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Under the background of the new power system, it is important to make full use of the distributed generation in the distribution network to achieve carbon peaking and carbon neutrality goals. With the wide access of distributed power sources, there is bi-directional power flow between the transmission and distribution networks. If the transmission and distribution networks still follow the traditional equivalence method for optimal power calculation, accurate results will not be obtained. Therefore, a transmission-distribution-network-coordinated optimal power flow model is established. The node tearing method is used to decouple the coupling variables of the transmission network and distribution networks. On this basis, the iterative form of the model with immobile points is obtained by mathematical derivation. The traditional heterogeneous decomposition algorithm only uses the latest calculation results for iterative computation. Hence, combining with the Anderson acceleration idea in mathematics, the heterogeneous decomposition algorithm based on Anderson acceleration is proposed by using the historical iteration values to optimize the iterations of immobile points. It is demonstrated by numerical experiments that, the established model can fully utilize the initiative of the distribution networks and reduce carbon emissions. The proposed algorithm has high accuracy. In addition, compared with the heterogeneous decomposition algorithm, its convergence performance is significantly improved.

Flow battery is an ideal choice for long-term and large-scale energy storage due to its advantages of numerous charge-discharge cycles, high capacity and long lifespan. However, the flow battery's complex structure poses high demands on battery control system, and traditional development methods are challenging to meet the flow battery system's diverse control requirements. To address this problem, a method for developing a flow battery control system based on event-driven technology is proposed, which aims to improve control precision and real-time performance. The proposed approach firstly presents a main-auxiliary stack synergistic architecture to address issues such as high stability requirements and large internal losses of flow batteries, and the architecture is modeled and analyzed. Then, the control system is designed by modularization based on event-driven technology, including the modules of flexible charging-discharging control, black start-up control strategy with auxiliary stacks, and state of charge (SOC) estimation based on Kalman filtering. Finally, a semi-physical simulation platform is built to validate the proposed architecture and strategies, which demonstrates that the proposed architecture and strategies can improve the energy conversion efficiency and stability of the system.

In traditional microgrid with droop control,the bus frequency decreases with the increase of load,and a corresponding frequency regulation control strategy is taken to keep frequency around the rated value. Control strategies based on proportional integral (PI) are commonly used to recover the bus frequency at present. Because of the possibility of changes in structure and system parameters of microgrid,the quick response and quick restoration of frequency may not be fulfilled with PI control. To solve this problem,the single neuron adaptive PI control method is used as frequency recovery algorithm in microgrid central controller in the second control layer to realize non-error control. In order to accelerate frequency recovery and improve the robustness of microgrid,the proportionality coefficient of the single neuron PI controller is optimized online by fuzzy controller. By comparing this method with original single neuron adaptive PI control with fixed neuron proportionality coefficient in simulation. It is proved that this modified method can improve transient performance of frequency recovery control and accelerate frequency recovery of microgrid.

In the context of intelligent power consumption, household users can promote the safe, stable and economic operation of the power system by participating in electricity demand response and obtain additional benefits. Therefore, a strategy for demand response based on family agglomeration is proposed in this paper. Firstly, the concept of family clustering is proposed to manage the aggregation of residents' families into communities and to design the response mechanism on the residents' side. Secondly, considering the residents' electricity consumption habits and influencing factors during demand response, the responding load function of household electricity consumption is constructed, which quantifies the potential of household demand response. On this basis, a response strategy is designed which takes both the degree of completion and user experience into account. Finally, three specific indicators are proposed to evaluate the response strategy, namely task completion rate, user discomfort and economic benefits. An example is given to verify the effectiveness and economy of the proposed strategy.

As the coupling between transmission systems and distribution systems is significantly enhanced, transmission–distribution coordinated optimisation becomes more and more necessary. Considering that the objectives of the transmission system operator (TSO) and the distribution system operator (DSO) are usually significantly different in the type and dimension, a synthesised‐objective collaborative model is proposed in this study, by normalising different objectives, introducing a weight factor to reflect the importance degree difference of multiple objectives and constructing a common objective. Then, a modified heterogeneous decomposition (M‐HGD) algorithm is proposed to solve the model. In the M‐HGD, two modifications on the update of iterative variables and the stop criteria are proposed, which effectively relieves the fluctuation of the iterative variables and restrains the tail effect of the convergence curve. The proposed model and algorithm can avoid privacy issues between the TSO and the DSO, achieve Pareto optimality solutions, and have powerful flexibility. Numerical experiments validate the effectiveness of the proposed model and demonstrate that the M‐HGD has high accuracy and has better convergence and efficiency than traditional distributed optimisation methods. [ABSTRACT FROM AUTHOR]