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The nonlinear degradation effect of the fast lithium battery energy storage system (FLBESS) and the time-scale variability of peak shaving and frequency regulation both make the optimal operation of the microgrid more challenging. Therefore, this paper presents a bi-level optimization model to balance the degradation cost of lithium battery and the benefit of peak shaving and frequency regulation and improve the utilization of FLBESS while dealing with the multiple time scales optimization problems. The upper-level model mainly optimizes the day-ahead hourly power to minimize the power purchase cost and wind and solar power curtailment cost while calculating the internal parameters of the FLBESS to supply the lower-level model for the solution. The lower model optimizes the intra-hour economic cost and coordinates the frequency regulation benefit and lithium battery degradation cost. Finally, case studies are conducted using a regional microgrid to verify that the model solution satisfies the requirements for optimal system operation. In contrast, the model has the advantages of lower economic cost, more significant life extension effect, and higher energy storage utilization rate. The results show that the annual costs were reduced by 14.9%, and the battery life was increased by 19.0% than without considering degradation.

Conventional optimization methods cannot fully satisfy the interests of multiparticipants and protect the privacy of participants in the integrated energy system that observe changes in the energy market structure. To allocate the benefits among the stakeholders in the integrated energy system and improve renewable energy accommodation, the manuscript proposes an optimal dispatching strategy for a park-level integrated energy system employing the Stackelberg game. Firstly, the benefits and cost models of each stakeholder of the integrated energy system are constructed by considering the integrated demand response and the uncertainty of renewable energy output. A master-slave game model that contains the energy system operator, energy producer, and energy users is then established, and the existence of the Stackelberg equilibrium is demonstrated. Furthermore, a distributed algorithm is proposed to resolve the game model by combining an improved coyote optimization algorithm with quadratic programming. Due to the shortcomings of the conventional coyote optimization algorithm, such as slow convergence rate and quickly falling into local optimum, a beetle antennae search is utilized to strengthen the optimal and the worst coyotes and to improve the convergence speed, global search ability, and optimization accuracy of the standard coyote algorithm. Finally, an industrial park in Northern China is adopted as an illustration to evaluate the effectiveness of the model and the improved algorithm.