Key Takeaways & Executive Findings
- •• • The bi-level optimization framework reduces total system cost by up to 12.3% compared to single-level planning, as demonstrated in the real urban park case study, by simultaneously optimizing capacity and operation. • • Flexible load control decreases peak-valley load difference by 18.7%, mitigating peak energy pressure and enhancing renewable energy accommodation, which is critical for grid stability in high-renewable-penetration parks. • • Energy storage integration improves load curve smoothing, reducing operational costs by 9.5% and enabling a 15.2% increase in renewable self-consumption, directly addressing the intermittency of wind and solar generation. • • Reliability constraints ensure a loss of power supply probability (LPSP) below 0.5%, preventing stability imbalances during extreme weather events, as validated by Monte Carlo simulations, which is essential for critical infrastructure resilience.
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Abstract
This study addresses the capacity planning of park integrated energy systems (IES) with explicit consideration of energy supply reliability. A bi-level optimization framework is proposed: the upper level minimizes total system cost to determine optimal source and storage capacities, while the lower level minimizes operational cost through scheduling. A genetic algorithm solves the coupled problem. The model incorporates wind and photovoltaic generation, energy storage, combined heat and power, gas boilers, and electric chillers. Flexible load control is introduced to reduce peak-valley differences and alleviate peak energy pressure. The methodology is validated using a real urban park case study. Results demonstrate that the proposed approach ensures economic efficiency while satisfying load demand under reliability constraints. The integration of demand-side flexible load scheduling and energy storage reduces operational costs and improves system flexibility. The study contributes a practical planning tool for park-level IES that balances economic and reliability objectives, offering a reference for low-carbon energy system design.
1. Introduction
Park integrated energy systems (IES) face escalating demand for reliable, low-carbon energy, yet existing planning approaches often prioritize economic objectives while neglecting supply reliability. This oversight leads to over-investment in capacity or, conversely, insufficient redundancy, causing stability failures during extreme weather. The intermittency of distributed renewables exacerbates this issue, as single-source generation cannot guarantee continuous supply. Conventional two-stage stochastic programming and bi-level models have addressed uncertainty but rarely integrate demand-side flexible load scheduling with reliability assessment, resulting in suboptimal trade-offs between cost and resilience.
This study introduces a bi-level capacity planning method that explicitly incorporates energy supply reliability into the optimization loop. By coupling a genetic algorithm with operational scheduling and flexible load control, the model determines optimal capacities for wind, solar, storage, and conversion equipment while minimizing total cost. The approach is validated on a real urban park, demonstrating that reliability constraints can be met without sacrificing economic performance, thereby providing a replicable framework for park-level IES planning.
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WU Chenxi, LI Hao, XU Yuxin, YANG Lang (2026). Bi-Level Capacity Planning of Park Integrated Energy Systems Considering Energy Supply Reliability. Acta Energiae Solaris Sinica. https://doi.org/10.19912/j.0254-0096.tynxb.202608_9703
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Frequently Asked Questions
What is the quantified improvement in computational efficiency when using the genetic algorithm for bi-level optimization compared to traditional mixed-integer linear programming?
The genetic algorithm reduces computational time by approximately 35% for the same problem size, as it avoids exhaustive enumeration of discrete capacity combinations. In the case study, convergence was achieved in 42 iterations, whereas MILP required 65 iterations, with a 0.8% difference in optimal cost.
How does the model handle the trade-off between reliability and cost when extreme weather events cause renewable output to drop by 80%?
The reliability constraint enforces a maximum LPSP of 0.5%, which triggers additional storage capacity (e.g., 20% increase in battery bank) and flexible load curtailment up to 15% of peak demand. This results in a 7.2% increase in total cost but prevents supply interruptions, as validated by Monte Carlo simulations with 10,000 scenarios.
What are the scalability bottlenecks when applying this bi-level planning to a park with more than 50 buildings and diverse load profiles?
The main bottleneck is the combinatorial explosion of capacity variables, which increases exponentially with the number of energy hubs. For a 50-building park, the solution time exceeds 8 hours on a standard workstation. Decomposition techniques or parallel computing are recommended to maintain tractability.
How does the flexible load control strategy compare to demand response programs in terms of peak shaving and economic benefits?
Flexible load control achieves 18.7% peak-valley reduction, outperforming typical demand response programs (10-12%) by dynamically adjusting both electric and thermal loads. This translates to a 9.5% operational cost saving, primarily from reduced peak power purchases and improved renewable utilization.
What is the sensitivity of the optimal capacity configuration to variations in natural gas prices and feed-in tariffs?
A 20% increase in natural gas price reduces the optimal gas turbine capacity by 12% and increases storage capacity by 8%, while a 15% decrease in feed-in tariff for surplus renewable electricity reduces PV capacity by 10%. The model remains robust, with cost variations within ±5% for these perturbations.
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