Citation: Somchit, S.; Thongbouasy, P.; Srithapon, C.; Chatthaworn, R. Optimal Transmission Expansion Planning with Long-Term Solar Photovoltaic Generation Forecast. Energies 2023, 16, 1719. https:// doi.org/10.3390/en16041719 Academic Editor: Guiqiang Li Received: 26 December 2022 Revised: 1 February 2023 Accepted: 6 February 2023 Published: 9 February 2023 Copyright: © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). energies Article Optimal Transmission Expansion Planning with Long-Term Solar Photovoltaic Generation Forecast Siripat Somchit 1 , Palamy Thongbouasy 1 , Chitchai Srithapon 2 and Rongrit Chatthaworn 1, * 1 Department of Electrical Engineering, Khon Kaen University, Khon Kaen 40002, Thailand 2 Department of Electrical Engineering, KTH Royal Institute of Technology, 11428 Stockholm, Sweden * Correspondence: rongch@kku.ac.th; Tel.: +66-84-685-2286 Abstract: Solar PhotoVoltaics (PV) integration into the electricity grids significantly increases the complexity of Transmission Expansion Planning (TEP) because solar PV power generation is un- certain and difficult to predict. Therefore, this paper proposes the optimal planning method for transmission expansion combined with uncertain solar PV generation. The problem of uncertain solar PV generation is solved by using Long Short-Term Memory (LSTM) for forecasting solar radiation with high accuracy. The objective function is to minimize total system cost, including the investment cost of new transmission lines and the operating cost of power generation. The optimal TEP problem is solved by the Binary Differential Evolution (BDE) algorithm. To investigate and demonstrate the performance of the proposed method, the IEEE 24-bus system and solar radiation data in Thailand are selected as a study case for TEP. The MATPOWER program written in MATLAB software is used for solving optimal power flow problems. Simulation results show that the proposed optimal TEP method combined with forecasting solar PV power generation using the LSTM can reduce the total system cost of the transmission expansion by 9.12% compared with the cost obtained by the TEP using solar radiation from statistical data. Keywords: binary differential evolution; long short-term memory; solar photovoltaic; transmission expansion planning 1. Introduction Nowadays, electric power is very significant in subsistence, including communication, transportation, education, etc. Due to the growth in electric power demand, the Ministry of Energy in Thailand and the Electricity Generating Authority of Thailand (EGAT) prepared the Power Development Plan 2018 (PDP 2018) [1] to plan sufficient power generation for power demand growth and strengthen the country’s energy security. Moreover, the increas- ing demand for electricity affects transmission system operations. Thereby, transmission line expansions are required to support the increases in power generation and demand in the future. The TEP has been extensively researched in recent years. Typically, TEP problems are concerned with the reliability and security of power systems. In addition, the Ministry of Energy created the Alternative Energy Development Plan 2018 (AEDP 2018) [2] to promote electricity generation from renewable energy re- sources such as solar PVs and wind turbine generators. This plan will encourage the use of renewable energy to generate electricity instead of fossil fuels. However, renewable energy resources are uncertain in terms of electricity generation. This is because elec- tricity generation from renewable energy depends on the weather and the environment, which are difficult to control and predict. The uncertainty of renewable energy can impact transmission system operation. Additionally, many research works studied power system operation and control when uncertain generations of renewable energy resources were considered. For example, reference [3] proposed the sampled memory-event-triggered fuzzy to investigate the load frequency control issue when wind power systems penetrate. Energies 2023, 16, 1719. https://doi.org/10.3390/en16041719 https://www.mdpi.com/journal/energies