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This paper proposes a new approach with two efficient metaheuristic algorithms, in combination with quadratic programming, to solve the nonlinear optimization problem Unit Commitment in a complex hydro-thermal power system i.e. Hydrothermal Unit Commitment (HTUC). The main goal is to minimize the total costs (which are a very non-linear and non-convex problem), while satisfying the many hydro-thermal constraints. Such constraints, together with the nonlinear non-convex and mixed-integer objective function, make the search space extremely complex. To solve such a complicated system, the paper proposes a hybridization of a developed binary-coded genetic algorithm (in which quadratic programming is integrated), with a particle swarm optimization (PSO) algorithm. PSO is applied to the final economic load dispatch (ELD), based on the optimal binary combination obtained from the genetic algorithm. A new approach has been proposed through the application of a repair mechanism, which is based on a priority list, in order to maintain the diversity of the population and prevent premature convergence. The entire algorithm was developed and tested in MATLAB and then applied to the IEEE 30 BUS test system. The experimental results show better performance of the proposed algorithm compared to the recently published algorithms, in terms of convergence, constraint handling, as well as better solution quality.

The continuous growth of communities leads to an increase in electrical energy usage. Besides the technological advance of power systems, still, there are outages which lead to an interruption in the power supply. The implementation of distributed generation provides usage of the local renewable energy sources for satisfying local power needs which in good manner complexes the distribution networks. However, because the distributed energy resources depend on the weather conditions, the islanded work is not quite reliable. Grid-connected distributed energy resources have enhanced power supply reliability. In this paper, a dynamic programming model is used for the optimisation of power generation by the local distributed energy resources. Assuming the distributed energy resources have storage systems, the model takes into account the battery charge at the moment and the availability of the distributed energy resources depending on the weather conditions and whether is day or night. It is assumed that the distributed energy resources have equal power capacities. Case study reviews a low voltage distribution network with plenty of distributed energy resources from different renewable energy sources implemented and a cluster of household consumers.

Proper operation of the power substations is of great importance for power network reliability, stability and uninterrupted power supply. Distributed generation provides higher reliability in power supply, but still, there are contingencies in the electric power production and supply process, which lead to outages in the power supply. In this paper, a method for substations’ reliability estimation with distributed generation is presented based on Markov Chain Monte Carlo method. The method considers the possible substation operation states and using random number generator in MATLAB, it simulates faults and calculates the substations’ reliability. The method is demonstrated on two cases of 110/35 kV substations, each consisting of two transformers and distributed generator, analysing the best placement for the distributed generation.