Sustainable Generation System Research Articles

Gas emission optimization and optimal energy management of a microgrid operating by renewable and sustainable generation systems

The economic operation of electric energy generating systems is one of predominant problems in energy systems. Due to the need for better reliability, high energy quality, lower losses, lower cost and clean environment, the application of renewable and sustainable energy sources such as wind energy, solar energy, fuel cells, etc. in recent years has become more widespread mainly. In this work, one of most general of all swarm intelligence algorithms inspired by collective intelligent behavior of natural or artificial systems called the Particle Swarm Optimization (PSO) is applied to solve the Gas emission Optimization (GEO) and the Optimal Energy Management (OEM) problems of a micro-grid (MG) operating by Renewable and Sustainable Generation Systems (RSGS). Our main goal is to minimize nonlinear objective function of an electrical micro-grid taking into account of equality and inequality constraints. The PSO approach was examined and tested on a standard MG system composed of different types of RSGS such as wind turbines (WT), photovoltaic systems (PV), fuel cells (FC), micro turbine (MT), diesel generator (DEG) and loads with energy storage systems (ESS). The results are promising and show the effectiveness and robustness of proposed approach to solve the GEO and the OEM problems. The results of proposed method have been compared and validated with those known references published recently.

Distributed Optimization of Dispatch in Sustainable Generation Systems via Dual Decomposition

Distributed generators (DGs) are being widely deployed in today’s power grid. These energy sources are highly variable posing practical challenges for deployment and grid management. In this paper, a novel scalable distributed power dispatch strategy is proposed to effectively manage DGs at the distribution substation level, capitalizing on the recent push to cyber-enable power grid operations. We demonstrate how the inherent separability of the power dispatch problem allows the use of dual decomposition that enables every participating DG to locally compute its dispatch strategy based on simple broadcast data by the utility. Results and comparisons indicate that the DGs are able to rapidly converge to an optimal economical dispatch vector with significantly less concentrated computational effort and communication overhead, promoting security and privacy.