考虑多重不确定性的园区综合能源系统优化运行策略及模糊参数敏感度分析

doi:10.19595/j.cnki.1000-6753.tces.260161

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Abstract

The integration of high-penetration renewable energy and diversified loads into a Community integrated energy system (CIES) introduces significant uncertainties on both the supply and demand sides, posing challenges to the system's optimal operation. Existing uncertainty handling methods each have limitations: stochastic optimization requires a large number of accurate probability scenarios; robust optimization tends to be overly conservative; and interval optimization suffers from high computational complexity. Furthermore, current uncertainty characterization approaches often discretize the uncertainty information, losing global information and flexibility. To address these issues, this paper proposes an optimal operation strategy for CIES considering multiple uncertainties, together with a fuzzy parameter sensitivity analysis method. The objective is to structurally characterize the source-load uncertainties, quantify the impact mechanism of fuzzy parameters on operational costs, and provide a quantitative basis for risk-controllable scheduling decisions.
First, a fuzzy parameter space for multiple uncertain parameters is constructed. This space captures both the inherent randomness of wind speed, solar irradiance, and electrical load fluctuations, and the epistemic uncertainty of their distribution parameters via fuzzy numbers. The membership degree reflects the confidence level in the parameter values. This dual-layer representation preserves complete uncertainty information.
Second, a scenario-slicing optimization method based on dimensional reduction and parsing of membership functions is proposed. The method slices the fuzzy parameter space at different membership levels. For each slice, Monte Carlo sampling is applied to generate multiple sets of fuzzy parameters. For each set, the bounds of wind power, photovoltaic output, and electrical load are calculated at a given confidence level, and the corresponding cost boundaries are solved through the CIES optimization model, which minimizes the total operational cost subject to power balance, conversion equipment, energy storage, and uncertainty constraints. By integrating the results from all slices, a complete cost-membership function is obtained, yielding a cost interval that covers the entire risk spectrum.
Third, a global sensitivity analysis method based on membership functions is proposed. By comparing the unconditional cost membership function with the conditional functions, the influence of each fuzzy parameter is quantified. The sensitivity index, calculated from the expected area difference between these functions, enables accurate identification of key uncertain factors and their impact intensity.
The proposed approach is validated using a typical CIES in East China with a 24-hour scheduling horizon. The results show that the sensitivity indices are consistent with the cost variations caused by parameter perturbations, verifying the correctness of the method. Simulation results indicate that the two shape parameters of photovoltaic output have the highest sensitivity indices, 0.5837 and 0.6303 respectively, identifying them as the most critical factors and guiding operators to prioritize photovoltaic forecasting. When the confidence level increases from 0.1 to 0.9, the cost interval expands, especially the upper bound, reflecting the additional cost required to cope with stricter uncertainty. Comparisons with deterministic optimization, stochastic optimization, and robust optimization demonstrate that the proposed method achieves a better balance between economy and flexibility: it provides a wider adjustable range than deterministic and stochastic optimization, and a lower cost upper bound than robust optimization, while offering richer cost information across risk levels.
The main conclusions are: (1) The proposed sensitivity analysis method correctly identifies the photovoltaic shape parameters as key uncertainty sources. (2) The scenario-slicing method efficiently obtains a complete cost-membership curve, supporting risk-aware decision-making. (3) The proposed method outperforms existing approaches in comprehensive performance, combining economic efficiency with regulation flexibility. (4) The confidence level analysis reveals a clear trade-off between operational risk and economic cost.

Key words： Community integrated energy system multiple uncertainties parameter sensitivity analysis optimal operation

Received: 27 January 2026

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TM73

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	Li Peng
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Cite this article:

Li Peng,Xu Weicheng,Hou Lei等. Optimal Operation and Sensitivity Analysis of a Community Integrated Energy System Considering Multiple Uncertainties[J]. Transactions of China Electrotechnical Society, 0, (): 260161-.

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https://dgjsxb.ces-transaction.com/EN/10.19595/j.cnki.1000-6753.tces.260161 OR https://dgjsxb.ces-transaction.com/EN/Y0/V/I/260161

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