Power Demand and Supply Optimization in Islanded Microgrids with Distributed Generation

In the power sector, a shift from the present fossil-dominated generation to renewable as well as energy-efficient generation and distribution is firmly underway. The transition is mostly driven by the digitalization of the energy systems to what has been coined ENERNET meaning energy network. Numerous benefits for both utility and consumers accrue. Digitalization enables more activity in the power trading market and a large amount of consumer data becomes available in the sector. Overall, strides are being made in the integration of Demand-Side Management (DSM) in the planning of Isolated/Islanded Microgrids (IMGs) as these will potentially reduce total OPEX costs at both customer and utility levels as well as increase renewable energy utilization. However, there is paucity in literature regarding distributed generators (DGs) non-convex cost function. Notably, not much has been covered regarding microgrid optimal load-dispatching especially with regards to optimizing algorithms. In this paper, we focus on formulating the day-ahead dispatch problem of microgrids with DGs subject to non-convex cost function and load dynamics. We first propose an operational framework that addresses the DG's 'valve point" loading effect as well as optimizing its performance. The impact of DSM on convex and non-convex EMS problems with different load participation levels is investigated. Further, the day-ahead scheduling horizon of fifteen-minute resolution time is considered to examine the effect of load dynamics in the microgrid. A Quantum Particle Swarm based approach is employed to solve non-convex DGs cost optimization. It is demonstrated that the proposed algorithm efficiently solves the non-convex EMS problem. Simulation results yield a 5% reduction in OPEX costs without compromising customer satisfaction.