The proposal of the “carbon neutrality” target promotes the rapid development of variable renewable energy, which leads to the reduction of inertia and the deficiency of flexible reserve in power systems. Wind turbines (WTs) now are asked for providing frequency support and reserve through proper control strategy. This proposes a new issue on how to coordinate the operation of conventional units and WTs. This paper proposes a frequency security constrained scheduling approach considering wind farms providing frequency support and reserve. Two dynamic frequency indices: RoCoF and nadir, are derived and introduced into the scheduling model. The frequency support capability of wind farm is accurately quantified by considering the real grid-connected WT capacity and wake effect. A wind power reserve model considering uncertainty is established by linking the reserve under the forecasted scenario and uncertain scenarios through reserve auxiliary variables. The contribution of wind farms to power system frequency support and reserve is quantified in the robust scheduling model. Case studies show that the frequency support and reserve from wind farms can effectively reduce the whole operation cost and VRE curtailment. Moreover, the value of wind farms in CO ₂ emission reduction is properly quantified.

With the popularity of plug-in electric vehicles (PEVs) and the development of vehicle to grid (V2G) technology, PEVs can be aggregated and behave as a controllable storage system via the internet of things. In this paper, we propose a dispatchable region formation approach of EV aggregation to capture its available flexibility in microgrid (MG) bidding. The dispatchable region of EV aggregation describes the feasible operation strategy as a single entity, characterized by its power and cumulative energy limits. Instead of scheduling an individual EV, the dispatchable region of large-scale EVs enables the MG operator to directly schedule the EV aggregation toward market revenue maximization. The MG bidding strategy is formulated as a risk-constrained stochastic programming, which maximizes day-ahead market profits considering real-time imbalance in a dual-pricing market. Case studies based on real-world datasets demonstrate that the proposed approach can significantly improve both computation efficiency and forecasting accuracy.

High penetration of renewable energy brings seasonal electricity imbalance in the power system and results in considerable energy curtailment. Such large-scale curtailed electricity could be used to produce hydrogen via power-to-hydrogen (P2H) technology. The introduction of P2H would largely affect the configuration of both the power grid and hydrogen supply chain (HSC). This article proposes a coordinated planning model of power system generation and transmission (GT) as well as HSC with transportable seasonal hydrogen storage. A coplanning model is put forward to investigate the optimal configuration of infrastructures in the GT-HSC and their coupling during operation. The model is formulated as an LP model so that the year-round energy balance of seasonal hydrogen storage with daily resolution could be considered. Case studies on 5-bus PJM system and Northwest China HRP-38 system demonstrate the effectiveness of the proposed model. Results of the case study also illustrate the benefits of incorporating GT with HSC to reduce the renewable energy curtailment and bring down the energy supply cost.

Concentrating solar power (CSP) has been advocated as a promising technology to mitigate the uncertainty in variable renewables generation due to its thermal storage capability. In de-regulated markets, however, it remains an open question as to how to identify different CSP plants' contributions to uncertainty mit-igation, and incentivize their participation in joint offering with variable renewable generation. In this paper, we propose a profit-sharing mechanism to incentivize the joint offering of CSP and wind power aggregation. The joint offering strategy is formulated as a two-stage stochastic optimization model, aimed at maximizing the day-ahead market revenues, considering real-time imbalance settlement. A Nash bargaining (NB)-based market mechanism is designed to allocate the surplus from the joint offering. In contrast to symmetric NB with equal bargaining power (BP), we develop a novel BP evaluation method by precisely quantifying the market contributions of different producers in the joint offering. To pre-serve the autonomy of each producer, a decentralized solution framework is presented. Case studies show that the joint offering of wind power and CSP aggregation can effectively mitigate real-time imbalance. The proposed profit-sharing mechanism can iden-tify market participants' contributions in both the day-ahead and balancing markets.

As data centers emerge as the information backbone of an increasingly digital world, the associated service demand has been rising rapidly. The operational nature of the data center brings great flexibility potential for energy systems. However, there remains an open question as to how to capture the flexibility potentials of geographically dispersed data centers and incentivize their flexibility provision for system operation. In this paper, we propose electricity–heat coordinated operation to capture the flexibility potential of data centers, i.e., temporal, spatial and integrated energy flexibility. To better depict the interaction between the system operator and the data centers, the centralized coordinated operation model is decomposed into a bilevel model, and an incentivized profit-sharing mechanism is designed to suitably motivate the flexibility provision of data centers. Case studies based on real-world datasets show that incorporating data centers at the system level can effectively improve operation efficiency and facilitate renewable energy integration. Hopefully, our work can provide novel insights into the coordination of data networks and power grids as well as the flexibility potential activation of data centers.