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Real-Time Optimal Controls for Active Distribution Networks - From Concepts to Applications

In the last few years, the entire power industry is undergoing profound changes towards a smarter electrical grid. Decentralized generation, distributed energy storage systems and active participation of end-users in the lower level of the electrical infrastructure, intelligently managed to provide support to the grid, define the notion of Active Distribution Networks (ADNs). The increased connection of distributed generation in ADNs, essentially composed of renewable energy resources, incurs severe impacts on planning and operational procedures of both transmission and distribution systems and calls for application of intelligent control techniques in order to achieve specific operation objectives. In this respect, this thesis focuses on the compelling problem of optimal operation and control of ADNs, with particular reference to the design of real-time control algorithms for voltage regulation and lines congestion management. In the first part of this thesis, we adopt a centralized control architecture for voltage regulation and lines congestion management in ADNs. The goal of the proposed controller is to schedule both the active and reactive power injections of a set of controllable resources, in coordination with other traditional resources, in order to achieve an optimal grid operation. The proposed online controller is based on a linearized approach that links control variables (e.g., power injections, transformers tap positions) and controlled quantities (e.g., voltages, current flows) by means of sensitivity coefficients. We first provide a straightforward analytical derivation of nodevoltages and line-currents sensitivity coefficients as a function of the power injections and transformers’ tap-changers positions that allows us to significantly reduce the computational time compared to traditional approaches. The proposed centralized scheme is suitable for the real-time control of a limited number of distributed energy resources, (e.g., medium-sized dispersed generation units) as it relies on a point-to-point communication from the distribution network operator (DNO) to each controllable resource. Once the proposed control algorithm is validated, as a further step, we relax the assumption that the DNO has an accurate knowledge of the system model, i.e., a correct network admittance matrix [Y] and we adapt the proposed centralized control architecture to such a scenario. To this end, we formulate the optimal control problem of interest as an uncertain mixed integer linear programming (MILP) one and we use a robust optimization framework to immunize the solution against the uncertainty in the system model. Additionally, we propose a methodology for the computation of voltage sensitivity coefficients for a multi-phase unbalanced grid without relying on