• Energy Research

Le système de distribution électrique (EDS) a subi des changements majeurs au cours de la dernière décennie en raison de l'intégration croissante de la production distribuée (DG), en particulier la DG des énergies renouvelables. Étant donné que les ressources énergétiques renouvelables ont une production incertaine, les systèmes de stockage d'énergie (SSE) dans l'EDS peuvent réduire l'impact de ces incertitudes. En outre, les véhicules électriques (VE) ont augmenté ces dernières années en raison des préoccupations environnementales, apportant de nouveaux défis à l'exploitation et à la planification de l'EDS. Dans ce contexte, les nouvelles approches pour le problème de la planification de l'expansion du réseau de distribution (DSEP) devraient prendre en compte les ressources énergétiques distribuées (unités DG, ESS et VE) et traiter les impacts environnementaux. Ce document propose un modèle de programmation linéaire à nombres entiers mixtes pour le problème du DSEP en tenant compte des unités DG, des ESS et des stations de recharge de VE, intégrant ainsi l'impact environnemental et les incertitudes associées à la demande (conventionnelle et VE) et à la production renouvelable. Contrairement à d'autres approches, le modèle proposé comprend l'optimisation simultanée des investissements dans les sous-stations, les circuits et les ressources énergétiques distribuées, y compris les aspects environnementaux (émissions de CO 2). La méthode d'optimisation a été développée dans le langage de modélisation AMPL et résolue via CPLEX. Les tests réalisés avec un système à 24 nœuds illustrent son efficacité en tant qu'outil précieux pouvant aider les planificateurs EDS à intégrer des ressources énergétiques distribuées. El sistema de distribución eléctrica (EDS) ha sufrido grandes cambios en la última década debido a la creciente integración de la generación distribuida (DG), en particular la DG de energías renovables. Dado que los recursos de energía renovable tienen una generación incierta, los sistemas de almacenamiento de energía (ESS) en el EDS pueden reducir el impacto de esas incertidumbres. Además, los vehículos eléctricos (VE) han ido en aumento en los últimos años aprovechados por las preocupaciones ambientales, lo que trae nuevos desafíos para la operación y planificación de la EDS. En este contexto, los nuevos enfoques para el problema de la planificación de la expansión del sistema de distribución (DSEP) deben considerar los recursos energéticos distribuidos (unidades DG, ESS y EV) y abordar los impactos ambientales. Este trabajo propone un modelo de programación lineal de enteros mixtos para el problema DSEP considerando unidades DG, ESS y estaciones de carga EV, incorporando así el impacto ambiental y las incertidumbres asociadas con la demanda (convencional y EV) y la generación renovable. A diferencia de otros enfoques, el modelo propuesto incluye la optimización simultánea de las inversiones en subestaciones, circuitos y recursos energéticos distribuidos, incluidos los aspectos ambientales (emisiones de CO 2). El método de optimización se desarrolló en el lenguaje de modelado AMPL y se resolvió a través de CPLEX. Las pruebas realizadas con un sistema de 24 nodos ilustran su eficacia como una herramienta valiosa que puede ayudar a los planificadores de EDS en la integración de los recursos energéticos distribuidos. The electrical distribution system (EDS) has undergone major changes in the last decade due to the increasing integration of distributed generation (DG), particularly renewable energy DG. Since renewable energy resources have uncertain generation, energy storage systems (ESSs) in the EDS can reduce the impact of those uncertainties. Besides, electric vehicles (EVs) have been increasing in recent years leveraged by environmental concerns, bringing new challenges to the operation and planning of the EDS. In this context, new approaches for the distribution system expansion planning (DSEP) problem should consider the distributed energy resources (DG units, ESSs, and EVs) and address environmental impacts. This paper proposes a mixed-integer linear programming model for the DSEP problem considering DG units, ESSs, and EV charging stations, thus incorporating the environmental impact and uncertainties associated with demand (conventional and EVs) and renewable generation. In contrast to other approaches, the proposed model includes the simultaneous optimization of investments in substations, circuits, and distributed energy resources, including environmental aspects (CO 2 emissions). The optimization method was developed in the modeling language AMPL and solved via CPLEX. Tests carried out with a 24-node system illustrate its effectiveness as a valuable tool that can assist EDS planners in the integration of distributed energy resources. شهد نظام التوزيع الكهربائي (EDS) تغييرات كبيرة في العقد الماضي بسبب التكامل المتزايد للتوليد الموزع (DG)، وخاصة توليد الطاقة المتجددة. نظرًا لأن موارد الطاقة المتجددة لها توليد غير مؤكد، يمكن لأنظمة تخزين الطاقة (ESSs) في EDS أن تقلل من تأثير هذه الشكوك. إلى جانب ذلك، تزايدت السيارات الكهربائية في السنوات الأخيرة بسبب المخاوف البيئية، مما جلب تحديات جديدة لتشغيل وتخطيط EDS. في هذا السياق، يجب أن تأخذ النهج الجديدة لمشكلة تخطيط توسيع نظام التوزيع في الاعتبار موارد الطاقة الموزعة (وحدات توليد الطاقة، ESS، والمركبات الكهربائية) ومعالجة الآثار البيئية. تقترح هذه الورقة نموذج برمجة خطي مختلط الأعداد لمشكلة DSEP مع الأخذ في الاعتبار وحدات توليد البيانات و ESS ومحطات شحن المركبات الكهربائية، وبالتالي دمج التأثير البيئي والشكوك المرتبطة بالطلب (المركبات التقليدية والمركبات الكهربائية) والتوليد المتجدد. على عكس الأساليب الأخرى، يتضمن النموذج المقترح التحسين المتزامن للاستثمارات في المحطات الفرعية والدوائر وموارد الطاقة الموزعة، بما في ذلك الجوانب البيئية (انبعاثات ثاني أكسيد الكربون). تم تطوير طريقة التحسين بلغة النمذجة AMPL وتم حلها عبر CPLEX. توضح الاختبارات التي أجريت باستخدام نظام مكون من 24 عقدة فعاليته كأداة قيمة يمكن أن تساعد مخططي EDS في دمج موارد الطاقة الموزعة.

This paper presents a comprehensive analysis on the latest advances in transactive energy systems. The main contribution of this work is centered on the definition of transactive energy concepts and how such systems can be implemented in the smart grid paradigm. The analyzed works have been categorized into three lines of research: (i) transactive network management; (ii) transactive control; and (iii) peer-to-peer markets. It has been found that most of the current approaches for transactive energy are available as a model, lacking the real implementation to have a complete validation. For that purpose, both scientific and practical aspects of transactive energy should be studied in parallel, implementing adequate simulation platforms and tools to scrutiny the results. Keywords: Transactive energy, P2P energy trading, Transactive control, Microgrids, Aggregators

Abstract Free and competitive energy markets are a recent and increasing phenomenon in several countries. Understanding these new energy markets and estimating their possible evolutions are current challenges of the research community. To avoid real market risks, the research community has developed autonomous traders and tested them in the Power Trading Agent Competition (Power TAC), a sophisticated energy market simulator. In this paper, we present COLDPower’16, a competitive autonomous trader composed of expert agents in specific kinds of markets and customers that combines local strategies into a global strategy to maximize profit. The local strategy of each tariff expert agent uses reinforcement learning algorithms, while the local strategy of the wholesale expert agent estimates future energy prices and the amount of energy that can be negotiated to buy energy when prices are low and sell energy when prices are high. COLDPower’16 was tested in Power TAC 2016. It achieved 2nd place in the final round of this international competition with 7 autonomous agent brokers.

With the high penetration of renewable generation in Smart Grids (SG), the uncertainty behavior associated with the forecast of weather conditions possesses a new degree of complexity in the Energy Resource Management (ERM) problem. In this paper, a Multi-Objective Particle Swarm Optimization (MOPSO) methodology is proposed to solve ERM problem in buildings with penetration of Distributed Generation (DG) and Electric Vehicles (EVs) and considering the uncertainty of photovoltaic (PV) generation. The proposed methodology aims to maximize profits while minimizing CO2 emissions. The uncertainty of PV generation is modeled with the use of Monte Carlo simulation in the evaluation process of the MOPSO core. Also, a robust optimization approach is adopted to select the best solution for the worst-case scenario of PV generation. A case study is presented using a real building facility from Brazil, to verify the effectiveness of the implemented robust MOPSO.

AbstractWith the advent of the smart grid era, the electrical grid is becoming a complex network in which different technologies coexist to bring benefits to both customers and operators. This paper presents a methodology for analyzing Key Performance Indicators (KPIs), providing knowledge about the performance and efficiency of energy systems, focusing on the demand side. In the first stage of the methodology, the baseline KPIs are calculated. In the second stage, all KPIs are updated to be compared with the baseline ones. In fact, due to the dynamic nature of players in a smart grid, this methodology plays a crucial role in the performance assessment. Moreover, the proper definition and selection of KPIs is usually a challenging task since KPIs can be applied to evaluate diverse areas within a smart grid. Such areas include building energy efficiency, home communications, and smart metering deployment, just to mention a few. In the proposed methodology, the information obtained from a KPI can be driven to distinct aspects such as efficiency, environment, reliability, power quality, safety, security, or cost reduction. Through a case study from a real implementation of an energy system, we show how to assess energy consumption efficiency, thus improving energy management.

Abstract Local electricity markets are a promising idea to foster the efficiency and use of renewable energy at the distribution level. However, as such a new concept, how these local markets will be designed and integrated into existing market structures, and make the most profit from them, is still unclear. In this work, we propose a local market mechanism in which end-users (consumers, small producers, and prosumers) trade energy between peers. Due to possible low liquidity in the local market, the mechanism assumes that end-users fulfill their energy demands through bilateral contracts with an aggregator/retailer with access to the wholesale market. The allowed bids and offers in the local market are bounded by a feed-in tariff and an aggregator tariff guaranteeing that end-users get, at most, the expected cost without considering this market. The problem is modeled as a multi-leader single-follower bi-level optimization problem, in which the upper levels define the maximization of agent profits. In contrast, the lower level maximizes the energy traded in the local market. Due to the complexity of the matter, and lack of perfect information of end-users, we advocate the use of evolutionary computation, a branch of artificial intelligence that has been successfully applied to a wide variety of optimization problems. Throughout three different case studies considering end-users with distinct characteristics, we evaluated the performance of four different algorithms and assessed the benefits that local markets can bring to market participants. Results show that the proposed market mechanism provides overall costs improvements to market players of around 30–40% regarding a baseline where no local market is considered. However, the shift to local markets in energy procurement can affect the conventional retailer/aggregator role. Therefore, innovative business models should be devised for the successful implementation of local markets in the future.

AbstractIn the coming years, several transformations in the transport sector are expected, associated with the increase in electric vehicles (EVs). These changes directly impact electrical distribution systems (EDSs), introducing new challenges in their planning and operation. One way to assist in the desired integration of this technology is to allocate EV charging stations (EVCSs). Efforts have been made towards the development of EVCSs, with the ability to recharge the vehicle at a similar time than conventional vehicle filling stations. Besides, EVs can bring environmental benefits by reducing greenhouse gas emissions. However, depending on the energy matrix of the country in which the EVs fleet circulates, there may be indirect emissions of polluting gases. Therefore, the development of this technology must be combined with the growth of renewable generation. Thus, this proposal aims to develop a mathematical model that includes EVs integration in the distribution system. To this end, a mixed-integer linear programming (MILP) model is proposed to solve the allocation problem of EVCSs including renewable energy sources. The model addresses the environmental impact and uncertainties associated with demand (conventional and EVs) and renewable generation. Moreover, an EV charging forecast method is proposed, subject to the uncertainties related to the driver's behavior, the energy required by these vehicles, and the state of charge of the EVs. The proposed model was implemented in the AMPL modelling language and solved via the commercial solver CPLEX. Tests with a 24-node system allow evaluating the proposed method application.