arXiv subjects

Ruike Lyu

Publications and source records attributed to Ruike Lyu.

14 recordsLinked to original sources

arXiv2026

Real-Time Operation Strategy of Virtual Power Plants With Optimal Power Disaggregation Among Heterogeneous Resources

The virtual power plant (VPP) can aggregate flexible resources on the demand side to provide frequency regulation for the grid, helping address the supply-demand balance challenges. When deploying regulation, the VPP disaggregates the requested power adjustment in real time among its internal heterogeneous resources. Achieving optimal power disaggregation in this process is challenging due to the temporal coupling characteristics of the resources, the uncertain regulation signals, and the requirement for fast response. Therefore, existing research relies on heuristic methods, such as proportional disaggregation, and fails to leverage the heterogeneity of multiple resources. Here, we propose an optimal operation strategy for VPPs to provide regulation, exploiting the complementary characteristics of heterogeneous resources by prioritizing the use of low-cost resources while considering temporal coupling. To reduce the computational overhead of online deployment, we further propose a fast disaggregation algorithm to eliminate the reliance on optimisation solvers. We conducted case studies on the operation of a VPP composed of resources including thermostatically controlled loads and industrial production processes. The results verified the reduced operation cost and increased profit of the VPP under the proposed strategy, with only milliseconds of online computation time. We believe that our work can help better exploit demand-side flexibility.

eess.SY↗

arXiv2026

Production Scheduling Identification: An Inverse Optimization Approach for Industrial Load Modeling Using Smart Meter Data

To cost-effectively manage the supply-demand balance of the power system, the flexibility of industrial users could be harnessed through demand-side response. To minimize the negative impact on the production of industrial users during demand-side response, general-purpose models such as the state-task network (STN) are widely used to model the energy-consuming constraints of industrial production processes. However, the required model parameters cannot be set because the required data are privately owned by industrial users and are not directly available, hindering the accurate modeling of industrial loads. In this paper, we propose production scheduling identification (PSI), an inverse-optimization-based approach for industrial load modeling under incomplete information. In PSI, industrial users' smart meter data are used to identify production scheduling parameters, thus addressing the problem of accurate load modeling when private data are unavailable. We implemented PSI with a modified STN and proposed a practical algorithm to obtain an effective solution. Numerical tests showed that PSI can identify the model parameters of a steel powder plant and a cement plant with acceptable accuracy, using only 21 days of hourly smart meter data. Compared with accurate models established with direct access to private data, the modeling error does not exceed 8.5% and 5.2%, respectively.

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arXiv2026

Efficient Scheduling of Discrete Industrial Processes Through Continuous Modeling

The resource-task network (RTN) model has been widely applied to represent the technical constraints of complex industrial processes (IPs) such as steel-making, providing the basis for industrial demand response. However, the legacy RTN model contains numerous binary variables and applies different formulations for non-flexible and flexible processes, restricting its computational efficiency and applicability. To systematically improve the computational performance of IP models, we propose continuous RTN model (cRTN), a novel modeling approach that uses continuous variables to represent production tasks and progresses, which are then integrated into unified as well as computationally favorable formulations for the technical constraints in discrete IPs, including resource balance, task execution, waiting time limits, and production targets. Compared to the legacy models, cRTN features fewer binary variables, shorter solving time, and better scalability while maintaining the same accuracy. Numerical tests based on a steel plant demonstrate that cRTN is in typical cases 10 times faster than legacy models and remains tractable with increasing batch sizes, which in legacy models leads to larger problem scales and infeasible solving time. cRTN also achieves a reduction in energy costs by resolving the issue of rounding errors reported in legacy models.

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arXiv2026

LSTN: A Linear Model of Industrial Production Process for Demand Response

Industrial production modeling provides operational constraints for industrial users participating in demand response (DR) programs. Conventional modeling of the production process introduces binary variables to model the discrete operating points of industrial equipment, which can be computationally infeasible in large-scale DR applications. To reasonably model industrial users' operational constraints while balancing computational complexity and modeling accuracy, we developed a linear model of the industrial production process for evaluating DR applications. Numerical results verify the accuracy of the proposed model and its great improvement in computational efficiency over competing approaches.

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arXiv2026

Approximating Energy-Regulation Feasible Region of Virtual Power Plants: A Data-driven Inverse Optimization Approach

System operators will probably allow virtual power plants (VPPs) to submit their feasible region (FR) for market clearing and dispatch. A VPP needs to determine its FR to submit as a whole based on the individual operation model of its internal distributed energy resources (DERs), which is an FR aggregation problem. Existing FR aggregation approaches rely on analytical methods, which have issues with generality and adaptability. In this paper, we propose a data-driven approach to approximate the energy-regulation FR of VPPs. It adopts the virtual battery model to approximate the aggregate FR of a VPP and determines the model parameters through inverse optimization based on generated multi-scenario operation data using the original operation model. Numerical tests verified the accuracy of the proposed method. We believe that our work helps to better leverage the flexibility of DERs.

math.OC↗

arXiv2026

Integrating Fast-response Capability into Virtual Power Plant Operation for Ancillary Services

Virtual power plants (VPPs) can aggregate distributed energy resources (DERs) to provide ancillary services for power systems, creating new profit opportunities. Ancillary services such as secondary frequency regulation require providers to have sufficient response capability to follow rapidly changing control commands. If overlooking the response requirement, the VPP will not be able to accurately measure its regulation capability, reducing its earnings in performance-based markets or risking disqualification. This paper integrates the requirement for fast-response capability into the operational framework of VPPs providing ancillary services. We leverage historical control commands to formulate chance constraints in the bidding model, mandating that the VPP's fast-response capability meets the requirement of ancillary services with a specified probability. Case studies verify that considering fast-response capabilities can enhance VPP operation.

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arXiv2026

Electric Vehicle Charging Right Trading: Concept, Mechanism, and Methodology

With the increasing penetration of electric vehicles (EVs), uncoordinated EV charging and the resulting chaos, disorder, and long waiting times at EV charging stations (EVCSs) will no longer be tolerable. An EV charging right (CR) is the right to reserve a predefined charging service. By purchasing CRs, EVs can reduce their charging waiting time, and the price of CRs can guide EVs toward optimized charging behaviors. In this article, we define CR, propose the CR trading mechanism (CRM), and analyze the effect of CRM on reducing waiting times and mitigating congestion in EV charging. In the proposed CRM, EVs can purchase CRs in advance, and the CRs are used to estimate the waiting time and update the price of charging. Queue theory is utilized in the waiting time estimation, in which the impact of disclosing queue states at EVCSs is considered for the first time. The simulation results verify the accuracy of the waiting time estimation and the effect of the proposed mechanism.

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arXiv2026

Co-optimizing Bidding and Power Allocation of an EV Aggregator Providing Real-time Frequency Regulation Service

The rapidly expanding scale of electric vehicle (EV) fleets and continuously decreasing battery costs are making vehicle-to-grid services a reality. In this paper, we study the interaction between the problems of an EV aggregator's bidding in the regulation market and power allocation (i.e., determining the (dis)charging powers of the EVs in regulation deployment). Although the two problems are coupled, they are often regarded as decoupled and optimized separately for complexity issues. However, failing to consider the coupling of bidding and power allocation can lead to a decline in the profit of the EV aggregator (EVA). In this paper, we propose a framework for co-optimizing EVA bidding and power allocation in the regulation market. The bidding model is formulated as a stochastic programming problem with embedded power allocation in discretized regulation signal scenarios. To meet the solution time requirement for regulation deployment, we further propose a power allocation model that can be solved online. It utilizes the Lagrange multipliers from the bidding problem to ensure that the allocation results correspond to the optimal solution of the bidding problem. The effect of the proposed framework on improving EVA profits and reducing degradation costs is verified in the case study.

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arXiv2026

Data-Driven Dimension Reduction for Industrial Load Modeling Using Inverse Optimization

The intricate mixed-integer constraints in industrial load models not only pose challenges for their direct integration into economic dispatch or market clearing processes but also render current analytical dimension-reduction methods ineffective. We propose a novel data-driven dimension-reduction approach for industrial load modeling, which uses the optimal energy usage data from industrial loads to train a dimension-reduced model that best fits the original constraints. Our approach, implemented by the adjustable load fleet model, outperformed analytical methods across three industrial load datasets.

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arXiv2026

Industrial Load Modeling and Optimization for Market-Based Interaction with Power Systems

Industrial loads account for over 60% of China's electricity consumption and can provide substantial flexibility for renewable-dominated power systems. Their market participation remains limited by complex production constraints, unavailable equipment parameters, and the computational scale of resource coordination. This dissertation addresses these barriers from the perspective of a load aggregator. It reformulates the State Task Network and Resource Task Network as the Linearized State Task Network and continuous Resource Task Network. In a standard grid-interaction case, the reformulation reduces solution time from 24 hours to 30 minutes and supports coordinated optimization of 2,000 industrial users. Production Scheduling Identification combines process knowledge and cost-minimizing behavior with hourly smart-meter data; using 21 training days, it achieves load-model errors of 5.2% and 8.5% for cement and steel-powder cases, compared with 13.4%-19.2% for machine-learning baselines. Data-Driven Dimension Reduction yields errors of 3.6%-10.3% across three industrial cases and replaces 10,208 integer variables with 24-48 continuous variables in the steelmaking case. For market interaction, a joint bidding and power-disaggregation framework uses shadow prices to allocate real-time commands among tens of thousands of resources through millisecond-level arithmetic. In a representative comparison, it reduces interaction costs by 40% while retaining the solution quality of the joint optimization model. These contributions keep detailed industrial process models available for executable scheduling while providing aggregators with compact models for flexibility assessment, portfolio optimization, and electricity-market participation.

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arXiv2026

Do We Still Need Demand Flexibility as Batteries Become Cheaper? A Levelized Cost Perspective

Energy storage and flexible loads both help balance power systems with high shares of variable renewables, but they do so differently. A battery moves electricity from one period to another. A factory or data center instead moves production or computing activity, while meeting demand for its product or service. As batteries become cheaper, it is natural to ask whether demand flexibility is still needed. Many assessments treat that controllable load as already available and focus on enabling or participation costs. For a large load that normally runs near full capacity, however, shifting electricity use can require extra equipment, delay sales, tie up working capital, and disrupt the process. Here, we compare energy storage technologies with industrial and data-center load shifting on a levelized cost basis, measuring the incremental cost of flexible relative to baseline operation. We find that energy storage becomes more expensive per shifted kilowatt-hour as the flexibility period lengthens and assets are cycled less often. Load shifting does not share that rise in capacity cost under a fixed avoided-hour share, but new productive capacity can still be too expensive to justify. Under representative central technology costs, batteries compete well for short-cycle shifting but are more than 80 times more expensive than load shifting of aluminum smelting when providing seasonal flexibility. The answer therefore depends on the timescale, the load, and whether the needed capacity is new or already sunk.

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arXiv2025

Industrial overcapacity can enable seasonal flexibility in electricity use

In many countries, declining demand in energy-intensive industries (EIIs) such as cement, steel, and aluminum is leading to industrial overcapacity. Although industrial overcapacity is traditionally envisioned as problematic and resource-wasteful, it could unlock EIIs' flexibility in electricity use. Here, using China's aluminum smelting industry as a case study, we evaluate the system-level cost-benefit of retaining EII overcapacity for flexible electricity use in decarbonized energy systems. We find that overcapacity can enable aluminum smelters to adopt a seasonal operation paradigm, ceasing production during winter load peaks that are exacerbated by heating electrification and renewable seasonality. This seasonal operation paradigm could reduce the investment and operational costs of China's decarbonized electricity system by 23-32 billion CNY/year (11-15% of the aluminum smelting industry's product value), sufficient to offset the increased smelter maintenance and product storage costs associated with overcapacity. It may also create labor complementarities between the aluminum and thermal power sectors.

physics.soc-ph↗

arXiv2025

Translation-Symmetric Market: Enabling Incentive Compatibility For DER Aggregation

Virtual power plants (VPPs) are important for coordinating the rapidly growing portfolios of distributed energy resources (DERs) and enabling them to deliver multiple services to higher-level electricity markets. However, profit allocation procedures for VPP participants become increasingly difficult to design in an incentive-compatible manner, owing to the increased market power of DERs within each VPP relative to their direct participation in wholesale markets. In this paper, we introduce translation symmetry in electricity markets and apply it to VPP aggregation of DERs for market participation to design an incentive-compatible profit allocation method. Under the stated assumptions, we prove that this translation symmetry induces an inductive property: once incentive compatibility holds at an upper level, it propagates to the internal settlements between the VPP and its constituent DERs, thereby supporting incentive compatibility throughout the hierarchy. We further show that service prices are invariant across levels, which helps preserve competitive conditions and enables transparent value assessment. Theoretical analysis and case studies illustrate how this translation-symmetry-based approach can enable incentive-compatible profit allocation when aggregating DERs to provide multiple services.

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arXiv2025

Learning for Feasible Region on Coal Mine Virtual Power Plants with Imperfect Information

The feasible region assessment (FRA) in industrial virtual power plants (VPPs) is driven by the need to activate large-scale latent industrial loads for demand response, making it essential to aggregate these flexible resources for peak regulation. However, the large number of devices and the need for privacy preservation in coal mines pose challenges to accurately aggregating these resources into a cohesive coal mine VPP. In this paper, we propose an efficient and reliable data-driven approach for FRA in the coal mine VPP that can manage incomplete information. Our data-driven FRA algorithm approximates equipment and FRA parameters based on historical energy dispatch data, effectively addressing the challenges of imperfect information. Simulation results illustrate that our method approximates the accurate feasible operational boundaries under dynamic and imperfect information conditions.

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