Departamento de Ingeniería Eléctrica

URI permanente para esta comunidadhttps://hdl.handle.net/10953/37

En esta Comunidad se recogen los documentos generados por el Departamento de Ingeniería Eléctrica y que cumplen los requisitos de Copyright para su difusión en acceso abierto.

Examinar

Mostrando 1 - 6 de 6

Dealing with contradictory objectives in energy communities: A game-oriented trilevel approach
(Elsevier, 2025-09) Tostado-Véliz, Marcos; Hasanien, Hany M.; Cruz-de-la-Torre, Carlos; Jurado-Melguizo, Francisco
Energy communities empower end users to partake actively in the operation of the system while lowering energy procurement through optimal sharing resources. The main objective of energy communities is reducing the collective bill by maximizing the usage of local assets such as photovoltaic and storage systems. However, the different community members may raise particular objectives that may eventually lie in contradiction with the reduction of the electricity cost. For example, prosumers may be interested in incrementing their consumption above a benchmark point in order to increase their comfort and satisfaction. Such contradictory objectives should be considered in energy management of communities in order to ensure its social stability and successful. To this end, a novel game-based trilevel day-ahead approach for cooperative communities is developed, in which two secondary objectives can be accommodated together with the cost minimization original target. As a sake of example, the developed tool tailors in this paper to the case in which prosumers aim at maximizing their consumption while storage pretend to minimize the degradation of assets. The original trilevel structure is reduced to a solvable single-level problem that provide an equilibrium point in the Nash sense. A number of results is provided in 5 and 15-bus cases in order to validate the new approach. Results show that the new proposal can be easily implemented in a variety of scenarios, showing a case-independent performance. The hierarchical decision-logic procedure has been illustrated and validated analysing the total community cost under different users’ preferences. Finally, it is shown that the developed methodology scales well with the storage capability and community size.
On the applicability of the alternating projections method for privacy-preserving scheduling in local energy communities
(Elsevier, 2025-09-01) Tostado-Véliz, Marcos; Dolatabadi, Mohammad; Siano, Pierluigi; Jurado-Melguizo, Francisco
Local energy communities enable proper infrastructure and management mechanisms to empower final users to partake actively in the operation of electrical systems while sharing resources to pursue common objectives. As an aggregated structure, suitable energy management and scheduling tools need to be developed and tested to ensure that local resources are properly operated to maximize the economy and efficiency of energy communities. However, final electricity users may be reluctant to share confidential information, which needs to be taken into account when developing novel computational tools for energy communities. This paper applies the well-known Alternating Projection Method (APM) and differential privacy (DP) to the day-ahead scheduling problem in energy communities. As a result, two novel iterative methodologies are proposed enabling decentralized privacy-aware resolution in energy communities. Different numerical results are discussed on 100 different community instances, analyzing both economic and energetic indicators. Specifically, with no added noise (sigma = 0) APM is numerically identical to the centralized benchmark across all cases . Additionally, for (0 < sigma < 1), the mean absolute percentage error in imported energy remains less than 20 %. Results reveal that the application of the APM is capable of reproducing exactly the results of the centralized approach, while the application of differential privacy may lead to large errors, especially regarding economic results when exportable capacity is large. Moreover, results reveal that the computational burden of the new methodologies is reasonable and therefore does not pose a barrier to their implementation. Indeed, as all steps in our implementation rely on Linear Programming (LP) and as there are many stable LP solvers (both open-source and commercial) it is easy for practitioners to deploy our approach for real-life scenarios. Our numerical experiments show that the considered privacy-aware techniques were quite efficient, achieving the solution in less than a minute in all cases. Moreover, the considered privacy-aware APM presents a highly parallelizable structure which allows the results to be even further improved.
Optimizing proton exchange membrane electrolyzer performance through dynamic pressure and temperature control: A mixed-integer linear programming approach
(Elsevier, 2025-12-01) Aguado-Molina, Roque; Tostado-Véliz, Marcos; Desideri, Umberto; Jurado-Melguizo, Francisco
Hydrogen is a key energy carrier for decarbonizing multiple sectors, particularly when produced via water electrolysis powered by renewable energy. Proton exchange membrane (PEM) electrolyzers are well suited for this application due to their ability to rapidly adjust to fluctuating power inputs. Despite being conventionally operated at high temperatures and pressures to reduce heating and compression needs, recent studies suggest that under partial loads, lower operating conditions may enhance efficiency. This study introduces a novel optimization framework for dynamically adjusting pressure and temperature in PEM electrolyzers. The model integrates an efficiency map within a Mixed-Integer Linear Programming (MILP) formulation and applies McCormick tightening to address nonlinearities. A one-week case study demonstrates operational cost reductions of up to 12.5 % through optimal control, favoring lower temperatures and pressures at low current densities and higher temperatures near rated load, while maintaining moderate pressures. The results show improved efficiency and reduced hydrogen crossover, enhancing safety and enabling scalable application over extended time horizons. These insights are valuable for long-term planning and evaluation of hydrogen production and storage systems.
Privacy-preserving energy management in local energy communities with EVs – An enhanced benders-like solution strategy
(Elsevier, 2025-09-15) Tostado-Véliz, Marcos; Borghetti, Alberto; Siano, Pierluigi; Jurado-Melguizo, Francisco
Local Energy Communities (LECs) are collectives of prosumers collaborating to reach common goals, such as the reduction of energy procurement costs and the provision of ancillary services to the network operator. They use the flexibility of modern residential installations, including rooftop photovoltaic (PV) systems and controllable loads, such as the charging stations of electric vehicles (EVs). A central unit, called community manager, usually coordinates the actions of prosumers. However, the need for large information exchange in this multi-agent framework is a problem for the widespread adoption of such models. Data privacy concerns between prosumers and the manager may deter participation. This paper presents a novel energy management strategy for LECs with EV charging stations that protects privacy. The proposed approach only shares dual variables with the community manager, while all primal variables, such as power schedules, remain private. The method uses Benders decomposition to solve the day-ahead energy management problem, which has a separable structure. To speed up the process, a Benders-bundle algorithm has been developed, which is faster than the basic Benders method. The method also makes it easy to include network constraints, so the results can be implemented in the network without congestions or voltage problems. The method is tested on a case of a community with 14 prosumers connected to a 15-bus radial low voltage distribution network. Results show that the new proposal performs as well as a centralized approach and is characterized by a good balance between solution accuracy and privacy preservation compared to other distributed and decentralized methods.
Risk-aware strategies for optimal participation of parking lots in day-ahead electricity markets
(Elsevier, 2025-05-01) Tostado-Véliz, Marcos; Hasanien, Hany M.; Carpio, José; Jurado-Melguizo, Francisco
The decarbonization of the mobility industry rules the massive deployment of charging infrastructures worldwide. Frequently, charging points are installed by private companies and entities, which pursue a monetary profit through providing charging services. It is therefore interesting looking for business opportunities that maximize the monetary profit of such infrastructures. In this regard, large-scale parking lots can partake in wholesale electricity markets, where they can buy or sell energy, thus actuating as a high-capacity virtual battery storage system. In this paper, a novel methodology for optimal participation of parking lots in day-ahead electricity markets is developed. The new proposal contributes with two main advantages compared to other similar research. On the one hand, we properly consider both charging and discharging modes of electric vehicles, which enable full participation in electricity markets as load or generator. On the other hand, an adaptive uncertainty-aware model is proposed and accommodated into the developed tool, thus casting as a unified framework that allows adopting both risk-averse and risk-seeker operational strategies. To this end, a tailored mathematical model is proposed, wherein uncertainties and binary variables related to operational statuses of batteries are properly accommodated via an original Benders’ decomposition algorithm. Moreover, different improvement strategies are proposed, thus resulting in a practical tool with potential fields of application in industry. A number of numerical results are provided to validate the new tool, as well as analyse how the adoption of risk-averse or risk-seeker strategies affects the strategic participation of parking lots in electricity markets.
Robust dynamic charging price in PV-assisted charging stations
(Elsevier, 2025-10-01) Tostado-Véliz, Marcos; Hasanien, Hany M.; Arévalo, Paul; Jurado-Melguizo, Francisco
With the increasing number of electric vehicles on road, the deployment of sufficient public charging infrastructures has become critical. To encourage the installation of new public charging points, such infrastructures need to be economically viable and profitable. In this regard, exploring economic activities within charging infrastructures has become a key topic to ensure the long-term financial sustainability of charging installations. In line with this objective, this paper develops a new robust methodology to setting dynamic charging prices in charging stations. Unlike to conventional charging prices based on flat tariffs, dynamic pricing strategies can follow wholesale electricity prices, potentially setting low prices and therefore displacing the fleet from domestic to public charging. The new proposal renders as a game-theoretical max-min bi-level optimization problem. To address the initial complexity of the formulation, a tailored solution algorithm is developed, which allows accessing to robust solutions efficiently. An adaptive robust modelling of uncertainties is proposed, based on intervals, which allows representing uncertainties as box-constrained variables. Moreover, this paper contributes with a new data-driven approach to determine limits on uncertainties based on bootstrapping. The new solution strategy is validated on a benchmark large-scale charging station installing a photovoltaic facility. Additionally, the effect of the risk level and photovoltaic size on final results is evaluated. In addition, the effectiveness of the charging pricing strategy is assessed, along with the influence of uncertainties on the final results.

RUJA: Repositorio Institucional de Producción Científica

Examinar

Examinando Departamento de Ingeniería Eléctrica por Materia "3306.02"