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• A new energy-as-a-service business model for aggregators of prosumers. • A new techno-economic simulation framework to compute business model benefits. • The new business model increases the average profitability of the aggregator by 438%. • The new business model provides free electricity to prosumers. Traditional retail business models price electricity using volumetric tariffs, which charge customers for the unit of energy consumed. These tariffs were designed for passive consumers with low flexibility. In this paper, we argue that these volumetric tariffs are unsuitable for prosumers with high flexibility since they are unable to adequately value the flexibility of their distributed energy resources in multiple electricity markets. This reduces the interest of prosumers participating in aggregators' business models. To address this issue, we propose a new business model for aggregators of prosumers, based on the concept of energy-as-a-service. In this business model, prosumers pay a monthly fee for aggregators to represent and optimize them in multiple wholesale electricity markets, including in energy and ancillary service markets. The monthly fee is computed by a new techno-economic simulation framework proposed in this paper, which can also be used to estimate the profitability of the new business model from the perspectives of both the aggregator and prosumers. Our experiments on a portfolio of real prosumers from Australia show that the new business model maximizes the economic benefits of both the aggregator and prosumers by increasing the average profit of the aggregator by 438% and reducing the average electricity cost of prosumers from $583/year to $0 when compared to two of the most common retail business models available in the Australian market. In other words, the economic benefit for prosumers is free electricity. In addition to this benefit, the new business model also provides simplicity and predictability to prosumers, as they are offered a guaranteed outcome before providing the services. [ABSTRACT FROM AUTHOR]

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]

• A framework for aggregating large-scale prosumers in electricity markets is proposed. • The proposed bi-level framework is linearized by duality theory and big-M method. • Cases studies based on 120,000 prosumers from Australia are conducted. • Increasing prosumers enlarges the gap between strategic and non-strategic bidding. Increased penetration of distributed energy resources is unleashing the flexibility of large-scale prosumers in deregulated markets. To explore prosumers' potential market revenues, some existing studies have focused on the strategic bidding of prosumers aggregation. A majority of those studies assume the price-taker role of the aggregator while a few studies assume the price-maker role of the aggregator. However, it remains an open question as to how the increasing number of prosumers influences the profit of a strategic aggregator. Therefore, we conduct a numerical analysis in this paper to quantify the profits of aggregating large-scale prosumers. A stochastic bi-level optimization model is proposed to depict the strategic behavior of prosumers aggregation bidding in joint energy and regulation markets. This bi-level model is transformed into a mixed-integer linear programming model by employing the Karush-Kuhn-Tucker conditions based on strong duality theory. Case studies based on 120,000 prosumers from Australia demonstrate that the strategic bidding behavior of an aggregator can lead to a 7.5% decrease in operation costs, and increasing the number of prosumers will lead to a larger gap between non-strategic and strategic behavior. [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]