Planning, operation and control of flexible distribution networks

Distribution networks are undergoing a rapid transition driven by high penetrations of distributed energy resources, fast growing power electronic devices and diversified load categories. This transition introduces stronger uncertainty and converter dominated dynamics, while proliferating diverse flexibility resources whose availability is highly time varying and constraint coupled. Therefore, flexible distribution networks (FDNs) must be planned, operated, and controlled in an integrated manner to translate flexibility into guaranteed network level benefits. This special issue focuses on the planning, operation and control of FDNs, aiming to highlight emerging methods for flexibility aware planning, multi-source coordination, incentive-compatible market and control mechanisms that deliver network wide improvements. This special issue has been the focus of considerable interest. The four papers selected for publication in this issue are briefly introduced as follows. In ‘Diagnostic analytics of electricity tariff design in Nepal’, Kandel et al. examine tariff reform under Nepal's monopsony electricity market and diagnose inefficiencies in existing pricing structures that suppress flexibility on both the demand and supply sides. The authors combine locational marginal pricing simulations across multiple load buses with revenue neutral time-of-use (TOU) redesign based on measured daily load profiles. The results reveal pronounced locational price variations and show that integrating distributed generation can lower residential prices while strengthening operational flexibility. Moreover, the four period TOU structure is identified as the most effective for peak shifting, together with an implementable roadmap that acknowledges metering and regulatory constraints. In ‘Performance analysis of DC microgrids with output resistance shaping in presence of constant power loads’, Prajapati et al. investigate flexibility-oriented stability and power sharing challenges introduced by constant power loads in droop-controlled DC microgrids. They propose a virtual output resistance (VOR) to offer dynamic flexibility. Then, adjusted with converter, the VOR is applied to improve damping while maintaining acceptable voltage profiles. Using both small signal analysis and an impedance based minor loop gain criterion, the paper derives feasible ranges of the virtual resistance for several droop variants, and notes that the minor loop gain design yields virtual output resistance values closer to stability boundaries observed in simulations. The results indicate that resistance shaping yields a consistent flexibility in current/power sharing, while its advantage becomes especially pronounced during plug-and-play transients and in meshed interconnections where coupling is stronger. Controller hardware-in-the-loop tests reproduce the same qualitative trends, indicating that the method remains effective when realistic delays are present. In ‘A distributed model-free adaptive voltage control algorithm for distribution systems with extensive integration of photovoltaics’, Tian et al. treat inverter VAR capability as a controllable flexibility resource to counter photovoltaics (PV) induced voltage excursions. To flexibly regulate voltage without a network model, they propose a model-free adaptive control scheme that allocates reactive power flexibility through neighbour-to-neighbour exchanges while respecting capability curves and voltage limits. Case studies demonstrate that the proposed approach recovers acceptable voltages over diverse irradiance and loading patterns, turning fast PV fluctuations into manageable control actions. Following the reconfiguration, it still restores affected buses within diverse conditions, demonstrating flexible response and robustness to PV variability and topology changes. In ‘Locational marginal capacity pricing for power system resilience’, Xiao et al. propose a pricing method that values capacity flexibility as a key factor for resilience enhancement, it pays resources for being able to provide firm capacity at specific locations during extreme events. Based on robust optimization, they compute a nodal ‘resilience price’ as the marginal benefit from adding one unit of resilience-resource capacity, reflected by lower expected load shedding and restoration costs. To make these prices enforceable, the settlement ties payments to emergency availability and grants paying customers resilience commitments and priority restoration. Case studies show prices arise only where shedding is likely, and increase with shedding severity and cost, pinpointing where additional capacity flexibility is most valuable. The guest editors sincerely thank all authors and reviewers for their outstanding contributions to the special issue of Energy Conversion and Economics. The Editors-in-Chief and Editorial Office of Energy Conversion and Economics are also recognized for their support throughout the editorial process. Shenxi Zhang (Senior Member, IEEE) received the B.S. degree in electrical engineering from Hohai University, Nanjing, China, in 2011. He received the Ph.D. degree in electrical engineering from Shanghai Jiao Tong University, Shanghai, China, in 2016. He is currently a Professor at the school of electrical engineering, Shanghai Jiao Tong University. His main research interests include power system optimization, renewable generation, and integrated energy system. Yichen Shen (Member, IEEE) received the B.Eng. degree in electrical and electronic engineering from the University of Bath, U.K, and in electrical power engineering from North China Electric Power University, China, in 2017, and the Ph.D degree from the University of Bath, U.K, in 2021. He is currently a Postdoc Researcher with Shanghai Jiao Tong University. His main research interests include resilient multi energy systems and power system planning. Peng Li (Senior Member, IEEE) received the B.S. and Ph.D. degrees in electrical engineering from Tianjin University, Tianjin, China, in 2004 and 2010, respectively. He is currently a Professor with the School of Electrical and Information Engineering, Tianjin University. His current research interests include operation, simulation and planning of active distribution networks. Prof. Li serves as an Associate Editor of IEEE Transactions on Sustainable Energy, CSEE Journal of Power and Energy Systems, and Applied Energy. Yue Zhou (Member, IEEE) received his bachelor and PhD degrees in electrical engineering from Tianjin University in 2011 and 2016, respectively. He is currently a professor at the School of Electrical and Information Engineering, Tianjin University. His research interests lie in power system demand side response, electricity market and cyber physical systems. Yue Xiang (Senior Member, IEEE) received the B.S. degree in electrical engineering and its automation and the Ph.D. degree in power system and its automation from Sichuan University, China, in 2010 and 2016, respectively. He is currently a Professor at the College of Electrical Engineering, Sichuan University. His main research interests are power system planning and optimal operation, low carbon integrated energy systems, electricity market, and AI application in power systems. Haozhong Cheng received the Ph.D. degree in power systems from Shanghai Jiao Tong University, Shanghai, China, in 1998. He is current a Professor in School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University. His research interests are mainly power system planning, operation, and deregulation. Pengfei Zhao (Member, IEEE) received the B.Eng. and Ph.D. degree from the University of Bath, U.K., in 2017 and 2021, respectively. From 2021 to 2024, he was an Associate Professor at the Institute of Automation, Chinese Academy of Sciences, Beijing, China. In 2024, he joined Cornell University as a Research Associate of System Engineering. He is currently a Postdoctoral Research Associate in the Department of Energy Science and Engineering, Stanford University. His research interests include Net Zero Energy Systems and Cyber-Physical Social Systems.