arXiv subjects

Alessandra Parisio

Publications and source records attributed to Alessandra Parisio.

8 recordsLinked to original sources

arXiv2026

Towards Network-Aware Operation of Integrated Energy Systems: A Comprehensive Review

Integrated Energy Systems (IES) are systems of interconnected electricity, gas, heating, and cooling networks, where the carriers interact and depend on one another. Beyond these core vectors, IES may also incorporate additional infrastructures, such as hydrogen, transportation and water networks, whenever sector coupling or cross-vector exchanges are relevant. Although modern cities already function as multi-energy systems, these networks are still planned and operated in isolation, which leads to inefficiencies and unused flexibility. As distributed energy resources (DERs) grow, local coupling among electricity, heating, and gas networks becomes stronger, so coordinated operation across carriers and infrastructures is essential. IES can improve efficiency, flexibility, and renewable integration, yet operation is challenging because of complex interdependencies, non-convex behaviors, and multi-scale dynamics of the energy networks. A key point that the literature often overlooks is the explicit role of network constraints and topology, which shape feasible operating regions, affect scalability, and determine how uncertainty and formal guarantees can be addressed. This review provides a first comprehensive analysis of network-aware modeling, optimization, and control methods for IES. We identify methodological limitations related to tractability, feasibility guarantees, and scalability. Building on these insights, we outline research directions that include distributed optimization with theoretical guarantees and control approaches informed by operational data. The review offers a foundation for scalable, network-aware operational frameworks for future low-carbon energy systems.

eess.SY↗

arXiv2025

A Hybrid Sequential Convex Programming Framework for Unbalanced Three-Phase AC OPF

This paper presents a hybrid Sequential Convex Programming (SCP) framework for solving the unbalanced three-phase AC Optimal Power Flow (OPF) problem. The method combines a fixed McCormick outer approximation of bilinear voltage-current terms, first-order Taylor linearisations, and an adaptive trust-region constraint to preserve feasibility and promote convergence. The resulting formulation remains convex at each iteration and ensures convergence to a stationary point that satisfies the first-order Karush-Kuhn-Tucker (KKT) conditions of the nonlinear OPF. Case studies on standard IEEE feeders and a real low-voltage (LV) network in Cyprus demonstrate high numerical accuracy with optimality gap below 0.1% and up to 2x faster runtimes compared to IPOPT. These results confirm that the method is accurate and computationally efficient for large-scale unbalanced distribution networks.

eess.SY↗

arXiv2025

Frequency-Varying Optimization: A Control Framework for New Dynamic Frequency Response Services

To address the variability of renewable generation, initiatives have been launched globally to provide faster and more effective frequency responses. In the UK, the National Energy System Operator (NESO) has introduced a suite of three new dynamic services, where aggregation of assets is expected to play a key role. For an Aggregated Response Unit (ARU), the required level of frequency response varies with grid frequency, resulting in a frequency-varying equality constraint that assets should meet collectively. We show that the optimal coordination of an ARU constitutes a Frequency-Varying Optimization (FVO) problem, in which the optimal trajectory for each asset evolves dynamically. To facilitate online optimization, we reformulate the FVO problem into Tracking of the Optimal Trajectory (TOT) problems, with algorithms proposed for two scenarios: one where the asset dynamics are negligible, and another where they must be accounted for. Under reasonable conditions, the ARU converges to the optimal trajectory within a fixed time, and within the maximum delivery time requested by NESO. The proposed framework can be readily distributed to coordinate a large number of assets. Numerical results verify the effectiveness and scalability of the proposed control framework.

eess.SY↗

arXiv2024

A New Definition of Demand Response in the Distributed Energy Resource Era

Demand response is a concept that has been around since the very first electric power systems. However, we have seen an explosion of research on demand response and demand-side technologies in the past 30 years, coinciding with the shift towards liberalized/deregulated electricity markets and efforts to decarbonize the power sector. Now we are also seeing a shift towards more distributed/decentralized electric systems; we have entered the era of "distributed energy resources," which require new grid management, operational, and control strategies. Given this paradigm shift, we argue that the concept of demand response needs to be revisited, and more carefully/consistently defined to enable us to better utilize this massive resource for economic, technical, environmental, and societal aims. In this paper, we survey existing demand response definitions, highlight their shortcomings, propose a new definition, and describe how this new definition enables us to more effectively harness the value of demand response in modern power systems. We conclude with a demand response research agenda informed by a discussion of demand response barriers and enablers.

eess.SY↗

arXiv2024

Distributed Feedback-Feedforward Algorithms for Time-Varying Resource Allocation

This paper studies distributed Time-Varying Resource Allocation (TVRA) where the local cost functions, global equality constraints, and Local Feasibility Constraints (LFCs) vary with time. Algorithms that mimic the structure of feedback-feedforward control systems are proposed. Feedback and feedforward laws are generated using local estimates from a distributed estimator, while a distributed controller enforces the stationarity condition within a fixed time and updates the candidate solution accordingly. To handle the LFCs, feedback laws based on projection and feedforward laws that switch between different modes are introduced as an initialization-free alternative to the barrier-based methods used in most related works. Our projection-based method guarantees that, for any infeasible initial value, the state trajectory enters the locally feasible set within a fixed time and remains within it thereafter, and that the set is forward invariant if the initial value is locally feasible. Convergence analyses are conducted under mild assumptions. For cases without LFCs, the proposed algorithm converges to the optimal trajectory within a fixed time. For cases with LFCs, the proposed algorithm is globally asymptotically stable at the optimal trajectory while exhibiting fixed-time convergence between consecutive switching instants. Numerical examples and a power system application verify their effectiveness.

eess.SY↗

arXiv2023

Deep Convolutional Neural Networks for Short-Term Multi-Energy Demand Prediction of Integrated Energy Systems

Forecasting power consumptions of integrated electrical, heat or gas network systems is essential in order to operate more efficiently the whole energy network. Multi-energy systems are increasingly seen as a key component of future energy systems, and a valuable source of flexibility, which can significantly contribute to a cleaner and more sustainable whole energy system. Therefore, there is a stringent need for developing novel and performant models for forecasting multi-energy demand of integrated energy systems, which to account for the different types of interacting energy vectors and of the coupling between them. Previous efforts in demand forecasting focused mainly on the single electrical power consumption or, more recently, on the single heat or gas power consumptions. In order to address this gap, in this paper six novel prediction models based on Convolutional Neural Networks (CNNs) are developed, for either individual or joint prediction of multi-energy power consumptions: the single input/single output CNN model with determining the optimum number of epochs (CNN_1), the multiple input/single output CNN model (CNN_2), the single input/ single output CNN model with training/validation/testing datasets (CNN_3), the joint prediction CNN model (CNN_4), the multiple-building input/output CNN model (CNN_5) and the federated learning CNN model (CNN_6). All six novel CNN models are applied in a comprehensive manner on a novel integrated electrical, heat and gas network system, which only recently has started to be used for forecasting. The forecast horizon is short-term (next half an hour) and all the predictions results are evaluated in terms of the Signal to Noise Ratio (SNR) and the Normalized Root Mean Square Error (NRMSE), while the Mean Absolute Percentage Error (MAPE) is used for comparison purposes with other existent results from literature.

cs.LG↗

arXiv2023

Data-Driven Load-Current Sharing Control for Multi-Stack Fuel Cell System with Circulating Current Mitigation

The global trend toward renewable power generation has drawn great attention to hydrogen Fuel Cells (FCs), which have a wide variety of applications, from utility power stations to laptops. The Multi-stack Fuel Cell System (MFCS), which is an assembly of FC stacks, can be a remedy for obstacles in high-power applications. However, the output voltage of FC stacks varies dramatically under variable load conditions; hence, in order for MFCS to be efficiently operated and guarantee an appropriate load-current sharing among the FC stacks, advanced converter controllers for power conditioning need to be designed. An accurate circuit model is essential for controller design, which accounts for the fact that the parameters of some converter components may change due to aging and repetitive stress in long-term operations. Existing control frameworks and parametric and non-parametric system identification techniques do not consider the aforementioned challenges. Thus, this paper investigates the potential of a data-driven method that, without system identification, directly implements control on paralleled converters using raw data. Based on pre-collected input/output trajectories, a non-parametric representation of the overall circuit is produced for implementing predictive control. While approaching equal current sharing within the MFCS, the proposed method considers the minimization of load-following error and mitigation of circulating current between the converters. Simulation results verify the effectiveness of the proposed method.

eess.SY↗

arXiv2022

Optimization-Based Ramping Reserve Allocation of BESS for AGC Enhancement

The transient behavior of Automatic Generation Control (AGC) systems is a critical aspect of power system operation. Therefore, fully extracting the potential of Battery Energy Storage Systems (BESSs) for AGC enhancement is of paramount importance. In light of the challenges posed by diverse resource interconnections and the variability associated, we propose an online optimization scheme that can adapt to changes in an unknown and variable environment. To leverage the synergy between BESSs and Conventional Generators (CGs), we devise a variant of the Area Injection Error (AIE) as a measure to quantify the ramping needs. Based on this measure, we develop a distributed optimization algorithm with adaptive learning rates for the allocation of the ramping reserve. The algorithm restores a larger learning rate for compliance with the ramping needs upon detecting a potentially destabilizing event. We demonstrate the effectiveness and scalability of the proposed scheme through comprehensive case studies. It is shown that the proposed scheme can improve the transient behavior of the AGC system by bridging the gap in ramping capability.

eess.SY↗