Demand Response Programs Definition Using Demand Price Elasticity to Define Consumers Aggregation for an Improved Remuneration Structure

The positioning of the consumers in the power systems operation has been changed in the recent years, namely due to the implementation of competitive electricity markets. Demand response is an opportunity for the consumers’ participation in electricity markets. Smart grids can give an important support for the integration of demand response. The methodology proposed in the present paper aims to create an improved demand response program definition and remuneration scheme for aggregated resources. The consumers are aggregated in a certain number of clusters, each one corresponding to a distinct demand response program, according to the economic impact of the resulting remuneration tariff. The knowledge about the consumers is obtained from its demand price elasticity values. The illustrative case study included in the paper is based on a 218 consumers’ scenario

A Study of Energy Management Systems and its Failure Modes in Smart Grid Power Distribution

abstract: The subject of this thesis is distribution level load management using a pricing signal in a smart grid infrastructure. The project relates to energy management in a spe-cialized distribution system known as the Future Renewable Electric Energy Delivery and Management (FREEDM) system. Energy management through demand response is one of the key applications of smart grid. Demand response today is envisioned as a method in which the price could be communicated to the consumers and they may shift their loads from high price periods to the low price periods. The development and deployment of the FREEDM system necessitates controls of energy and power at the point of end use. In this thesis, the main objective is to develop the control model of the Energy Management System (EMS). The energy and power management in the FREEDM system is digitally controlled therefore all signals containing system states are discrete. The EMS is modeled as a discrete closed loop transfer function in the z-domain. A breakdown of power and energy control devices such as EMS components may result in energy con-sumption error. This leads to one of the main focuses of the thesis which is to identify and study component failures of the designed control system. Moreover, H-infinity ro-bust control method is applied to ensure effectiveness of the control architecture. A focus of the study is cyber security attack, specifically bad data detection in price. Test cases are used to illustrate the performance of the EMS control design, the effect of failure modes and the application of robust control technique. The EMS was represented by a linear z-domain model. The transfer function be-tween the pricing signal and the demand response was designed and used as a test bed. EMS potential failure modes were identified and studied. Three bad data detection meth-odologies were implemented and a voting policy was used to declare bad data. The run-ning mean and standard deviation analysis method proves to be the best method to detect bad data. An H-infinity robust control technique was applied for the first time to design discrete EMS controller for the FREEDM system.Dissertation/ThesisMasters Thesis Electrical Engineering 201

Optimización multiobjetivo del consumo eléctrico basado en la respuesta a la demanda

The following document deals with the use of energy saving systems supported by multiobjective methods that allow to minimize the electric consumption of a commercial user in a certain time and to reduce the excessive payment in the electric template, this was realized applying the use of the answer the electric demand by means of the transfer of electric charges without representations of the commercial user towards schedules outside the peaks countries within the national interconnected system, this application is directed to an energy saving as well as the reduction in the use of obsolete equipment that are connected to The electricity grid can also reduce CO2 emissions to the environment by starting and operating thermal power plants that are considered backup power stations in the hours of maximum electrical demand. The mechanisms of demand management allow the distributors of the companies give a better service without affecting the customers by the disconnection of the power abruptly, they also allow to apply mechanisms of control depending on the client and the form of programs of electrical reliability that is handled in each of the countries to which it applies.El siguiente documento trata sobre la utilización de sistemas de ahorro energético apoyado en métodos multiobjetivos que permitan minimizar el consumo eléctrico de un usuario comercial en un determinado tiempo y bajar el pago excesivo en la planilla eléctrica, esto se lo realizará aplicando el uso de la respuesta a la demanda eléctrica mediante el traslado de cargas eléctricas no representativas del usuario comercial hacia horarios fuera de los considerados picos dentro del sistema nacional interconectado, esta aplicación es dirigida a un ahorro energético así como la reducción en el uso de equipos obsoletos que están conectados a la red eléctrica, además se puede disminuir las emisiones de CO2 al ambiente por el arranque y funcionamiento de centrales térmicas que son consideradas centrales de respaldo en las horas de máxima demanda eléctrica. Los mecanismos de manejo de la demanda permiten a las empresas distribuidoras dar un mejor servicio sin afectar al cliente por la desconexión de la energía bruscamente, además permite aplicar mecanismos de control dependiendo del cliente y la forma de programas de confiabilidad eléctrica que se maneje en cada uno de los países a los que se aplique

Application of demand response programs for peak reduction using load aggregator

O aumento do consumo de energia requer atenção. Os especialistas propuseram muitas soluções para otimizar o uso de energia e propõem um sistema de gestão de energia eficiente. No entanto, desenvolver um sistema de energia que contempla agregadores de carga é óbvio para aprimorar o processo de gestão de energia. Este trabalho discute um sistema de gestão de energia para implementar programas de Demand Response (DR) usando abordagens de agregação de carga. Neste trabalho, dois estudos de caso comparam as diferentes respostas do sistema. O objetivo principal é discutir o papel de diferentes modelos de agregador de carga no sistema de energia, implementando um programa de DR. Esses agregadores de carga controlam diferentes tipos de cargas. Neste contexto, vários tipos de cargas domésticas são consideradas cargas controláveis. No processo de agregação, o objetivo é agregar as cargas que possuem as mesmas características usando a análise de agrupamento das cargas. A contribuição científica desta dissertação está relacionada com a redução da ponta e a agregação de cargas, considerando as cargas controláveis e os recursos de geração no sistema. Para atingir o objetivo anterior, foram realizados dois estudos de caso. Cada estudo de caso consiste em três cenários baseados no modelo de agregação de carga. Os resultados dos estudos indicam as respostas do sistema aos diferentes cenários e ilustram os méritos do modelo de agregador de carga. Além disso, os resultados demonstram como o agrupamento dos dispositivos de carga no sistema pode efetivamente fornecer redução de pico com recurso a programas de DR.The increment of energy consumption takes a high level of attention. The experts have proposed many solutions to optimize energy use and propose an efficient energy management system. However, verifying the load aggregators' role energy system is obvious to enhance the energy management process. This work discusses an energy management system to implement DR programs using load aggregation approaches. In this work, two case studies compare the different responses of the system. The main goal is to discuss the role of different load aggregator models in the power system by implementing a DR program. Those load aggregators control different types of loads. In this context, various types of domestic loads are considered controllable loads. In the aggregation process, the goal is to aggregate the loads that have the same features using the clustering analysis of the loads. The scientific contribution of this thesis is related to the integration of providing the peak reduction and the clustered aggregation of loads, considering the controllable loads and generation resources in the system. To achieve the previous goal, two case studies have been done. Each case study consists of three scenarios based on the load aggregation model. The results of the case studies indicate the system responses to the different scenarios and illustrate the merits of the load aggregator model. Furthermore, the results demonstrate how clustering the load appliances in the system can effectively provide peak reduction due to the DR programs

Análise de otimização energética de um edifício comercial e residencial

Com as alterações climáticas a surgirem cada vez mais em todo o planeta, como sociedade devemos procurar por formas de reduzir e mitigar o nosso impacto. A otimização energética e o uso eficiente de energia, visam melhorar a eficácia de consumo e reduzir perdas. O presente trabalho apresenta a análise energética de um edifício com quatro pisos, áreas residenciais, comerciais e escritórios, e a comparação dos seus desempenhos e balanços energéticos. Este estudo vem em seguimento do trabalho feito anteriormente, e procura expandir o escopo de resultados obtidos, para outros tipos de consumidores de energia. O edifício e os equipamentos de consumo de energia elétrica foram definidos para posteriormente serem sujeitos a simulação. A otimização foi feita por um algoritmo de múltiplos períodos, tendo em conta o conceito de resposta da procura (demand response). Foram estudados dois cenários com os dados de entrada usados no estudo anterior, como referência, e novos dados selecionados especificamente para o edifício modelado. Os cenários, provaram o funcionamento do algoritmo de otimização com reduções de consumo de 5%, ao longo do dia. Adicionalmente, o algoritmo foi capaz de deslocar com sucesso o funcionamento de máquinas de lavar, e da bombagem do edifício. Por fim, foi possível comprovar o funcionamento da otimização, com utilizadores residenciais e comerciais, e considerando o conforto dos mesmos.With climate change emerging more and more across the planet, as a society we must look for ways to reduce and mitigate our impact. The optimization and the efficient use of energy aim to improve the efficacy of consumption and reduce losses. The present work presents the energy analysis of a building with four floors, residential, commercial and office areas, and the comparison of its performance and energy balances. This study follows on from the work done previously, and seeks to expand the scope of results obtained, for other types of energy consumers. The building and the appliances used to consume electricity were defined to be subsequently subject to simulation. The optimization was done by a multi-period algorithm, considering the demand response concept. Two scenarios were studied with the input data used in the previous study, as a reference, and new data selected specifically for the modeled building. The scenarios proved the operation of the optimization algorithm with consumption reductions of 5%, throughout the day. Additionally, the algorithm was able to successfully displace the operation of washing machines, and the pumping of the building. Finally, it was possible to prove the operation of the optimization, with residential and commercial users, and considering their comfort

Modelo computacional para manejo educativo de la respuesta a la demanda de energía eléctrica en el hogar

En el constante consumo de energía eléctrica en los hogares, oficinas y demás, es importante un monitoreo para verificar y cuantificar el gasto por electrodoméstico y equipo electrónico dentro del consumo diario, dependiendo de la hora en la que se desee utilizar dicho aparato. Una estrategia pedagógica es la mejor forma de aprendizaje para el consumidor cotidiano, usuario final del sistema para conocer sus gastos y mejorar sus hábitos de consumo. Debido a las tecnologías disponibles y el uso de teléfonos celulares inteligentes y tabletas, la forma más versátil de llegar a los consumidores es a través de una aplicación móvil de interfaz sencilla y práctica, que de manera didáctica provea las herramientas necesarias para participar en un programa de respuesta a la demanda. Los hogares según su consumo pueden hacer parte de diferentes programas de respuesta a la demanda, por eso, a través de los medidores inteligentes de energía eléctrica se adquirió información del consumo de un hogar, para establecer un comportamiento y posteriormente las horas pico y valle en dicha casa. Una vez hecho esto y basándose en programas de respuesta a la demanda, se asignó uno al usuario garantizando reducir el costo de su consumo. Todo esto fue hecho a partir de un algoritmo que, con los datos adquiridos por los medidores inteligentes, cuantificó y almacenó el consumo de dicha casa en una base de datos y por medio de esta disponibles en la aplicación móvil, que con los datos de consumo y a través de una aplicación didáctica, cada usuario aprende de su consumo y como reducirlo por medio de un programa de respuesta a la demanda previamente diseñado

GAIC: Um sistema inteligente e flexível para simulação e apoio à participação de pequenos e médios consumidores na gestão ativa de cargas no âmbito de smart grids

A liberalização dos mercados de energia e a utilização intensiva de produção distribuída tem vindo a provocar uma alteração no paradigma de operação das redes de distribuição de energia elétrica. A continuidade da fiabilidade das redes de distribuição no contexto destes novos paradigmas requer alterações estruturais e funcionais. O conceito de Smart Grid vem permitir a adaptação das redes de distribuição ao novo contexto. Numa Smart Grid os pequenos e médios consumidores são chamados ao plano ativo das participações. Este processo é conseguido através da aplicação de programas de demand response e da existência de players agregadores. O uso de programas de demand response para alcançar benefícios para a rede encontra-se atualmente a ser estudado no meio científico. Porém, existe a necessidade de estudos que procurem benefícios para os pequenos e médios consumidores. O alcance dos benefícios para os pequenos e médios consumidores não é apenas vantajoso para o consumidor, como também o é para a rede elétrica de distribuição. A participação, dos pequenos e médios consumidores, em programas de demand response acontece significativamente através da redução de consumos energéticos. De modo a evitar os impactos negativos que podem provir dessas reduções, o trabalho aqui proposto faz uso de otimizações que recorrem a técnicas de aprendizagem através da utilização redes neuronais artificiais. Para poder efetuar um melhor enquadramento do trabalho com as Smart Grids, será desenvolvido um sistema multiagente capaz de simular os principais players de uma Smart Grid. O foco deste sistema multiagente será o agente responsável pela simulação do pequeno e médio consumidor. Este agente terá não só que replicar um pequeno e médio consumidor, como terá ainda que possibilitar a integração de cargas reais e virtuais. Como meio de interação com o pequeno e médio consumidor, foi desenvolvida no âmbito desta dissertação um sistema móvel. No final do trabalho obteve-se um sistema multiagente capaz de simular uma Smart Grid e a execução de programas de demand response, sSendo o agente representante do pequeno e médio consumidor capaz de tomar ações e reações de modo a poder responder autonomamente aos programas de demand response lançados na rede. O desenvolvimento do sistema permite: o estudo e análise da integração dos pequenos e médios consumidores nas Smart Grids por meio de programas de demand response; a comparação entre múltiplos algoritmos de otimização; e a integração de métodos de aprendizagem. De modo a demonstrar e viabilizar as capacidades de todo o sistema, a dissertação inclui casos de estudo para as várias vertentes que podem ser exploradas com o sistema desenvolvido.The liberalization of electricity markets and the intensive use of distributed generation have been changing the paradigm of the electrical distribution networks operation. The continuity of distribution networks reliability in the context of these new paradigms requires structural and functional changes. The smart grid context allows the adaptation of the distributed generation to this new context. In a smart grid, small and medium consumers have to participate. This process is accomplished by the application of demand response programs and by aggregated players. The use of demand response programs to achieve benefits for the network is currently being studied in the scientific field. However, studies that seek for benefits for small and medium consumers are necessary. Reaching the benefits for small and medium consumers is not only advantageous for the consumer, but also for the electrical distribution network. The participation of small and medium consumers in demand response programs takes place mostly by reducing the energy consumption. In order to avoid negative impacts that may arise from these reductions, the work proposed makes use of optimizations that use learning through the artificial neural networks. For a better explaining the work with Smart Grids, a multiagent system capable of simulating the main players of a Smart Grid will be developed. The aim of this multiagent system will be the agent responsible for the simulation of the small and medium consumers. This agent must replicate the small and medium consumer, as well as enable the integration of real and virtual loads. As a means of interaction with the small and medium consumer, a mobile system has been developed in the scope of this dissertation. At the end of this work, it was obtained a multiagent system capable of simulating a smart grid, and the implementation of demand response programs. In this way, the agent responsible for the small and medium consumers capable of taking action and reactions in order to respond autonomously to the demand response programs of the network. The development of the system allows: the study and analysis of the integration of small and medium consumers in smart grids through demand response programs; comparing multiple optimization algorithms; and the integration of learning methods. For demonstrating and facilitating the capabilities of the whole system, the dissertation comprises case studies for the several ways that can be explored with the developed system

Multi agent system framework for demand response management in distribution networks

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonThe inexorable increase in penetration of clean energies and responsive loads in the Distribution Network (DN), introduces new technical challenges for network operators. The responsibilities of Distribution Network Operators (DNOs) are being adjusted to cope with current system challenges and they are transitioning to Distribution System Operators (DSOs), taking a more active role in dynamically managing power flows across the network. Further, the advancement in distribution automation technologies provides greater opportunities for energy consumers to take more effective participation in demand reduction schemes and DSOs can be enablers of Demand Response (DR). Hence, the functionality of DR can be considered an alternative, lower cost, carbon-saving and flexible solution to defer network reinforcement. This forms the rationale behind this thesis, which aims to provide an in-depth investigation of the potential responsiveness in residential demand and its effect on constraint management of the DN. The main contribution of this thesis is the design, development and implementation of a Multi Agent System (MAS) framework for active DN management through residential DR. One advantage of the proposed platform is the capability of integrating both centralised and decentralised DR control mechanisms. It employs the responsiveness of demand from both loads shifting and shedding through price-based and incentive-based DR respectively. The feasibility and effectiveness of such a platform has been evaluated by developing and implementing the DR mechanism in three levels. The DR algorithm for several static and dynamic electricity tariffs, (Time of Use (ToU), Day-Ahead (DA) and Real Time Pricing (RTP)), is designed and developed in Low Voltage (LV) feeders. This is then expanded and implemented in a Medium Voltage (MV) feeder under an RTP environment. Finally, two incentive-based DR schemes: emergency and local community DR, are merged in the MV/LV network to improve its reliability and security. The implementation of the MAS framework demonstrated its configurability and scalability through three case studies under different scenarios. One novel aspect of this research is the consideration of customers’ characteristics in the design of the DR algorithms. In addition, at MV level, the tariffs and the required DR are allocated to each LV feeder specifically taking into account their DR potential and participation effects on the overall network performance. The simulation results at LV level show that maximum peak demand reduction and the most flattened load profile are achieved with RTP. At MV/LV network, MAS provides a community environment where the consumers can collaborate to decrease their overall demand. Moreover, the local community can reduce their dependency on the grid during daytime with PV generation. It is concluded that DR trading can benefit all players economically and also lessen DN violations from stipulated limits