This paper presents a novel mechanism for integrating renewable energy into the grid by linking forecast reliability with energy storage configuration. A comprehensive reliability score, combining forecast accuracy and energy storage gains, is introduced to determine feed-in tariffs and incentivize optimal energy storage deployment. To address the inherent uncertainty of renewable generation, typical wind power output scenarios are identified using K-means clustering based on the Elbow Method, reducing scenario complexity while preserving statistical characteristics. A genetic algorithm is applied to optimize energy storage capacity and charging/discharging strategies, subject to practical constraints including allocation ratio, storage duration, grid balance, and operational limits. Case studies of two 200 MW wind farms demonstrate that the proposed mechanism significantly reduces output deviations, enhances reliability scores, and increases net revenue. Energy storage exhibits a marginal saturation effect: initial deployment yields substantial improvements, while further expansions provide diminishing incremental benefits. Plants with lower forecast accuracy benefit more from storage optimization, highlighting the value of site-specific strategies. The mechanism also improves system-level stability by smoothing renewable output fluctuations and promoting efficient storage utilization. The framework offers actionable guidance for policymakers and operators to design incentive-based grid-connection mechanisms that align financial rewards with operational performance and is scalable to other renewable sources and hybrid systems, supporting high-penetration renewable energy power systems.

Jiamin Fang. A Study on a New Mechanism for Grid Integration of Renewable Energy Based on Forecast Reliability and Energy Storage Configuration. International Journal of New Developments in Engineering and Society (2026), Vol. 10, Issue 1: 46-56. https://doi.org/10.25236/IJNDES.2026.100107.

[1] Deng L, Li Z, Sun H, et al. Generalized locational marginal pricing in a heat-and-electricity-integrated market[J]. IEEE Transactions on Smart Grid, 2019, 10(6): 6414-6425.

[2] Yi X, Guo Y, Sun H, Qin X, Wu Q. Energy-grade double pricing for combined heat and power systems[J]. IEEE Transactions on Power Systems, 2024, 39(2): 3769-3784.

[3] Gu B, Shen H, Lei X, Hu H, Liu X. Forecasting and uncertainty analysis of day-ahead photovoltaic power using a novel forecasting method[J]. Applied Energy, 2021, 299: 117291.

[4] Zheng F. Research on market mechanisms and institutional optimization of renewable energy power grid integration[D]. Yunnan University, 2021.

[5] Fu Y, Song Y L, Deng L R, Yang L. Integrated energy market mechanism design with embedded renewable energy uncertainties[J]. Power System Technology, 2025(5): 1848-1858.

[6] Bai Y X, Dai M R, Ji T Y, et al. Trading mechanism of spot electric energy and frequency regulation markets considering renewable energy under the background of new power system[J/OL]. Southern Power System Technology, 2025: 1-11.

[7] Nan X Q, Li Q Z, Zhao Y Z, et al. Economic dispatch and auxiliary decision methods considering wind power forecast credibility[J]. Automation of Electric Power Systems, 2013, 37(19): 61-67.

[8] Xu X, Xie L R, Liang W X, et al. Bi-level optimization model considering temporal characteristics of wind power forecast errors and credibility[J]. Transactions of China Electrotechnical Society, 2023, 38(6): 1620-1632, 1661.

[9] Yuan T, Chao Q, Turson I, et al. Modeling of dynamic clean economic dispatch for large-scale wind power grid integration[J]. Proceedings of the CSEE, 2010, 30(31): 7-13.

[10] Yuan T, Chao Q, Li Y, et al. Economic dispatch optimization model based on wind power limit penetration[J]. Power System Protection and Control, 2011, 39(1): 15-22.