Search

Congestions are becoming a significant issue with an increasing number of occurrences in distribution networks due to the growing penetration of distributed generation and the expected development of electric mobility. Fair congestion management (CM) policies and prices require proper indices of congested areas and contributions of customer to congestions. This paper presents spatial and temporal indices for rapidly recognizing the seriousness of congestions from the perspectives of both magnitude violation and duration to prioritize the affected areas where CM procedures should be primarily activated. Besides, indices are presented which describe the contributions of customers to the congestions. Simulation tests on IEEE 123-bus and Australian 23-bus low-voltage distribution test feeders illustrate the calculation and capabilities of the proposed indices in balanced and unbalanced systems.

Distribution system state estimation (DSSE) is a critical analysis tool for active distribution networks (DNs). Unlike weighted least squares techniques, which are static DSSE methods, the augmented complex Kalman filter (ACKF) is a novel technique that considers the system’s dynamic behavior. Currently, most DNs integrate a large number of unmonitored residential photovoltaic (PV) generations. Existing unmeasured PV sources violate the white noise assumption in Kalman and least-squares-based estimators, causing the estimator to be biased. Because the one-step difference of aggregated customer demand is characterized as white noise, the suggested PV estimation technique based on the differencing strategy is used to decouple PV from the measured load. Using the specified contribution factors, the new online pseudo current injections are generated. In addition, the estimator’s accuracy is improved by using a new PV-scaling-aided ACKF approach based on the PV separation strategy. For validation purposes, this method is applied to real DN case studies. This study also makes use of an actual dataset to illustrate the efficacy of the proposed technique. The proposed technique outperforms the existing snapshot and dynamic DSSE techniques, and significant improvements are achieved in terms of accuracy and computational cost.

The enormous concerns of climate change and traditional resource crises lead to the increased use of distributed generations (DGs) and electric vehicles (EVs) in distribution networks. This leads to significant challenges in maintaining safe and reliable network operations due to the complexity and uncertainties in active distribution networks, e.g., congestion and reliability problems. Effective congestion management (CM) policies require appropriate indices to quantify the seriousness and customer contributions to congested areas. Developing an accurate model to identify the residual life of aged equipment is also essential in long-term CM procedures. The assessment of network reliability and equipment end-of-life failure also plays a critical role in network planning and regulation. The main contributions of this thesis include a) outlining the specific characteristics of congestion events and introducing the typical metrics to assess the effectiveness of CM approaches; b) proposing spatial, temporal and aggregate indices for rapidly recognizing the seriousness of congestion in terms of thermal and voltage violations, and proposing indices for quantifying the customer contributions to congested areas; c) proposing an improved method to estimate the end-of-life failure probabilities of transformers and cables lines taking real-time relative aging speed and loss-of-life into consideration; d) quantifying the impact of different levels of EV penetration on the network reliability considering end-of-life failure on equipment and post-fault network reconfiguration; and e) proposing an EV smart charging optimization model to improve network reliability and reduce the cost of customers and power utilities. Simulation results illustrate the feasibility of the proposed indices in rapidly recognizing the congestion level, geographic location, and customer contributions in balanced and unbalanced systems. Voltage congestion can be significantly relieved by network reconfigu

Virtual power plants (VPPs) are an effective way to increase renewable integration. In this PhD research, the concept design and the detailed costs and benefits of implementing a realistic VPP in Western Australia (WA), comprising 67 dwellings, are developed. The VPP is designed to integrate and coordinate an 810kW rooftop solar PV farm, 350kW/700kWh vanadium redox flow batteries (VRFB), heat pump hot water systems (HWSs), and smart appliances through demand management mechanisms. This research develops a robust bidding strategy for the VPP to participate in both load following ancillary service (LFAS) and energy market in the wholesale electricity market in WA considering the uncertainties associated with PV generation and electricity market prices. Using this strategy, the payback period can be improved by 3 years (to a payback period of 6 years) and the internal rate of return (IRR) by 7.5% (to an IRR of 18%) by participating in both markets. The daily average error of the proposed robust method is 2.7% over one year when compared with a robust mathematical method. The computational effort is 0.66 sec for 365 runs for the proposed method compared to 947.10 sec for the robust mathematical method. To engage customers in the demand management schemes by the VPP owner, the gamified approach is adopted to make the exercise enjoyable while not compromising their comfort levels. Seven gamified applications are examined using a developed methodology based on Kim’s model and Fogg’s model, and the most suitable one is determined. The simulation results show that gamification can improve the payback period by 1 to 2 months for the VPP owner. Furthermore, an efficient and fog-based monitoring and control platform is proposed for the VPP to be flexible, scalable, secure, and cost-effective to realise the full capabilities and profitability of the VPP.

Recommendations are A. The guidelines need to be expressed recognizing both adverse and positive impacts. B. Symmetry in rewards and penalties is necessary to avoid perception of gouging C. The long run marginal cost needs a defined sample rate to reflect the loss of life of equipment D. There is an opportunity to have customer investment which is positive to network operation and this is one mechanism which will facilitate it.

In this article, an augmented complex Kalman filter (ACKF) is proposed for distribution networks as a noniterative forecasting-aided state estimator. Although most of the distribution state estimator (DSE) algorithms deal with real and imaginary parts of distribution networks’ states independently, the proposed algorithm in this article considers the states as complex values. In the case of real-time DSE and the presence of a large number of customer loads in the system, employing DSEs in one single estimation layer is not computationally efficient. Consequently, our proposed method performs in several estimation layers hierarchically as a multilayer DSE based on an ACKF (DSE-MACKF). In the proposed method, a distribution network can be divided into one main area and several subareas. The aggregated loads in each subarea act like a big customer load in the main area. Customer load aggregation results in lower variability and higher spatial-temporal correlation. This increases the accuracy of the estimated states in the main estimation layer. Additionally, the proposed method is formulated to include unbalanced loads in low-voltage radial distribution networks. This approach is applied to two real distribution networks for comparison and evaluation. The effectiveness of the proposed method is discussed using several criteria such as computational time, standard deviation, and maximum and average voltage error. The performance of the proposed method has been assessed against the weighted least square estimation method, while the computational time and the average voltage error have been decreased from 14 to 2 s and 1.13% to 0.23%, respectively.

The widespread adoption of small-scale distributed energy resources (DERs) amongst energy users has drastically changed the operation of distribution networks. To date, there has not been a consolidated model to incorporate the investment decisions of the end-users in the distribution network planning. The contribution of this paper is a distribution network planning model for the utility which considers the neighborhood energy trading (NET) as a platform for end-users to directly exchange energy between them. The proposed mixed-integer second-order cone programming (MISOCP) problem provides the optimal decisions for line and transformer upgrades, as well as for photovoltaic (PV) and battery in end-users’ premises. Moreover, it indicates a fair allocation of network charges among the participants to NET schemes. The simulation results on the IEEE-33 bus test system confirm the effectiveness of the proposed model in lowering the total cost of the planning and the operation. This platform can be used by government-owned utilities as a guide to avoid sunk investments while motivating the increased installation of renewable distributed generation and storage units by end-users.

Recent technological advances, the global understanding of climate change and the role that renewable energy resources can play, along with the rise in electricity prices and little incentive for feeding the excess PV generation back to the grid have led to the growing interest among end-users in residential solar photovoltaic (PV) systems with battery storage systems. The recently released Western Australian Climate Policy sets the goal of achieving net-zero greenhouse gas emissions (GGE) by 2050. The rapid growth in distributed energy resources (DER) has changed the load pattern in distribution networks (DNs). As more DER facilities are introduced to the electric power systems, the power utilities undertake more investments in infrastructure to tackle the uncertainties pertaining to DER and manage the voltage issues due to increasing DER penetration. In these cases, the conventional planning models will result in over- or under-investment choices due to limited knowledge about end-users, which ultimately leads to financial losses for both the utility and customers. In order to effectively plan DNs for the future utilities need to understand the possible changes at the end-users’ side, which is missing in the existing literature. To achieve the optimal plan for the modern power distribution networks, all parties should be considered including the utility and end-users. There is also a critical need for better network charge tariffs designs to reflect the true contribution of customer DER in the cost of poles and wires. This thesis studies planning models for power utilities incorporating a model of end-users’ decisions. This enables utilities to see the most likely possible scenarios of end-users’ investment in DER. The main contributions of this thesis are: 1. Development of a DN planning model that incorporates the expected end-users’ investments in DER. This model enables the utility to investigate the opportunities and challenges offered by end-users’ DER and p

A standalone remote area microgrid may frequently experience overloading due to lack of sufficient power generation or excessive renewable-based generation that can cause unacceptable voltage and frequency deviation. This can lead the microgrid to operate with less resiliency and reliability. Such problems are conventionally alleviated by load-shedding or renewable curtailment. Alternatively, autonomously operating microgrids in a geographical area can be provisionally connected to each other to facilitate power exchange for addressing the problems of overloading or overgeneration. The power exchange link among the microgrids can be of different types such as a three-phase ac, a single-phase ac, or a dc-link. Power electronic converters are required to interconnect such power exchange networks to the three-phase ac microgrids and control the power-sharing amongst them. Such arrangement is also essential to interconnect microgrid clusters to each other with proper isolation while maintaining autonomy if they are operating in different standards. In this thesis, the topologies, and structures of various forms of power exchange links are investigated and appropriate operation and control frameworks are established under which power exchange can take place properly. A decentralised control mechanism is employed to facilitate power-sharing without any data communication. The dynamic performance of the control mechanism for all the topologies is illustrated through simulation studies in PSIM® while the stability and robustness of the operation are evaluated using numerical studies in MATLAB®.

Today, high renewable energy penetration in distribution networks (DNs), such as rooftop photovoltaic (PV) generations increases the complexity of DNs, where new control and monitoring methodology is needed. State Estimation (SE) methods are commonly used in DNs to improve the visibility of the low voltage (LV) feeders. However, having PV in these areas violates the assumptions of the white noise, the basis for SE methods, since it imposes a correlation between different measurements. In this work, the proposed weighted least square method is deployed to disaggregate the solar fraction from the measured total load using one reference PV in the area. The proposed methodology is validated on five different LV areas, using the Australian distribution network real dataset with a resolution of 30 minutes. According to the results, the proposed method can improve the PV estimation efficiency in each LV area compared with the ordinary method.

Increasing the number of active consumers in distribution networks necessitates transforming the current control, monitoring, and protection schemes. However, on one hand, installing high-frequency measurement devices and fast communication platforms in low-voltage (LV) distribution networks is not cost effective and scalable. On the other hand, the fault detection approaches, which can provide acceptable accuracy by relying only on low-frequency measured data (with 1-30-min sampling rates), are not developed yet. Currently, the overcurrent fault detectors work mainly based on fixed current thresholds, which makes them inefficient in a system with high-distributed-energy resources. This is due to high volatility and uncertainty in the measured profile of the current. In this article, a data-driven fault detection framework with dynamic fault current thresholds is proposed. The motivation here is to develop a framework that can locally detect and isolate faults within the LV distribution networks without requiring high-frequency sampling meters. The proposed model is based on quantile regression as a statistical method to generate the quantiles of distributions of the current measurements. Two different fault current thresholds are formulated for instantaneous and definite time fault detection schemes. The thresholds are dynamically predicted for each next time step. The proposed framework is evaluated using data from a real distribution network with 169 houses. The results suggest that the proposed model is very promising for LV residential distribution networks.

As distribution networks worldwide are experiencing the adoption of residential solar photovoltaic (PV) more than ever, the need for transiting from the concept of load forecasting to net energy forecasting, i.e. predicting the blend of PV and load as a whole, is pressing. While most of the existing literature has focused on load forecasting, this paper, for the first time, contributes to this transition at both single household and low aggregate levels through a comprehensive study. The paper also proposes a multi-input single-output (MISO) model based on an efficient long short-term memory (LSTM) neural network, by which different household energy profiles help provide more accurate forecasts for other households or aggregate energy profile. This technique, indeed, considers the spatial dependencies of households' profile indirectly. Through this study, the underlying problem of short-term net energy forecasting is compared to load forecasting, and it is shown how the inclusion of PV generation behind the meter could deteriorate forecasting accuracy. Moreover, the impact of the level of granularity associated with smart meter data on the aggregated net energy forecasting is discussed, and it is revealed that the higher resolution data can potentially alleviate the accuracy lost. Furthermore, online LSTM, as opposed to proposed batch learning MISO LSTM, is used as a forecasting tool. The results show online LSTM is more resilient to sudden changes at the single household level, while MISO LSTM is efficient for aggregate level. The proposed framework is conducted on two real Ausgrid and Solar Analytics case studies in Australia.

The current searching schemes in reliability evaluation of radial distribution systems require complicated and extensive codes, that contribute to the majority of the CPU times. This paper proposes a new one-step method operating with a set of binary descriptor matrices, replacing the searching, to calculate load and system reliability indices. In this technique, 'fuse to branch' and 'switch to branch' are introduced and employed to facilitate the solutions along with the use of bus-injection to branch-current matrices. In this approach, the cross-connects operation, islanding mode of small-scale DGs, and network constraints are also considered as part of the adequacy evaluation. The proposed technique is highly efficient for long-term distribution network planning and optimization where many cross-connects are involved. The proposed technique is applied to RBTS Bus 2 and 4, as well as a realistic 747-node distribution network. Results for the large distribution planning of the 747-node network demonstrate the computational time is reduced from 61 days using the traditional method to 4.6 hours based on the proposed technique.

This paper proposes a metaheuristic optimisation algorithm named enhanced breeding swarms (EBS), which combines the strengths of particle swarm optimisation (PSO) with those of genetic algorithm (GA). In addition, EBS introduces three modifications to the original breeding swarms to improve the performance and the accuracy of the optimisation algorithm. These modifications are applied on the acceptance criteria based on the improved glowworm swarm optimisation, velocity impact factor, and the mutation operator. The EBS algorithm is tested and compared against GA, PSO, and original BS algorithms, using unrotated and rotated six recognised optimisation benchmark functions. Results indicate that the EBS outperforms GA, PSO, and BS in most cases in terms of accuracy and speed of convergence, especially when the dimension of optimisation increases. As an application of the proposed EBS algorithm, a load flow analysis on a 6-bus network is performed, and the comparison results against another heuristic algorithm and the Newton-Raphson are reported.

Distribution networks have experienced significant changes in the last two decades, and have moved from passive networks to active ones. These critical changes require careful control and monitoring, involved with measurement devices and communication platforms. However, in distribution networks with a very large number of customer loads, it is not cost-effective to consider extensive measurement devices. As a result, distribution state estimation (DSE) methods can be used to estimate the states of the unmeasured nodes, where pseudo measured data is used for unmeasured nodes to make DSE method observable. However, pseudo data brings high uncertainty to DSE algorithm, which decreases the accuracy of the estimated states. To increase the accuracy in pseudo data, conditional multivariate complex Gaussian distribution (CMCGD) is employed in this study. CMCGD method increases the accuracy of pseudo data in presence of high correlation between the sets of aggregated load data. As a result, loads aggregation is considered in this study to decrease uncertainty and increase the correlation in pseudo data. Although loads aggregation increases the correlation, it decreases the size of the impedance matrix by reducing the number of branches. Ignoring the cable impedances in distribution networks, will decrease the accuracy of the estimated states, even with a highly correlated set of pseudo data. In this paper, we aim to show that each distribution network with its own cable impedances requires a balance between loads aggregation and the reduction in the size of the impedance matrix. An investigation is provided on a typical distribution network to show how to achieve a minimum estimation error, when both factors of loads aggregation and size of the impedance matrix are considered.

The increasing complexity of distribution networks calls for advancement in distribution system state estimation (DSSE) to monitor the operating conditions more accurately. A sufficient number of measurement devices is imperative for a reliable and accurate state estimation. The limitation on the measurement devices is generally tackled with using the so-called pseudo measured data. However, the errors in pseudo data by current techniques are quite high leading to a poor DSSE. As customer loads in distribution networks show high cross-correlation in various locations and over successive time steps, it is plausible that deploying the spatial-temporal dependencies can improve the pseudo data accuracy and estimation. Although the role of spatial dependencies in DSSE has been addressed in the literature, one can hardly find an efficient DSSE framework capable of incorporating temporal dependencies present in customer loads. Consequently, to obtain a more efficient and accurate state estimation, we propose a new non-iterative DSSE framework to involve spatial-temporal dependencies together. The spatial-temporal dependencies are modeled by conditional multivariate complex Gaussian distributions and are studied for both static and real-time state estimations, where information at preceding time steps are employed to increase the accuracy of DSSE. The efficiency of the proposed approach is verified based on the quality and accuracy of the indices, standard deviation and computational time. The method applied to a combination of residential and industrial customer loads in three different balanced medium voltage (MV), and one unbalanced low voltage (LV) distribution case studies for evaluations.

This paper forms a framework for allocating a central electric energy storage (EES) in discrete communities, forming as segmentations along the rural feeders, where the installation of cross connects are not economic or even feasible. In this framework, EESs that centrally can be installed in each community are owned and operated by an aggregator/retailer, trading bidirectional energy transactions with both the grid and the customers. The objective is to find optimum investment in storage capacity in rural feeders for minimum annual energy purchase cost using energy arbitrage opportunities, while maintaining an allowable level of system average interruption duration index (SAIDI). As a part of EES sizing and siting optimization in this approach, annual hourly network data are utilized for charge/discharge scheduling and constraint, and for system reliability assessment-using the k-means clustering technique. This method is applied to a rural network in Queensland, Australia, and the results are examined to show the effectiveness of this method.

The Public Transport Authority (PTA) of Western Australia aims to provide a safe and reliable transport service to Western Australians. The PTA is accountable to ensure that uninterrupted, reliable and safe power is supplied and distributed through overhead line equipment (OLE) to the train railcars. Rail cars on the network operate with 25kV AC supply. There are protection systems in place to protect the electrical network and isolate faulty sections from the rest of the network and prevent any further damage to equipment. The traction power network supplies the required power for the rail cars through the catenary and contact wire in the electrical network. The return current from the rail cars flows back through the rail tracks and return conductor (RC) wire to the feeder station using booster transformer (BT). Over the years PTA has decided to remove the BTs from the network due to strategic and financial reasons. This project is focused on the Midland line, as this is the only line where all the BTs are disconnected from the system. As result, the overall impedance of the traction power network was changed. This change caused the relay’s accuracy in the network, which results in operational delays. In other words, the distance to fault readings by the relays were no longer correct and, eventually, this affects the train operations and could cause delays in fault findings in the OLE network. Therefore, PTA has decided to update the distance protection relay settings. However, the first step performed under this project was to provide a test plan and measure the overhead line impedances and verify the measured data by theoretically calculate and modelling traction power system (in OpenPowerNet). All of the mentioned works were performed under this project except the traction power system modelling which were performed in collaboration with a PTA electrical engineer and all the input data in system modelling were provided under this project.

In recent times the need to deploy additional sustainable generation means has become more apparent due to the ever-changing landscape of the global energy generation sector. Australia’s changing consumer needs means new technologies like renewable generation sources such as solar PV systems have increased in popularity over time, though their full capability has not yet been met. Though their intermittent generation is cause for concern in maintaining a stable and quality power supply. This thesis aims to address the issues by developing a probabilistic methodology for the day ahead estimate of the maximum hosting limits capacity and minimum operating reserve requirements of a microgrid containing high levels of PV penetration. Before the commencement and development of the project, a wide range of methods from literature were analysed regarding microgrids and their use in this project. The comprehensive range of concepts of microgrids and their distributed generation were divulged and incorporated into the project methodology. To understand how to provide the probabilistic estimate of the maximum hosting capacity, three previously methods in literature were analysed, each providing more technically advanced approaches than the last. The same research approach was used to understand the methodology of developing a probabilistic estimate of the operating reserve. These methods range in methodology, from the Monte Carlo simulation method to advanced artificial neural networks. To provide the day ahead estimates, an artificial neural network is developed to generate the network parameter forecasts required, providing with it, a probabilistic range of input to a network model. The maximum hosting capacity limit will ensure the amount of renewable generation expected will not exceed the performance indexes required for, voltage level, line loading limits and generator reverse power flow. The minimum operating reserve will provide an estimate of the reserve generation re

As technology continues to develop and improve, the modern world is experiencing a significant transition from the traditional concept of large-scale, centralised power generation to a future incorporating distributed energy resources (DER) closer to local loads. Microgrids provide a sustainable solution for growing energy demands by integrating renewable energy (RE) generation with energy storage (ES) technologies, backed up with traditional generation methods, placing a higher reliance on the abundance of renewable resources such as solar or wind energy and a decreased reliance upon diminishing fossil fuels. With careful planning and design regarding placement and sizing of DER and ES, as well as a robust control and energy management strategy, the implementation of the embedded microgrid concept at a large commercial entity such as Murdoch University (MU) can have significant benefits including enhanced system reliability and reduced operational expenditure. The significant objective of this project was to analyse the reliability of the current electrical network that exists within the Substation 13 precinct at MU and compare the results with a reliability analysis of the proposed upgraded Substation 13 electrical network. The aim of such a comparison was to determine whether the electrical performance and reliability had improved following the implementation of the proposed upgraded network and that electrical supply can be maintained to critical loads during a grid outage scenario. The analysis and simulations of either network models was to be carried out using the PowerFactory software package, with a number of critical reliability indices identified within literature to provide the basis of comparison. The output results from the model simulations and reliability analysis demonstrated a notable and favourable increase in system reliability following the network upgrades under grid outage scenarios. This demonstrated that under the new proposed network config

Microgrid is based on smaller decentralised low voltage system with the use of modern power technology puts different types of Distributed Energy sources solar power, wind power, and energy storage devices together, improving the electrical supply reliability, reducing the feeder loss and ensures the stability of the voltage. The current trend of incorporating energy storage devices in the microgrid is aimed to mitigate the power imbalance and improve the electrical supply reliability. The thesis uses Kalbarri, Western Australia as a case study site with an aim to investigate the appropriate battery technology and formulate control algorithm for the microgrid. The thesis starts by examining the Australian electrical market including the: socio‐economic, political, and regulatory environment and presents the rationale of having an Energy Storage System in rural Australia. The thesis investigates the various available BESS battery technology options and suggests the most appropriate options for the BESS comprised Kalbarri microgrid model. The MATLAB/Simulink BESS control algorithm design model is presented with an aim to test voltage and frequency regulation under different load condition, including the process of seamless transition from the grid‐connected operation to a grid‐disconnected operation of the microgrid. The research presents a theoretical control model based on the Power Control theory and existing academic literature on the topic. The thesis examines the control algorithm design to regulate the frequency and voltage using the BESS system to connect to the main three phase AC grid. The overall site model includes a power conversion of two DC sources: BESS and PV system. The BESS control algorithm model comprises of a Power Conversion system that use three‐phase full bridge Insulated Gate Bipolar Transistors (IGBTs) with LCL filter and a Power Control System based on Phased Lock Loop to synchronise with the grid frequency. The Power Control system uses a

The growth of renewable energy, especially PV system has been accelerated in the past decades. With this growth, several problems have been identified that can interfere with the power quality on the distribution network. These problems include; voltage rise, voltage unbalance, voltage sag/swell, flickering, and harmonic issue. IEEE 13 bus distribution network has been chosen to be the base of this research due to its flexibility and it has been modeled in DigSILENT Power Factory. The results of the study shown that the identified problems were real, and it needs to be addressed before damages can be done to the equipment. The proposed solution to the network was to double feed the network. Double feeding is a solution that proposed an extra connection to the grid in the farthest node in the distribution network. The proposed solution has been tested and promising results were found. Overall the study has been helpful to test the capability of a distribution network to handle a various degree of PV production level and how double feeding affected the power quality of the network. More research in the network is needed, especially with a real distribution network involved as a real network will behave differently compared to the simulated network.

With increasing photovoltaic (PV) penetration in low-voltage (LV) distribution networks, voltage violation during the peak PV generation period is one of the main power quality concerns. To minimize the PV power curtailment caused by voltage violations, this paper proposes a new approach utilizing one dynamic voltage restorer (DVR) at the secondary of an existing urban distribution transformer as a continuous voltage compensator with an accompanying control algorithm. The voltage compensation algorithm controls the DVR in real-time to prevent over-voltage and under-voltage at all network nodes, thereby maximizing PV injection and preventing equipment damage. The controller uses the equivalent line impedance of network to estimate the average voltage of load points without the need for any communication links for measuring load voltages. In this approach, the fixed tap changer of the distribution transformer is also optimally adjusted to minimize the DVR rating using a proposed offline optimization method. Simulation results on IEEE LV test feeder prove the ability of the DVR in maintaining all network node voltages within the allowable range in peak demand and peak PV generation periods with the benefit of injecting only a small amount of active and reactive power to the system, in contrast to alternative algorithms.

Demand Response (DR) eliminates the need for expensive capital expenditure on the electricity distribution, transmission and the generation systems by encouraging consumers to alter their power usage through electricity pricing or incentive programs. However, modelling of DR programs for residential consumers is complicated due to the uncertain consumption behavious of consumers and the complexity of schedulling a large number of household appliances. This thesis has investigated the design and the implementation challenges of the two most commonly used DR components in the residential sector, i.e., time of use (TOU) and direct load control (DLC) programs for improving their effectiveness and implementation with innovative strategies to facilitate their acceptance by both consumers and utilities. In price-based DR programs, the TOU pricing scheme is one of the most attractive and simplest approaches for reducing peak electricity demand in the residential sector. This scheme has been adopted in many developed countries because it requires less communication infrastructure for its implementation. However, the implementation of TOU pricing in low and lower-middle income economies is less appealing, mainly due to a large number of low-income consumers, as traditional TOU pricing schemes may increase the cost of electricity for low income residential consumers and adversely affect their comfort levels. The research in this thesis proposes an alternative TOU pricing strategy for the residential sector in developing countries in order to manage peak demand problems while ensuring a low impact on consumers’ monthly energy bills and comfort levels. In this study, Bangladesh is used as an example of a lower-to-middle income developing country. The DLC program is becoming an increasingly attractive solution for utilities in developed countries due to advances in the construction of communication infrastructures as part of the smart grid concept deployment. One of the main chal

This book highlights the latest research advances in the planning and management of electric distribution networks. It addresses various aspects of distribution network management including planning, operation, customer engagement, and technology accommodation. Given the importance of electric distribution networks in power delivery systems, effectively planning and managing them are vital to satisfying technical, economic, and customer requirements. A new planning and management philosophy, techniques, and methods are essential to handling uncertainties associated with the integration of renewable-based distributed generation, demand forecast, and customer needs. This book covers topics on managing the capacity of distribution networks, while also addressing the future needs of electric systems. The efficient and economical operation of distribution networks is an essential aspect of ensuring the effective use of resources. Accordingly, this book addresses operation and control approaches and techniques suitable for future distribution networks.

Introduction of new electronic technologies that are more sensitive to disturbances in the power network has made it necessary to monitor the quality of power supplied. This project presents a study of the power quality at Murdoch University, and aims at identifying the causes of poor power quality and provide the solutions to these power quality problems. The main objective of this project is to analyse the power quality of the electricity system of Murdoch University in order to identify the causes of poor power quality and provide solutions to improve the power quality. Furthermore, for all the specific goals that had been set before the commencement of this project have all been accomplished. The most common power quality issues in most electrical networks globally include voltage sags, voltage swells, voltage transients, and frequency harmonics. Other power quality issues that can be experienced include voltage flicker, voltage unbalance, inter-harmonics, brownouts, direct current, short-term and long-term power interruption. A discussion of all these issues has been conducted in this report with their impacts on equipment in the network identified to be wear and tear, overheating, tripping of circuit breakers, and improper functioning of equipment. The compensating devices that can be used for improving power quality such as Active and Passive filters, Distribution Static Compensator, Dynamic Voltage Restorer, and Unified Power Quality Conditioner have also been analysed in this report. The direct and indirect costs of power quality have been reviewed in this project with the finding that 50 % of the global turnover of the electricity sector equating to 500 billion Euros is lost annually due to poor power quality. IEEE 519, AS/NZS 61000.3.2.2003, ANSI C84.1, IEEE 1459, EN 50160, and IEC 61000 are some of the power quality standards that have been discussed in Power Quality Analysis at Murdoch University this project. Based on the data collected from Murdoch Un

The objective of this thesis is to examine the microgrid concept as a viable economic alternative to the centralised electrical network in Western Australia when applied to industrial loads. The key focus was on formulating an economic optimisation model as it is applied specifically to new Western Australian industrial developments. The Nambeelup Industrial Area, located approximately 9 km northeast of the city of Mandurah, is the electrical and thermal load considered for this task. Analysis was conducted to determine a range of technically viable microgrid configurations that display economically superior characteristics when compared to the benchmark. This benchmark is a proposed $31.1M infrastructure upgrade to supply the Nambeelup Industrial Area through the Meadow Springs Substation which is part of the South West Interconnected System. Using HOMER1 software and the acquired industry data, a range of distributed energy resources (DER), energy storage systems, thermal recovery systems and varying states of grid connection were modelled over a 25-year project life. The studies show that the initial capital expenses of proposed microgrids were often many times the benchmark cost but had a decidedly lower project net present cost. Specifically, results revealed that increased DER penetration correlated with an average discounted savings of $350M over the project lifetime, which lead to an average payback period of less than three years when compared to the benchmark. Economically optimised architectures often featured combined heat and power (CHP) equipped gas-fired combustion generation with large-scale wind turbines. A final set of architectures were proposed based on their respective optimisation variables with a main featured configuration of a single 32 MVA CHP equipped gas turbine, an 18.15 MW wind farm and a 60 MWh vanadium redox flow battery bank installation. The featured system provides a more reliable and environmentally superior thermal and electrical

Currently, fossil fuels such as coal, oil and natural gas are mainly responsible for the devastating effects of environmental air pollution caused by burning fossil fuels to produce electricity. The future demands for energy and power consumption is on the rise and enabling renewable technologies such as solar power through Photovoltaic systems (PV) will be able to harness the ever-increasing demands for energy. PV power generation systems are exponentially growing in use and are currently the most common and reliable renewable energy source available on the market. With increased penetration of rooftop PV, there are still many significant challenges to overcome to optimize the distribution systems. Further studies on PV have shown that the impacts of PV systems on the power grid are a significant concern facing the development of renewable energies. The aims of this project is to investigate the penetration of a large scale PV system into Murdoch University’s electrical network and to discuss the potential impacts on power quality, voltage profiles and power losses. Studies have indicated that Murdoch University has 43 buildings that are suitable for PV installations, up to a total rated power of 2 MW. The area of each building has been taken into account to effectively install suitable PV arrays. Due to the limitation of data, 15 substations in the network have been analysed for before and after the PV installation where the total power for the grid is 832.9 KW. Three different modelling scenarios have been finalised based on alternative loads on particular days with a maximum PV generation. In scenario 1 the total load power is 554 KW, and this was the lowest load that was measured on the 25/12/2016. The results of the second scenario indicated a load of 841.71 KW which occurred on the 21/03/2017, which is close to the output of the PV generation system. Scenario 3 had the highest load of 1.0094 MW that occurred on the 06/09/2017. To assess the impacts of the PV

The objective of this thesis is to illustrate the effectiveness of customer-side voltage regulation in unbalanced distribution networks, under increasing levels of distributed generation, with the overarching aim of reducing voltage magnitude and voltage imbalance violations. The voltage regulators will be installed between the point of common coupling and the customer point of access. This thesis focusses on a static timeseries load flow study that has been developed using Python 3.6. Load flow simulations have been carried out at PV penetrations levels from 30 to 100 per cent, utilising three different loads models––constant power, constant impedance and an equal impedance-power ratio. An algorithm has been developed for selecting the location of the voltage regulators, which uses the performance of the network in terms of voltage magnitude and voltage imbalance. The test network is based on a real four-wire multipleearthed neutral distribution network in Perth, Western Australia. Real and reactive power readings from customer meters have been used. The voltage regulator model is constructed around an autotransformer regulator with 32 steps and an effective adjustment range of ±10%. The proposed voltage regulation methodology in this thesis is effective in addressing the problems of voltage magnitude violations and to a lesser extent, voltage imbalance, in the presence of high PV penetration. However, the benefits this solution offers in terms of voltage violation reduction, loss reduction and autonomous operation, are not enough to overcome the current cost of these devices. The existing on-load tap changer solution modelled in this thesis for comparison, is shown to deliver better technical outcomes in terms of network performance, for less cost.

Modern societies depend on the continuous supply of electricity, which has become a key focus for power system engineers. It is critical to the economy that a high quality and an uninterruptable power supply is maintained to ensure there are no costly losses. This thesis looks at addressing two network issues: security and reliability, by incorporating the use of a Resistive Superconductor Fault Current Limiter (R-SFCL) device. The technical analysis investigates the two scenarios independently. The first case assesses the installation of a peak-lopping diesel generating facility with a series installed R-SFCL to address the shortfall forecasted in the future. The second case evaluates the functionality of the R-SFCL as a busbar coupler to improve the substation’s reliability performance. Both applications of the R-SFCL are to reduce the fault current levels on the network to within manageable levels. An economic assessment is handled to provide the most inexpensive and reliable solution for addressing the network drivers for a review time of 25 years. The fault analysis is conducted in a steady-state assumption to evaluate the efficiency of reducing the fault current under contingency conditions. The simulations are conducted on two real 132/22 kV substation networks to investigate the fault current levels experienced on the network. The results from the simulations were then used for an economic assessment of the device against traditional network solutions. The device proved effective in limiting the fault current for the two scenarios, but the economic assessment impacted the application of the R-SFCL. The economic assessment proved that the R-SFCL is beneficial for improving the reliability performance of a substation that has outage difficulties, but to address network security, the application of a R-SFCL is not the favourable option today.