Hz and lower) without training the system. In addition, the proposed algorithm is simple, and requires information on the average power signatures of each appliance. The method shows good performance in terms of standard measures when tested on the popular REDD and AMPds datasets. [ABSTRACT FROM AUTHOR]

An essential component of smart grid applications is the ability to solve the power flow (PF) problem in real-time. As numerical methods are too slow, the use of neural networks (NNs) is rapidly increasing. Graph Neural Networks (GNNs) and their variants have become one of the leading methods to learn graph representations. Power systems and in particular, distribution systems can be represented as graphs, and are characterized by often topology changes, which makes the consideration of the topology structure to be an important aspect when searching for a solution approach. Although GNNs have promising results for certain applications such as computer vision ones, considering its limitations, it still has a long way to go until becoming a leading candidate for PF based applications. This paper highlights the existing gaps and challenges in fully accepting ANNs and particularly GNNs as real-time solution engines for the PF problem in DSs. These gaps are analyzed under three categories: suitable architectures for the solution of the PF problem in DS, explicit vs. implicit incorporation of the DS topology information impact on the models' generalization, and the limiting factors for GNNs implementation aimed at the solution of the PF problem in DSs. The paper also includes a discussion, suggestions and insights of overcoming these gaps in future research. • State-of-the-art ANN based solutions for the power flow problem are reviewed. • Discussing barriers of adopting GNNs as a valid method for DSs PF based applications. • Incorporation of the DS topology information impact on the models' generalization. • Insights and suggestions of overcoming these gaps in future research are presented. [ABSTRACT FROM AUTHOR]

As a response to increased power demand, electrical utilities have started to use Demand Response (DR) programs to improve the stability, efficiency and reliability of their networks. One type of DR, an Emergency Demand Response Program (EDRP), addresses stability challenges by sending curtailment orders to consumers associated with the program. There are different methods for calculating the amount of power that should be curtailed and how it should be divided among consumers. An important aspect of curtailment assignment is fairness, yet there is no agreed-upon manner of achieving fairness in curtailment allocation. This paper presents a new fairness-based EDRP for a residential area. The fairness is manifested in the algorithm by taking into account the number of times and the amount of energy a customer is asked to curtail for a predetermined period of time. The proposed algorithm is demonstrated and verified using simulations. • A set of four criteria are presented for fair curtailment of loads in emergency demand response operation. • Algorithm that takes into consideration the above criteria is developed. • The algorithm is based on a weigh function for deciding the chosen concentrator by scores. • The algorithm takes into account the time interval for each request. • Two test cases are simulated to support the theoretical method. [ABSTRACT FROM AUTHOR]

Energy storage may improve power management in microgrids that include renewable energy sources. The storage devices match energy generation to consumption, facilitating a smooth and robust energy balance within the microgrid. This paper addresses the optimal control of the microgrid's energy storage devices. Stored energy is controlled to balance power generation of renewable sources to optimize overall power consumption at the microgrid point of common coupling. Recent works emphasize constraints imposed by the storage device itself, such as limited capacity and internal losses. However, these works assume flat, highly simplified network models, which overlook the physical connectivity. This work proposes an optimal power flow solution that considers the entire system: the storage device limits, voltages limits, currents limits, and power limits. The power network may be arbitrarily complex, and the proposed solver obtains a globally optimal solution. [ABSTRACT FROM PUBLISHER]

Power flow calculations are an essential stage in many planning and control applications for distribution systems.To use these in control applications, however, the calculation time needs to be improved, and this can be done by the use of a trained ANN. This paper presents the considerations for constructing ANNs for DSs, and describes a method for training the system in order to support switching events representing a change in topology. The solutions for three DSs, balanced as well as unbalanced, are presented and the various considerations affecting the most appropriate ANN construction are discussed. The results are compared to the solution from the classical complex Newton‐Raphson and the fixed‐point iterative methods. The solutions have very high precision and good results are found for switched laterals. The computational performance is also compared and an improvement of two orders of magnitude is observed. [ABSTRACT FROM AUTHOR]

• This paper suggests to use PCA as an efficient dimension reduction method for NILM. • The method can be used with any NILM classification technique, various datasets and sample-rates. • The method is tested using the public dataset AMPds and a private dataset. • The results show that the run time is reduced while the accuracy is preserved, i.e. there is minimal loss of information. • The suggested PCA method may be useful in applications in which run-time is an important factor, and therefore cannot use complex NILM algorithms, with a high-dimensional solution space. Non-intrusive load monitoring (NILM) techniques estimate the consumption of individual appliances in a household or facility, based on readings of a centralized meter. Usually, NILM techniques are shown to be improved when various power features and additional power quality parameters are included. However, adding power features leads to increased time complexity which is a disadvantage to real-time operation. Therefore, in this work we offer a process based on principal component analysis (PCA) which reduces the dimension of NILM power features. The suggested method can be used with any NILM classification technique, and shows good performance in terms of standard measures and time complexity when tested on popular datasets. [ABSTRACT FROM AUTHOR]