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Coordinated management of residential loads in large-scale systems

Safdarian, A ; Sharif University of Technology | 2018

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  1. Type of Document: Article
  2. DOI: 10.1007/978-3-319-74412-4_10
  3. Publisher: Springer International Publishing , 2018
  4. Abstract:
  5. Recent developments in communication systems and protocols led to the greater attention of energy industry to future smart grids. As one of the key aspects of smart grids, demand response is a promising tool for increasing the capabilities, efficiency, and reliability of power systems. Demand response programs focus on the flexibility potentials in demand, instead of paying full attention to the efficiency of energy system infrastructures. So far, several utilities around the world have planned to realize potential benefits of flexible demand of their consumers. However, participation in most of the programs is dominated by large industrial consumers. This focus on large consumers is mostly due to the lack of knowledge among small consumers on how to participate in demand response programs as well as their difficulty in manually responding to the utility signals. This is in spite of the key role that residential consumers can play since more than one-third of electricity produced worldwide is consumed by residential sector. In order to achieve potential benefits of the consumers, automated home load management modules have been proposed to optimize operating status of flexible appliances in response to what happens in the system. These modules autonomously operate the appliances just to simplify consumers response to the signals broadcasted by the operators. The autonomous operation of the modules, however, may threaten the network efficiency since negative consequences like peak rebound during periods with lower prices are likely. In order to prevent such consequences, effective coordination frameworks are necessary to coordinate operating status of the appliances belong to different consumers. In this regard, a few coordination frameworks have been proposed in the literature. Some of them are in the centralized fashion wherein individual appliances data and consumers preferences are gathered in a control center where an optimization model is solved to optimize operation of all appliances. These centralized frameworks, although effective in avoiding the consequences, may jeopardize consumers privacy, lead to congestion in communication infrastructures, and result in crash of control center processors. In order to avoid these challenging issues, some distributed approaches have been proposed in the literature. The approaches are based on different theories such as game theory. The main characteristics of the approaches refer to their affordable run time and computational burden as well as to their ability in attaining the global or near-optimal solution. This chapter focuses on the concepts, models, and approaches associated with coordinating demand response potentials by residential consumers. After a brief explanation on the importance of activating demand response by residential consumers, mathematical model for optimal operation management of individual residential consumers is thoroughly presented. Then, some descriptions and observations are provided to highlight the necessity of coordination framework to avoid negative consequences of autonomous responses. A four-consumer system is simulated to demonstrate how autonomous response by consumers may lead to severe peak rebounds. These descriptions are followed by a review on centralized coordination frameworks and the associated challenging issues. Applying the coordination framework to the four-consumer system, the raised peak rebound is significantly alleviated. However, it is demonstrated how complexity of the model in centralized framework grows as the number of consumers increases. Then, two sequential and non-sequential distributed coordination frameworks are presented. The two frameworks are applied to the four-consumer system. Although both of the frameworks are effective in mitigating the peak rebound, the non-sequential framework is faster. Finally, the chapter is concluded by providing a summary over the provided materials. © Springer International Publishing AG, part of Springer Nature 2018
  6. Keywords:
  7. Autonomous response ; Centralized approach ; Coordinated response ; Demand response ; Distributed approach ; Energy expense ; Non-sequential approach ; Peak rebound ; Residential consumer ; Sequential approach ; Smart grid ; System-wide coordination
  8. Source: Studies in Systems, Decision and Control ; Volume 145 , 2018 , Pages 151-171 ; 21984182 (ISSN)
  9. URL: https://link.springer.com/chapter/10.1007%2F978-3-319-74412-4_10