Ning Qi

Ning Qi, Peng Li, Lin Cheng et al.

Energy storage (ES) and virtual energy storage (VES) are key components to realizing power system decarbonization. Although ES and VES have been proven to deliver various types of grid services, little work has so far provided a systematical framework for quantifying their adequacy contribution and credible capacity value while incorporating human and market behavior. Therefore, this manuscript proposed a novel evaluation framework to evaluate the capacity credit (CC) of ES and VES. To address the system capacity inadequacy and market behavior of storage, a two-stage coordinated dispatch is proposed to achieve the trade-off between day-ahead self-energy management of resources and efficient adjustment to real-time failures. And we further modeled the human behavior with storage operations and incorporate two types of decision-independent uncertainties (DIUs) (operate state and self-consumption) and one type of decision-dependent uncertainty (DDUs) (available capacity) into the proposed dispatch. Furthermore, novel reliability and CC indices (e.g., equivalent physical storage capacity (EPSC)) are introduced to evaluate the practical and theoretical adequacy contribution of ES and VES, as well as the ability to displace generation and physical storage while maintaining equivalent system adequacy. Exhaustive case studies based on the IEEE RTS-79 system and real-world data verify the significant consequence (10%-70% overestimated CC) of overlooking DIUs and DDUs in the previous works, while the proposed method outperforms other and can generate a credible and realistic result. Finally, we investigate key factors affecting the adequacy contribution of ES and VES, and reasonable suggestions are provided for better flexibility utilization of ES and VES in decarbonized power system.

Ning Qi, Ningkun Zheng, Bolun Xu

This paper proposes a novel framework to price energy storage in economic dispatch with a social welfare maximization objective. This framework can be utilized by power system operators to generate default bids for storage or to benchmark market power in bids submitted by storage participants. We derive a theoretical framework based on a two-stage chance-constrained formulation which systematically incorporates system balance constraints and uncertainty considerations. We present tractable reformulations for the joint chance constraints. Analytical results show that the storage opportunity cost is convex and increases with greater net load uncertainty. We also show that the storage opportunity prices are bounded and are linearly coupled with future energy and reserve prices. We demonstrate the effectiveness of the proposed approach on an ISO-NE test system and compare it with a price-taker storage profit-maximizing bidding model. Simulation results show that the proposed market design reduces electricity payments by an average of 17.4% and system costs by 3.9% while reducing storage's profit margins, and these reductions scale up with the renewable and storage capacity.

Ning Qi, Xiaolong Jin, Kai Hou et al.

This paper proposes a novel privacy-preserving uncertainty disclosure framework, enabling system operators to release marginal value function bounds to reduce the conservativeness of interval forecast and mitigate excessive withholding, thereby enhancing storage dispatch and social welfare. We develop a risk-averse storage arbitrage model based on stochastic dynamic programming, explicitly accounting for uncertainty intervals in value function training. Real-time marginal value function bounds are derived using a rolling-horizon chance-constrained economic dispatch formulation. We rigorously prove that the bounds reliably cap the true opportunity cost and dynamically converge to the hindsight value. We verify that both the marginal value function and its bounds monotonically decrease with the state of charge (SoC) and increase with uncertainty, providing a theoretical basis for risk-averse strategic behaviors and SoC-dependent designs. An adjusted storage dispatch algorithm is further designed using these bounds. We validate the effectiveness of the proposed framework via an agent-based simulation on the ISO-NE test system. Under 50% renewable capacity and 35% storage capacity, the proposed bounds enhance storage response by 38.91% and reduce the optimality gap to 3.91% through improved interval predictions. Additionally, by mitigating excessive withholding, the bounds yield an average system cost reduction of 0.23% and an average storage profit increase of 13.22%. These benefits further scale with higher prediction conservativeness, storage capacity, and system uncertainty.

Daniyaer Paizulamu, Lin Cheng, Ning Qi et al.

Uncertainties in balancing generation and load in low-carbon industrial microgrids (IMGs) make hybrid energy storage systems (HESS) crucial for their stable and economic operation. Existing model predictive control (MPC) techniques typically enforce periodic state of charge (SOC) constraints to maintain long term stability. However, these hard constraints compromise dispatch flexibility near the end of the prediction horizon, preventing sufficient energy release during critical peaks and leading to optimization infeasibility. This paper eliminates the periodic SOC constraints of individual storage units and proposes a novel full-timescale hierarchical MPC scheduling framework. Specifically, comprehensive physical and cost models are established for the HESS composed of flywheel, battery, compressed-air, and hydrogen-methanol energy storage. The control problem is decoupled into a hierarchical MPC architecture. Furthermore, a novel adaptive feedback mechanism based on micro trajectory inverse projection (MTIP) is embedded into the scheduling process, accurately mapping the high frequency dynamic buffering capabilities of lower tier storages into the upper decision space to generate dynamic boundaries. Experiments using 14 consecutive months of second-level data from a real-world IMG validate the effectiveness of the proposed method, demonstrating its significant superiority over existing approaches. By effectively preventing limit violations and deadlocks in lower-tier storages under extreme fluctuations, it achieves a 97.4\% net load smoothing rate and a 62.2\% comprehensive cycle efficiency.

Eliza Cohn, Ning Qi, Upmanu Lall et al.

This paper proposes a real-time control policy for cascaded hydropower systems that incorporates decision-dependent uncertainty (DDU) to capture the coupling of streamflow uncertainties across the network. The framework jointly models exogenous forecast errors and endogenous uncertainty propagation, explicitly characterizing the dependence between upstream releases and downstream inflow variability through a heteroskedastic variance model conditioned on past errors, variance, and control actions. We formulate a joint chance-constrained optimization problem to ensure reliable system operation under uncertainty, and develop a tractable supporting hyperplane algorithm that enables explicit and adaptive risk allocation under DDU. We establish convergence of the proposed method and show that it recovers the Bonferroni approximation under steady-state conditions. A randomized case study based on Columbia River data demonstrates that the proposed framework improves both energy generation and reservoir reliability by accounting for DDU. Sensitivity analyses on drought severity and model parameters further highlight the value of adaptive risk allocation for resilient hydropower operations.

Ning Qi, Yousuf Baker, Bolun Xu

This paper proposes a novel non-anticipatory long-short-term coordinated dispatch framework for isolated microgrid with hybrid short-long-duration energy storages (LDES). We introduce a convex hull approximation model for nonconvex LDES electrochemical dynamics, facilitating computational tractability and accuracy. To address temporal coupling in SoC dynamics and long-term contracts, we generate hindsight-optimal state-of-charge (SoC) trajectories of LDES and netloads for offline training. In the online stage, we employ kernel regression to dynamically update the SoC reference and propose an adaptive online convex optimization (OCO) algorithm with SoC reference tracking and expert tracking to mitigate myopia and enable adaptive step-size optimization. We rigorously prove that both long-term and short-term policies achieve sublinear regret bounds over time, which improves with more regression scenarios, stronger tracking penalties, and finer convex approximations. Simulation results show that the proposed method outperforms state-of-the-art methods, reducing costs by 73.4%, eliminating load loss via reference tracking, and achieving an additional 2.4% cost saving via the OCO algorithm. These benefits scale up with longer LDES durations, and the method demonstrates resilience to poor forecasts and unexpected system faults.

Qiang Ji, Lin Cheng, Yue Zhou et al.

The integration of the high penetration of intermittent renewable energy sources (RES) and the retirement of thermal units have significantly aggravated the flexibility scarcity and real-time balancing challenges in power systems. Low-carbon steel production systems, based on green-hydrogen ironmaking and electrified melting, possess substantial demand response (DR) potential. This paper proposes a process-aware DR evaluation framework for hydrogen-integrated zero-carbon steel plants coupled with methanol production (H2-DRI-EAF-MeOH). First, a novel zero-carbon steel production system architecture is established to explicitly represent the energy-material flow coupling relationships among electricity, hydrogen, heat, iron, steel, CO2, and methanol. Second, to explicitly capture electric arc furnace (EAF) operational constraints while preserving optimization tractability, an operating feasible region model is developed and validated using field data from a pure hydrogen direct reduced iron and EAF plant, yielding an average relative error of 4.1%. Finally, a process-aware DR scheduling model is formulated by incorporating the proposed process deviation penalties to balance economic performance against process disturbance costs and operational acceptability. Additionally, dual-side evaluation metrics are developed to quantify grid-side regulation performance and load-side flexibility characteristics. Case studies demonstrate that under real-time pricing, the proposed system achieves an average DR capacity of 275.4 MW, improves the RES-load matching degree from 0.262 to 0.508, and reduces total operational costs by 17.78% compared with the baseline scheduling scheme. The proposed framework provides a theoretical foundation for RES-steel-chemical synergies.

Yingrui Zhuang, Lin Cheng, Yuji Cao et al.

Price signals from distribution networks (DNs) guide energy communities (ECs) to adjust energy usage, enabling effective coordination for reliable power system operation. However, this coordination faces significant challenges due to the limited availability of information (i.e., only the aggregated energy usage of ECs is available to DNs), and the high computational burden of accounting for uncertainties and the associated risks through numerous scenarios. To address these challenges, we propose a quantum learning and estimation approach to enhance coordination between DNs and ECs. Specifically, leveraging advanced quantum properties such as quantum superposition and entanglement, we develop a hybrid quantum temporal convolutional network-long short-term memory (Q-TCN-LSTM) model to establish an end-to-end mapping between ECs' responses and the price incentives from DNs. Moreover, we develop a quantum estimation method based on quantum amplitude estimation (QAE) and two phase-rotation circuits to significantly accelerate the optimization process under numerous uncertainty scenarios. Numerical experiments demonstrate that, compared to classical neural networks, the proposed Q-TCN-LSTM model improves the mapping accuracy by 69.2% while reducing the model size by 99.75% and the computation time by 93.9%. Compared to classical Monte Carlo simulation, QAE achieves comparable accuracy with a dramatic reduction in computational time (up to 99.99%) and requires significantly fewer computational resources.