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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.