Effects of Producer and Transmission Reliability on the Sustainability Assessment of Power System Networks

Abstract

Details are presented of the development and incorporation of a generation and transmission reliability approach in an upper-level sustainability assessment framework for power system planning. This application represents a quasi-stationary, multiobjective optimization problem with nonlinear constraints, load uncertainties, stochastic effects for renewable energy producers, and the propagation of uncertainties along the transmission lines. The Expected Energy Not Supplied (EENS) accounts for generation and transmission reliability and is based on a probabilistic as opposed to deterministic approach. The optimization is developed for three scenarios. The first excludes uncertainties in the load demand, while the second includes them. The third scenario accounts not only for these uncertainties, but also for the stochastic effects related to wind and photovoltaic producers. The sustainability-reliability approach is applied to the standard IEEE Reliability Test System. Results show that using a Mixture of Normals Approximation (MONA) for the EENS formulation makes the reliability analysis simpler, as well as possible within a large-scale optimization. In addition, results show that the inclusion of renewable energy producers has some positive impact on the optimal synthesis/design of power networks under sustainability considerations. Also shown is the negative impact of renewable energy producers on the reliability of the power network.

Optimum Choice of Energy System Configuration and Storages for a Proper Match between Energy Conversion and Demands

“…Vargas-Jaramillo et al [5] emphasize the importance of properly taking into account the reliability of power networks, which depends on the uncertainties associated with generation, transmission and distribution, load demand, and the presence of unexpected catastrophic events. All these factors affect the sustainability of these networks, making their planning a difficult problem to solve.…”

Solution to the Economic Emission Dispatch Problem Using Numerical Polynomial Homotopy Continuation

“…Power dispatch is a complicated task for the energy industry because a highly variable and unpredictable load demand from the customers needs to be satisfied using the most suitable (less expensive) mix of producers [1,2]. The available producers use different types of fuel (including fossil and renewable energy sources), have different capacities, have different efficiencies, and there is the need to decide whether to operate them at full-or part-load [3,4]. In addition, the highest percentage of electricity is generated using fossil fuels, which increases the problem of pollutant emissions to the environment, of which CO 2 , SO 2 , and NO x are of most concern [5,6].…”

“…Three main different approaches are commonly used to solve the economic emission dispatch problem, i.e., deterministic techniques, stochastic techniques, and combinations of these two techniques. Deterministic techniques [3,4,11,15,20] have the advantage that can solve large scale systems with good accuracy [15,20] in a short convergence time [12,15]. However, these techniques can get stuck at local optimum points easily, are very sensitive to the starting point [21,22], and have difficulties with solving nonconvex problems as well as those with nonsmooth objective functions [21,23].…”