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Microgrids are extensively integrated into electrical systems due to their many technical, economic, and environmental advantages. However, they encounter a challenge as they experience high-frequency fluctuations caused by the stochastic nature of renewable energy generation, electric loads, and the presence of Electric Vehicles (EVs). Therefore, various techniques, algorithms, and controllers have been introduced to ensure effective Load Frequency Control (LFC) and maintain a stable power system in microgrids. These methods aim to ensure that the system's frequency remains stable and within an acceptable range, especially when faced with changing load demands and other factors. This paper presents a novel enhanced control approach, Particle Swarm Optimization-Trained Artificial Neural Network (PSO-TANN), to optimize the load frequency model of a microgrid with vehicle-to-grid integration. The results are then compared under various scenarios, including renewable energy integration, EV charging and discharging dynamics, and varying load demands. The comparative analysis involves assessing the performance of the conventional Proportional–Integral–Derivative (PID) controller, the PSO-PID controller, and the newly proposed controlling technique. The suggested controller attains 99.904% efficiency with a negligible mean squared error of 1.1112 × 10−7, decreasing the integrated time absolute error to 1.0 × 10−4. It shows rapid response, precise targeting, and quick peak output ability, with marginal overshoot and undershoot, and a transient time of 28.5626 s, efficiently controlling microgrid frequency. Stability analysis validates the effectiveness of the proposed PSO-TANN controller in ensuring stability within the microgrid's LFC system during uncertainties and disturbances. This establishes resilience, diminishes settling time, and maintains reliable performance while controlling frequency. • An investigation into an islanded microgrid's Load Frequency Control (LFC) model framework has been conducted, comprising diesel generator, solar PV system, wind turbine, and EVs operating in vehicle-to-grid mode to supply power to electric loads, consequently impacting the frequency dynamics of the microgrid system. • A novel enhanced control approach, Particle Swarm Optimization-Trained Artificial Neural Network (PSO-TANN) has been implemented to optimize the load frequency model of a microgrid. • An extensive comparative analysis with other controllers, including PID and PSO-PID, is conducted across diverse scenarios involving renewable energy integration and EV charging and discharging dynamics under various load disturbances to reduce settling time, control system frequency, and ensure reliable performance. • The stability analysis validates the efficiency of the proposed PSO-TANN controller in ensuring reliability during uncertainties and disturbances, maintaining overall robustness and stability of the LFC system within the microgrid. [ABSTRACT FROM AUTHOR]

This paper examines the cost-effectiveness of subsidies (feed-in tariffs and renewable energy credits) paid for by electricity consumers to support the uptake of roof top photovoltaic (PV) systems by households in Australia. We estimate annual payback periods, and then regress these against the actual uptake of household PV and associated emission reductions, creating a relationship not apparent in other research. Sensitivity analysis reveals that the declining cost of PV panels had most impact on PV uptake followed by feed-in tariffs, renewable energy credits and the increasing cost of household electricity tariffs. Our modelling shows that feed-in tariffs were higher than necessary to achieve the resultant levels of PV uptake and that the low cost of PV panels and comparatively high electricity tariffs are likely to result in a continuing strong uptake of household PV in Australia. Our modelling shows that subsidies peaked in 2011 and 2012, with payback periods of three to four years, having since increased to five to six years. Emission reduction costs are expected to reduce from over AU$200 per t CO 2 e in 2013 to between AU$65 and AU$100 per t CO 2 e in 2020. Household PV reduced Australia’s emissions by 3.7 million t CO 2 e in 2013 (1.7% of Australia’s total emissions) and is expected to reach eight million tonnes (3.7% of Australia’s total emissions) by 2020. [ABSTRACT FROM AUTHOR]

• The proposed method can solve voltage issues and enable higher PV penetration. • PV imbalance makes traditional methods less effective on overvoltage mitigation. • A joint centralized-decentralized method is developed for unbalanced PV scenarios. • The new approach controls fast PV fluctuations in a coordinated and distributed way. Due to random connection of small-size single-phase rooftop photovoltaic (PV) generators in residential areas, low voltage distribution systems tend to have unbalanced PV integration across phases. In such situations, traditional reactive power compensation methods may cause unintended Voltage-Reactive Power responses across phases due to line coupling, which consequently affects their effectiveness on overvoltage mitigation. Such overvoltage problems significantly hinder the future integration of clean and renewable solar energy resources in power networks. In this paper, the limitations of traditional methods in PV imbalance scenarios are revealed and comprehensively analyzed by a voltage sensitivity method for the first time. On this basis, an innovative joint centralized-decentralized method is developed to overcome the PV imbalance-induced voltage regulation challenge. Specifically, the inverter's reactive power response to PV power variations in different phases is adaptively scheduled in order to avoid adverse effect of inter-phase Voltage-Reactive Power interaction on voltage regulation. The effectiveness of the proposed method is validated by time-series simulations with recorded data and a real-life low voltage distribution system in Australia. With the traditional method (i.e. power factor droop control strategy), 93 minutes' overvoltage can be observed in the case study, which will trigger overvoltage protection and disconnect PV inverters from the grid. While, the proposed method can always control the system voltage within the allowable range, and it consequently avoids PV disconnection. By solving this bottleneck issue, more PV systems can be integrated into distribution networks, which consequently contributes to CO 2 emission reduction in future. [ABSTRACT FROM AUTHOR]