In this paper, fuzzy inductive reasoning (FIR) is applied to the problem of short-term load forecasting (STLF) in power systems for a day in advance. The FIR model learns both past and future relations from the load and the temperature. The proposed optimization model uses an evolutionary algorithm based on a local random controlled search - simulated rebounding algorithm (SRA) - to choose the inputs to the FIR model. Using an optimization method to determine linear and nonlinear relationships between the variables, a parsimonious set of input variables can be identified improving the accuracy of the forecast. The input variables are updated when a new load pattern is happened or when relative errors are unacceptable. With this update is achieved, a better monitoring of the load trend due to the process is not strictly stationary. The FIR and SRA methodology is applied to the Ecuadorian power system as an application example. Results and comparisons with other STLF methodologies (autoregressive integrated moving average, artificial neural networks, and adaptive neuro-fuzzy inference system) are shown, and conclusions are derived.

This paper presents a new formulation for the preventive security-constrained unit commitment problem modeling N - k line outages and transmission losses. The pre- and post-contingency transmission constraints, representing N - k line outages, are explicitly included by using generalized generation distribution factors. To account for security, a contingency selection procedure based on line outage distribution factors finds a list of worst-case contingencies. Transmission losses are incorporated using piecewise linear expressions. The proposed model is formulated as an instance of mixed-integer linear programming. The effectiveness of the proposed approach is illustrated with the IEEE 57-bus system and the 1,354-bus portion of the European transmission system. As empirically evidenced, the explicit consideration of N-k line outages and transmission losses leads to different decisions in the generation scheduling and dispatch, ensuring secure power system operation.

This study proposes a very effective formulation to carry out the security-constrained direct current (DC)-based optimal power flow (OPF) problem using two linear factors: (i) the power transmission distribution factors (PTDF) and (ii) the line outage distribution factors (LODF). The security-constrained (SC) DCOPF problem has been reformulated using these linear distribution factors, and mainly the pre- and post-contingency constraints have been added into the optimization problem based on the active power unit generation (decision variables). The main advantage of this formulation is the reduction of decision variables as well as equality and inequality constraints. To validate the introduced formulation, several experiments have been conducted using MatPower, DIgSILENT Power Factory and Gurobi. Simulation results demonstrate both the feasibility to carry out the SCOPF problem and the potential applicability of the proposed formulation to medium and large-scale power systems.