The integration of soft open point (SOP) effectively improves the flexibility of active distribution networks (ADNs). However, in practical operation, accurate network parameters are difficult to obtain and the operation state changes rapidly with distributed generators (DGs). With the development of information technologies, massive operation data can be acquired in ADNs. How to utilize multisource data has become the key to realize the intelligent operation of ADNs. This article proposes a data-driven operation strategy of SOP based on model-free adaptive control (MFAC). First, considering the inaccurate parameters and frequent change of operation states, a data-driven framework is formulated for the real-time operation of SOP. Then, the operation strategies of multiple SOPs are further improved with interarea coordination. The results of case studies show that driven by the measurement data, the potential benefits of SOPs are explored to adaptively respond to system state changes and improve the operational performance of ADNs.

With the increasing penetration of distributed generators, various operational problems, especially severe voltage violation, threaten the secure operation of active distribution networks. To effectively cope with the voltage fluctuations, novel controllable power electronic equipment represented by soft open points has been used in active distribution networks. Meanwhile, the communication has dramatically increased due to the rise of the variety and number of devices within the network. This paper proposes a decentralized voltage control method of soft open points based on voltage-to-power sensitivity. The method reduces the burden of communication, storage, and calculation effectively in a decentralized manner and fulfills the rapid requirements of large-scale active distribution networks. First, the network is divided into several sub-areas; each is under the control of one soft open point at most. The initial strategies of soft open points are adjusted by local voltage-to-power sensitivity and the voltage information within the sub-areas. If some nodal voltages still exceed the expected range after the sub-area autonomy, the operation strategies of soft open points are further improved by inter-area coordination with the alternating direction method of multipliers algorithm. The effectiveness of the proposed decentralized control method is verified on the IEEE 33-node system.

The flexible power distribution devices, represented by soft open points (SOPs), can facilitate power exchange among regional distribution networks. However, given the substantial investment in SOPs, there exists an urgent need for their reasonable configuration and fair allocation among multi-stakeholders. Promisingly, the peer-to-peer (P2P) electricity trading based on SOPs can not only effectively reduce operational cost, but also impart revenue-generating abilities to SOP investment. Aiming at the optimal SOP configuration under multi-stakeholder investment, this paper proposes an asymmetric bargaining-based planning method for SOPs considering P2P trading. First, a planning-operation two-layer coupling framework of SOPs is established under multi-stakeholder games. In the planning layer, analyzing the game behaviors among multiple distribution companies (DISCOs), an asymmetric bargaining-based planning model is formulated to obtain the configuration and investment schemes of SOPs. In the operational layer, the P2P trading and the profitability of multiple DISCOs are driven by price incentives. Then, a two-layer coupling model is built and efficiently solved using the generalized Benders decomposition algorithm. Finally, the effectiveness of the proposed method is validated on a practical distribution network. The proposed method incentivizes investment in SOPs by balancing the interests of multiple DISCOs, while efficiently improving the operational performance of distribution networks.

The economic operation and scheduling of community integrated energy system (CIES) depend on accurate day-ahead multi-energy load forecasting. Considering the high randomness, obvious seasonality, and strong correlations between the multiple energy demands of CIES, this paper proposes an adaptive forecasting method for diverse loads of CIES based on deep transfer learning. First, a one-dimensional convolutional neural network (1DCNN) is formulated to extract hour-level local features, and the long short-term memory network (LSTM) is constructed to extract day-level coarse-grained features. In particular, an attention mechanism module is introduced to focus on critical load features. Second, a hard-sharing mechanism is adopted to learn the mutual coupling relationship between diverse loads, where the weather information is added to the shared layer as an auxiliary. Furthermore, considering the differences in the degree of uncertainty of multiple loads, dynamic weights are assigned to different tasks to facilitate their simultaneous optimization during training. Finally, a deep transfer learning strategy is constructed in the forecasting model to guarantee its adaptivity in various scenarios, where the maximum mean discrepancy (MMD) is used to measure the gradual deviation of the load properties and the external environment. Simulation experiments on two practical CIES cases show that compared with the four benchmark models, the electrical and heating load forecasting accuracy (measured by MAPE) increased by at least 4.99 and 18.22%, respectively.