Centralized and decentralized control for demand response
Pacific Northwest Nat. Lab., Richland, WA, USA
DOI: 10.1109/ISGT.2011.5759191 Conference: Innovative Smart Grid Technologies (ISGT), 2011 IEEE PES
Demand response has been recognized as an essential element of the smart grid. Frequency response, regulation and contingency reserve functions performed traditionally by generators are now starting to involve demand side resources. Additional benefits from demand response include peak reduction and load shifting, which will defer new infrastructure investment and improve generator operation efficiency. Technical approaches designed to realize these functionalities can be categorized into centralized control and decentralized control, depending on where the response decision is made. This paper discusses these two control philosophies and compares their response performances in terms of delay time and predictability. A distribution system model with detailed household loads and controls is built to demonstrate the characteristics of the two approaches. The conclusion is that the promptness and reliability of decentralized control should be combined with the controllability and predictability of centralized control to achieve the best performance of the smart grid.
Available from: Innocent Kamwa
- "Authors in  classified these types of DR into two control categories: a centralized one in which we find DLC and a decentralized one that contains ARC. All types of DR control are applicable to medium and large industrial and commercial loads, but only dynamic pricing, DLC and ARC could be available for domestic appliances. "
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ABSTRACT: High penetration of renewable energy, bringing generation closer to consumption and splitting power system to micro grids after disturbances are trends and actions of our future power system. Thus, providing ancillary services will be a challenging task due to the lacks of conventional spinning reserves and storage devices. Demand response is an attractive way to provide ancillary services for smart grid due to its two way communication and costumer contribution in the control of the grid. This paper presents a centralized-decentralized control of responsive demand to enable primary and 10-min reserves. Two control technics are applied on each control mode: Multi-Band power system stabilizer and a droop control (DC). The effectiveness of these control strategies is demonstrated on the modified IEEE 14 bus system connected to a 14 bus distribution operating at 100% of its capacity and subjected to a severe loss of generation.
- "Hubert & Grijalva (2011) have proposed decentralized optimization techniques based on user behaviour patterns. Lu et al. (2011) have showed that combining centralized and decentralized control mechanisms may lead to the best performing SG. The approach considered, tackles the problem of decentralized control mechanisms only relying on unidirectional PCF signals. "
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ABSTRACT: Renewable power generation using wind and solar energy is central to most strategies to reduce carbon emissions and mitigate climate change. As the share of these power sources grows, their inherently transient behavior effects substantial changes in traditional electricity pricing patterns. Therefore, stock market electricity prices have been proposed as cost functions to drive optimal control techniques for demand side management. De-mand shifting to no-peak-times can thus be formulated as an optimization problem, min-imizing operational costs of devices. We use one-way communicated pseudo cost func-tions to drive optimization. Mathematical models of the controlled devices provide the state of the system and hence information about the quality of service, which in turn is used to formulate optimization constraints. This one-way communication approach pro-tects user privacy. Furthermore, local data acquisition without feedback to a central con-trol system allows for fast and continuous adaptation to user and environmental influ-ences as well as system changes. This general framework is used to develop and com-pare optimization techniques for resistive hot water heaters (RHWH), which have a long history of use for load balancing by centralized control mechanisms such as teleswitching and time-of-use (TOU) tariffs. Optimization algorithms, based on different thermal RHWH models (fully-mixed and multi-node), are implemented in MATLAB. By data mining historic hot water draws, the future demand is estimated using a k-nearest neighbor algorithm. The implementation is tested in an annual simulation using EXAA’s historic (2013) day-ahead prices together with simulated hot water demand profiles. Resulting cost and ener-gy savings relative to rigid night tariff switching are presented.
Available from: jocet.org
- "Our objective is to manage the available resources efficiently and effectively so as to save both energy and cost. In order to achieve this, we use the demand-response (DR) model . Based on a customer's demand, the available sources of energy are scheduled to obtain an optimized solution. "
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