Abstract
This paper tackles the efficient Power-Flow solution of ill-conditioned cases. In that sense, those methods based on the Continuous Newton's philosophy look very promising, however, these methodologies still present some issues mainly related with the computational efficiency or the robustness properties. In order to overcome these drawbacks, we suggest several modifications about the standard structure of the Continuous Newton's method. Thus, the standard Continuous Newton's paradigm is firstly modified with a frozen Jacobian scheme for reducing its computational burden; secondly, it is extended for being used with High-order Newton-like method for achieving higher convergence rate and, finally, a regularization scheme is introduced for improving its robustness features. On the basis of the suggested improvements, a Power-Flow solution paradigm is developed. As example, a novel Power-Flow solver based on the introduced solution framework and the 4th order Runge-Kutta formula is developed. The novel technique is validated in several realistic large-scale ill-conditioned systems. Results show that the suggested modifications allow to overcome the drawbacks presented by those methodologies based on the Continuous Newton's method. On the light of the results obtained it can be also claimed, that the developed solution paradigm constitutes a promising framework for developing robust and efficient Power-Flow solution techniques. © 2020 Elsevier Ltd

Abstract

Abstract
The penetration of distributed generation in medium (MV) and low (LV) voltage distribution grids has been steadily increasing every year in multiple countries, thus creating new technical challenges in grid operation and motivating developments in distributed optimization for flexibility management. The traditional centralized optimal power flow (OPF) algorithm can solve technical constraints violation. However, computational efficiency, new technologies (e.g., edge computing) and control architectures (e.g., web-of-cells) are demanding for distributed approaches. This work formulates a novel distributed multi-period OPF for three-phase unbalanced grids that is essential when integrating energy storage units in operational planning (e.g., day-ahead) of LV or local energy community grids. The decentralized constrained optimization problem is solved with the alternating direction method of multipliers (ADMM) adapted for unbalanced LV grids and multi-period optimization problems. A 33-bus LV distribution grid is used as a case-study in order to define optimal battery storage scheduling along a finite time horizon that minimizes overall grid operational costs, while complying with technical constraints of the grid (e.g., voltage and current limits) and battery state-of-charge constraints. © 2020

Abstract
The adoption of Electric Vehicles (EVs) will revolutionize the storage capacity in the power system and, therefore, will contribute to mitigate the uncertainty of renewable generation. In addition, EVs have fast response capabilities and are suitable for frequency regulation, which is essential for the proliferation of intermittent renewable sources. To this end, EV aggregators will arise as a market representative party on behalf of EVs. Thus, this player will be responsible for supplying the power needed to charge EVs, as well as offering their flexibility to support the system. The main goal of EV aggregators is to manage the potential participation of EVs in the reserve market, accounting for their charging and travel needs. This work follows this trend by conceiving a chance-constrained model able to optimize EVs participation in the reserve market, taking into account the uncertain behavior of EVs and their charging needs. The proposed model, includes penalties in the event of a failure in the provision of upward or downward reserve. Therefore, stochastic and chance-constrained programming are used to handle the uncertainty of a small fleet of EVs and the risk profile of the EV aggregator. Two different relaxation approaches, i.e., Big-M and McCormick, of the chance-constrained model are tested and validated for different number of scenarios and risk levels, based on an actual test case in Denmark with actual driving patterns. As a final remark, the McCormick relaxation presents better performance when the uncertainty budget increases, which is appropriated for large-scale problems.

Abstract