Distributed Communication Architecture for Smart Grid Applications

One big challenge in building a smart grid arises from the fast growing amount of data and limited communication resources. The traditional centralized communication architecture does not scale well with the explosive increase of data and has a high probability of encountering communication bottlenecks due to long communication paths. To address this challenging issue, this article presents a distributed communication architecture that implements smart grid communications in an efficient and cost-effective way. This distributed architecture consists of multiple distributed operation centers, each of which is connected to several data concentrators serving one local area and only sends summary or required integrated information to a central operation center. Using this distributed architecture, communication distance is much shortened, and thus data will be delivered more efficiently and reliably. In addition, such a distributed architecture can manage and analyze data locally, rather than backhauling all raw data to the central operation center, leading to reduced cost and burden on communication resources. Advanced metering infrastructure is chosen as an example to demonstrate benefits of this architecture on improving communication performance. The distributed communication architecture is also readily applicable to other smart grid applications, for example, demand response management systems

Proactive Monitoring, Anomaly Detection, and Forecasting of Solar Photovoltaic Systems Using Artificial Neural Networks

The world of energy sustainability landscape is witnessing high proliferation of smartgrids and microgrids, it has become significant to use intelligent tools to design, operate and maintain such crucial systems in our lives. Solar energy is an intermittent source and purely Photovoltaic (PV) based, or PV and storage based smartgrids require characterization and modelling of PV resources for an effective planning and effective operations. This dissertation familiarizes briefly the existing tools for design, monitoring, forecasting and operation of a solar system in smart electric grids infrastructure and proposes a unique application-based infrastructure to monitor, operate, forecast and troubleshoot a working PV of a smartgrid. A resilient smartgrid communication is proposed which enables monitoring and control of different elements in any PV system. This communication architecture is used to facilitate a feedback-oriented monitoring of different elements in a microgrid ecosystem and investigated thoroughly. This integrated architecture which is a combination of sensors, network elements, database and computation elements is designed specifically for solar photovoltaic (PV) powered grids on modular basis. Apart from this, the network resilience and redundancy for smooth and loss less communication is another characteristic factor in this research work. Subsequently, a deep neural network algorithm is developed to diagnose the underperformance in the generation of a PV system connected to a smartgrid. As PV generation is predominantly dependent on climatic parameters, it is necessary to have a mechanism for understanding and diagnosing performance of the system at any given instance. To address this challenge, this deep neural network architecture is presented for instantaneous performance diagnosis. The proposed architecture enabled modeling and diagnose of soiling and partial shade conditions prevalent with an accuracy of 90+%. Features of monitoring and regulating the generation and demand side of the grid were integrated through network along with feedback-based measures for effective performance in the PV system of a smartgrid or microgrid using the same network. The novelty in this work lies in real-time calculation of ideal performance and comparison for diagnosing critical performance issues of solar power generation like soiling and partial shading. Furthermore, long-short term memory (LSTM), which is a recurrent neural network model, is created for forecasting the PV solar resources, in which can assist in quantifying PV generation in various time intervals (hourly, daily, weekly). PV based smartgrids often experience expensive or inaccurate resources planning due to the lack of accurate forecasting tools where the projected methodology would eliminate such losses. This research work in its whole provides a different proposition of vertical integration which can transform into a new concept called Internet of Microgrid (IoMG). Planning, monitoring and operation form the core of smartgrids administration and if intelligent tools intertwined with network are being used as integral part in each of these aspects, then it forms a holistic view of smartgrids

ICT-Enabled Control and Energy Management of Community Microgrids for Resilient Smart Grid Operation

Our research has focused on developing novel controllers and algorithms to enhance the resilience of the power grid and increase its readiness level against major disturbances. The U.S. power grid currently encounters two main challenges: (1) the massive and extended blackouts caused by natural disasters, such as hurricane Sandy. These blackouts have raised a national call to explore innovative approaches for enhanced grid resiliency. Scrutinizing how previous blackouts initiated and propagated throughout the power grid, the major reasons are lack of situational awareness, lack of real-time monitoring and control, underdeveloped controllers at both the transmission and distribution levels, and lack of preparation for major emergencies; and (2) the projected high penetration of renewable energy resources (RES) into the electric grid, which is mainly driven by federal and state regulatory actions to reduce GHG emissions from new and existing power plants, and to encourage Non Wire Solutions (NWS). RESs are intermittent by nature imposing a challenge to forecast load and maintain generation/demand balance. The conceived vision of the smart grid is a cyber-physical system that amalgamates high processing power and increased dependence on communication networks to enable real-time monitoring and control. This will allow for, among other objectives, the realization of increased resilience and self-healing capabilities. This vision entails a hierarchical control architecture in which a myriad of microgrids, each locally controlled at the prosumer level, coordinates within the distribution level with their correspondent distribution system operator (i.e. area controllers). The various area controllers are managed by a Wide Area Monitoring, Protection and Control operator. The smart grid has been devised to address the grid main challenges; however, some technical barriers are yet to be overcome. These barriers include the need to develop new control techniques and algorithms that enable flexible transitions between operational modes of a single controller, and effective coordination between hierarchical control layers. In addition, there is a need to understand the reliability impacts of increased dependence on communication networks. In an attempt to tackle the aforementioned barriers, in my work, novel controllers to manage the prosumer and distribution networks were developed and analyzed. Specifically, the following has been accomplished at the prosumer level, we: 1) designed and implemented a DC MG testbed with minimal off-the-shelf components to enable testing new control techniques with significant flexibility and reconfiguration capability; 2) developed a communication-based hybrid state/event driven control scheme that aims at reducing the communication load and complexity, processor computations, and consequently system cost while maintaining resilient autonomous operation during all possible scenarios including major emergencies; and 3) analyzed the effect of communication latency on the performance of centralized ICT-based DC microgrids, and developed mathematical models to describe the behavior of microgrids during latency. In addition, we proposed a practical solution to mitigate severe impacts of latency. At the distribution level, we: 1) developed a model for an IEEE distribution test network with multiple MGs integrated[AM1] [PL2] ; 2) developed a control scheme to manage community MGs to mitigate RES intermittency and enhance the grid resiliency, deferring the need for infrastructure upgrade; and 3) investigated the optimal placement and operation of community MGs in distribution networks using complex network analysis, to increase distribution networks resilience. At the transmission level (T.L), New York State T.L was modeled. A case study was conducted on Long Island City to study the impact of high penetration of renewable energy resources on the grid resilience in the transmission level. These research accomplishments should pave the way and help facilitate a smooth transition towards the future smart grid.

Planning considerations for smart meter implementations in South Africa

Smart meter implementations are still in their infancy in many African countries. This is evident by the lack of research on the subject in the African context. Most of the research studies are either Eurocentric or US-centric. Although these studies are important and informative, they might not address the African challenges in context. Hence, South Africa was chosen as the testbed for an investigation that addresses the apparent knowledge gap. This study set out to formulate a framework for planning considerations in the implementation of smart meter technology within South Africa. Through extensive literature review and analysis, the technology acceptance model (TAM) was chosen as a foundational framework for this study. Although TAM is widely used for researching technology acceptance and use, its applicability was found to be inadequate in explaining customer centric factors in smart metering. Therefore, it was supplemented with factors from the theory of reasoned action (TRA), the theory of planned behavior (TPB), privacy calculus theory (PCT), as well as the unified theory of acceptance and use of technology (UTAUT). A total of 11 consumer-centric factors were identified, and these were statistically analysed using the structural equation modelling technique (SEM). Ten (10) consumer-centric factors was found to be significant. These were attitude, perceived value, monetary cost, privacy risk, perceived ease of use, perceived usefulness, facilitating conditions, social norms, trust in technology and behavioral intention. Hypothesis testing confirmed that, not one acceptance model could adequately be used to identify and explain the consumer-centric factors that can be incorporated for planning considerations for smart meter implementation in South Africa. It was further observed that the consumer-centric factors such as environmental issues, security, reliability and health issues that were important in developed countries were not deemed so in South Africa. From a methodological perspective, the study attests to contextual localised application as opposed to universal meaning and measurement invariance when incorporating planning consideration for smart meter implementation in South Africa as compared to European countries and the United States of America. Finally, the findings hold some practical implications, as they showed the practical utility of the model in predicting the consumer-centric factors that can be incorporated for planning considerations. In support, the Business Model Canvas (BMC) was found to be a useful tool in deriving and reporting on the formulation of planning consideration guidelines. Using the BMC, five planning consideration guidelines were derived: customer segmentation, partnerships, benefits communication, value identification and customer attitude. These planning considerations will allow smart meter providers to identify their customers, partners and value propositions they might need to offer consumers to facilitate a higher smart meter acceptance and use. The proposed planning consideration guidelines can practically be used by policymakers and regulators for several aspects for future pervasive technology acceptance studies. This research has, therefore, created a platform for further research in the smart technology domain while providing a usable predictive framework for the identification of consumer-centric factors and formulation of planning considerations guidelines for smart meter implementation within the South African context

Decoupled voltage sensitivity analysis for cluster-oriented smart grid operations

The power systems of today use “smart grids” to improve grid operations and energy efficiency. State-of-the-art technologies and advanced control mechanisms have meant the renewable energy sources (RESs) can now be increasingly integrated into power grids. The grid integration of the RESs makes power generation more sustainable but concurrently causes bidirectional energy flow. This can lead to imbalances between phases. Instability during grid operations is consequently concerned, such as overcurrent in power lines and over/under bus voltages. In the power systems, distribution grids are especially affected, since they were not originally designed to handle power generation. The traditional grid operation, which is a centralised architecture, is therefore impractical for smart grids. Accordingly, an active distribution network is required. In this thesis, an impedance network model and a method for decoupled voltage sensitivity analysis are proposed. Their key contribution to the academic community in the field of smart grids is to enable distributed steady-state analysis based on a clustering power systems approach (CPSA), resulting in decentralised active operations in distributed areas of the smart grids. The voltage sensitivity analysis proposed in this thesis examines the response of voltage magnitude and angle in relation to bus current in sequence systems, active power, and reactive power. The results from the analysis therefore indicate that there are impacts between buses in term of the voltage magnitudes, which can be further used for power management and voltage regulation. The proposed analysis method is derived from a mathematical description of complex bus voltage, based on the proposed impedance model. It requires only measurement data gathered from the phasor measurement unit, without the information from grid topology. The required measurement data consist of bus voltages, bus currents, and the line currents of the connecting line between the distributed areas. As the foundation of the proposed method, first, the impedance model for each distributed area is determined from the measurement data. Only bus impedances between buses of concern are produced in this step. The impedance model is further used together with the measured voltage of the concerned bus in the sensitivity analysis. The proposed analysis method is devised to deal with both balanced and unbalanced grid conditions. The accuracy of the proposed analysis method was verified by simulations in three case studies. The results from the first two case studies demonstrated the accurate voltage sensitivity analysis in all selected grid cases under the balanced and unbalanced grid conditions, including the case of the measurement errors up to the maximum of 1% total vector error. Use of the outcome from voltage sensitivity analysis for regulating voltage profile was then examined in the third case study. Once verification was achieved, the proposed analysis method enabled decoupled voltage sensitivity analysis by using only the measurement data. This makes the proposed method suitable for further use in smart grids. Further research is recommended, which should give consideration to possible additional measurement errors, dynamic characteristics of the power grid, and the implementation of the proposed method