Artículos (Ingeniería Eléctrica)

URI permanente para esta colecciónhttps://hdl.handle.net/11441/11352

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Transporte de energía en HVDC. El resurgir tecnológico de la corriente continua
(Publicaciones DYNA, 2022-05) Serrano-González, Javier; Riquelme Santos, Jesús Manuel; Arcos Vargas, Ángel; Ingeniería Eléctrica; Organización Industrial y Gestión de Empresas I
Industrial electricity prices in Spain: A discussion in the context of the European internal energy market
(Elsevier, 2021-01) Serrano-González, Javier; Álvarez Alonso, César; Ingeniería Eléctrica; Centro para el Desarrollo Tecnológico y la Innovación (CDTI)
This paper analyses the current state of electricity prices for large industrial consumers in Spain and compares the regulatory framework with other main industrial countries in Europe. The cost of electrical energy is a key factor in a country's industrial competitiveness. In a regulatory context shaped by the new European regulation of the internal electricity market in Europe, it would be expected that electricity prices in the Member States will converge in the long term, reaching similar values throughout the European Union. However, the limited electricity interconnection capacity in the case of some countries, such as Spain, means that electricity market prices are higher than in many Member States. This means that electricity-consuming industries face problems of competitiveness due to their higher energy bill costs. This paper focuses on the current situation for large industrial consumers in Spain, studying the main causes of the differences with other European countries, as well as analysing the various support mechanisms that have been implemented at both the national and European levels. This study also discusses the future prospects and possible solutions and recommendations for energy policy to promote the competitiveness of large industry in Spain within the context of a single European market.
Optimal design of neighbouring offshore wind farms: A co-evolutionary approach
(Elsevier, 2018-01) Serrano-González, Javier; Burgos Payán, Manuel; Riquelme Santos, Jesús Manuel; Ingeniería Eléctrica; Ministerio de Economia, Industria y Competitividad (MINECO). España; European Commission (EC)
This paper presents a new approach for the optimization of neighbouring offshore wind farms. Offshore wind energy is one of the most promising and developed low-carbon generation technologies. However, the high capital costs, which are strongly dependent on seabed depth, currently limit the geographical expansion of this technology to areas with relatively shallow waters and appropriate wind resource. This, along with the advantages of sharing a submarine transmission system among several projects, leads to a high concentration of offshore wind farms in certain zones, as happens, for example, in the North Sea. The presence of other neighbouring offshore wind farms has to be taken into account when a developer plans a new project, since the wake effect of wind turbines belonging to other neighbouring wind farms will affect the annual energy production and, consequently, the profitability of the project under study. However, not only already operating or installed neighbouring projects have to be borne in mind, but also the possible design of future neighbouring wind farms yet to be developed. In order to tackle this issue, an innovative co-evolutionary algorithm is proposed in this paper with the objective of determining a Nash equilibrium solution that would provide the best possible configuration of the wind farm under study by taking into account and limiting the disturbance introduced by other neighbouring projects. The performance of the proposed methodology has been successfully tested through the analysis of a realistic case and compared with other collaborative approaches and the classic single-project optimization methods already existing in the literature.
Optimal wind-turbine micro-siting of offshore wind farms: A grid-like layout approach
(Elsevier, 2017-08) Serrano-González, Javier; Trigo García, Ángel Luis; Burgos Payán, Manuel; Riquelme Santos, Jesús Manuel; González Rodríguez, Ángel Gaspar; Ingeniería Eléctrica; Ministerio de Economía y Competitividad (MINECO). España; European Commission (EC)
This paper presents a new approach for the optimization of the layout of offshore wind farms. Almost all previous work on optimal micro-siting for large offshore wind farms have been based on irregular arrangements of wind turbines. However, most offshore wind farms already built are configured in symmetrical/regular layouts. From a mathematical point of view, the geometrical relationships of such symmetrical layouts enable the problem to be defined by just a few variables. This presents a considerable advantage compared with irregular arrangements where the number of variables is directly linked to both the number of wind turbines and the number of cells in which the computational domain is discretized. In contrast, symmetrical layouts are more demanding with regard to the optimization process, since the problem constraints, such as the shape of the available exploration area to deploy the project, the maximum surface allowed, and the maximum number of wind turbines, drastically increase the non-linearity of the objective function, which affects the ability of the optimization algorithm to achieve the optimal solution. This work compares the behaviour of two meta-heuristic optimization algorithms (the Genetic Algorithm and Particle Swarm Optimization) in solving the addressed problem and, more importantly, it introduces a series of improvements on the objective function, which enhance the behaviour of the optimization algorithms when dealing with realistic constraints, such as the shape of the concession zone and maximum deployable area. Finally, the performance of the proposed methodologies has been tested under two situations. The first scenario is a small-sized hypothetical offshore wind farm. In the second scenario, the layout of a real project (Horns Rev 3 offshore wind farm) has been optimized and compared with the solutions proposed by the Danish transmission system operator. The results obtained show the ability of the proposed tools to successfully show the ability of the proposed tools to optimize offshore wind farms under realistic considerations.
Hybrid AC/DC Topologies for the CIGRE Low-Voltage Benchmark Performance Evaluation
(Multidisciplinary Digital Publishing Institute (MDPI), 2026) Kamoona, Mustafa A.; Mauricio, Juan Manuel; Ingeniería Eléctrica
This paper presents three hybrid AC/DC topologies for the CIGRE European low-voltage benchmark grid to evaluate their impact on voltage regulation, current compliance, and power-sharing capability under realistic operating conditions. The proposed topologies integrate a dedicated DC network in parallel with the existing AC infrastructure through voltage source converters (VSCs), enabling controlled power exchange between the two subsystems. This structure facilitates improved voltage support and more flexible integration of distributed renewable energy resources, many of which inherently operate in DC. A decentralized droop-based control strategy is employed as a uniform baseline to control the VSCs and assess the intrinsic performance of each topology. The proposed architectures are evaluated using realistic 24-h load profiles under scenarios with and without droop control. The results demonstrate significant improvements in voltage stability and feeder current management, particularly under high DC penetration conditions. Overall, the study provides a reproducible benchmark framework for topology-level comparison of hybrid AC/DC low-voltage distribution networks.
Adaptive ANN–HHO framework with gradient-informed energy boosting for voltage stability and power loss reduction in hybrid AC/DC microgrid
(Elsevier, 2026) Kamoona, Mustafa A.; Saleh, Ameer L.; Mauricio, Juan Manuel; Számel, László; Ingeniería Eléctrica; Ministerio de Ciencia e Innovación (MICIN). España; Agencia Estatal de Investigación. España; European Commission (EC)
This paper introduces an enhanced Artificial Neural Network-Harris Hawks Optimization (ANN–HHO) framework for voltage regulation and power losses minimization in a 9-bus hybrid AC/DC microgrid with a 100 kW photovoltaic (PV) subsystem. The proposed controller optimizes the inverter duty cycle to achieve optimal power flow and maintain voltage stability within strict limits ( ± 5% of nominal base voltage 230 V) under balanced and unbalanced load conditions. Key enhancements are introduced to the standard HHO, including a variance-based adaptive weighting mechanism for multi-objective balance, ANN-weight-normalized constraint penalties, and a gradient-informed energy boosting strategy that dynamically refines the HHO exploration–exploitation equilibrium. The framework is validated through 24-h MATLAB/Simulink simulations under time-varying load conditions. The results demonstrate the effectiveness of the proposed adaptive optimization framework in enhancing efficiency, voltage stability, and dynamic performance of hybrid AC/DC microgrids, and show improved performance over the traditional method. The proposed method achieves voltage regulation within 0.95–1.05 pu, improves active power performance by up to 18.5%, enhances reactive power support exceeding 60% at critical buses, and reduces line power losses by 85% on average, with stable and efficient operation. The findings provide a robust foundation for future developments incorporating multi-source integration and advanced AI-based control.
The effect of hydropower bidding strategy on the iberian day-ahead electricity market
(Elsevier, 2024-09) Roldán Fernández, Juan Manuel; Serrano-González, Javier; González Rodríguez, Ángel Gaspar; Burgos Payán, Manuel; Riquelme Santos, Jesús Manuel; Ingeniería Eléctrica; Junta de Andalucía
In 2021, the Iberian Electricity Market, which includes Portugal and Spain, experienced a significant increase in electricity prices. On average, prices tripled compared to pre-pandemic levels in 2019, despite stable demand. This trend, though less pronounced in other European markets, has raised concerns about the efficiency and competitiveness of wholesale electricity markets. This study investigates the bidding behavior of non-pumped hydropower plants in the Iberian market, employing a merit order model based on real data from 2019 to 2021. The analysis reveals a clear mismatch between hydropower bid prices and actual production costs. Although this practice is legal, it conflicts with the European Union's goal of promoting fair competition within the internal electricity market. Our findings suggest that aligning hydropower bid prices with 2019 levels could have reduced annual market costs by approximately 2324 million euros under a moderate scenario. This indicates substantial potential for cost savings and improved integration of hydropower. However, this strategy also benefits companies that own these hydropower plants, as they often possess large portfolios and can capitalize on increased market prices through other plants, particularly renewable energy units, which offer electricity at very low prices and have a high likelihood of being dispatched. Furthermore, the study highlights regulatory gaps allowing hydropower plants to price low-cost energy similarly to thermal plants, raising consumer costs and distorting market dynamics. These findings emphasize the need for stronger regulatory oversight and reforms to prevent excessive price inflation by hydropower, which plays a critical role in renewable energy integration and grid stability. Policymakers must ensure fair pricing while compensating hydropower plants for their flexibility and essential contributions to the energy system.
Gradient descent algorithm with greedy repositioning using power deficit aggregation of wakes to accelerate the offshore wind farm layout optimization problem in irregular concession areas
(Elsevier, 2024-12) González Rodríguez, Ángel Gaspar; Roldán Fernández, Juan Manuel; Serrano-González, Javier; Muñoz-Díez, José Vicente; Ingeniería Eléctrica; Ministerio de Ciencia, Innovación y Universidades (MICIU). España; Agencia Estatal de Investigación. España
Wind farm layout optimization is essential to maximize the energy production of renewable energy systems. A new layout optimization method for offshore wind farms is proposed to minimize power deficits due to the wake effect without limitation on the number of turbines, the shape, or the extension of the concession area. The main engine of the algorithm is a gradient-descent method in which throughout the optimization process, new turbines are progressively and randomly included within the concession area and quickly expand outward, looking for areas with less perturbation, in turn, pushing the previous ones. When the optimization process ends, to avoid local maxima, it enters into a process of suppression of the set of locations that cause the greatest potential (power deficit). Then, a map of potential for the entire area is created, and a greedy algorithm places new turbines to complete the layout with the final number of turbines. The overall process is completed in 25 s. To drastically speed up the search process and the creation of the potential map, a simplification has been validated and added: for turbines affected by multiple wakes, the resulting power has been calculated by using a linear aggregation of power deficits, instead of the usual linear (or quadratic) aggregation of speed deficits. Owing to this type of aggregation, an analogy is established between power deficit and repulsive non-isotropic electrostatic potential energy, which allows using the properties of conservative fields. Thanks to this, the process is 20 times faster than any other layout optimization algorithm found in the revised literature. Irregular concession areas are easily treated using Stokes’ theorem to detect outer points.
Identifying interaction patterns to speed-up the AEP evaluation in large offshore wind farms with uniformly distributed turbines and exclusion zones
(Elsevier, 2025-12) González Rodríguez, Ángel Gaspar; Serrano-González, Javier; Roldán Fernández, Juan Manuel; Burgos Payán, Manuel; Ingeniería Eléctrica; Ministerio de Ciencia, Innovación y Universidades (MICIU). España
The evaluation of annual energy production (AEP) is the key step for estimating wind farm income and assessing project viability through metrics like the levelized cost of energy. However, calculating AEP is highly time-intensive, particularly in wind farm layout optimization algorithms, which must evaluate AEP for numerous potential layouts. Even with simplified models, evaluating a single layout for medium or large farms can take several seconds, limiting the exploration of feasible solutions. In uniformly distributed offshore wind farms, repeated interaction patterns between turbines lead to redundant calculations of wind speed deficits in traditional methods. This work addresses the issue by identifying geometric interaction patterns and their frequency within a given layout. By leveraging these patterns, the method drastically reduces AEP computation times, achieving up to a 3000-fold improvement for a 16 × 16 turbine farm. The approach is especially effective for uniformly spaced, rhomboid-shaped offshore wind farms, reducing computation times to less than 20 ms regardless of farm size. For irregularly shaped farms or those with exclusion zones, the method still achieves significant time reductions, performing over 20 times faster than traditional methods. This innovation enables deeper exploration of the solution space in optimization processes, greatly enhancing efficiency in wind farm design.
A novel weight-based ensemble method for emerging energy players: an application to electric vehicle load prediction
(Elsevier, 2025-05) Villalonga Palou, Joan Tomás; Serrano-González, Javier; Riquelme Santos, Jesús Manuel; Roldán Fernández, Juan Manuel; Ingeniería Eléctrica; Ministerio de Ciencia e Innovación (MICIN). España; European Commission (EC). Fondo Europeo de Desarrollo Regional (FEDER)
The emergence of new resources and services in the electricity system implies that more and more agents need to obtain more accurate forecasts to optimize their operations. It is common for these agents to have different sources of forecasts (from specialized consultants or meteorological services, among others). The proposed approach aims to obtain more accurate predictions by optimally combining a set of predictions obtained by different techniques. In this way it is possible to obtain a resulting prediction that improves the error and uncertainty associated with each of the individual forecasts. The objective is achieved by the analytical minimization of the errors obtained by each of the individual predictors. This allows to obtain dynamically the optimized weights assigned to each of the algorithms so that the combination outperforms the individual behaviour of each of them. The proposed ensemble approach has been successfully tested on a real time series of electric vehicle charging. Likewise, the results obtained have been compared exhaustively with other ensemble techniques consolidated in the literature based on different methods, including dynamic ensembles as machine learning approaches. The results obtained show an appreciable improvement of the errors obtained in the predictions using the proposed techniques.
Evaluation of the Impact of Environmentally Driven Curtailment Regulations on Wind Farm Energy Production
(Springer, 2026) Serrano-González, Javier; González Rodríguez, Ángel Gaspar; Gómez Expósito, Antonio; Riquelme Santos, Jesús Manuel; Ingeniería Eléctrica; Ministerio de Ciencia e Innovación (MICIN). España
Environmentally driven curtailment strategies—based on increasing wind turbine cut-in speeds during high bat activity—are increasingly adopted in wind energy regulations worldwide. These measures, typically enforced during summer nights, aim to reduce bat mortality but lead to energy losses that can compromise the economic performance of wind farms. This study assesses the impact of such regulations by analyzing three key variables that influence curtailed energy: mean wind speed, wind variability, and turbine design, characterized through specific power (the ratio of rated power to rotor swept area). A combined approach is proposed, integrating a theoretical energy model with real performance data from 130 commercial wind turbines, enabling a comprehensive evaluation across a wide range of wind regimes and turbine configurations. The results show that curtailed energy increases significantly at low-wind sites and for turbines with lower specific power. Wind variability also has a notable influence, with contrasting effects depending on the wind class. Although the study is based on the regulatory framework under development in Spain, the methodology is generalizable to other regions. The findings provide valuable insights for developers and policymakers, supporting the design of strategies that balance biodiversity conservation with the economic sustainability of wind energy deployment.
Data-driven energy sharing in collective self-consumption communities: a medium-term predictive ensemble framework
(Elsevier, 2026-10) Serrano-González, Javier; Villalonga Palou, Joan Tomás; Riquelme Santos, Jesús Manuel; Riquelme Domínguez, José Miguel; Ingeniería Eléctrica; Junta de Andalucía; Ministerio de Ciencia e Innovación (MICIN). España
This paper presents a predictive ensemble framework for the allocation of energy in collective self-consumption communities. The proposed approach combining several simple yet complementary models—seasonal persistence, previous month, previous year, rolling average, k-nearest neighbors —within a minimum-variance optimization that dynamically adjusts model weights according to recent predictive performance. The framework can deliver stable and accurate results with limited historical data, reaching full reliability after approximately six months of hourly observations. Using multi-year data from a representative energy community, the ensemble method was benchmarked against individual predictors and a reference baseline representing perfect foresight. Results show that the ensemble consistently minimizes deviations from the baseline across all consumers and time periods, outperforming each single model. Sensitivity analyses with respect to photovoltaic capacity, import prices, and export remuneration confirm the robustness of the approach: the ensemble systematically achieves the lowest cost gap relative to the baseline under all tested conditions, with the greatest advantage observed under high import tariffs and low photovoltaic capacity. Overall, the proposed architecture provides a realistic and scalable solution for managing collective self-consumption under current regulatory frameworks, which often require predefined allocation coefficients set months in advance. Beyond its economic performance, the framework opens the door to future developments incorporating uncertainty management, fairness criteria, and online learning, paving the way toward intelligent and equitable energy communities.
Optimal reliability thresholds for stochastic flexibility aggregators in European reserve markets
(Elsevier, 2027-01) Paredes-Parrilla, Ángel; Zhou, Yihong; Aguado, José A.; Morstyn, Thomas; Ingeniería Eléctrica; Ministerio de Ciencia, Innovación y Universidades (MICIU). España; European Commission (EC). Fondo Europeo de Desarrollo Regional (FEDER); Universidad de Sevilla
The integration of stochastic renewable energy sources has made flecan be reformuxibility aggregators crucial for power system balancing, yet the uncertainty they introduce challenges system security and market efficiency. Current trends show a move towards probabilistic reliability rules for bid qualification, but a key research gap remains: how can a Transmission System Operator (TSO) optimally set these reliability thresholds while considering competitive aggregators in the market? This paper proposes a novel methodology using a Strengthened Faster Linear Approximation (SFLA) to reformulate the game-theoretic problem, which includes lower-level distributionally robust joint chance constraints, into a tractable single-level optimization problem. A case study with real-world data from Spanish Reserve Markets demonstrates the method’s ability to balance system costs and security, quantifying the trade-off between reserve procurement and delivery risk. This research provides TSOs with a computationally tractable decision-making tool to set reliability thresholds for aggregators while ensuring security.
Practical sensitivity-based optimization technique to solve the hosting capacity problem in unbalanced low voltage networks
(Elsevier, 2026-03) Carmona-Pardo, Rubén; Morán-Corbacho, Rafael; Rodríguez del Nozal, Álvaro; Romero Ramos, Esther; Ingeniería Eléctrica; Ministerio de Ciencia, Innovación y Universidades (MICIU). España; Agencia Estatal de Investigación. España; European Commission (EC). Fondo Europeo de Desarrollo Regional (FEDER); Centro para el Desarrollo Tecnológico y la Innovación (CDTI)
This work addresses the problem of computing the hosting capacity of distributed energy resources, both generation and demand, in a three-phase four-wire low voltage network. A new methodology, based on the use of voltage and current sensitivity coefficients to model the system, allows defining a second-order cone programming formulation that guarantees the convexity of the problem. This approach results in a very practical and accurate tool capable of solving the hosting capacity problem, for generation and demand hosting capacity, in large unbalanced distribution networks, regardless of the initial operating conditions or its radial or meshed topology, and considering the limiting constraints on voltages, currents, reverse power flows and voltage unbalances. Tests on numerous different real low-voltage networks demonstrate the practical usefulness of the tool, highlighting the accuracy of the results obtained. For the largest tested distribution network, a comparison has been included between the results obtained with the proposed methodology and those derived from using a Monte Carlo-based probabilistic approach, demonstrating the computational advantage of the new method and the good accuracy of the optimum obtained. With the new hosting capacity computation tool, it has been possible to identify technically safe scenarios that allow for the accurate quantification and localization of the nodes and phases to which the new generation/demand must be connected, reaching penetration levels of up to 32%/21% with respect to the transformer’s rated power.
Gamifying Renewable Energy: Enhancing Pre-University Students’ Knowledge and Attitudes Toward Science and Technology
(Multidisciplinary Digital Publishing Institute (MDPI), 2025-11) Pablo-Lerchundi, Iciar; Sastre-Merino, Susana; Riquelme Domínguez, José Miguel; Mahtani, Kumar; Mendoça, Hugo; Ingeniería Eléctrica; European Union (UE)
Science and Technology (S&T) education is fundamental for advancing sustainability and preparing new generations to face global challenges. However, there is growing concern about the decline in interest and positive attitudes of pre-university students towards S&T and STEM fields, which affects the future workforce and the ability to address complex problems such as energy transition. Research highlights the importance of early interventions and innovative teaching methods to sustain motivation and foster positive attitudes towards S&T and STEM fields. Among these, gamified learning strategies, such as escape rooms, have emerged as promising tools for making S&T education more engaging and accessible. This study investigates whether such approaches can enhance knowledge in renewable energy and attitudes towards S&T in pre-university students. A total of 101 secondary education students participated in a gamified escape room on renewable energy, followed by pre- and post-intervention surveys assessing knowledge and attitudes towards S&T. Responses from 96 students were analyzed using non-parametric statistical tests. The activity improved students’ knowledge of renewable energy but did not lead to measurable changes in their attitudes towards S&T, suggesting that one-time interventions may raise awareness but are insufficient to shift perceptions; therefore, sustained and immersive educational strategies are needed to foster lasting engagement with STEM fields.
Smart Grid Origins, Definitions, Technologies, and Emerging Trends: A Power Community Perspective
(Institute of Electrical and Electronics Engineers (IEEE), 2025-10) Malik, O.; Liu, Jay; Simoes, Marcelo; Dent, Christ; Strunz, Kai; Wischkaemper, Jeffrey; Miranda, Vladimiro; Gaunt, Trevor; Gómez Expósito, Antonio; Ribeiro, Paulo; Ingeniería Electrónica
This paper captures an engaging—and at times heated-Power-Globe (PG) discussion of evolving definitions of smart grid technologies. The exchange took place between December 2024 and January 2025. The primary objective of this paper is to clarify some of the ambiguities surrounding the term “smart grid” over the past two decades, as highlighted in the spirited PG debate. “Smart grid” has sometimes been advocated as a panacea to resolve the tension between competing objectives for the provision of electricity (specifically, making it reliable, clean, and affordable). This paper examines the term “smart grid” in terms of raw technical functionalities, applications., and use cases, some of which may get closer than others to meeting the aspirational promises. While smart technology should expand our menu of options, it will not absolve us of the need to make hard decisions.
Independent aggregators securing end user Wasserstein distributionally robust flexibility through bilevel incentives
(Elsevier, 2026-04) Paredes-Parrilla, Ángel; Zhou, Yihong; Aguado, José A.; Morstyn, Thomas; Ingeniería Eléctrica; Ministerio de Ciencia e Innovación (MICIN). España; European Union (UE)
The imperative for increased power system flexibility, driven by the energy transition, positions Independent Aggregators (IAs) as central to integrating Distributed Energy Resources (DERs). However, the inherent uncertainty of DERs limits their participation in reserve markets and complicates the design of economic incentives through bi-level optimization methods. To address this challenge, this paper proposes a bi-level optimization framework that employs a novel reformulation of Wasserstein distributionally robust joint chance constraints. The approach enables IAs to mobilize stochastic DER flexibility through robust incentives while securing reserve provision. The problem is reformulated as a single-level mixed-integer linear program using Karush-Kuhn-Tucker conditions and a Faster Inner Convex Approximation (FICA) technique. This provides computationally fast and accurate probability guarantees for reserve delivery. Empirical validation using Spanish market data demonstrates that the proposed FICA-enabled framework for DER aggregation substantially enhances economic efficiency and ex-post risk compliance over benchmarks. FICA increases the aggregator profits under stringent robustness while determining optimal incentive combinations that unlock higher flexibility volumes, with less computational burden as single-level approaches. This research offers IAs a practical, robust tool for effective reserve market participation, facilitating DER integration in reserve markets.
Storage deployment and its impact on wholesale electricity prices
(Elsevier, 2026-06) Alonso Pérez, Javier Florencio; Arcos Vargas, Ángel; Ingeniería Eléctrica; Organización Industrial y Gestión de Empresas I; Ministerio de Ciencia, Innovación y Universidades (MICIU). España; European Union (UE)
The European Union’s ambitious renewable energy targets highlight the pivotal role of Battery Energy Storage Systems (BESS) in mitigating the inherent variability of renewable generation and matching it to fluctuating electricity demand. This paper analyzes how storage capacity additions influences day-ahead electricity prices through a deterministic Real-Time Optimization model that replicates the strategic behavior of market producers. The model incorporates day-ahead bidding curves as proxies for price–demand elasticity and accommodates alternative producer strategies, both price-taking and price-making behaviors. Its output includes projected market prices and the revenues of storage operators in the day-ahead market, excluding other potential income streams. By comparing these projections with investment parameters, the study provides insights into the economic viability of storage expansion scenarios proposed in national energy plans. The methodology is applied to the Spanish electricity system using 2024 market data.
Droop-control-aided state estimation and false data detection in active distribution systems
(Elsevier, 2025) Dimoulias, Stelios C.; Kryonidis, Georgios C.; Malamaki, Kyriaki-Nefeli D.; Folletis, Fotios P.; Milioudis, Apostolos N.; Romero Ramos, Esther; Ingeniería Eléctrica; European Union (UE)
In this paper, an enhanced state estimation (SE) method for active distribution systems (DSs) is presented. The advancement of the proposed method lies in the integration of the local control logic of the distributed renewable energy sources, i.e., the droop control, into the mathematical formulation of the SE optimization problem. The proposed method is evaluated in comparison with the conventional weighted least squares SE method, both as a stand-alone state estimator and under false data injection cyberattacks. Simulations on two real-world medium-voltage DSs demonstrate the improved performance of the proposed method in terms of estimation accuracy and system observability, as well as its superiority against both random and sophisticated false data injection attacks.
The cost of ancillary services in high PV penetration scenarios: the case of Spain
(Elsevier, 2025) Alonso Pérez, Javier Florencio; Arcos Vargas, Ángel; Martínez Ramos, José Luis; Organización Industrial y Gestión de Empresas I; Ingeniería Eléctrica; Ministerio de Ciencia, Innovación y Universidades (MICIU). España
Renewables should reach a 42.5 % share of total energy consumption by 2030 to meet the EU agenda, which translates to 75–80 % of the electricity generation mix for intermittent renewable resources (wind, solar…). In this context, running out-of-merit thermal power plants just to provide ancillary services (AS) has undesirable side effects, namely: increased supply cost and CO2 emissions, and reduced renewables share in the mix. This article proposes a methodology to compare the impact of AS provision in future scenarios with high renewables penetration, mainly photovoltaics (PV), under two different alternatives: 1) AS fully provided by conventional power plants, as is done today; and 2) AS provided by renewable sources, according to marginal market criteria. The future scenarios are built considering the current generation portfolio plus additional wind, PV and battery storage facilities, all of them competing with the marginal thermal technology (combined cycles). The methodology is applied to the Spanish case, keeping in mind the revised National Energy and Climate Plan for 2030. In conclusion, a series of recommendations are made regarding ancillary service provision and storage deployment.

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