A techno-economic planning model for integrated generation and transmission expansion in modern power systems with renewables and energy storage using hybrid Runge Kutta-gradient-based optimization algorithm

Abstract

This paper presents an integrated generation and transmission expansion planning (G&TEP) model embedding with energy storage systems (ESSs) to reduce G&TEP projects’ cost, enhance power system’s reliability, decrease carbon emission, and increase the penetration of renewable energy systems. ESSs are a key component of modern power grids for their ability to solve many challenges and problems. Electrical system operators and planners often resort to ESSs, which are considered promising technology and are sometimes the only economically viable way to address deficiencies in the planning phase, specifically in the presence of renewable energy sources. However, choosing the right ESS type to solve a problem is still a challenge and essential to cost-effectively integrating ESSs into power systems. In this work, a techno-economic planning model is formulated to decide on a suitable ESS type for the optimal configuration of two power systems, taking into account the technical and economic aspects of ESSs. Three long-term and seven medium-term types of ESSs were tested for this purpose. A hybrid scheme of Runge Kutta optimizer and gradient-based optimizer is applied to solve the problem. The proposed planning model is implemented on the known Garver system and a real system in Egypt (Egyptian west delta network). The numerical results found that pumped hydroelectric storage is the most effective type in achieving N-1 reliability constraints, increasing the use of RESs, and reducing the planning cost. ESSs enhanced systems’ security and reduced the total planning cost by 0.86%–2.35% for the Garver network. The results showed that using ESSs is necessary for some power systems, like the west delta network, to avoid rolling blackouts. The investment and operating costs of ESSs, in the presence of RESs, reach more than 50 % of the total planning cost in some case studies. However, ESSs, in the absence of RESs and reliability constraints, increase the planning cost by 5%–50%. Finally, the hybrid scheme proved its superiority in solving the proposed problem.