This paper proposes a new approach to model stability and security constraints in optimal power flow (OPF) problems based on a neural network (NN) representation of the system security boundary (SB). The novelty of this proposal is that a closed form, differentiable function derived from the system's SB is used to represent security constraints in an OPF model. The procedure involves two main steps: First, an NN representation of the SB is obtained based on back-propagation neural network (BPNN) training. Second, a differentiable mapping function extracted from the BPNN is used to directly incorporate this function as a constraint in the OPF model. This approach ensures that the operating points resulting from the OPF solution process are within a feasible and secure region, whose limits are better represented using the proposed technique compared to typical security-constrained OPF models. The effectiveness and feasibility of the proposed approach is demonstrated through the implementation, as well as testing and comparison using the IEEE two-area and 118-bus benchmark systems, of an optimal dispatch technique that guarantees system security in the context of competitive electricity markets.

This paper presents the development of a heuristic-based algorithm for a Home Electric Energy Management System (HEEMS). The novelty of the proposal resides in the fact that solutions of the Pareto front, minimizing both the energy consumption and cost, are obtained by a Genetic Algorithm (GA) considering the renewable energy availability as well as the user activity level (AL) inside the house. The extensive solutions search characteristic of the GAs is seized to avoid the calculation of the full set of Pareto front solutions, i.e., from a reduced set of non-dominated solutions in the Pareto sense, an optimal solution with the best fitness is obtained, reducing considerably the computational time. The HEEMS considers models of the air conditioner, clothes dryer, dishwasher, electric stove, pool pump, and washing machine. Models of the wind turbine and solar PV modules are also included. The wind turbine model is written in terms of the generated active power exclusively dependent on the incoming wind profiles. The solar PV modules model accounts for environmental factors such as ambient temperature changes and irradiance profiles. The effect of the energy storage unit on the energy consumption and costs is evaluated adapting a model of the device considering its charge and discharge ramp rates. The proposed algorithm is implemented in the Matlab^® platform and its validation is performed by comparing its results to those obtained by a freeware tool developed for the energy management of smart residential loads. Also, the evaluation of the performance of the proposed HEEMS is carried out by comparing its results to those obtained when the multi-objective optimization problem is solved considering weights assigned to each objective function. Results showed that considerable savings are obtained at reduced computational times. Furthermore, with the calculation of only one solution, the end-user interaction is reduced making the HEEMS even more manageable than previously proposed approaches.

This paper provides a systematic approach to address the fundamental electricity auction issue regarding how to include security boundaries associated with system stability into a security-constrained DC-OPF-based model. These boundaries are linearly approximated by a reduced number of hyperplanes, from which a set of linear inequality security/stability constraints is derived and included into a proposed voltage security-constrained DC-OPF auction model. This allows the proper implementation of a true DC formulation, taking full advantage of its associated simplicity and robustness in the solution algorithm. Case studies are presented to illustrate the applicability of the proposed method and to demonstrate that the market clearing and power dispatch resulting from the optimization solution process are within the desired feasible and secure region, contrary to the typical security constrained DC-OPF auction model, which may fail to meet this condition.

State estimation is one of the most important processes to perform a reliable monitoring and control of the steady-state operating condition of modern electric power systems; thus, it is currently a fundamental part in the development of research to enhance the monitoring and security of the smart grids operation. This important topic is taught in advanced courses of operation and control of power systems, for graduate and undergraduate power engineering students. However, the most used software packages for simulation and analysis of power systems by researchers, students, and educators have put little attention on the state estimation module. Due to this fact, this paper proposes an approach to develop the computational implementation of a practical educational tool for state estimation of electric power systems using the MATLAB optimization toolbox. In this proposal, the formulation of the state estimation problem consists of developing a general digital code to implement an objective function based on the weighted least squares method. While the lsqnonlin function of the MATLAB optimization toolbox solves the formulated state estimation problem. Simplifying both research and educational processes, this tool helps graduate and undergraduate students to improve learning, understanding, and the times of implementation and development of research in state estimation. Simulations of an equivalent model of the Mexican interconnected power system consisting of 190 buses and 46 machines are used to test and validate the proposal performance.

A new approach for identifying multiple line outages in electric power systems by using data obtained from synchrophasor measurements is proposed in this paper. A novelty of the proposal is the application of the sensitivity theory to the system's alternating current power flow model to determine the most likely contingency scenario, with not previous knowledge of the number of line outages. Since the proposal considers both voltage and current synchrophasor measurements, a set of observable lines is defined such that their outages are identified directly from those measurements. In this context, the more observed transmission lines there are, the smaller the search space for identifying potential outages in unobserved transmission lines. On the other hand, the detection problem of multiple-line outages is decomposed into the sequential uncovering of different single-line outages by using the concept of total derivative. The reduction of the search space and the decomposition of the multiple-line outages problem significantly reduce the computational burden of the proposed approach. Lastly, numerical results demonstrate that the proposal presents a very satisfactory percentage of success for identifying the correct set of line outages, even when a limited number of PMUs is embedded in the system.