arXiv subjects

Jingguan Liu

Publications and source records attributed to Jingguan Liu.

9 recordsLinked to original sources

arXiv2026

Cost-Informed Learning for Aggregating Building HVAC Flexibility

This paper develops a cost-informed aggregation framework that learns an aggregate flexibility set of building heating, ventilation, and air-conditioning (HVAC) loads to minimize the aggregator's dispatch cost. Existing aggregation methods mainly use volume-oriented objectives and treat flexibility aggregation and downstream utilization as separate stages. Consequently, the resulting aggregate set may fail to preserve the flexibility most valuable for reducing downstream dispatch costs. To address this limitation, we represent aggregate HVAC flexibility using a parameterized storage-form surrogate and jointly learn the surrogate parameters and the inner-approximation objective from downstream dispatch-cost feedback. This cost-informed feedback allocates the surrogate's limited representation capacity to cost-relevant regions of the aggregate flexibility set. To account for electricity-price uncertainty, we formulate a distributionally robust conditional value-at-risk (DR-CVaR) downstream problem that captures both distributional ambiguity and tail risk. For efficient learning, we reformulate the DR-CVaR problem as a convex second-order cone program. We estimate cost gradients using randomized smoothing and a score-function estimator, avoiding differentiation through large-scale building-level optimization problems. Case studies using NYISO price data show that the proposed framework reduces dispatch costs relative to a volume-oriented aggregation benchmark and that suitable risk and ambiguity settings can lower the out-of-sample CVaR of dispatch costs.

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arXiv2026

Minimizing Bid Cost Recovery for Energy Storage with Uniform Pricing

We study in-market uniform pricing and out-of-market bid cost recovery (BCR) payments in rolling-window dispatch for real-time power system operations with energy storage resources (ESRs). Due to intertemporal state-of-charge (SOC) constraints, ESR operations are temporally coupled, and existing in-market locational marginal pricing (LMP) may fail to compensate ESR's intertemporal opportunity costs, thereby triggering out-of-market BCR payments. We show that positive BCR is unavoidable when dispatched generators or ESRs have supply-side bids higher than the demand-side bid. We further identify an intertemporal coupling indicator associated with binding SOC constraints and demonstrate empirically that positive BCR arises only when this indicator is active, revealing that BCR is fundamentally driven by intertemporal coupling. In the simulation, we compare a BCR-minimizing uniform pricing scheme (UP-BCR) with existing real-time pricing methods under forecast uncertainty and show that UP-BCR substantially reduces BCR and demand payments relative to LMP while maintaining zero merchandising surplus in a copper-plate model.

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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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