Model-less Measurement-based Computation of Voltage Sensitivities in Unbalanced Electrical Distribution Networks

Within the context of microgrids optimal voltage control, most schemes proposed in the literature either rely on (i) droop-control methods or (ii) methods involving the computation of explicit nodal power set-points as a solution to a given optimization problem. The first category of approaches is in general suboptimal as it relies on locally sensed measurements. The second category guarantees some level of optimality but requires an accurate and up-to-date model of the network that is, in general, not always available in low voltage grids. To overcome the aforementioned limitations, in this work we propose a methodology suitable for voltage control in generic low voltage 3-phase unbalanced grids. It can be used for the computation of either explicit power set-points or to define the droops of local voltage regulators. Its main characteristic is that it does not rely on the knowledge of the system model and its state. In particular, the goal is to compute linearized dependencies between voltage magnitude and nodal power injections, i.e., voltage sensitivity coefficients. The proposed method assumes availability of a monitoring infrastructure and the computation of the desired sensitivities involves the solution of an over-determined system of linear equations constructed solely using available measurements of nodal power injections and voltage magnitudes. The proposed method is also capable to account for the measurement errors and their time correlation. The performance evaluation of the proposed method is carried out using real measurements coming from a real low voltage feeder located in Switzerland equipped with an appropriate metering infrastructure

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

Decentralized generation, distributed energy storage systems and active participation of end-users in the lower level of the electrical infrastructure, intelligently managed to provide grid support, define the notion of Active Distribution Networks (ADNs). The presence of distributed generation in ADNs incurs severe impacts on planning and operational procedures and calls for intelligent control techniques. This thesis focuses on the compelling problem of optimal operation and control of ADNs, with particular reference to the design of real-time voltage control and lines congestion management algorithms. In the first part of the thesis, we adopt a centralized architecture for voltage control and lines congestion management in ADNs. The goal of the proposed controller is to schedule the active and reactive power injections of a set of controllable resources, in coordination with traditional resources, in order to achieve an optimal grid operation. The controller relies on a linearized approach that links control variables and controlled quantities using sensitivity coefficients. Once the proposed 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 admittance matrix and we adapt the proposed control architecture to such a scenario. When the controllable resources are heterogeneous and numerous, control schemes that rely on two-way communication between the controllable entity and the DNO cannot scale in the number of network buses and controllable resources. In this direction, in the second part of this thesis, we propose the use of broadcast-based control schemes that rely on state estimation for the feedback channel. We propose a low-overhead broadcast-based control mechanism, called Grid Explicit Congestion Notification (GECN), intended for provision of grid ancillary services by a seamless control of large populations of distributed, heterogeneous energy resources. Two promising candidates in terms of controllable resources are energy storage systems and elastic loads. Therefore, we choose to validate GECN in the case of aggregations of thermostatically controlled loads, as well as of distributed electrochemical-based storage systems. In the last part of the thesis, we formulate the control problem of interest as a non-approximated AC optimal power flow problem (OPF). The AC-OPF problem is non-convex, thus difficult to solve efficiently. A recent approach that focuses on the branch-flow convexification of the problem is claimed to be exact for radial networks under specific assumptions. We show that this claim, does not hold, as it leads to an incorrect system model. Therefore, there is a need to develop algorithms for the solution of the non-approximated, inherently non-convex OPF problem. We propose an algorithm for the AC-OPF problem in radial networks that uses an augmented Lagrangian approach, relies on the method of multipliers and does not require convexity. We design a centralized algorithm that converges to a local minimum of the original problem. When controlling multiple dispersed energy resources, it is of interest to define also a distributed method. We investigate the alternating direction method of multipliers (ADMM) for the distributed solution of the OPF problem and we show cases for which it fails to converge. As a solution we present a distributed version of the proposed OPF algorithm that is based on a primal decomposition