Distributed generation optimization in future smart grids

Abstract

Ever-surging global power(energy) demands coupled with the need to avail it in a reliable, as well as efficient manner, have led to the modernization of legacy and cur-rent power system grids into Smart Grid (SGs) equivalents. This is mostly achieved by blending the existing systems with an information subsystem that will facilitate duplex communication, i.e., electrical power flowing towards the end users while information characterising the grid’s performance can also be relayed, mostly in the reverse direction. Thus, the information subsystem interconnects other core (key) entities such as generation, distribution, transmission, and end-user terminals to interrelate in real-time, and in the process, achieving a well reliable, robust as well as efficiently managed SG power system. As such, in the emerging distributed power systems of the future, Demand Side Management (DSM) will play an important role in dealing with stochastic renewable power sources and loads. A near-unity load factor can be secured by employing De-mand Response methods with storage systems as well as regulatory control mechanisms. Increasing deployment of Renewable Energy generation and other forms of unconventional loads such as Plug-In Electric Vehicles will aid DR implementation with attendant better results for both prosumers and the utilities. The central objective of DSM is to minimize peak-to-average ratio (PAR) and energy costs by switching to cheaper RES as well as reduction of CO2 emissions. This work focused on emergent techniques and microgrid optimization with special attention to load scheduling. Techniques for DSM, mathematical models of DSM, and optimization methods have been reviewed. State-of-the-art methodologies entering the DSM mainstream are data science, advanced metering infrastructure, and blockchain technologies. An improved atom search optimization technique is applied for DSM to substantially reduce power and energy costs in typical standalone or grid-tied microgrids. Further the day ahead dispatch problem of MGs with DEGs subject to a non-convex cost function is solved and simulated using quadratic particle swarm optimization. In the later case, the objective function includes the DEGs ‘valve-point’ loading effect in the ‘fuel-cost’ curve. The impact of DSM on convex and non-convex energy management problems with different load participation levels is investigated. Ultimately, it is demonstrated that the quadratic particle swarm optimization algorithm efficiently solves the non-convex energy management system (EMS) problem. In addition, we propose a hierar-chical optimal dispatch framework that relies on several objectives to achieve the overall design goal of a reliable and stable power supply, coupled with economic ben-efits to prosumers who elect to participate in power trading. Evaluation of the pro-posed framework is carried out analytically and by way of simulation. Overall, it is deduced from the obtained analytical as well as simulation results that the combination of appropriately sized battery storage systems (BESS) and renewable type generators such as PVs and WTs will help achieve a stable and reliable power supply to all users in the SG (or MG) and at the same time, it affords resilience. Final-ly, in our closing chapter, we also spell out possible future research directions.