• Energy Research
• 2021-2025close

Les réseaux électriques du monde entier sont en transition vers une structure décentralisée. Dans le cadre d'une telle transition, la technologie blockchain apparaît comme une solution potentielle aux problèmes techniques, de déploiement et de décentralisation, compte tenu de sa sécurité, de son intégrité, de sa nature décentralisée et de son infrastructure requise. De plus, la technologie blockchain offre d'excellentes fonctionnalités telles que la non-répudiation et l'immuabilité, ce qui en fait une application prometteuse pour l'intégration et la gestion des DER sur les facteurs de fiabilité. Dans cet article, un examen complet des applications blockchain pour la gestion et l'intégration des DER est présenté. Tout d'abord, une revue de la littérature basée sur la blockchain des activités de recherche dans le domaine de l'intégration DER et des tâches connexes, y compris les efforts entrepreneuriaux, est effectuée. Ensuite, les différentes opportunités et défis de l'intégration et de la gestion des DER dans les réseaux électriques, c'est-à-dire la centralisation, le soutien réglementaire, les coûts de développement sont discutés. Enfin, certains défis et opportunités de recherche clés liés à l'intégration de la technologie blockchain aux problèmes d'intégration et de gestion des DER sont présentés. Las redes eléctricas de todo el mundo están en transición hacia una estructura descentralizada. Bajo tal transición, la tecnología blockchain está emergiendo como una posible solución para problemas técnicos, de implementación y descentralización, dada su seguridad, integridad, naturaleza descentralizada e infraestructura requerida. Además, la tecnología blockchain ofrece excelentes características como el no repudio y la inmutabilidad, lo que la convierte en una aplicación prometedora para la integración y gestión de DER en factores de fiabilidad. En este documento, se presenta una revisión exhaustiva de las aplicaciones de blockchain para la gestión e integración de DER. En primer lugar, se lleva a cabo una revisión bibliográfica basada en blockchain de las actividades de investigación en el área de integración de DER y las tareas relacionadas, incluidos los esfuerzos empresariales. A continuación, se discuten las diferentes oportunidades y desafíos de la integración y gestión de DER en las redes eléctricas, es decir, centralización, soporte regulatorio, costes de desarrollo. Finalmente, se presentan algunos desafíos y oportunidades clave de investigación para incluir la tecnología blockchain en los problemas de integración y gestión de DER. Power grids all over the world are transitioning towards a decentralized structure. Under such a transition, blockchain technology is emerging as a potential solution for technical, deployment and decentralization issues, given its security, integrity, decentralized nature and required infrastructure. Moreover, blockchain technology offers excellent features like non-repudiation and immutability which makes it a promising application for DER integration and management on reliability factors. In this paper, a comprehensive review of blockchain applications for DER management and integration is presented. First, a blockchain-based literature review of research activities in the DER integration area and related tasks including entrepreneurial efforts is carried out. Next, the different opportunities and challenges of DER integration and management in power grids, i.e., centralization, regulatory support, development costs are discussed. Finally, some key research challenges and opportunities of including blockchain technology to DER integration and management issues are presented. تنتقل شبكات الطاقة في جميع أنحاء العالم نحو هيكل لامركزي. في ظل هذا التحول، تظهر تقنية البلوك تشين كحل محتمل للقضايا التقنية وقضايا النشر واللامركزية، نظرًا لأمنها ونزاهتها وطبيعتها اللامركزية والبنية التحتية المطلوبة. علاوة على ذلك، توفر تقنية البلوك تشين ميزات ممتازة مثل عدم الإنكار والثبات مما يجعلها تطبيقًا واعدًا لتكامل DER وإدارتها فيما يتعلق بعوامل الموثوقية. في هذه الورقة، يتم تقديم مراجعة شاملة لتطبيقات blockchain لإدارة وتكامل DER. أولاً، يتم إجراء مراجعة للأدبيات القائمة على البلوك تشين للأنشطة البحثية في مجال تكامل دير والمهام ذات الصلة بما في ذلك جهود ريادة الأعمال. بعد ذلك، تتم مناقشة الفرص والتحديات المختلفة لتكامل DER وإدارتها في شبكات الطاقة، أي المركزية والدعم التنظيمي وتكاليف التطوير. أخيرًا، يتم تقديم بعض التحديات والفرص البحثية الرئيسية لتضمين تقنية البلوك تشين لقضايا تكامل وإدارة DER.

Generation dispatching is a challenge in islanded microgrids due to the operational and economic restrictions in isolated zones. Furthermore, the impact of usual operational network changes in topology, load demand, and generation availability may become significant considering the grid size. This research paper presents a detailed multiple cost function modeling methodology of an optimal power flow algorithm applied to a non-interconnected zone in Colombia. The optimal power flow (OPF) formulation includes cost functions related to renewable resources as presented in the isolated zone and a complete model of the charging and discharging of batteries. Additionally, the flexibility of the proposal is tested using three different network topologies with a characteristic daily load curve from the zone. The main contribution of this paper lies in the implementation of an optimal power flow including cost functions of renewable sources for isolated microgrids. A test case for a non-interconnected zone in Colombia is performed for various operation cases.

The integration and management of distributed energy resources (DERs), including residential photovoltaic (PV) production, coupled with the widespread use of enabling technologies such as artificial intelligence, have led to the emergence of new tools, market models, and business opportunities. The accurate forecasting of these resources has become crucial to decision making, despite data availability and reliability issues in some parts of the world. To address these challenges, this paper proposes a deep and machine learning-based methodology for PV power forecasting, which includes XGBoost, random forest, support vector regressor, multi-layer perceptron, and LSTM-based tuned models, and introduces the ConvLSTM1D approach for this task. These models were evaluated on the univariate time-series prediction of low-volume residential PV production data across various forecast horizons. The proposed benchmarking and analysis approach considers technical and economic impacts, which can provide valuable insights for decision-making tools with these resources. The results indicate that the random forest and ConvLSTM1D model approaches yielded the most accurate forecasting performance, as demonstrated by the lowest RMSE, MAPE, and MAE across the different scenarios proposed.

Today, the power system operation represents a challenge given the security and reliability requirements. Mathematical models are used to represent and solve operational and planning issues related with electric systems. Specifically, the AC optimal power flow (ACOPF) and the DC optimal power flow (DCOPF) are tools used for operational and planning purposes. The DCOPF versions correspond to lineal versions of the ACOPF. This is due to the fact that the power flow solution is often hard to obtain with the ACOPF considering all constraints. However, the simplifications use only active power without considering reactive power, voltage values and losses on transmission lines, which are crucial factors for power system operation, potentially leading to inaccurate results. This paper develops a detailed formulation for both DCOPF and ACOPF with multiple generation sources to provide a 24-h dispatching in order to compare the differences between the solutions with different scenarios under high penetration of wind power. The results indicate the DCOPF inaccuracies with respect to the complete solution provided by the ACOPF.

The increased use of distributed energy resources, especially electrical energy storage systems (EESS), has led to greater flexibility and complexity in power grids, which has led to new challenges in the operation of these systems, with particular emphasis on frequency regulation. To this end, the transmission system operator in Great Britain has designed a control scheme known as Enhanced Frequency Response (EFR) that is especially attractive for its implementation in EESS. This paper proposes a Type-2 fuzzy control system that enables the provision of EFR service from a battery energy storage system in order to improve the state-of-charge (SoC) management while providing EFR service from operating scenarios during working and off-duty days. The performance of the proposed controller is compared with a conventional FLC and PID controllers with similar features. The results showed that in all scenarios, but especially under large frequency deviations, the proposed controller presents a better SoC management in comparison without neglecting the EFR service provision.

Electricity markets provide valuable data for regulators, operators, and investors. The use of machine learning methods for electricity market data could provide new insights about the market, and this information could be used for decision-making. This paper proposes a tool based on multi-output regression method using support vector machines (SVR) for LMP forecasting. The input corresponds to the active power load of each bus, in this case obtained through Monte Carlo simulations, in order to forecast LMPs. The LMPs provide market signals for investors and regulators. The results showed the high performance of the proposed model, since the average prediction error for fitting and testing datasets of the proposed method on the dataset was less than 1%. This provides insights into the application of machine learning method for electricity markets given the context of uncertainty and volatility for either real-time and ahead markets.

This paper describes the development of a deep neural network architecture based on transformer encoder blocks and Time2Vec layers for the prediction of electricity prices several steps ahead (8 h), from a probabilistic approach, to feed future decision-making tools in the context of the widespread use of intra-day DERs and new market perspectives. The proposed model was tested with hourly wholesale electricity price data from Colombia, and the results were compared with different state-of-the-art forecasting baseline-tuned models such as Holt–Winters, XGBoost, Stacked LSTM, and Attention-LSTM. The findings show that the proposed model outperforms these baselines by effectively incorporating nonlinearity and explicitly modeling the underlying data’s behavior, all of this under four operating scenarios and different performance metrics. This allows it to handle high-, medium-, and low-variability scenarios while maintaining the accuracy and reliability of its predictions. The proposed framework shows potential for significantly improving the accuracy of electricity price forecasts, which can have significant benefits for making informed decisions in the energy sector.