Service Restoration Strategy in Active Distribution Networks

In the last decade, the massive penetration of Distributed Generators (DGs) has brought significant technical challenges to all the aspects related to the planning and operation of distribution networks. Thus, smart grid concept has evolved as a new operating paradigm replacing conventional approaches that were deployed in passive distribution networks. This new operation framework is equipped with intelligent measurement, communication and control infrastructures enabling an efficient electric power transfer from different point of supplies to the end consumers. This modernization and, at the same time, the ever-increasing utilization of sensitive loads in the industrial, commercial and residential distribution networks implies a higher level of requirement regarding the quality of supply. Availability is the most important factor when it comes to power quality. Therefore, the restoration of distribution systems, considering their new active status, is a timely topic that deserves to be revisited. In this regard, a novel analytical optimization model of the restoration problem is first proposed for passive distribution networks. The considered self-healing actions besides the switching operation are the tap setting modification of voltage regulation devices including I) On-Line Tap-Changing (OLTC) transformers, II) Step Voltage Regulators (SVRs), and III) Capacitor Banks (CBs). Next, the developed formulation is modified and extended such that it is applicable also for active distribution networks. In order to account for the requirements imposed by the start-up process of disconnected DGs, a novel âmulti-step restorationâ strategy is introduced. For ensuring the feasibility of the obtained solution concerning the technical constraints (e.g. voltage and current limits), a recently published method for the exact and convex relaxation of the Optimal Power Flow (OPF) problem is incorporated into the developed formulation of the restoration problem. In the next step, we propose a solution strategy making the multi-period restoration problem tractable for analytical solvers in case of a grid of realistic size. According to this strategy, the line switching variables and the AC power flow model are decomposed into master and sub problems, which are solved through successive iterations. At each iteration, the solution of the sub problem is used to augment the constraints of the master problem with the proposed feasibility or optimality cuts. The numerical results indicate that the proposed decomposition algorithm provides, within a short time (after a few iterations), a restoration solution with a quality that is close to the proven optimality, when it can be exhibited. In this thesis, along with the network security constraints in steady-state, the transient constraints associated with the starting of induction motor loads are also accounted for in the developed restoration problem. In this regard, an analytical and convex model is derived representing the starting transients of an induction motor in a semi-static fashion. This model is then integrated into a load restoration problem aiming to find the optimal energization sequence of different loads (static and motor loads). We validate the feasibility of the optimization results in terms of these transient operational limits using I) off-line time-domain simulations, and II) a Power Hardware-In-the-Loop experiment.

Integrated voltage control and line congestion management in Active Distribution Networks by means of smart transformers

Konstantina Christakou, Giovanni De Carne, Mario Paolone

Within the context of Active Distribution Networks (ADNs), smart transformers represent very powerful devices able to provide fast and efficient control actions with respect to different ADNs ancillary services. This paper discusses the benefits, in terms of ADNs voltage and line flows controls, achieved by interfacing distributed generators with the power grid by means of a smart transformer. Among several benefits, these devices allow for a phase-per-phase control of the generators active and reactive power injections. This peculiarity enables to deploy new control schemes that are analyzed and discussed in the paper with reference to a case study based on a modified IEEE 34 node test distribution feeder.

IEEE Industrial Electronics Society2014

Real-Time Optimal Controls for Active Distribution Networks

Konstantina Christakou

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.

EPFL2015

Exact Convex Modeling of the Optimal Power Flow for the Operation and Planning of Active Distribution Networks with Energy Storage Systems

Mostafa Nick

The distribution networks are experiencing important changes driven by the massive integration of renewable energy conversion systems. However, the lack of direct controllability of the Distributed Generations (DGs) supplying Active Distribution Networks (ADNs) represents a major obstacle to the increase of the penetration of renewable energy resources characterized by a non-negligible volatility. The successful development of ADNs depends on the combination of i) specific control tools and ii) availability of new technologies and controllable resources. Within this context, this thesis focuses on developing practical and scalable methodologies for the ADN planning and operation with particular reference to the integration of Energy Storage Systems (ESSs) owned, and directly controlled, by the Distribution Network Operators (DNOs). In this respect, an exact convex formulation of Optimal Power Flow (OPF), called AR-OPF, is first proposed for the case of radial power networks. The proposed formulation takes into account the correct model of the lines and the security constraints related to the nodal voltage magnitudes, as well as, the lines ampacity limits. Sufficient conditions are provided to guarantee that the solution of the AR-OPF is feasible and optimal (i.e., the relaxation used is exact). Moreover, by analyzing the exactness conditions, it is revealed that they are mild and hold for real distribution networks. The AR-OPF is further augmented by suitably incorporating radiality constraints in order to develop an optimization model for optimal reconfiguration of ADNs. Then, a two-stage optimization problem for day-ahead resource scheduling in ADNs, accounting for the uncertainties of nodal injections, is proposed. The Adaptive Robust Optimization (ARO) and stochastic optimization techniques are successfully adapted to solve this optimization problem. The solutions of ARO and stochastic optimization reveal that the ARO provides a feasible solution for any realization of the uncertain parameters even if its solution is optimal only for the worst case realization. On the other hand, the stochastic optimization provides a solution taking into account the probability of the considered scenarios. Finally, the problem of optimal resource planning in ADNs is investigated with particular reference to the ESSs. In this respect, the AR-OPF and the proposed ADN reconfiguration model, are employed to develop optimization models for the optimal siting and sizing of ESSs in ADNs. The objective function aims at finding the optimal trade-off between technical and economical goals. In particular, the proposed procedures accounts for (i) network voltage deviations, (ii) feeders/lines congestions, (iii) network losses, (iv) cost of supplying loads (from external grid or local producers) together with the cost of ESS investment/maintenance, (v) load curtailment and (vi) stochasticity of loads and renewables production. The use of decomposition methods for solving the targeted optimization problems with discrete variables and probable large size is investigated. More specifically, Benders decomposition and Alternative Direction Method of Multipliers (ADMM) techniques are successfully applied to the targeted problems. Using real and standard networks, it is shown that the ESSs could possibly prevent load and generation curtailment, reduce the voltage deviations and lines congestions, and do the peak shaving.