Integration of solar and flexible resources into expected security cost with dynamic optimal power flow problem using a Novel DE algorithm

Abstract

Numerous power systems are undergoing dramatic changes in their generation portfolios. Solar electricity generation capacity has increased significantly in response to global environmental and national energy security concerns, as well as rising fuel prices. Integration of these solar resources with limited dispatchability into existing power systems with varying levels of availability, demand and system flexibility is challenging. This article proposes an expected security cost dynamic optimal power flow (ESCDOPF) that integrates solar and flexible resources. The proposed model is formulated and implemented with a “Novel Differential Evolution (NDE)” optimization algorithm to obtain the global solution for the total expected system operating cost which includes contingency costs by satisfying the model constraints over a day. Solar output is predicted using the Beta probability distribution function with direct, under-estimation and over-estimation costs. Similarly, flexible resources such as demand response management (DRM), battery energy storage systems (BESS) and high-voltage direct current (HVDC) systems are modelled in order to manage the supply-demand portfolio at all times. To handle inequality constraints and feasible solution superiority, this NDE algorithm employs a penalty function method. To validate the proposed model and the solution process, illustrative cases of various scenarios are performed on an IEEE-30 bus system. Results show that the proposed NDE algorithm satisfies all of the model constraints and also provides cost savings from the integration of solar and flexible resources into the power system.