Conference Paper

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
Source: IEEE Xplore

ABSTRACT 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.

  • [Show abstract] [Hide abstract]
    ABSTRACT: This paper proposes an advanced distribution management system (DMS) that a) monitors each component and performs protection functions using a dynamic state estimation, b) the estimated states are transmitted to the DMS where the real time model of the entire feeder is synthesized, c) uses the real time model to perform upper level optimization (operations planning) and lower level optimization (real time control) via a hierarchical optimization procedure; and d) applies proper controls to operate the system at optimal points. The proposed approach for protection, operations planning, and real time control of the system provides the infrastructure for additional important applications. As an example, the paper presents a novel application for monitoring available reserves from all resources in the system. We propose the concept of Reserve-O-Meter that monitors in real time the available reserves from all resources (utility and customer owned).
    IEEE Transactions on Smart Grid 01/2013; 4(4):2109-2117.
  • [Show abstract] [Hide abstract]
    ABSTRACT: This paper presents a novel dynamic parameter selection process to optimize the performance of a centralized load controller designed to provide intra-hour load balancing services using thermostatically controlled appliances (TCAs). An optimal set of control parameters for the controller are selected by exhaustive simulations of control variables such as the sampling time of the forecaster, the magnitude of the load balancing signal, and the temperature deadband. The effects of TCA lock-off times, ambient temperatures, heat gains, and two-way communication delays on the controller design are also modeled. Customer comfort, device life cycles, and control errors are used as metrics to evaluate the performance. The results demonstrate that the optimized controller offers satisfactory performance considering all the operational uncertainties.
    IEEE Transactions on Smart Grid 05/2013;
  • [Show abstract] [Hide abstract]
    ABSTRACT: Demand response and dynamic retail pricing of electricity are key factors in a smart grid to reduce peak loads and to increase the efficiency of the power grid. Air-conditioning and heating loads in residential buildings are major contributors to total electricity consumption. In hot climates, such as Austin, Texas, the electricity cooling load of buildings results in critical peak load during the on-peak period. Demand response (DR) is valuable to reduce both electricity loads and energy costs for end users in a residential building. This paper focuses on developing a control strategy for the HVACs to respond to real-time prices for peak load reduction. A proposed dynamic demand response controller (DDRC) changes the set-point temperature to control HVAC loads depending on electricity retail price published each 15 minutes and partially shifts some of this load away from the peak. The advantages of the proposed control strategy are that DDRC has a detailed scheduling function and compares the real-time retail price of electricity with a threshold price that customers set by their preference in order to control HVAC loads considering energy cost. In addition, a detailed single family house model is developed using OpenStudio and Energyplus considering the geometry of a residential building and geographical environment. This HVAC modeling provides simulation of a house. Comfort level is, moreover, reflected into the DDRC to minimize discomfort when DDRC changes the set-point temperature. Our proposed DDRC is implemented in MATLAB/SIMULINK and connected to the EnergyPlus model via building controls virtual test bed (BCVTB). The real-time retail price is based on the real-time wholesale price in the ERCOT market in Texas. The study shows that DDRC applied in residential HVAC systems could significantly reduce peak loads and electricity bills with a modest variation in thermal comfort.
    IEEE Transactions on Smart Grid 01/2014; 5(1):121-129.