Surplus electricity energy from renewable sources can be efficiently utilized by converting it into other forms of energy in the integrated energy system (IES). Local electricity energy sharing can be realized through effective dispatching and independent transaction between producers and consumers. This features the merits on energy consumption and operational cost reduction. In this paper, a novel energy scheduling regime is proposed for the electricity-hydrogen-heat IES clusters under a peer-to-peer (P2P) electricity and heat energy transaction structure. To instantly control the robustness of the scheduling decisions, a weighted MPC strategy is newly proposed for online decision-makings. Furthermore, an adaptive alternating direction method of multipliers (ADMM) with varying penalties algorithm that can effectively preserve prosumer privacy is proposed, which enables the operators to coordinate various parties in the multi-energy microgrids. Extensive case studies based on a typical testing system are carried out, of which the simulation results demonstrate the effectiveness of the proposed scheduling regime and suggest potential for practical application.

Virtual power plants (VPPs) and electric vehicle (EV) charging stations (CSs) have been attracting much attention in recent years. However, existing research rarely concerns the cooperation between VPPs and CSs that are managed by different stakeholders. To facilitate the cooperation between VPPs and CSs, this work proposes a cooperative operation framework for a multi-stakeholder VPP-CSs system. In the proposed cooperative framework, day-ahead offering and real-time balancing models are developed to maximize the total benefit of the VPP-CSs system. To support a more flexible operation of the VPP-CSs system with EV energy flexibility, an EV user incentive program is proposed for acquiring EV battery access rights. The conflicting interests of different stakeholders are addressed by a t-value cost allocation method. To alleviate the computational burden in calculating the t-values, a maximum right cost estimation approach is proposed. Case studies confirm that the proposed methods can provide superior performance by increasing 4.6% of VPP profit, increasing 20.7% of CS profit, reducing 16.3% of EV user charging fees, and achieving 99.2% of t-value estimation accuracy.

Virtual power plant (VPP) has become an important resource for reserve provision owing to its fast-responding capability. In this paper, an optimal VPP operational regime considering reserve uncertainty is proposed, which includes a novel day-ahead offering strategy and a real-time dispatching model. At the day-ahead stage, the offering strategy gives the VPP’s price-dependent offers in the energy market under multiple uncertainties on market price, renewable generation, and calls of reserve deployment. A hybrid stochastic minimax regret (MMR) model is proposed to facilitate making offering decisions in the electricity market. At the real-time dispatching stage, generation scheduling can be realized based on the MMR criterion in an online fashion. To alleviate the intrinsic conservativeness of the dispatching model, a self-adaptive algorithm is also proposed to instantly modify the confidence bounds. The proposed regime is comprehensively tested through extensive case studies, which demonstrate the effectiveness of our method in obtaining operational decisions that are less conservative.

With the mass roll-out of electric vehicles (EVs) and rapid progress in battery technology, utilizing EV charging flexibility has become a promising solution for supporting economic and secured power system operations. This work proposes a novel hybrid incentive program, which encourages EV owners to sell their charging flexibility to a charging station (CS) and achieve a win-win situation for both EV owners and the CS. Unlike existing approaches, the proposed hybrid incentive program is simultaneously featured with simplicity, consistency, and controllability. To determine the incentive payment parameters, an optimal incentive price selection model is developed. In the solution methodology, we first linearize the original problem, then develop an adaptive ADMM algorithm to efficiently solve the formulated problem. Case studies confirm the superiority of the proposed hybrid incentive program over the state-of-the-arts, achieving 22.51% of EV owners’ cost reduction, 31.18% of energy market bill reduction, and 64.13% of potential charging flexibility utilization.

Extreme weather events have led to long-duration outages in the distribution system (DS), necessitating novel approaches to blackstart and restore the system. Existing blackstart solutions utilize blackstart units to establish multiple microgrids (MGs), sequentially energize non-blackstart units, and restore loads. However, these approaches often result in isolated MGs. In DERs-aided blackstart, the continuous operation of these MGs is limited by the finite energy capacity of commonly used blackstart units like battery energy storage (BES)-based gridforming inverters (GFMIs). To address this issue, this article proposes a holistic blackstart and restoration framework that incorporates synchronization between dynamic MGs and the entire DS with the transmission grid (TG). To support synchronization, we leveraged virtual synchronous generator-based control for GFMIs to estimate their frequency response to load pick-up events using only initial/final quasi-steady-state points. Subsequently, a synchronization switching condition is developed to model synchronizing switches, aligning them seamlessly with a linearized branch flow problem. Finally, we designed a bottomup blackstart and restoration framework that considers the switching structure of the DS, energizing/synchronizing switches, DERs with grid-following inverters, and BES-based GFMIs with frequency security constraints. The proposed framework is validated in IEEE-123-bus system, considering cases with two and four GFMIs under various TG recovery instants.

The scheduled electric vehicle (EV) charging flexibility has great potential in supporting the operation of power systems, yet achieving such benefits is challenged by the uncertain and user-dependent nature of EV charging behavior. Existing research primarily focuses on modeling the uncertain EV arrival and battery status yet rarely discusses the uncertainty in EV departure. In this paper, we investigate the EV charging scheduling strategy to support load flattening at the distribution level of the utility grid under uncertain EV departures. A holistic methodology is proposed to formulate the unexpected trip uncertainty and mitigate its negative impacts. To ensure computational efficiency when large EV fleets are involved, a distributed solution framework is developed based on the alternating direction method of multipliers (ADMM) algorithm. The numerical results reveal that unexpected trips can severely damage user convenience in terms of EV energy content. It is further confirmed that by applying the proposed methodology, the resultant critical and sub-critical user convenience losses due to scheduled charging are reduced significantly by 83.5% and 70.5%, respectively, whereas the load flattening performance is merely sacrificed by 17%.