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Enabling Automated Dynamic Demand Response: From Theory to Practice
Abstract and Figures
Demand response (DR) is a technique used in smart grids to shape customer load during peak hours. Automated DR offers utilities a fine grained control and a high degree of confidence in the outcome. However the impact on the customer's comfort means this technique is more suited for industrial and commercial settings than for residential homes. In this paper we propose a system for achieving automated controlled DR in a heterogeneous environment. We present some of the main issues arising in building such a system, including privacy, customer satisfiability, reliability, and fast decision turnaround, with emphasis on the solutions we proposed. Based on the lessons we learned from empirical results we describe an integrated automated system for controlled DR on the USC microgrid. Results show that while on a per building per event basis the accuracy of our prediction and customer selection techniques varies, it performs well on average when considering several events and buildings.
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... Scalable solutions will be required to enable fast and reliable control [1] during demand response (DR) [6] events. The scale of the required interconnections raises many challenges in terms of privacy and security [2], automated control strategies [3], and software solutions for efficient near real-time responsiveness [4] to predictable and unpredictable events. The first steps are already being taken in the form of smart devices, and monitoring and energy management systems (cf. ...
As smart homes and smart grids become ubiquitous their interactions will become crucial for optimizing energy consumption at large scale at residential level. Scalable solutions will be required to enable fast and reliable control during demand response. While management solutions have been proposed they do not focus on the scalability issues of the processing system. Handling continuous and variable Big Data streams can easily saturate existing systems. In this paper we propose a scalable cloud based architecture and prototype system for handling smart home data flows. The system can support near real time decisions for 10,000 customers each having 10 sensors with only 35 commodity machines running free cloud software. The platform is automated and can be used to directly control the customers’ smart home or to send recommendations. Some initial experiments are performed to show the benefits of smart recommendations.
... The OpenADR protocol is applied to a microgrid system. Frincu et al. develop a campus-level microgrid that consists of heterogeneous energy equipment and an automated building control center [21,22]. They implement the protocol to run numerous demand response scenarios. ...
An Automated Demand Response is the most fundamental energy service that contributes to balancing the power demand with the supply, in which it realizes extensive interoperations between the power consumers and the suppliers. The OpenADR specification has been developed to facilitate the service communications, and several facilities offer primitive forms of services in a retail market. However, few researches have reported the details of such a real-world service yet, and thus we are still unaware of how it works exactly. Instead, we rely on our textbooks to design next generations of the ADR service. To overcome the discrepancy of our understanding, this paper shares our hand-on experiences on the commercialized ADR service. In particular, we deploy smart submeters to manage energy loads and install an energy management system in a small commercial facility, helping the owner participate in the ADR service that a local utility offers. The building owner makes a service contract with a qualified load aggregator based on her curtailment rate, a reference point that decides the success of her load curtailment. With the rate, the customer facility participates in three DR events for tests that last for 2, 1, and 3 hours, respectively. Our experimental results are illustrated with discussions on various aspects of the service.
Various aspects of machine to machine (M2M) technology in demand responsive commercial buildings are discussed. M2M is used to describe technologies that enable computers, embedded processors, smart sensors and actuators to communicate with one another, take measurement, and take decisions, often without human intervention. Using Extensible Markup Language (XML), the server provided a constant stream of 15-min-ahead pricing. It has been found that the remote monitoring and control of enterprise and energy-management control system (EMCS) and EIS data available over the Internet prior to the test.
This paper presents the technical and architectural issues associated with automating Demand Response (DR) programs. The paper focuses on a description of the Demand Response Automation Server (DRAS), which is the main component used to automate the interactions between the Utilities and their customers for DR programs. Use cases are presented that show the role of the DRAS in automating various aspects of DR programs. This paper also describes the various technical aspects of the DRAS including its interfaces and major modes of operation. This includes how the DRAS supports automating such Utility/Customer interactions as automated DR bidding, automated DR event handling, and finally real-time pricing.
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