• Energy Research

This paper presents a methodology to address reactive power compensation using Evolutionary Particle Swarm Optimization (EPSO) technique programmed in the MATLAB environment. The main objective is to find the best operation point minimizing power losses with reactive power compensation, subjected to all operational constraints, namely full AC power flow equations, active and reactive power generation constraints. The methodology has been tested with the IEEE 14 bus test system demonstrating the ability and effectiveness of the proposed approach to handle the reactive power compensation problem.

Editorial Complex Optimization and Simulation in Power Systems

The electrical grid is undergoing an unprecedented evolution driven mainly by the adoption of smart grid technologies. The high penetration of distributed energy resources, including renewables and electric vehicles, promises several benefits to the different market actors and consumers, but at the same time imposes grid integration challenges that must adequately be addressed. In this paper, we explore and propose potential business models (BMs) in the context of distribution networks with high penetration of electric vehicles (EVs). The analysis is linked to the CENERGETIC project (Coordinated ENErgy Resource manaGEment under uncerTainty considering electrIc vehiCles and demand flexibility in distribution networks). Due to the complex mechanisms needed to fulfill the interactions between stakeholders in such a scenario, computational intelligence (CI) techniques are envisaged as a viable option to provide efficient solutions to the optimization problems that might arise by the adoption of innovative BMs. After a brief review on evolutionary computation (EC) applied to the optimization problems in distribution networks with high penetration of EVs, we conclude that EC methods can be suited to implement the proposed business models in our future CENERGETIC project and beyond.

La creciente tendencia de los vehículos eléctricos (EV) y la construcción de sistemas fotovoltaicos integrados (BIPV) es un medio prometedor para reducir los problemas relacionados con el cambio climático. Las cargas de EV se pueden gestionar a través de un agregador para maximizar el uso de energía verde producida por unidades fotovoltaicas (PV) a través de estrategias de carga inteligentes que explotan la demanda de EV controlable conectada a BIPV. Los trabajos anteriores se han centrado en la coordinación de la carga de vehículos eléctricos en un BIPV inteligente, aunque sin una optimización que fomente la carga de vehículos eléctricos con la energía producida por las unidades fotovoltaicas. Este documento propone una estrategia de agregación que maximiza un índice de energía verde (GEI) para la coordinación de carga inteligente de los vehículos eléctricos, que aprovecha los períodos con alta disponibilidad fotovoltaica para cargar las baterías de los vehículos eléctricos; además, una etapa de posprocesamiento para el GEI proporciona a los propietarios de vehículos eléctricos información sobre el porcentaje de energía cargada, período por período, que proviene de la generación fotovoltaica. Los resultados de un estudio de caso con 510 vehículos eléctricos integrados con 17 BIPV inteligentes muestran que la estrategia optimiza de manera efectiva el uso de la energía producida por las unidades fotovoltaicas para cargar los vehículos eléctricos, contribuye a reducir el consumo de energía no renovable del sector de la construcción y satisface los requisitos de energía de los propietarios de vehículos eléctricos para el transporte. La tendance croissante des véhicules électriques (VE) et de la construction de systèmes photovoltaïques intégrés (BIPV) est un moyen prometteur de réduire les problèmes liés au changement climatique. Les charges de VE peuvent être gérées via un agrégateur pour maximiser l'utilisation de l'énergie verte produite par les unités photovoltaïques (PV) grâce à des stratégies de charge intelligentes qui exploitent la demande de VE contrôlable connectée au BIPV. Les travaux précédents se sont concentrés sur la coordination de la charge des véhicules électriques dans un BIPV intelligent, mais sans optimisation qui encourage la charge des véhicules électriques avec l'énergie produite par les unités photovoltaïques. Cet article propose une stratégie d'agrégation qui maximise un indice d'énergie verte (GEI) pour la coordination de la charge intelligente des VE, qui tire parti des périodes de disponibilité PV élevée pour charger les batteries de VE ; en outre, une étape de post-traitement pour le GEI fournit aux propriétaires de VE des informations sur le pourcentage d'énergie chargée, période par période, qui provient de la production PV. Les résultats d'une étude de cas avec 510 VE intégrés à 17 BIPV intelligents montrent que la stratégie optimise efficacement l'utilisation de l'énergie produite par les unités photovoltaïques pour charger les VE, contribue à réduire la consommation d'énergie non renouvelable du secteur du bâtiment et satisfait les besoins énergétiques des propriétaires de VE pour le transport. The growing trend of electric vehicles (EVs) and building integrated photovoltaics (BIPVs) is a promising means to reduce related climate change issues. EV loads can be managed via an aggregator to maximize the usage of green energy produced by photovoltaic units (PV) through smart charging strategies that exploit controllable EV demand connected to BIPV. Previous works have focused on the EV charging coordination in a smart BIPV, although without an optimization that encourages EV charging with the energy produced by the PV units. This paper proposes an aggregation strategy that maximizes a green energy index (GEI) for the smart charging coordination of EVs, which takes advantage of periods with high PV availability to charge the EV batteries; moreover, a post-processing stage for the GEI provides EV owners with information about the percentage of charged energy, period by period, that comes from PV generation. The results for a case study with 510 EVs integrated with 17 smart BIPVs show that the strategy effectively optimizes the usage of the energy produced by the PV units to charge the EVs, contributes to reduce non-renewable energy consumption of the building sector, and satisfies the EV owners' energy requirements for transportation. الاتجاه المتنامي للسيارات الكهربائية وبناء الخلايا الكهروضوئية المتكاملة (BIPVs) هو وسيلة واعدة للحد من قضايا تغير المناخ ذات الصلة. يمكن إدارة أحمال السيارات الكهربائية عبر مجمع لزيادة استخدام الطاقة الخضراء التي تنتجها الوحدات الكهروضوئية من خلال استراتيجيات الشحن الذكية التي تستغل الطلب على السيارات الكهربائية التي يمكن التحكم فيها والمتصلة بـشركة بي آي بي في. ركزت الأعمال السابقة على تنسيق شحن السيارة الكهربائية في BIPV الذكية، على الرغم من عدم وجود تحسين يشجع شحن السيارة الكهربائية بالطاقة التي تنتجها الوحدات الكهروضوئية. تقترح هذه الورقة استراتيجية تجميع تزيد من مؤشر الطاقة الخضراء (GEI) لتنسيق الشحن الذكي للمركبات الكهربائية، والتي تستفيد من الفترات ذات التوافر الكهروضوئي العالي لشحن بطاريات المركبات الكهربائية ؛ علاوة على ذلك، توفر مرحلة ما بعد المعالجة لمالكي المركبات الكهربائية معلومات حول النسبة المئوية للطاقة المشحونة، فترة تلو الأخرى، التي تأتي من توليد الطاقة الكهروضوئية. تُظهر نتائج دراسة حالة شملت 510 سيارات كهربائية مدمجة مع 17 سيارة ذكية من طراز BIPV أن الاستراتيجية تعمل على تحسين استخدام الطاقة التي تنتجها الوحدات الكهروضوئية لشحن السيارات الكهربائية بشكل فعال، وتسهم في تقليل استهلاك الطاقة غير المتجددة في قطاع البناء، وتلبي متطلبات مالكي السيارات الكهربائية من الطاقة للنقل.

Abstract Worldwide microgrid capacity is expected to reach 7 GW and a market value of $35 billion dollars in the next few years. The decentralization of the generation dispatch role and different ownership models concerning microgrids, will contribute to increase the complexity of the future power systems. Analyzing new policies and strategies as well as evaluating those impacts is only possible with the use of sophisticated simulation tools. This paper presents a scalable computational simulation to address microgrid dispatch and the impact in the electricity market. Computational intelligence techniques are integrated to improve the effectiveness of the simulation tool. These techniques include CPLEX; differential search algorithm and quantum particle swarm optimization. Each microgrid player is able to solve a day-ahead scheduling problem and submit bids to the electricity market agent (spot market), which calculates the market clearing price. The developed case study with a large number of players totaling about 150,000 consumers suggest the relevance of the developed computational framework.

Abstract Several initiates have been taken promoting clean energy and the use of local flexibility towards a more sustainable and green economy. From a residential point of view, flexibility can be provided to operators using home-appliances with the ability to modify their consumption profiles. These actions are part of demand response programs and can be utilized to avoid problems, such as balancing/congestion, in distribution networks. In this paper, we propose a model for aggregators flexibility provision in distribution networks. The model takes advantage of load flexibility resources allowing the re-schedule of shifting/real-time home-appliances to provision a request from a distribution system operator (DSO) or a balance responsible party (BRP). Due to the complex nature of the problem, evolutionary computation is evoked and different algorithms are implemented for solving the formulation efficiently. A case study considering 20 residential houses equipped each with seven types of home-appliances is used to test and compare the performance of evolutionary algorithms solving the proposed model. Results show that the aggregator can fulfill a flexibility request from the DSO/BRP by re-scheduling the home-appliances loads for the next 24-h horizon while minimizing the costs associated with the remuneration given to end-users.

The relationships between the environment and the energy sector are particularly relevant. The production and consumption of electricity are directly and indirectly responsible for some of the major negative impacts of human activity on the environment. Residential buildings have a strong impact on the electricity sector, and energy resource management models may be explored to minimize costs. This paper proposes the optimization of an energy storage system (ESS) capacity for residential use, in a single-family household, with the integration of photovoltaic (PV) generation and the use of electric vehicles (EVs) aiming to minimize electricity consumption costs. An economic viability study of the obtained solutions is also reported. The obtained results point that the optimal ESS capacity was 5.6 kWh. Furthermore, economic results show that the incentive householders' investment should be for environmental reasons.