arXiv subjects

Enrique Baeyens

Publications and source records attributed to Enrique Baeyens.

13 recordsLinked to original sources

arXiv2026

Graph-Induced Tensor Liftings for Networked SEIR Models: Dimensional Reduction and Residual Analysis

Networked SEIR models describe epidemic spread within and between interacting subpopulations through contact-supported nonlinear transmission. Standard polynomial liftings based on complete ordered Kronecker tensors yield linear higher-dimensional representations, but their dimensions grow rapidly because they retain interactions absent from the transmission graph. This paper develops a graph-induced tensor lifting whose observables are selected from the effective transmission support. An exact edge-based quadratic representation separates linear compartmental transitions from nonlinear infection terms. A homogeneous hierarchy is then constructed recursively. The quadratic transmission field generates the next degree. The linear compartmental field saturates the resulting dictionary within that degree. The first edge-closure dynamics are linear up to an explicit cubic truncation residual, and higher-order truncations contain only next-degree terms. The first lifted dimension scales with the numbers of subpopulations and effective transmission channels. At fixed order, graph-induced dictionaries grow linearly with network size under uniformly bounded local connectivity, whereas complete polynomial liftings retain order-dependent polynomial growth. Uniform first edge-closure residual bounds depend on the transmission rate and the maximum weighted incoming transmission intensity. Numerical illustrations compare equal intensity per active channel with equal total incoming intensity. They confirm that dictionary dimensions depend only on graph support, whereas residual trajectories also reflect weight accumulation, weight distribution, and nonlinear propagation. These results provide a structured basis for reduced modeling and subsequent model-specific analysis and control.

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arXiv2025

A Comparative Analysis of Electricity Consumption Flexibility in Different Industrial Plant Configurations

The increasing integration of renewable energy sources into power systems is intensifying the demand for greater flexibility among industrial electricity consumers. However, operational constraints, production requirements, and market dynamics pose significant challenges to achieving optimal flexibility. This paper presents an enhanced mixed integer linear programming (MILP) model that directly optimizes electricity consumption flexibility in manufacturing plants. Unlike previous approaches, the proposed model determines optimal transactions with both day-ahead and intraday continuous electricity markets, while ensuring production continuity and adhering to plant-specific operational constraints. The methodology is validated through annual simulations of two real world industrial configurations, cement manufacturing and steel production, using 2023 market data. Comparative results highlight that the steel plant achieved average electricity cost savings through flexibility of 0.41 euro/MWh, whereas the cement plant achieved 0.24 euro/MWh, reflecting differences in storage capacities, production rates, and operational flexibility. A comprehensive sensitivity analysis further identifies key parameters affecting flexibility potential, such as the production to demand ratio, storage capacity, and minimum operation periods. The findings offer valuable insights for industrial operators aiming to reduce energy costs, enhance operational flexibility, and support the decarbonization of electricity systems.

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arXiv2025

A Robust Optimization Framework for Flexible Industrial Energy Scheduling: Application to a Cement Plant with Market Participation

This paper presents a scenario based robust optimization framework for short term energy scheduling in electricity intensive industrial plants, explicitly addressing uncertainty in planning decisions. The model is formulated as a two-stage Mixed Integer Linear Program (MILP) and integrates a hybrid scenario generation method capable of representing uncertain inputs such as electricity prices, renewable generation, and internal demand. A convex objective function combining expected and worst case operational costs allows for tunable risk aversion, enabling planners to balance economic performance and robustness. The resulting schedule ensures feasibility across all scenarios and supports coordinated use of industrial flexibility assets, including battery energy storage and shiftable production. To isolate the effects of market volatility, the framework is applied to a real world cement manufacturing case study considering only day-ahead electricity price uncertainty, with all other inputs treated deterministically. Results show improved resilience to forecast deviations, reduced cost variability, and more consistent operations. The proposed method offers a scalable and risk-aware approach for industrial flexibility planning under uncertainty.

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arXiv2025

Enhancing Industrial Flexibility and Market Participation in Cement Manufacturing Through Optimized Production Scheduling

The growing share of variable renewable energy (VRE) sources in power systems is increasing the need for short term operational flexibility, particularly from large industrial electricity consumers. This study proposes a practical, two stage optimization framework to unlock this flexibility in cement manufacturing and support participation in electricity balancing markets. In Stage 1, a mixed integer linear programming (MILP) model minimizes electricity procurement costs by optimally scheduling the raw milling subsystem. In Stage 2, a flexibility assessment model evaluates profitable deviations, targeting participation in Spain manual Frequency Restoration Reserve (mFRR) market. A real world case study in a Spanish cement plant (including PV and battery storage) shows that flexibility services can yield monthly revenues of up to 800 EUR and paybacks as short as six years. This framework offers a replicable pathway for industrial flexibility in energy intensive sectors.

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arXiv2024

Aggregated demand flexibility prediction of residential thermostatically controlled loads and participation in electricity balance markets

The aggregate demand flexibility of a set of thermostatically controlled residential loads (TCLs) can be represented by a virtual battery (VB) in order to manage their participation in the electricity markets. For this purpose, it is necessary to know in advance and with a high level of reliability the maximum power that can be supplied by the aggregation of TCLs. A probability function of the power that can be supplied by a VB is introduced. This probability function is used to predict the demand flexibility using a new experimental probabilistic method based on a combination of Monte Carlo simulation and extremum search by bisection algorithm (MC&ESB). As a result, the maximum flexibility power that a VB can provide with a certain guaranteed probability is obtained. The performance and validity of the proposed method are demonstrated in three different case studies where a VB bids its aggregate power in the Spanish electricity balancing markets (SEBM).

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arXiv2024

AC/DC optimal power flow and techno-economic assessment for hybrid microgrids: TIGON CEDER demonstrator

In the recent years, the interest in electric direct current (DC) technologies (such as converters, batteries, electric vehicles, etc.) is increasing due to its potential on energy efficiency and sustainability. However, the vast majority of electric systems and networks are based on alternating current (AC), as they also have certain advantages regarding cost-effective transport and robustness. In this paper, an AC/DC optimal power flow method for hybrid microgrids and several key performance indicators (KPIs) for its techno-economic assessment are presented. The combination of both calculations allows users to clearly determine the viability of their hybrid microgrids. AC/DC networks have been modelled considering their most common elements. For the power flow method, a polynomial optimisation is formulated considering four different objective functions: the minimisation of energy losses, voltage deviation and operational costs, and also the maximisation of the microgrid generation. The power flow method and the techno-economic analysis have been implemented in Python and validated in the Centro de Desarrollo de Energías Renovables (CEDER) demonstrator for TIGON. The results show that the calculated power flow variables and the ones measured at CEDER are practically the same. In addition, the KPIs have been obtained and compared for four operating scenarios: baseline, no battery, battery flexibility and virtual battery (VB) flexibility. The last one result in the most profitable option.

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arXiv2022

Optimal Storage and Solar Capacity of a Residential Household under Net Metering and Time-of-Use Pricing

Incentive programs and ongoing reduction in costs are driving joint installation of solar PV panels and storage systems in residential households. There is a need for optimal investment decisions to reduce the electricity consumption costs of the households further. In this paper, we first develop analytical expression of storage investment decision and then of solar investment decision for a household which is under net metering billing mechanism with time of use pricing condition. Using real data of a residential household in Austin, TX, USA, we study how the investment decisions would provide benefit for a period of one year. Results show significant profit when using storage devices and solar panels optimally for the system. It is important to note that though our approach can help significantly to take investment decisions, the solution will still be sub-optimal for somebody who needs optimal investment jointly on both storage and solar systems.

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arXiv2021

Flexibility management with virtual batteries of thermostatically controlled loads: real-time control system and potential in Spain

Load flexibility management is a promising approach to face the problem of balancing generation and demand in electrical grids. This problem is becoming increasingly difficult due to the variability of renewable energies. Thermostatically controlled loads can be aggregated and managed by a virtual battery, and they provide a cost-effective and efficient alternative to physical storage systems to mitigate the inherent variability of renewable energy sources. But virtual batteries require of an accurate control system being capable of tracking frequency regulation signals with minimal error. A real-time control system allowing virtual batteries to accurately track frequency or power signals is developed. The performance of this controller is validated for a virtual battery composed of 1,000 thermostatically controlled loads. Using virtual batteries equipped with the developed controller, a study focused on residential thermostatically controlled loads in Spain is performed. The results of the study quantify the potential of this technology in a country with different climate areas and provides insight about the feasibility of virtual batteries as enablers of electrical systems with high levels of penetration of renewable energy sources.

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arXiv2020

Incentive Design in a Distributed Problem with Strategic Agents

In this paper, we consider a general distributed system with multiple agents who select and then implement actions in the system. The system has an operator with a centralized objective. The agents, on the other hand, are selfinterested and strategic in the sense that each agent optimizes its own individual objective. The operator aims to mitigate this misalignment by designing an incentive scheme for the agents. The problem is difficult due to the cost functions of the agents being coupled, the objective of the operator not being social welfare, and the operator having no direct control over actions being implemented by the agents. This problem has been studied in many fields, particularly in mechanism design and cost allocation. However, mechanism design typically assumes that the operator has knowledge of the cost functions of the agents and the actions being implemented by the operator. On the other hand, cost allocation classically assumes that agents do not anticipate the effect of their actions on the incentive that they obtain. We remove these assumptions and present an incentive rule for this setup by bridging the gap between mechanism design and classical cost allocation. We analyze whether the proposed design satisfies various desirable properties such as social optimality, budget balance, participation constraint, and so on. We also analyze which of these properties can be satisfied if the assumptions of cost functions of the agents being private and the agents being anticipatory are relaxed.

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arXiv2019

A Minimal Incentive-based Demand Response Program With Self Reported Baseline Mechanism

In this paper, we propose a novel incentive based Demand Response (DR) program with a self reported baseline mechanism. The System Operator (SO) managing the DR program recruits consumers or aggregators of DR resources. The recruited consumers are required to only report their baseline, which is the minimal information necessary for any DR program. During a DR event, a set of consumers, from this pool of recruited consumers, are randomly selected. The consumers are selected such that the required load reduction is delivered. The selected consumers, who reduce their load, are rewarded for their services and other recruited consumers, who deviate from their reported baseline, are penalized. The randomization in selection and penalty ensure that the baseline inflation is controlled. We also justify that the selection probability can be simultaneously used to control SO's cost. This allows the SO to design the mechanism such that its cost is almost optimal when there are no recruitment costs or at least significantly reduced otherwise. Finally, we also show that the proposed method of self-reported baseline outperforms other baseline estimation methods commonly used in practice.

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arXiv2019

Control Strategy Design for Power Quality Management in Active Distribution Networks

Uncertainty and variability of renewable energy sources present an imperative technical challenge for Electrical Distribution Utilities. Power Quality indices represent quality of energy delivered and reliability. In this work, a control strategy design for real-time PQ management in active distribution systems is presented. The present work addresses management of voltage fluctuations induced by the variability in renewable generation. A zero energy reserve approach to tackle fluctuations of renewable energy generators is developed. The power consumption of flexible loads is modulated to reduce the technical losses and peak load of the feeder in this reactive power control strategy. A Volt VAR control strategy formulation, using the capability of smart inverters to provide dynamic reactive power is presented. Unbalanced power flow, different load profiles and flexible loads as virtual energy storages are used to improve voltage profile and reduce technical losses while maintaining system reliability. IEEE 13 bus distribution system is used for control strategy design validation. Comparative results indicate reduction in the system technical losses and the stress on automatic voltage regulators. The ease of design of control strategy indicate potential real-life application.

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arXiv2018

Analysis of Solar Energy Aggregation under Various Billing Mechanisms

Ongoing reductions in the cost of solar photovoltaic (PV) systems are driving their increased installations by residential households. Various incentive programs such as feed-in tariff, net metering, net purchase and sale that allow the prosumers to sell their generated electricity to the grid are also powering this trend. In this paper, we investigate sharing of PV systems among a community of households, who can also benefit further by pooling their production. Using cooperative game theory, we find conditions under which such sharing decreases their net total cost. We also develop allocation rules such that the joint net electricity consumption cost is allocated to the participants. These cost allocations are based on the cost causation principle. The allocations also satisfy the standalone cost principle and promote PV solar aggregation. We also perform a comparative analytical study on the benefit of sharing under the mechanisms favorable for sharing, namely net metering, and net purchase and sale. The results are illustrated in a case study using real consumption data from a residential community in Austin, Texas.

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arXiv2017

Sharing Storage in a Smart Grid: A Coalitional Game Approach

Sharing economy is a transformative socio-economic phenomenon built around the idea of sharing underused resources and services, e.g. transportation and housing, thereby reducing costs and extracting value. Anticipating continued reduction in the cost of electricity storage, we look into the potential opportunity in electrical power system where consumers share storage with each other. We consider two different scenarios. In the first scenario, consumers are assumed to already have individual storage devices and they explore cooperation to minimize the realized electricity consumption cost. In the second scenario, a group of consumers is interested to invest in joint storage capacity and operate it cooperatively. The resulting system problems are modeled using cooperative game theory. In both cases, the cooperative games are shown to have non-empty cores and we develop efficient cost allocations in the core with analytical expressions. Thus, sharing of storage in cooperative manner is shown to be very effective for the electric power system.

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