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Waste-to-energy (WTE) is a technology that converts inexhaustible waste into electricity, thus significantly alleviating energy crisis. Integrating hydrogen storage into WTE provides an effective way to store and utilize surplus electricity from WTE on-site (by producing hydrogen). Additionally, integrating power-to-gas (P2G) into WTE provides an effective way to reduce CO 2 emission from WTE on-site (by producing natural gas). To this end, a novel waste-driven renewable energy system (WRES), which is consisted of WTE, hydrogen storage, and P2G, has been proposed. However, the uncertain waste supply and electrical load markedly interfere with WRES operation. Meanwhile, when WTE, hydrogen storage, and P2G belong to different agents, the collaborative benefit from WRES operation should be rationally allocated among agents for system sustainability. This paper endeavors to achieve WRES scheduling and collaborative benefit allocation for WTE, hydrogen storage, and P2G under uncertainties. Firstly, a temporal relevance based distributionally robust optimization model is proposed for WRES scheduling under uncertainties, in which the possible range of the joint distribution for uncertainties is depicted by data covariance matrices involved ambiguity set. Secondly, collaborative benefit is allocated according to WTE, hydrogen storage, and P2G contributions, in which Gauss-Legendre quadrature formula is integrated with Aumann-Shapley value method to reduce calculation complexity. Finally, simulation results show that 1) the proposed scheduling model guarantees the economic and stable operation of WRES under uncertainties. 2) after considering the temporal relevance, WRES operation benefit is 8.35 % higher, which indicates that the proposed scheduling model has superiority in decision conservatism by introducing temporal relevance to remove impractical distributions in ambiguity set. 3) the proposed allocation model rationally distributes the collaborative benefit according to contributions, and presents lower calculation complexity, e.g., the calculation time is 95.52 % lower than the Shapely value method. This paper provides policy insights to promote widespread application and sustainable operation of WRES. • Collaborative scheduling and benefit allocation are studied for WRES. • Temporal relevance based DRO model is proposed for collaborative scheduling. • Aumann-Shapley value method plus Gauss-Legendre quadrature for benefit allocating. • The proposed scheduling model can reduce conservatism and resist uncertainty. • The proposed allocation model can reduce calculation complexity and keep fairness. [ABSTRACT FROM AUTHOR]

Pumped storage is crucial for maintaining energy balance and smoothing out the fluctuations from renewable sources. Yet, it is limited by its fixed capacity and lack of expandability post-construction, posing challenges to its long-term adaptability in the context of increasing installed renewable sources capacity. Underwater hydrogen storage, however, characterized by its green, low-carbon profile and ability for rapid energy release and long-term storage, complements pumped storage by enhancing the system's overall energy storage capacity and flexibility. Therefore, this paper proposes an innovative way for the pumped storage power station capacity expansion based on the underwater hydrogen storage introduction. On the basis of technical support of underwater hydrogen storage and time-series attribute consideration of uncertainties, a multi-objective distributionally robust optimization model with temporal correlation is constructed for underwater hydrogen storage planning and further scheduling pumped storage power station and underwater hydrogen storage to operate. Firstly, the system structure and operation mode after introducing underwater hydrogen storage into pumped storage power station are designed. Secondly, the temporal covariance conditions are introduced in a moment-based ambiguity set, with the aim of removing those distributions that do not match the temporal correlation of the historical forecasting errors samples. Finally, considering the "worst-case" distribution within the narrowed ambiguity set, an improved multi-objective distributionally robust optimization is constructed, which optimizes the capacity of each equipment in underwater hydrogen storage and the operation strategy of pumped storage power station and underwater hydrogen storage. Simulation mainly verifies: 1) it increases the economic revenue, electric load supply and photovoltaic output accommodation by 3.35 × 108 $, 2033.091 MW and 67584.054 MW, respectively, due to the introduction of underwater hydrogen storage for pumped storage power station expansion. 2) it improves cost savings, load supply reliability and photovoltaic output accommodation by 0.224 %,3.231 % and 2.722 % respectively, due to the introduction of temporal covariance to modify distributionally robust optimization model. • Underwater hydrogen storage introducing for pumped storage power station expansion. • Improved distributionally robust optimization is employed for planning and operating. • Temporal covariance conditions are introduced to modify moment-based ambiguity set. • Economy and reliability are improved due to underwater hydrogen storage introduction. • The proposed model can resist multiple uncertainties and reduce conservatism. [ABSTRACT FROM AUTHOR]