# Towards Flexible Distribution Systems: Future Adaptive Management Schemes
## 3 Future Adaptive Control and Management Methods
For example, non-linear mathematical models of flexible resources increase the complexity of the optimization problem. In addition, stochasticity can adversely affect the optimal operating points of flexible resources. Risk level should also be considered, i.e., what is the certainty that the offered flexibility can be provided as promised/planned? In general, it can be assumed that urban area flexibilities could be typically restricted by (1) thermal capacity limits as well as (2) customer uncertain behavior and preferences. Correspondingly, in the rural area, flexibilities could be typically restricted by (1) voltage and thermal limits and (2) customer preferences as well as (3) potential local community-related targets in some areas. However, it must be noted that future price signals related to the future market structures (energy, flexibility/ancillary service, and local markets) with dynamic pricing as well as potential dynamic distribution network tariffs, can have a substantial effect on customers' and energy communities' behavior in terms of flexibility services offerings. From the DSOs' viewpoint, a new regulation is needed to enable enhanced use of DER flexibility services as a non-wire alternative and to avoid building excessive new line and transformer capacity [28; 29; 30; 31; 32]. Conflict of interest between DSO and TSO regarding use of flexibility services from distribution network-connected resources should be avoided by proper TSO-DSO coordination [33; 34; 35; 36; 37; 38; 39] and advanced state-monitoring [40] as well as state-forecasting [41; 42] in DSO networks. A conflict of interest between DSO and TSO interest has also been observed in real-life pilots, such as in [43]. However, in [43], most of the time, the flexibility needs between DSO and TSO did not have mutual effects, and sometimes the flexibility needs were also in the same direction. In addition to increased TSO-DSO coordination and collaboration in operation, cooperation will be increasingly needed already in the planning [44] stage. Due to different changes, such as increased DER, in the future, more focus must be on control, and estimation [45] of reactive power flows between HV (TSO) and MV (DSO) networks. This is also needed if dedicated markets for reactive power [46; 47; 48; 49] will be created. Improved TSO-DSO collaboration enabling new functionalities, flexibility platforms, and marketplaces will be of key importance to foster the evolution and development of future distribution networks [28; 50; 51; 52; 53; 54; 55; 56; 57; 58]. In the future, improved forecasting (weather, active and reactive power of generation/storage/load/EV, available/needed flexibility, market prices, network state, and \(PQ\) flows between voltage levels, etc.) will be an essential part of different management and market schemes enabling flexibility services improved use and provision for DSOs and TSOs. Advanced ICT technologies such as AI- and ML-based data analytics will be needed to create more accurate forecasts for optimal operation of future DSO networks [59; 60; 61; 62]. Simultaneous control of multiple small-scale DER units (flexibilities) also requires new standards and guidelines. Therefore, new reference control methods [63], and standards and guidelines for DER aggregation [64], communication, and group management are developed and are also constantly under development. These are important from the point of view of the DER units' flexibility services provision and flexibility market participation. New practices and architectures for TSO-DSO data exchange are also needed [65]. Related to reactive power flow control between voltage levels, control stability challenges and oscillations are possible [66], and the control functions operation should be tested, for example, in a real-time simulation environment before field installations.