Optimal Design of Integrated Energy Systems Based on Reliability Assessment

Dong-Min Kim, In-Su Bae, Jae-Ho Rhee, Woo-Chang Song () and Sunghyun Bae ()
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Dong-Min Kim: Department of Electrical Engineering, Kangwon National University, Samcheok 25913, Republic of Korea
In-Su Bae: Department of Electrical Engineering, Kangwon National University, Samcheok 25913, Republic of Korea
Jae-Ho Rhee: Department of Electrical Engineering, Kangwon National University, Samcheok 25913, Republic of Korea
Woo-Chang Song: Department of Electrical Engineering, Kangwon National University, Samcheok 25913, Republic of Korea
Sunghyun Bae: Department of Quantum Information Science and Engineering, Sejong University, Seoul 05006, Republic of Korea

Mathematics, 2025, vol. 13, issue 23, 1-15

Abstract: This paper presents an optimal-design methodology for small-scale Integrated Energy Systems (IESs) that couple electricity and heat in distributed networks. A hybrid reliability assessment integrates probabilistic state enumeration with scenario-based simulation. Mathematically, the design is cast as a stochastic, reliability-driven ranking: time-sequential Monte Carlo (MC) produces estimators of Loss of Load Probability (LOLP), Expected Energy Not Supplied (EENS), and Self-Sufficiency Rate (SSR), which are normalized and combined into a Composite Reliability Index (CRI) that orders candidate siting/sizing options. The case study is the D-campus microgrid with Photovoltaic (PV), Combined Heat and Power (CHP), Fuel Cell (FC), Battery Energy Storage Systems (BESSs), and Heat Energy Storage Systems (HESSs; also termed TESs), across multiple siting and sizing scenarios. Results show consistent reductions in LOLP and EENS and increases in SSR as distributed energy resource capacity increases and resources are placed near critical nodes, with the strongest gains observed in the best-performing configurations. The CRI also reveals trade-offs across intermediate scenarios. The operational concept of the campus Energy Management System (EMS), including full operating modes and scheduling logic, is developed to maintain a design focus on reliability-driven decision making. Probability-based formulations, reliability metrics, and the sequential MC setup underpin the proposed ranking framework. The proposed method supports Distributed Energy Resource (DER) sizing and siting decisions for reliable, autonomy-oriented IESs.

Keywords: integrated energy system; reliability assessment; optimal design; composite reliability index; Monte Carlo simulation; microgrid; distributed energy resources (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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