Power distribution systems are experiencing large deployment of behind-the-meter distributed generation and storage along with electrified transport and heat, requiring fundamental changes in planning and operation. There are increasing efforts to directly model real large-scale networks and conduct detailed analysis beyond conventional practices. Utilizing optimization methods to leverage distributed generation to the benefit of the distribution system and all customers is the ideal. However, progress towards adoption of such controls by system operators are impeded by concerns over privacy, regulatory and market issues in many jurisdictions. While these issues are being addressed, there may be opportunity to exploit the synergistic effect of mixing low carbon technologies (LCT). This paper presents a large-scale medium voltage (MV)-low voltage (LV) integrated system model building, scenario determination and analysis approach for distribution systems. Data with different formats from several databases are used in model building. In collaboration with the national distribution system operator (DSO) in Ireland (ESB Networks), a pilot study is conducted for a rural system in the Southwest of Ireland, highlighting the challenges of directly modelling real distribution systems and investigating the potential synergies between multiple low carbon technologies.
Future distribution grids are expected to face an increasing penetration of electric vehicles (EVs) and heterogeneous distributed energy resources (DERs). This demands a distributed energy management (EM) to manage power generation and delivery of energy sources to maintain power quality under the impact of EV charging, to save operating costs, and to enhance resiliency. However, the global optimality of the distributed EM's optimization problem is still an issue in existing work because of the non-convex nature of the optimization problem. In this paper, a distributed EM strategy for grid-connected distribution networks is proposed. In particular, the EM strategy is composed of two steps. In the first step, some conditions of the EM optimization task are relaxed to apply an algorithm converging to the global optimality. The results of the first step are used to reconfigure constraints of the full optimization problem in Step 2. The proposed scheme is validated by implementing the real-time controller-hardware-in-the-loop (CHIL) experimentation on the IEEE 33 bus system. To study the impact of EV charging, EV data is
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