Método para optimizar los costos del servicio de energía eléctrica de grandes usuarios en Colombia, incorporando flexibilidad de la demanda

Mediante un análisis de los diferentes métodos utilizados a nivel internacional para incentivar la respuesta de la demanda de energía a los precios del mercado, en este trabajo se propone el método que mejor se adapta al sistema y al mercado de energía en Colombia, con el objetivo de lograr beneficios para todos los actores que participan del negocio de la energía eléctrica en el país -- El análisis microeconómico, con casos de simulación de las transacciones en el mercado de energía, permite concluir que, en Colombia, el uso generalizado del modelo que considera Tiempo de Uso –TOU–, con la participación de los grandes Usuarios No Regulados –UNR–, en respuesta a una señal de precio de bolsa en Tiempo Real, mediante la flexibilidad de sus procesos productivos, representa beneficios importantes, sin necesidad de realizar modificaciones regulatorias ni efectuar inversiones en infraestructura para lograr la implementación del modelo -- Este método es muy beneficioso para el mercado eléctrico colombiano, ya que incentiva a los grandes usuarios nacionales para que en el transcurso del día consuman energía eléctrica en las horas de demanda mínima, denominadas horas de valle, y adicionalmente reduzcan los consumos de energía en los periodos de demanda máxima, llamados horas de pico, en consideración a los altos costos de la energía en estos periodos del día, buscando de este modo reducir los consumos en demanda máxima y aumentarlos en demanda mínima, lo que finalmente representaría una menor variabilidad de los consumos de energía diaria -- El resultado será lograr un aplanamiento de la curva de carga, lo cual conlleva una optimización de los costos de la producción de energía eléctrica en Colombi

Near Optimal Demand-Side Energy Management Under Real-time Demand-Response Pricing

1

Smart home energy management: An analysis of a novel dynamic pricing and demand response aware control algorithm for households with distributed renewable energy generation and storage

Home energy management systems (HEMS) technology can provide a smart and efficient way of optimising energy usage in residential buildings. One of the main goals of the Smart Grid is to achieve Demand Response (DR) by increasing end users’ participation in decision making and increasing the level of awareness that will lead them to manage their energy consumption in an efficient way. This research presents an intelligent HEMS algorithm that manages and controls a range of household appliances with different demand response (DR) limits in an automated way without requiring consumer intervention. In addition, a novel Multiple Users and Load Priority (MULP) scheme is proposed to organise and schedule the list of load priorities in advance for multiple users sharing a house and its appliances. This algorithm focuses on control strategies for controllable loads including air-conditioners, dishwashers, clothes dryers, water heaters, pool pumps and electrical vehicles. Moreover, to investigate the impact on efficiency and reliability of the proposed HEMS algorithm, small-scale renewable energy generation facilities and energy storage systems (ESSs), including batteries and electric vehicles have been incorporated. To achieve this goal, different mathematical optimisation approaches such as linear programming, heuristic methods and genetic algorithms have been applied for optimising the schedule of residential loads using different demand side management and demand response programs as well as optimising the size of a grid connected renewable energy system. Thorough incorporation of a single objective optimisation problem under different system constraints, the proposed algorithm not only reduces the residential energy usage and utility bills, but also determines an optimal scheduling for appliances to minimise any impacts on the level of consumer comfort. To verify the efficiency and robustness of the proposed algorithm a number of simulations were performed under different scenarios. The simulations for load scheduling were carried out over 24 hour periods based on real-time and day ahead electricity prices. The results obtained showed that the proposed MULP scheme resulted in a noticeable decrease in the electricity bill when compared to the other scenarios with no automated scheduling and when a renewable energy system and ESS are not incorporated. Additionally, further simulation results showed that widespread deployment of small scale fixed energy storage and electric vehicle battery storage alongside an intelligent HEMS could enable additional reductions in peak energy usage, and household energy cost. Furthermore, the results also showed that incorporating an optimally designed grid-connected renewable energy system into the proposed HEMS algorithm could significantly reduce household electricity bills, maintain comfort levels, and reduce the environmental footprint. The results of this research are considered to be of great significance as the proposed HEMS approach may help reduce the cost of integrating renewable energy resources into the national grid, which will be reflected in more users adopting these technologies. This in turn will lead to a reduction in the dependence on traditional energy resources that can have negative impacts on the environment. In particular, if a significant proportion of households in a region were to implement the proposed HEMS with the incorporation of small scale storage, then the overall peak demand could be significantly reduced providing great benefits to the grid operator as well as the households

Sistema de otimização de resposta à demanda para redes elétricas inteligentes

Resumo: O Gerenciamento pelo Lado da Demanda (GLD) é o planejamento e implementação de atividades para influenciar o uso de eletricidade do consumidor de maneira que produza mudanças desejadas na curva de carga de um sistema elétrico. Embora seja um tema discutido desde meados da década de 1980, o advento de redes elétricas inteligentes traz, devido a uma maior integração da Tecnologia da Informação e Comunicação (TIC) com Sistemas Elétricos de Potência (SEP), simultaneamente novas oportunidades e desafios para o GLD, possibilitando uma efetiva extensão das atividades da concessionária de energia para o cliente e abrindo uma nova dimensão do planejamento e operação da distribuição de energia elétrica. Análises criteriosas são fundamentais quando do planejamento de um programa de GLD para que a concessionária obtenha benefícios técnico-econômicos tais como a postergação de investimentos e alívio de sobrecarga, mas não perca receita desnecessariamente. A fim de auxiliar concessionárias no planejamento de programas de Resposta à Demanda com Base em Tarifas (RDBT) (uma das alternativas para o GLD), essa dissertação propõe um sistema para otimização desses programas que foca na seleção de clientes residenciais em um alimentador. Para isso, foram desenvolvidas duas abordagens: híbrida e heurística. A primeira contou com duas técnicas distintas, Fluxo de Potência Ótimo (FPO) para determinação inicial de reduções por barra seguido de otimização binária por enxame de partículas (do inglês, Binary Particle Swarm Optimization (BPSO)) para a seleção dos clientes via otimização global ou por barra. A segunda realizou otimização global somente com BPSO. O sistema foi testado utilizando curvas de carga de clientes residenciais, dados de um alimentador de distribuição radial, matrizes de elasticidade dos clientes perante sinais tarifários, assim como a tarifa branca. Os principais resultados apontam que o sistema é de grande valia para concessionárias de energia analisarem e otimizarem programas de RDBT. A abordagem híbrida com otimização por barra apresentou o melhor compromisso entre custo computacional e atingimento do objetivo de redução. A abordagem heurística apresentou resultados melhores no atingimento da meta de redução, todavia com custo computacional que pode inviabilizar sua aplicação em concessionárias. As diferentes abordagens desenvolvidas apresentam um panorama para compreensão da utilização de técnicas de otimização de seleção de clientes e permitem visualização de aplicações futuras

Stigmergy-based Load Scheduling in a Demand Side Management Context

This work proposes an approach, based on a fundamental coordination mechanism from nature, namely stigmergy. The proposed meta-heuristic is utilized to distributively calculate global schedules for a population of customers provided with intelligent devices. These schedules maximize renewable energy sources utilization. Furthermore, this approach is adapted and utilized as a coordination mechanism of autonomous customers to modify their consumption behavior in a real-time optimization context

Proceedings of the 6th International Conference EEDAL'11 Energy Efficiency in Domestic Appliances and Lighting

This book contains the papers presented at the sixth international conference on Energy Efficiency in Domestic Appliances and Lighting. EEDAL'11 was organised in Copenhagen, Denmark in May 2011. This major international conference, which was previously been staged in Florence 1997, Naples 2000, Turin 2003, London 2006, Berlin 200h9a s been very successful in attracting an international community of stakeholders dealing with residential appliances, equipment, metering liagnhdti ng (including manufacturers, retailers, consumers, governments, international organisations aangde ncies, academia and experts) to discuss the progress achieved in technologies, behavioural aspects and poliacineds , the strategies that need to be implemented to further progress this important work. Potential readers who may benefit from this book include researchers, engineers, policymakers, and all those who can influence the design, selection, application, and operation of electrical appliances and lighting.JRC.F.7-Renewable Energ