Collaborative scheduling and benefit allocation for waste-to-energy, hydrogen storage, and power-to-gas under uncertainties with temporal relevance.

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]