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Partial discharge inception voltage (PDIV) at different frequencies is of great interest for PD tests conducted at nonpower frequency. However, in the existing literature, there is no agreement on PDIV characteristics with respect to frequency. There is a lack of comprehensive analysis of frequency-dependent PDIV characteristics either. In this article, the PDIV of a cylindrical void at different frequencies (from 0.5 to 300 Hz) is investigated from the perspective of the first PD time lag. First, an appropriate time interval between consecutive measurements is experimentally determined. Then, PDIV measurements at different frequencies are conducted by increasing voltage at a fixed ramp rate. Furthermore, the PDIVs defined by peak voltage and instantaneous voltage are compared. After that, a bipolar square wave is applied to measure the first PD time lag. Finally, based on the first PD time-lag distribution with respect to voltage at different frequencies, the cumulative probability of the first PD under sinusoidal voltage is computed and analyzed. The results suggest that the first PD time lag has a significant influence on the measured PDIV at frequencies above 10 Hz. This is because the mean time lag is nearly half the time period of half voltage cycle at low voltages. Thus, high voltages (HVs) are needed to obtain a high probability of the first PD, i.e., a large measured PDIV. [ABSTRACT FROM AUTHOR]

The sustaining evolution of sensing and advancement in communications technologies has revolutionized prognostics and health management for various electrical equipment toward data-driven ways. This revolution delivers a promising solution for the health monitoring problem of the heat pump (HP) system, a vital device widely deployed in modern buildings for heating use, to timely evaluate its operation status to avoid unexpected downtime. Many HPs were practically manufactured and installed many years ago, resulting in fewer sensors available due to technology limitations and cost control at that time. It raises a dilemma to safeguard HPs at an affordable cost. In this article, we propose a hybrid scheme by integrating industrial Internet-of-Things (IIoT) and intelligent health monitoring algorithms to handle this challenge. To start with, an IIoT network is constructed to sense and store measurements. Specifically, temperature sensors are properly chosen and deployed at the inlet and outlet of the water tank to measure water temperature. Second, with temperature information, we propose an unsupervised learning algorithm named mixture slow feature analysis (MSFA) to timely evaluate the health status of the integrated HP. Characterized by frequent operation switches of different HPs due to the variable demand for hot water, various heating patterns with different heating speeds are observed. Slowness, a kind of dynamics to measure the varying speed of steady distribution, is properly considered in MSFA for both heating pattern division and health evaluation. Finally, the efficacy of the proposed method is verified through a real integrated HP with five connected HPs installed ten years ago. The experimental results show that MSFA is capable of accurately identifying health status of the system, especially failure at a preliminary stage compared to its competing algorithms. [ABSTRACT FROM AUTHOR]

This paper proposes a novel data driven framework for detecting topology transitions in a distribution network. The framework analyzes data from phasor measurement units (PMUs) and relies on the fact that changes in network topology results in changes in the structure and admittance of the network. Using voltage and current phasors recorded by PMUs, the proposed method approximates network parameters using an ensemble-based deep learning model and thus, it does not require any knowledge of network parameters and load models. Using the prediction error of the proposed model, a connectivity matrix which shows the status of switches is constructed. In contrast to other methods, this proposed framework does not require a library of voltage and current transients associated with possible network transitions. It can also detect simultaneous switching actions and is robust to noise and load variations. The model yields a lower error detection rate, and its performance is validated using a modified version of the IEEE 33 bus network and a real feeder located in Queensland, Australia, under full and partial observability conditions. The proposed model has also been compared with another data driven method in terms of inference time and error detection rates. [ABSTRACT FROM AUTHOR]

Silver sulphide corrosion is a recently identified failure mechanism of transformer onload tap changers (OLTC). The corrosive sulphur in transformer oil reacts with the silver coated components of OLTC tap selector and forms silver sulphide films. During OLTC operation, the silver sulphide flakes can be detached from the tap selector and mix with the transformer oil. The silver sulphide is a semi conductive material. As a result, the dielectric strength in the transformer oil around the OLTC silver coated contacts or elsewhere in the transformer, wherever the semi conductive particles travel, will be reduced. Eventually, due to either high electric field stress or aged oil with a low dielectric strength, oil breakdown can occur between adjacent contacts. This can cause a catastrophic failure of the OLTC and quite often the tapping winding as well. Since such failures affect the transformer reliability, utilities are interested in methods of detecting and mitigating silver sulphide corrosion on OLTCs. This paper presents a risk assessment criterion to identify the degree of significance of silver sulphide corrosion in transformers using existing diagnostic techniques. [ABSTRACT FROM AUTHOR]

Inverter transformers are one of the key components in a PV plant and their operating patterns are considerably different from that of transformers in a substation. Moreover, these transformers experience higher level of harmonics generated by the inverters. Therefore, investigating the ageing profile of the inverter transformer is a timely requirement. In this paper, the ageing of inverter transformers in a medium-scale solar PV plant is studied by utilizing various real-time measurements including load current, top-oil temperature (TOT), ambient temperature, moisture in top oil, solar irradiance and individual current harmonics. An updated moisture-dependent thermal model (MDTMH), with consideration of current harmonics is developed to estimate the hot-spot temperature (HST). The influence of weather conditions, which directly determine the load current of inverter transformers on the loss of life (LOL) of an inverter transformer is also investigated. Furthermore, the effect of current harmonics levels on the inverter transformer's HST and LOL is explored. Finally, comparisons of HST and LOL of inverter transformers connected to PV arrays with three different tracking technologies, i.e., fixed-tilt, single-axis and Dual-axis PV arrays are performed. [ABSTRACT FROM AUTHOR]

In this paper, the feasibility of peer-to-peer (P2P) energy trading in a voltage-constrained grid-connected network is studied. In particular, a local voltage management scheme is proposed that takes network constraints into consideration to instruct the prosumers to trade energy frequently in the P2P market. A coalition graph game-based P2P energy trading framework is developed, in which prosumers can form the coalition to negotiate and decide on the energy trading parameters, such as trading quantities and prices. The Myerson value rule is used to allocate the total payoff of the proposed game fairly among the participating prosumers. Further, the stability of the proposed coalition structure is confirmed. Several simulation results are provided to verify the effectiveness of the developed P2P trading model. The simulation results show that the proposed P2P trading framework can enable prosumers to export power without causing high voltage problem in the network, and help prosumers cut down a significant portion of their overall electricity costs compared to the feed-in-tariff and coalition game model without mutual negotiations. [ABSTRACT FROM AUTHOR]

Today many large-scale photovoltaic (PV) plants have been equipped with sky imaging systems. The sky images contain abundant spatio-temporal information of the local climate condition at the PV plant. Meanwhile, power outputs of the PV systems located at different sites in a certain geographical area also exhibit spatio-temporal correlation. It is of interest to improve the accuracy of PV nowcasting by fully utilizing spatio-temporal information embedded in both sky images and PV output measurements of distributed PV systems. In this article, we incorporate the above two aspects into a unified framework and propose the Bi-level spatio-temporal (BILST) PV nowcasting model. The proposed model learns features from local spatio-temporal information embedded in sky images, global spatio-temporal correlations embedded in PV output datasets of a number of distributed PV systems and weather characteristics embedded in exogenous dataset simultaneously. Then the obtained three types of hidden features are aggregated and applied to predict the PV output at the PV site of interest. Experiments using real-world datasets show that the proposed BILST model can enable sky images to contribute to the PV nowcasting task and achieve the desirable accuracy of PV nowcasting consistently. [ABSTRACT FROM AUTHOR]

This article proposes a novel algorithm employing space vector ellipse (SVE) in a complex plane to classify and visualize power quality disturbance-events (PQDEs). In the proposed method, at first, the time-domain signal and a reference signal, which are separated by 90°, are mapped in a complex 2D coordinates. Thus, the tip of resultant rotating vector traces an ellipse, from which three parameters, namely, semi-major axis, semi-minor axis and inclination angle, are obtained. Then, the ellipse parameters are exploited to classify and visualize nine types of PQDEs, namely, voltage sag, swell, interruption, harmonic, sag-harmonic, swell-harmonic, notch, flicker and transient. To validate the practicability of the proposed approach, an extensive real-time simulation study is carried out on RTDS platform using a test microgrid network to generate a large number of PQDEs. The test events were successfully classified and visualized in complex plane. Moreover, the noisy and practical signals, recorded by IEEE 1159.2 Working Group, were successfully classified to demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

System strength and inertia inherently provided by synchronous generators (SGs) empower a power system to ride through voltage and frequency disturbances. The requirements of system strength and inertia were not enforced in the National Electricity Market (NEM) of Australia since SGs dominated the generation fleet in the past. However, the increasing wind and solar generation continuously displaces SGs and consequently reduces system strength and inertia in the NEM. This paper proposes a formulation of system strength and inertia constrained generator dispatch to reassure NEM operational security in light of emerging high renewable penetration. A fault current iterative solver is developed to evaluate system strength, in which the current limitation and voltage control logic of inverter-based generators, and the fault current contribution from VAR compensators are properly modelled in the phasor domain. The system strength contribution factor of an SG is defined to linearize system strength constraint for unit commitment (UC). System and sub-network inertia constraints are also formulated for the UC to limit the rate of change of frequency (RoCoF) in the event of generator/interconnector trip. The proposed generator dispatch formulation can fully meet system strength and inertia requirements in the NEM. [ABSTRACT FROM AUTHOR]

This article presents a new approach for classification and localization of voltage sags triggered by symmetrical and unsymmetrical faults in electricity networks. The proposed method utilizes five unique parameters of three-dimensional (3-D) polarization ellipse (PE) obtained from the mapping of three-phase voltage signals in 3-D coordinates. The decision boundaries, which are set by the parametric equations exploiting the five ellipse parameters, namely, elevation, azimuth, tilt angles, semi-major axis, and semi-minor axis, are used to classify the fault-initiated sags. The localization of faults is conducted from the parametric equations demonstrating the variation of ellipse parameters with respect to distance from the fault-location. These parametric equations would be helpful to make decision faster and thereby, eliminate the uncertainty in decision-making process. The applicability of the proposed method is tested and validated using real data measured by DOE/EPRI, the recorded waveforms of IEEE 1159.2 Working Group, and the data extracted from the simulation of faults at several feeders of IEEE 14-bus test system using RSCAD and Real Time Digital Simulator (RTDS) platform. [ABSTRACT FROM AUTHOR]

Currently, many remote communities rely on costly diesel engine (DE) generators as their only energy resource. Therefore, to reduce cost and CO2 emissions, renewable energy such as photovoltaic (PV) power is widely used. However, such integration may have reliability issues due to intermittency and uncertainty of PV power. Hence, energy storage is further implemented to compensate for the variability of PV output. As a result, the critical problem is to find an optimized PV-storage control strategy and capacity considering the techno-economic aspects, cost, and reliability. Previous research has not provided convincing results due to the little attention paid to the practical operational constraints of existing DEs (time delay management, spinning reserve strategies, ramp rate, etc.), and rough hourly usage data in a time-series model. Besides, no applicable systematic evaluation method has been established. This paper develops a new model which fully considers the DE generator's operational characteristics. Furthermore, based on the recorded 1-min load and PV generation data in a real microgrid, a systematic evaluation method is proposed, which searches for the best results for both control strategies and capacities of all components. This method can be utilized for the integration of high renewable (PV) penetration into existing DE powered remote networks. [ABSTRACT FROM AUTHOR]

Peer-to-peer trading is a next-generation energy management technique that economically benefits proactive consumers (prosumers) transacting their energy as goods and services. At the same time, peer-to-peer energy trading is also expected to help the grid by reducing peak demand, lowering reserve requirements, and curtailing network loss. However, large-scale deployment of peer-to-peer trading in electricity networks poses a number of challenges in modeling transactions in both the virtual and physical layers of the network. As such, this article provides a comprehensive review of the state-of-the-art in research on peer-to-peer energy trading techniques. By doing so, we provide an overview of the key features of peer-to-peer trading and its benefits of relevance to the grid and prosumers. Then, we systematically classify the existing research in terms of the challenges that the studies address in the virtual and the physical layers. We then further identify and discuss those technical approaches that have been extensively used to address the challenges in peer-to-peer transactions. Finally, the paper is concluded with potential future research directions. [ABSTRACT FROM AUTHOR]

Synchronous generators are operating for less time than before or being decommissioned in the National Electricity Market (NEM) of Australia, due to the proliferation of asynchronous wind and solar generation. Sub-networks of the NEM will face inertia shortages in the near future. This paper develops a formulation of zonal inertia constrained generator dispatch for power systems with a diversified generator portfolio including synchronous generators, synchronous condensers, inverter-interfaced generators and energy storages. Zonal inertia constraints are formulated in unit commitment and optimal power flow to limit the rate of change of frequency (RoCoF) in the event of network separation. Load frequency relief is also considered to reduce the ramp rate requirement of primary reserve. The proposed formulation can reduce the average cost of primary reserve and maintain zonal inertia adequacy to constrain RoCoF in case of the trip of the interconnector(s). [ABSTRACT FROM AUTHOR]

Broadband responses under random excitations can reflect the modal characteristics of a structure. This paper investigates the applicability of operational modal analysis (OMA) to extract modal parameters of transformer windings under random excitations. Firstly, random vibration signals are extracted from measured vibration signals by estimating an accurate power frequency and eliminating vibration harmonics. Then, modal characteristics are obtained by applying OMA to the extracted random vibration signals. The numeric study on a winding axial vibration model demonstrates the effectiveness of the proposed method in obtaining its modal characteristics. Laboratory experiments show that the natural frequencies calculated by OMA are a subset of the actual natural frequencies of windings and the tank. The field test results of a 110 kV power transformer show that the number of natural frequencies increase when winding deformation occurs. OMA of transformer windings may pave a way for online winding condition monitoring. [ABSTRACT FROM AUTHOR]

Spatiotemporal analysis has been recognized as one of the most promising techniques to improve the accuracy of photovoltaic (PV) generation forecasts. In recent years, PV generation data of a number of PV systems distributed in a geographical locale have become increasingly available. This paper conducts a thorough investigation of the spatial–temporal correlation amongst PV generation data of distributed PV systems. PV generation data of different PV systems located at different sites may exhibit similar time varying patterns. To quantify such spatial correlation, a suitable spatial similarity metric is chosen and its applicability is examined. To evaluate the temporal correlations amongst the PV generation data collected from distributed PV systems, a shape-based distance metric is proposed. A data-driven inference model, built on a Bayesian network, is developed for a very short-term PV generation forecast (less than 30 min). The model utilizes historic PV generation and weather data, and incorporates the abovementioned spatial similarity and temporal correlation to support the PV output forecast. The experiment results show that the proposed method achieves a promising performance compared to a number of baseline methods. [ABSTRACT FROM AUTHOR]

This paper proposes a peer-to-peer (P2P) energy trading scheme that can help a centralized power system to reduce the total electricity demand of its customers at the peak hour. To do so, a cooperative Stackelberg game is formulated, in which the centralized power system acts as the leader that needs to decide on a price at the peak demand period to incentivize prosumers to not seek any energy from it. The prosumers, on the other hand, act as followers and respond to the leader’s decision by forming suitable coalitions with neighboring prosumers in order to participate in P2P energy trading to meet their energy demand. The properties of the proposed Stackelberg game are studied. It is shown that the game has a unique and stable Stackelberg equilibrium, as a result of the stability of prosumers’ coalitions. At the equilibrium, the leader chooses its strategy using a derived closed-form expression, while the prosumers choose their equilibrium coalition structure. An algorithm is proposed that enables the centralized power system and the prosumers to reach the equilibrium solution. Numerical case studies demonstrate the beneficial properties of the proposed scheme. [ABSTRACT FROM AUTHOR]

Renewable energy resources are utilized in distribution networks based on an active front end technology as a bidirectional power flow energy conversion system. Low and high frequency harmonics generated by switching pattern of the power electronics converter should be reduced by an appropriate output filter. According to international regulations, harmonics injected to the grid by pulsewidth modulated (PWM) power converters must be handled to maintain power quality indices within standard limits. LCL filters with passive damping resistors are the most reliable and renowned devices to fulfil the standards. The main objectives in the design of an efficient LCL filter are to reduce the cost and weight of the filter, as well as to increase the robustness and stability of the power electronics converter. In this paper, a comprehensive and mathematical approach is proposed to calculate the maximum current ripple of a PWM-based inverter, which helps to precisely calculate the size of the inverter-side inductors in the LCL filter. The mathematical approach to solve the problem of optimal damping is presented, which can be implemented analytically in different configurations of the LCL filters. The proposed method also works to solve an optimal damping problem for any type of filter. [ABSTRACT FROM AUTHOR]

The number of large-scale photovoltaic (PV) and wind farms is rapidly growing in Australia and all around the world. When these resources participate in the wholesale electricity market, their uncertain nature of generation results in revenue loss due to the penalty incurred by deviating from day-ahead and real-time commitments. In an attempt to avoid financial losses, they typically bid in the market conservatively. This, in turn, might lead to wasting clean energy and lowering overall profit for the producers. To address these issues, various energy storage devices are considered as a potential solution by academic and industrial researchers alike. In this study, an optimal battery sizing methodology is proposed to improve renewable generation predictability using “Seasonal-Trend decomposition based on LOESS” 1 locally weighted regression. decomposition technique, self-similarity estimation, and enhancing it through filtering. The ultimate goal is to determine the optimal battery size that enhances predictability of renewable generation regardless of the prediction technique and time horizon, which necessarily improves the accuracy of predicted values. The goal is achieved by the proposed method through designing a forecasting-technique-agnostic algorithm. For optimal battery sizing, an optimization formulation is proposed including battery degradation through its useful lifetime. Moreover, prediction studies are carried out to prove predictability enhancement using four prediction techniques and three prediction horizons. The simulation results show the effectiveness of the proposed method in improving self-similarity index (i.e., Hurst exponent) in the PV production time series and economic viability of the proposed methodology in a particular application. [ABSTRACT FROM AUTHOR]

The measurement of water inside a transformer is essential to maintain its reliable operation, as the water content can increase as the transformer ages. This measurement is frequently performed by a utility as a part of its periodic maintenance activities. In this paper, the application of optical fiber sensors as an online method to measure water content of paper inside an operating transformer is presented. There are several advantages of using these sensors, including their size, lightweight, flexibility, multiplexing, no-electromagnetic interference, etc. A unique benefit of fiber optic technology is that sensors can be embedded into the winding structure and operate in areas of high electric field strength. For this work, fiber optic water and temperature sensors are investigated by comparing their measurements to a commercial water activity probe immersed in oil. The study found that the determination of water content by fiber optic sensors compared closely with other techniques, which lead to the conclusion that they can be an effective technology for water measurement in an operational transformer. [ABSTRACT FROM AUTHOR]

Commercial-scale, grid-connected battery energy storage system (BESS) typically operates on price-driven or peak shaving charging cycles. However, when installed in a distribution grid next to medium or large-scale photovoltaic (PV) generation, BESS can also play an important role in voltage regulation processes. Therefore, this paper proposes a new, model-driven controller which incorporates BESS into a voltage regulation scheme in order to counteract voltage variation caused by PV power fluctuations. In addition, the method optimizes control efforts and coordinates regulation actions of BESS and remaining grid devices. The algorithm was tested on a model implemented in the real-time digital simulator of a real grid equipped with a medium-scale BESS (0.6 MW/0.76 MWh) and 3.15 MWp of PV generation. As a result, the developed controller managed to regulate grid voltages quickly and without a significant influence on BESS charging cycle. The algorithm can be applied to distribution grids which already suffer from voltage fluctuations or to enhance PV generation in weak feeders by providing much faster and precise voltage control. [ABSTRACT FROM AUTHOR]

The power transformer is a key element in a power system and its condition needs to be monitored and evaluated. However, subject to electrical, thermal, and mechanical stresses, the condition of a power transformer can eventually deteriorate causing the loss of the transformer's useful life. Utilizing various condition monitoring data of the transformer, this paper applies a state-space model method to the transformer's remaining useful life estimation. In the state-space model, a state dynamic equation considering the transformer aging mechanism is developed. Three measurement equations using different types of condition monitoring data are established. To solve the nonlinear state-space model, a particle filtering approach is applied. The posterior probability density function of the state variable obtained from the particle filtering is used to determine the transformer's remaining useful life. A number of case studies are carried out to demonstrate the applicability of the proposed method. [ABSTRACT FROM AUTHOR]

In long distribution feeders, step voltage regulators (SVRs) with the line drop compensation have been widely implemented to control voltage profiles. After integration of photovoltaic (PV) systems, reactive power support from PV inverters can also be utilized in voltage regulation. Although both SVR and reactive power support can be effective to manage system voltage without coordination, problems such as large voltage variations and excessive SVR tap operations still exist in some strong PV power fluctuating days. In order to solve these issues, SVR and reactive power support should be assigned to different voltage regulation tasks according to their voltage regulation characteristics. Specifically, in a distribution system, an SVR should mainly deal with slowly changing quantities (e.g., load, upstream voltage), while the limited reactive power support should be used to counter fast fluctuating PV power. In this paper, the power factor droop parameters applied on PV inverters are optimally selected to achieve such coordination, so that voltage problems and excessive SVR tap operations can be successfully mitigated. The effectiveness of the proposed method is demonstrated via case studies. Future PV integration project in weak distribution systems can benefit from the innovative and practical methodology proposed in this paper. [ABSTRACT FROM AUTHOR]

Buildings consume 60% of global electricity. However, current building management systems (BMSs) are highly expensive and difficult to justify for small- to medium-sized buildings. The Internet of Things (IoT), which can collect and monitor a large amount of data on different aspects of a building and feed the data to the BMS's processor, provides a new opportunity to integrate intelligence into the BMS for monitoring and managing a building's energy consumption to reduce costs. Although an extensive literature is available on, separately, IoTbased BMSs and applications of signal processing techniques for some building energy-management tasks, a detailed study of their integration to address the overall BMS is limited. As such, this article will address the current gap by providing an overview of an IoT-based BMS that leverages signal processing and machine-learning techniques. We demonstrate how to extract high-level building occupancy information through simple, low-cost IoT sensors and study how human activities impact a building's energy use-information that can be exploited to design energy conservation measures that reduce the building's energy consumption. [ABSTRACT FROM AUTHOR]

Vibro-acoustic measurement on a transformer's on-load tap changer (OLTC) can provide indications on its mechanical condition. Recently, a joint vibro-acoustic and arcing measurement system has been proposed, which can correlate the vibro-acoustic signal to mechanical events of OLTC's operation. However, there are still considerable difficulties in extracting useful information from both vibro-acoustic signal and arcing signal in a synchronized manner without any distortions in the extracted signals. In this paper, Savitzky–Golay filter is introduced to process the signals acquired from a joint vibro-acoustic and arcing measurement system installed on in-service OLTCs. It proves that the Savitzky–Golay filter can process both vibro-acoustic and arcing signals induced by OLTC, extract essential information without any time delay from both types of signals, and retrieve voltage phase information from the arcing signal. The methodologies developed in this paper can improve the visibility of OLTC's mechanical operation for an effective online condition monitoring. [ABSTRACT FROM PUBLISHER]

Conventional distribution systems are transforming into smarter networks by accommodating renewable energy resources, communication infrastructure, and control devices. While the grids are transforming to be smart grids, issues relating to grid stability and control are emerging due to proximity and interactions among generator units and associated controllers. The existence of low damped oscillations could be harmful to power transfer capacity and generator stability. This paper presents small signal stability and control issue of emerging distribution systems. A robust control methodology for motor load current is proposed for stability enhancement. System loads are ranked based on their relative influence on small signal instability. The proposed methodology controls current of the selected motor load during system disturbances to improve overall small signal stability of the distribution system. The proposed methodology has been successfully implemented on a 16-bus distribution network. [ABSTRACT FROM AUTHOR]

With the rapid development of photovoltaic (PV) technology, more utility-scale PV systems are being integrated into rural areas where abundant and low-cost land is available. In these areas, step-voltage regulators (SVRs) with an open-delta configuration are widely used for voltage regulation. However, since SVRs were initially designed for tackling slow and smooth load variations, frequent PV output fluctuations can cause excessive tap changes. Moreover, rural communication facilities are normally expensive and underdeveloped, so only limited parameters are recorded with low resolution and reliability. Thus, much needed tap information cannot be properly monitored in a cost-effective way. In contrast, a downstream load center or utility-scale PV site is commonly well equipped with modern sensors and data acquisition systems, which provide the possibility for tap evaluation. Therefore, this paper proposes a new remote tap position estimation approach to accurately assess the upstream tap positions of open-delta SVRs from downstream measurements. Furthermore, the proposed approach is firmly validated through field testing under the support of the local utility and PV plant owner. This novel method not only enables reliable tap position monitoring with high precision for utilities to research PV–SVR interaction, but also provides valuable information to PV owners for correctly analyzing downstream voltage performance. [ABSTRACT FROM AUTHOR]

Open-delta connected step voltage regulators (SVRs) have been widely installed into distribution networks, and their characteristic model (auto-transformer model) has been established over the years for power system analysis. However, these models are not fully developed to a complete bus admittance matrix form, which can be readily used for unbalanced load flow programs. Further, the debate on the neutral shift after implementation of open-delta SVRs has added difficulties on the modeling. There have been confusions on what is the neutral shift for a three-wire network without neutral and where the neutral is shifted to. Recently, accurate modeling of such a network component has become exceptionally important especially with the rapid proliferation of photovoltaic (PV), and there is a strong requirement of evaluating PV integration impacts on distribution systems. Propelled by all these necessity of modeling, this paper develops a new mathematical model of the open-delta SVR in a bus admittance matrix form which can be easily incorporated into suitable unbalanced load flow programs. Further, the neutral shift issue is also clarified during the model development, and in addition a method for tap position estimation is derived. All the established models are validated by field measurement. [ABSTRACT FROM PUBLISHER]

There are more large-scale photovoltaic (PV) plants being established in rural areas due to availability of low-priced land. However, distribution grids in such areas traditionally have feeders with low X/R ratios, which makes the independent reactive power compensation method less effective on voltage regulation. Consequently, upstream step voltage regulator (SVR) may suffer from excessive tap operations with PV-induced fast voltage fluctuations. Although a battery energy storage system (BESS) can successfully smooth PV generation, frequent charge/discharge will substantially affect its cost effectiveness. In this paper, a real-time method is designed to coordinate PV inverters and BESS for voltage regulation. To keep up with fast fluctuations of PV power, this method will be executed in each 5 s control cycle. In addition, charging/discharging power of BESS is adaptively retuned by an active adjustment method in order to avoid BESS premature energy exhaustion in a long run. Finally, through a voltage margin control scheme, the upstream SVR and downstream PV inverters and BESS are coordinated for voltage regulation without any communication. This research is validated via a real-time digital simulator MatLab cosimulation platform, and it will provide valuable insights and applicable strategies to both utilities and PV owners for large-scale PV farm integration into rural networks. [ABSTRACT FROM PUBLISHER]

The installed capacity of wind generation and photovoltaics (PV) in many countries is going to dominate generation fleets in a bid to meet growing renewable energy targets. Synchronous inertia has never been problematic as there was more available than needed, but it is being significantly reduced due to the increasing integration of nonsynchronous renewable generation. When the low bidding priced generation of wind and PV becomes considerably large, conventional economic dispatch algorithms can result in less online synchronous inertia and put power system security at risk. However, the compromise of power system security due to synchronous inertia shortage is not well studied in the literature. This paper develops a synchronous inertia constrained economic dispatch algorithm to satisfy the minimum required synchronous inertia of frequency control. Synchronous condensers and wind reserve are economically allocated to alleviate any shortage of synchronous inertia and frequency control ancillary services (FCAS). A Gaussian particle swarm optimization algorithm is introduced to simultaneously co-optimize the dispatch of synchronous generators and their FCAS, wind reserve, and synchronous condensers. [ABSTRACT FROM AUTHOR]

Any mismatch between load and generation (due to a generator or an interconnection trip) is intended to be balanced and stabilized by contingency reserve, which is also known as contingency Frequency Control Ancillary Services (FCAS) requirement. Load Frequency Relief (LFR), which represents the effect of frequency dependent loads on power system frequency excursion, is crucial for correctly evaluating contingency reserve requirement during generation dispatch to ensure an adequate frequency response. Over estimation of LFR can be accountable for less planned reserve during an economic dispatch that may cause undesirable frequency performance. On the other hand, under estimation of LFR can result in an excessive reserve and hence could unnecessarily increase system operational cost. Conventionally, LFR is considered as a fixed quantity during the evaluation of FCAS requirement. However, recent experience in the Australian power grid suggests that such an assumption may lead to an inaccurate outcome. To explore the above issue, this research investigates the LFR using field measurement data, which were captured at different locations of the southern states of Australia (e.g., Tasmania and Victoria). An approach is developed to identify the predominating factors affecting the LFR and subsequently a technique is proposed to appropriately determine contingency FCAS requirement. [ABSTRACT FROM PUBLISHER]