arXiv subjects

Jinyu Wen

Publications and source records attributed to Jinyu Wen.

12 recordsLinked to original sources

arXiv2026

Reachable-Set Decomposition for Real-Time Aggregation of Multi-Zone HVAC Fleets

Aggregating building heating, ventilation, and air-conditioning (HVAC) fleets can provide substantial real-time flexibility to power systems. However, practical deployment requires scalable characterization of multi-zone, multi-period flexibility and guaranteed feasible disaggregation as temperature states and exogenous inputs are revealed sequentially. This paper develops an offline-online reachable-set decomposition framework that combines offline temporal decomposition with a scalable online fleet coordination policy. Offline, backward reachable sets encode remaining-horizon feasibility as per-period state constraints, while tailored polytopic inner approximations provide tractable representations of these sets for thermally coupled multi-zone buildings. Online, aggregate flexibility is computed through parallel, single-period building-level linear programs followed by the fleet-level closed-form Minkowski summation of power intervals, thereby avoiding operations on high-dimensional full-horizon sets. The resulting endpoint profiles enable a closed-form, time-causal policy that disaggregates any aggregate HVAC power command within the reported interval while preserving recursive feasibility. Case studies show that, relative to the considered fixed full-horizon baselines, the proposed framework captures greater aggregate flexibility by recomputing per-period intervals using the latest state and exogenous information, while maintaining feasible disaggregation under sequentially revealed uncertainty. Its building-separable computation further enables scalable implementation for large fleets of multi-zone buildings.

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arXiv2026

Preference-Oriented Aggregation of Heterogeneous Distributed Energy Resources for Reserve Dispatch

Aggregating distributed energy resources (DERs) aims to encode their collective flexibility into a single set for efficient grid dispatch. However, existing aggregation methods are overly conservative for heterogeneous DERs due to two main challenges: 1) dimensional heterogeneity, which complicates the combination of flexibilities across different time dimensions, and 2) type heterogeneity, where diverse and irregular DER profiles hinder accurate approximations, resulting in significant flexibility loss. To resolve these challenges, this paper propose a novel preference-oriented aggregation method for reserve dispatch. For dimensional heterogeneity, we extend existing techniques by reformulating the Minkowski sum as a polytope projection problem using a matrix transformation technique. By unifying DERs in a higher-dimensional space and projecting them back into the aggregate feasible region, the proposed technique effectively aggregates dimensionally heterogeneous DERs. For type heterogeneity, we further develop a distributed aggregation-dispatch coordination framework that incorporates reserve dispatch preferences into aggregation. This framework effectively captures the critical, active aggregate flexibility prioritized in optimal reserve dispatch, thereby significantly reducing the flexibility loss when aggregating type-heterogeneous DERs. Numerical tests validate the effectiveness of our method in addressing both heterogeneities and highlight its promising potential for power systems with high reserve requirements.

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arXiv2026

Coupling-Aware Aggregation of Multi-Zone HVAC Loads under Uncertainty: A Two-level Framework

Aggregating building heating, ventilation, and air-conditioning (HVAC) loads unlocks substantial demand-side flexibility for power systems. Yet multi-zone coupling creates intricate interdependencies and uncertainty propagation, complicating the quantification of aggregate flexibility. To address this issue, this paper proposes a coupling-aware two-level aggregation framework. At the building level, tailored Gaussian elimination and coordinate transformation techniques are employed to recast the high-dimensional thermal dynamics as an equivalent lower-dimensional analytical expression. This expression streamlines the subsequent aggregator-level stage by (i) clarifying the propagation of zone-level uncertainties to the building-level interface, (ii) decoupling intra-building multi-zone coupling from inter-building aggregation, and (iii) providing full-dimensional building-level flexibility sets that enable tractable reformulation. At the aggregator level, existing geometric aggregation approaches are generalized by a newly developed matrix-transformation technique. This technique effectively constructs inner approximations between polytopes of different dimensions, producing closed-form images of high-dimensional multi-zone HVAC flexibility in power subspace. The resulting inner approximation is then recast as a customized separatable linear program that efficiently determines the optimal aggregate parameters. Case studies validate the effectiveness of our framework, highlighting its accuracy, reliability, and scalability.

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arXiv2026

Dimension-Reduced ADP for Real-Time Microgrid Operation with Massive Air-Conditioning Loads under Multiple Uncertainties

This paper proposes a dimension-reduced approximate dynamic programming (ADP) method for real-time microgrid operation with massive air-conditioning loads under multiple uncertainties. The operation problem is formulated as a multi-stage Markov decision process, and a post-decision value function is introduced to characterize the impact of current decisions on future operating costs. To address the curse of dimensionality caused by massive air-conditioning loads, a consistency-based value function projection is developed to map the high-dimensional state space at each node into a tractable aggregated state space. Based on the reduced states, piecewise linear approximation is further employed for efficient value function training. Case studies on 33-bus and 123-bus systems show that the proposed method achieves near-optimal operation performance with low computational cost and good scalability under both deterministic and stochastic conditions.

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arXiv2026

Continuous-Time Aggregation of Massive Flexible HVAC Loads Considering Uncertainty for Reserve Provision in Power System Dispatch

Heating, ventilation, and air conditioning (HVAC) loads, with their rapid response capabilities, can provide considerable intra-hour flexibility on the demand side for reserve provision in order to follow the fast variations of renewables. However, scheduling massive HVACs is challenging due to computation complexity and the uncertainty of outdoor temperature. In this paper, we first introduce a novel continuous-time (CT) aggregation model to reveal the potential intra-hour flexibility of HVACs. For accurate aggregation, a new affine transformation is designed to handle the heterogeneity in high-dimensional feasible region. Further, for reliable aggregation in practical environment, the outdoor temperature uncertainty is constructed by distributionally robust chance constrains and integrated into the aggregation model. Secondly, for the tractable calculation of the proposed CT aggregation model, a cascade of tailored reformulation techniques is proposed, including the Bernstein polynomial spline, polytope projection, and linearization transformation. Thirdly, a customized hierarchical dispatch framework is proposed via incorporating the proposed CT aggregation model into reserve provision in power system dispatch, so as to efficiently schedule massive HVACs to cope with the renewable uncertainty. Case studies verify the effectiveness and scalability of the proposed CT aggregation model in aggregation accuracy, intra-hour flexibility utilization, and uncertainty handling.

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arXiv2026

Leveraging Time-Causal State Variable Aggregation for Real-Time Schedule of Massive Air Conditioners

Air conditioner (AC) loads offer promising flexibility for active distribution networks to manage uncertainties, such as those in renewable energy generation, electricity prices, and load demand. However, real-time scheduling of ACs is challenging due to their massive temporal coupling constraints and time-causal uncertainties. To address this, a novel time-causal aggregation-based approximate dynamic programming (TCA-ADP) algorithm is proposed for efficient scheduling. The time-causality requirements for aggregating state variables are first analyzed to align with the real-time sequential decision-making process. Subsequently, an enhanced aggregation model is developed to ensure both high accuracy and adherence to time causality. The aggregation process is further reformulated as a linear program to optimize aggregation parameters and enable tractable computation. Accordingly, the TCA-ADP leverages aggregated state variables to approximate the value function as a new way, balancing computational efficiency and economy against the large value function space of massive ACs. By training the value function offline using historical data, the TCA-ADP efficiently achieves near-optimal real-time scheduling of massive ACs through parallel and closed-form disaggregation. Case studies demonstrate the effectiveness and scalability of the TCA-ADP, highlighting its aggregation accuracy, uncertainty handling, and the trade-off between economy and tractability.

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arXiv2026

Mean-Field Learning for Storage Aggregation

Distributed energy storage devices can be aggregated to provide operational flexibility for power systems. This requires representing a massive device population as a single, tractable surrogate that is computationally efficient and accurate. However, surrogate identification is challenging due to heterogeneity, nonconvexity, and high dimensionality of storage devices. To address these challenges, this paper develops a mean-field learning framework for storage aggregation. We interpret aggregation as the average behavior of a large storage population and show that, as the population grows, aggregate performance converges to a unique, convex mean-field limit, enabling tractable population-level modeling. This convexity further yields a price-responsive characterization of aggregate storage behavior and allows us to bound the mean-field approximation error. We construct a convex surrogate model with physically interpretable parameters that approximates the aggregate behavior of large storage populations and can be embedded directly into power system operations. Surrogate parameter identification is formulated as an optimization problem using historical price-response data, and we adopt a gradient-based algorithm for efficient learning. Case studies validate the theoretical findings and demonstrate the effectiveness of the proposed framework in approximation accuracy and data efficiency.

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arXiv2026

Knowledge-data fusion framework for frequency security assessment in low-inertia power systems

The integration of renewable energy via power electronics is transforming power grids into low-inertia systems, heightening the risks of frequency insecurity and widespread outages. Therefore, frequency security assessment (FSA) methods are urgently needed to ensure the reliable system operation. Recently, knowledge-data fusion models attempt to address the limitations of knowledge-driven (accuracy) and data-driven (generalization) FSA methods. However, current methods remain confined to shallow knowledge-data integration due to challenges in representing heterogeneous knowledge and establishing interactive mechanisms. Here, by classifing FSA domain knowledge into physics-guided and physics-constrained categories, we propose a guided learning-constrained network (GL-CN) framework, which deeply integrates domain knowledge across both network architecture and training process. In this framework, a data-driven model with dual input channels combining graph convolutional networks (GCN) and multilayer perceptrons (MLP) is proposed to extract both nodal and system-level power system features. Furthermore, guided learning enhances model generalization through data augmentation in pre-training utilizing physics-guided knowledge, while constrained network encodes physics-constrained knowledge into the network architecture and loss function to ensure physics-consistent and robust predictions. Validated on Yunnan Provincial Power Grid in China, our method reduces FSA time from days to seconds compared to traditional simulation, achieving 98% accuracy, robustness against 39.0% knowledge error, and generalization for 40%-60% renewable penetration. This provides a solid solution for mitigating blackouts caused by frequency insecurity and offers a generalizable paradigm for broader cross-domain problems.

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arXiv2025

Optimization and Control Technologies for Renewable-Dominated Hydrogen-Blended Integrated Gas-Electricity System: A Review

The growing coupling among electricity, gas, and hydrogen systems is driven by green hydrogen blending into existing natural gas pipelines, paving the way toward a renewable-dominated energy future. However, the integration poses significant challenges, particularly ensuring efficient and safe operation under varying hydrogen penetration and infrastructure adaptability. This paper reviews progress in optimization and control technologies for hydrogen-blended integrated gas-electricity system. First, key technologies and international demonstration projects are introduced to provide an overview of current developments. Besides, advances in gas-electricity system integration, including modeling, scheduling, planning and market design, are reviewed respectively. Then, the potential for cross-system fault propagation is highlighted, and practical methods for safety analysis and control are proposed. Finally, several possible research directions are introduced, aiming to ensure efficient renewable integration and reliable operation.

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arXiv2025

MsaMIL-Net: An End-to-End Multi-Scale Aware Multiple Instance Learning Network for Efficient Whole Slide Image Classification

Bag-based Multiple Instance Learning (MIL) approaches have emerged as the mainstream methodology for Whole Slide Image (WSI) classification. However, most existing methods adopt a segmented training strategy, which first extracts features using a pre-trained feature extractor and then aggregates these features through MIL. This segmented training approach leads to insufficient collaborative optimization between the feature extraction network and the MIL network, preventing end-to-end joint optimization and thereby limiting the overall performance of the model. Additionally, conventional methods typically extract features from all patches of fixed size, ignoring the multi-scale observation characteristics of pathologists. This not only results in significant computational resource waste when tumor regions represent a minimal proportion (as in the Camelyon16 dataset) but may also lead the model to suboptimal solutions. To address these limitations, this paper proposes an end-to-end multi-scale WSI classification framework that integrates multi-scale feature extraction with multiple instance learning. Specifically, our approach includes: (1) a semantic feature filtering module to reduce interference from non-lesion areas; (2) a multi-scale feature extraction module to capture pathological information at different levels; and (3) a multi-scale fusion MIL module for global modeling and feature integration. Through an end-to-end training strategy, we simultaneously optimize both the feature extractor and MIL network, ensuring maximum compatibility between them. Experiments were conducted on three cross-center datasets (DigestPath2019, BCNB, and UBC-OCEAN). Results demonstrate that our proposed method outperforms existing state-of-the-art approaches in terms of both accuracy (ACC) and AUC metrics.

cs.CV↗

arXiv2021

Double Deep Q-learning Based Real-Time Optimization Strategy for Microgrids

The uncertainties from distributed energy resources (DERs) bring significant challenges to the real-time operation of microgrids. In addition, due to the nonlinear constraints in the AC power flow equation and the nonlinearity of the battery storage model, etc., the optimization of the microgrid is a mixed-integer nonlinear programming (MINLP) problem. It is challenging to solve this kind of stochastic nonlinear optimization problem. To address the challenge, this paper proposes a deep reinforcement learning (DRL) based optimization strategy for the real-time operation of the microgrid. Specifically, we construct the detailed operation model for the microgrid and formulate the real-time optimization problem as a Markov Decision Process (MDP). Then, a double deep Q network (DDQN) based architecture is designed to solve the MINLP problem. The proposed approach can learn a near-optimal strategy only from the historical data. The effectiveness of the proposed algorithm is validated by the simulations on a 10-bus microgrid system and a modified IEEE 69-bus microgrid system. The numerical simulation results demonstrate that the proposed approach outperforms several existing methods.

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arXiv2020

Network-Constrained Transactive Control for Multi- Microgrids-based Distribution Networks with SOPs

Different from most transactive control studies only focusing on economic aspect, this paper develops a novel network-constrained transactive control (NTC) framework that can address both economic and secure issues for a multi-microgrids-based distribution network considering uncertainties. In particular, we innovatively integrate a transactive energy market with the novel power-electronics device (i.e., soft open point) based AC power flow regulation technique to improve economic benefits for individual microgrids and meanwhile ensure the security of the entire distribution network. In this framework, a dynamic two-timescale NTC model consisting of slow-timescale pre-scheduling and real-time scheduling stages is formulated to work against multiple system uncertainties. Moreover, the original bilevel game problems are transformed into single-level mixed-integer second-order cone programming problems through KKT conditions, duality, linearization and relaxation techniques to avoid iterations of transitional methods, so as to improve computational efficiency. Finally, numerical simulations on a modified 33-bus test system with 3 MGs verify the effectiveness of the proposed framework.

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