Aggregator-Based Optimization of Community Solar Energy Trading Under Practical Policy Constraints: A Case Study in Thailand

Sanvayos Siripoke, Varinvoradee Jaranya, Chalie Charoenlarpnopparut (), Ruengwit Khwanrit, Puthisovathat Prum and Prasertsak Charoen
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Sanvayos Siripoke: School of Information, Computer, and Communication Technology (ICT), Sirindhorn International Institute of Technology, Thammasat University, Khlong Luang 12120, Pathum Thani, Thailand
Varinvoradee Jaranya: School of Information, Computer, and Communication Technology (ICT), Sirindhorn International Institute of Technology, Thammasat University, Khlong Luang 12120, Pathum Thani, Thailand
Chalie Charoenlarpnopparut: School of Information, Computer, and Communication Technology (ICT), Sirindhorn International Institute of Technology, Thammasat University, Khlong Luang 12120, Pathum Thani, Thailand
Ruengwit Khwanrit: School of Information, Computer, and Communication Technology (ICT), Sirindhorn International Institute of Technology, Thammasat University, Khlong Luang 12120, Pathum Thani, Thailand
Puthisovathat Prum: School of Information, Computer, and Communication Technology (ICT), Sirindhorn International Institute of Technology, Thammasat University, Khlong Luang 12120, Pathum Thani, Thailand
Prasertsak Charoen: School of Information, Computer, and Communication Technology (ICT), Sirindhorn International Institute of Technology, Thammasat University, Khlong Luang 12120, Pathum Thani, Thailand

Energies, 2025, vol. 18, issue 13, 1-38

Abstract: This paper presents SEAMS (Solar Energy Aggregator Management System), an optimization-based framework for managing solar energy trading in smart communities under Thailand’s regulatory constraints. A major challenge is the prohibition of residential grid feed-in, which limits the use of conventional peer-to-peer energy models. Additionally, fixed pricing is required to ensure simplicity and trust among users. SEAMS coordinates prosumer and consumer households, a shared battery energy storage system (BESS), and a centralized aggregator (AGG) to minimize total electricity costs while maintaining financial neutrality for the aggregator. A mixed-integer linear programming (MILP) model is developed to jointly optimize PV sizing, BESS capacity, and internal buying price, accounting for Time-of-Use (TOU) tariffs and local policy limitations. Simulation results show that a 6 kW PV system and a 70–75 kWh shared BESS offer optimal performance. A 60:40 prosumer-to-consumer ratio yields the lowest total cost, with up to 49 percent savings compared to grid-only systems. SEAMS demonstrates a scalable and policy-aligned approach to support Thailand’s transition toward decentralized solar energy adoption and improved energy affordability.

Keywords: smart home; battery energy storage system; photovoltaic; optimization; energy trading; smart community; Thailand (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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