Multi-objective techno-economic generation expansion planning to increase the penetration of distributed generation resources based on demand response algorithms

Abstract

Generation expansion planning in the power system is of particular importance. In traditional systems, investment in the generation expansion was made by the electricity company, but with the restructuring in the electricity industry, the owners of different parts of the system submit their proposals to the independent system operator and the independent system operator chooses the optimal design. Slowly increasing energy production from renewable sources can pose challenges for the grid. Increasing the penetration of renewable resources due to uncertainty in their production can reduce network reliability and thus increase system costs. The investigation on generation expansion planning is a multifaceted issue (technical and economic) that has been analyzed in various aspects in recent years. In this study, a multidimensional structure of generation expansion planning based on increasing the penetration level of distributed generation resources (renewable and non-renewable) as well as the application of load management and demand response algorithms is proposed. The proposed model is scheduled based on two levels of primary and secondary development. In the primary, the development of generation and transmission based on large-scale power plants as well as solar and wind farms are presented. In the secondary, in order to reduce the power fluctuations caused by the distributed generation’s units, non-stochastic power generation units such as micro turbines, gas turbines and combined heat and power have been utilized. To overcome the difficulties in solving the problem of hybrid and non-convergent mixed-integer problem, the adaptive particle swarm optimization has been hired. The simulation results indicate that in the second scenario, where the development of the generation expansion planning is based on the integration of distributed generation resources and power plants, it is more cost-effective. In addition to, these simulation results represent the accuracy of the proposed probabilistic method in planning of dynamic generation systems in order to estimate the probability density function and the optimal output variables in multi-objective techno-economic planning.