Dynamic rating monitoring is a solution to face challenges in both the operation and the planning of power systems. With thermal monitoring, dynamic overloads of transformers are allowed, however, the thermal model used to predict hot-spot must be capable of ensuring the assessment of insulation life expectancy. Hence, this paper presents an online algorithm to estimate and predict transformers' rating in order to assess the overload capability during short and long times. The overload factors are computed using estimations of top-oil temperature. The main characteristic of this proposal is that the environmental cooling conditions are considered by the fitting of the parameters of a simple equivalent thermal circuit using an Extended Kalman Filter during the transformer operation. To validate the implementation of the algorithm, the information provided by typical transformer rating monitoring systems is used. Thus, the performance and effectiveness of the algorithm are verified using field measurement data during the normal operation of two power transformers.

This paper addresses a methodology to optimize PV panel cleaning schedule. PV system yield is impacted due to soiling on panel surface. This soiling changes during the system operation as a consequence of environmental conditions. In order to reduce the impact of soiling accumulation, the panel surface must be cleaned. However, cost and frequency of cleaning can affect plant revenue. Therefore, to enhance financial performance, an optimal cleaning strategy must be selected. This strategy depends mostly on energy cost, soiling rate, cleaning costs, and system efficiency. Hence, the model proposed in this work takes into account these variables in order to support the decisions during the assessment of PV yield. The methodology was evaluated through two irradiation profiles with different associated costs. As a result, the methodology made it possible to estimate a cleaning strategy optimizing the incomes under different scenarios.

Increased use of renewable energies in smart microgrids (SMGs) present new technical challenges to system operation. SMGs must be self-sufficient and operate independently; however, when more elements are integrated into SMGs, as distributed energy resources (DER), traditional explicit mathematical formulations will demand too much data from the network and become intractable. In contrast, tools based on optimization with metaheuristics can provide near optimal solutions in acceptable times. Considering this, this paper presents the variable neighborhood search differential evolutionary particle swarm optimization (VNS-DEEPSO) algorithm to solve multi-objective stochastic control models, as SMGs system operation. The goal is to control DER while maximizing profit. In this work, DER considered the bidirectional communication between energy storage systems (ESS) and electric vehicles (EVs). They can charge/discharge power and buy/sell energy in the electricity markets. Also, they have elements such as traditional generators (e.g., reciprocating engines) and loads, with demand response/control capability. Sources of uncertainty are associated with weather conditions, planned EV trips, load forecasting and the market prices. The VNS-DEEPSO algorithm was the winner of the IEEE Congress on Evolutionary Computation/The Genetic and Evolutionary Computation Conference (IEEE-CEC/GECCO 2019) smart grid competition (with encrypted code) and also won the IEEE World Congress on Computational Intelligence (IEEE-WCCI) 2018 smart grid competition (these competitions were developed by the group GECAD, based at the Polytechnic Institute of Porto, in collaboration with Delft University and Adelaide University). In the IEEE-CEC/GECCO 2019, the relative error improved between 32% and 152% in comparison with other algorithms.

Power systems are susceptible to disturbances due to their nature. These disturbances can cause overloads or even contingencies of greater impact. In case of an extreme situation, load curtailment is considered the last resort for reducing the contingency impact, its activation being necessary to avoid the collapse of the system. However, load shedding systems seldom work optimally and cause either excessive or insufficient reduction of the load. To resolve this issue, the present paper proposes a methodology to enhance the load curtailment management in medium voltage distribution systems using Particle Swarm Optimization (PSO). This optimization seeks to minimize the amount of load to be cut off. Restrictions on the optimization problem consist of the security operation margins of both loading and voltage of the system elements. Heuristic optimization algorithms were chosen, since they are considered an online basis (allowing a short processing time) to solve the formulated load curtailment optimization problem. Best performances regarding optimal value and processing time were obtained using a PSO algorithm, qualifying the technique as the most appropriate for this study. To assess the methodology, the CIGRE MV distribution network benchmark was used, assuming dynamic load profiles during an entire week. Results show that it is possible to determine the optimal unattended power of the system. This way, improvements in the minimization of the expected energy not supplied (ENS) as well as the System Average Interruption Frequency Index (SAIDI) at specific hours of the day were made.

This paper presents a decomposition methodology using constraint handling rules to improve the computation time of a security-constrained optimal power flow (SCOPF) problem. In order to evaluate methodology performance, tests over small (500 buses), medium (4,918 buses), and large scale (11,615 buses) transmission networks were carried out. The methodology consisted in the decomposition of the SCOPF problem into a base case problem and contingency sub-problems using constraint handling rules to solve the complete problem in an iterative fashion. The first stage involved solving an OPF problem using a base case network. The second stage dealt with the modification of the initial base case by updating some of the constraint limits according to the evaluation of potentially relevant contingencies. The entire algorithm resorted to parallel computing tools. The methodology, along with active power re-dispatch through droop control and PV/PQ switching in post-contingency scenarios, successfully solved the tested networks with the set of proposed constraints.

Smart microgrids (SMGs) may face energy rationing due to unavailability of energy resources. Demand response (DR) in SMGs is useful not only in emergencies, since load cuts might be planned with a reduction in consumption but also in normal operation. SMG energy resources include storage systems, dispatchable units, and resources with uncertainty, such as residential demand, renewable generation, electric vehicle traffic, and electricity markets. An aggregator can optimize the scheduling of these resources, however, load demand can completely curtail until being neglected to increase the profits. The DR function (DRF) is developed as a constraint of minimum size to supply the demand and contributes solving of the 0-1 knapsack problem (KP), which involves a combinatorial optimization. The 0-1 KP stores limited energy capacity and is successful in disconnecting loads. Both constraints, the 0-1 KP and DRF, are compared in the ranking index, load reduction percentage, and execution time. Both functions turn out to be very similar according to the performance of these indicators, unlike the ranking index, in which the DRF has better performance. The DRF reduces to 25% the minimum demand to avoid non-optimal situations, such as non-supplying the demand and has potential benefits, such as the elimination of finite combinations and easy implementation.