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Publicações

Publicações por CPES

2020

An orthogonal method for solving maximum correntropy-based power system state estimation

Autores
Freitas, V; Costa, AS; Miranda, V;

Publicação
IET Generation, Transmission & Distribution

Abstract

2020

DER Adopter Analysis using Spatial Autocorrelation and Information Gain Ratio under different Census-data Aggregation Levels

Autores
Heymann, F; Lopes, M; vom Scheidt, F; Silva, JM; Duenas, P; Soares, FJ; Miranda, V;

Publicação
IET Renewable Power Generation

Abstract

2020

Favorable properties of Interior Point Method and Generalized Correntropy in power system State Estimation

Autores
Pesteh, S; Moayyed, H; Miranda, V;

Publicação
Electric Power Systems Research

Abstract
The paper provides the theoretical proof of earlier published experimental evidence of the favorable properties of a new method for State Estimation – the Generalized Correntropy Interior Point method (GCIP). The model uses a kernel estimate of the Generalized Correntropy of the error distribution as objective function, adopting Generalized Gaussian kernels. The problem is addressed by solving a constrained non-linear optimization program to maximize the similarity between states and estimated values. Solution space is searched through a special setting of a primal-dual Interior Point Method. This paper offers mathematical proof of key issues: first, that there is a theoretical shape parameter value for the kernel functions such that the feasible solution region is strictly convex, thus guaranteeing that any local solution is global or uniquely defined. Second, that a transformed system of measurement equations assures an even distribution of leverage points in the factor space of multiple regression, allowing the treatment of leverage points in a natural way. In addition, the estimated residual of GCIP model is not necessarily zero for critical (non-redundant) measurements. Finally, that the normalized residuals of critical sets are not necessarily equal in the new model, making the identification of bad data possible in these cases. © 2019 Elsevier B.V.

2020

Information Theoretic Generalized State Estimation in power systems

Autores
Meneghetti, R; Simões Costa, A; Miranda, V; Ascari, LB;

Publicação
Electric Power Systems Research

Abstract

2020

Distributed multi-period three-phase optimal power flow using temporal neighbors

Autores
Pinto, R; Bessa, RJ; Sumaili, J; Matos, MA;

Publicação
Electric Power Systems Research

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

2020

A two-stage strategy for security-constrained AC dynamic transmission expansion planning

Autores
Gomes, PV; Saraiva, JT;

Publicação
Electric Power Systems Research

Abstract
This paper presents a new and promising strategy organized in two stages to solve the dynamic multiyear transmission expansion planning, TEP, problem. Specifically, the first stage is related to the reduction of the search space size and it is conducted by a novel constructive heuristic algorithm (CHA). The second one is responsible for the refinement of the optimal solution plan and it uses a novel evolutionary algorithm based on the best features of particle swarm optimization (PSO) and genetic algorithm (GA). The planning problem is modelled as a dynamic and multiyear approach to ensure that it keeps a holistic view over the entire planning horizon and it aims at minimizing the total system costs comprising the investment and operation costs. Additionally, the N-1 contingency criterion is also considered in the problem. The developed approach was tested using the IEEE 118-Bus test system and the obtained results demonstrate its advantages in terms of efficiency and required computational time. Furthermore, the results demonstrated that the novel strategy can enable the utilization of the AC optimal power flow (OPF) in a faster and reliable way when compared to the standard and widespread DC-OPF model. © 2019 Elsevier B.V.

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