Kui Shan

A power limiting control strategy based on adaptive utility function for fast demand response of buildings in smart grids

Power imbalance in electrical grid operation has become a most critical issue that results in a series of problems to grids and end-users. The end-users at demand side can actually take full advantage of their power reduction potentials to alleviate the power imbalance of an electrical grid. Buildings, as the major energy end-users, could play an important role on power demand response in smart grids. This article presents a fast power demand limiting control strategy in response to the sudden pricing changes or urgent requests of grids within a very short time, i.e., minutes. The basic idea is to shut down some of active chillers during demand response events for immediate power demand reduction. The article focuses on the solutions to address the operation problems caused by the conventional control logics, particularly the disordered flow distribution in chilled water system. A water flow supervisor based on an adaptive utility function is developed for updating the chilled water flow set-point of every individual zone online. The objective is to maintain even indoor air temperature change among all zones during a demand response period. A case study is conducted in a simulation platform to test and validate the novel control strategy. Test results show that the proposed control strategy can achieve fast power reduction after receiving a demand response request. Simultaneously, the proposed control strategy can effectively solve the problem of disordered water distribution and achieve the similar changing profiles of the thermal comfort among different zones under the reduced cooling supply.

Building demand response and control methods for smart grids: A review

Demand response provides a solution to the grid imbalance problems which restrict the use of renewable energy. Since buildings possess a high amount of flexible load, they could contribute to smart grid stability and achieve cost savings for buildings and the other participants in the smart grid. However, the research and application of building demand response are still at the beginning stage. Majority of the existing demand response studies are from the view point of the grid side, rather than the demand side. This article, therefore, provides an overview on the studies on building demand response from the view point of buildings at the demand side. It mainly consists of two parts: (1) an overview of different types of demand response programs, and the status of the demand response programs in several countries and regions; (2) a review on the control methods for demand response in commercial and residential buildings. This review intends to support the further development of demand response methods for future smart grid applications and their implementation in buildings.

Sensitivity and uncertainty analysis of measurements in outdoor airflow control strategies

Control of HVAC systems is critical to building energy efficiency and indoor environment. Almost all control systems require real-time measurements. However, the measurements inherently contain errors that inevitably impact the outputs of control systems. Therefore, it is of great interest to analyze the impacts of the measurement sensitivity and uncertainty on the control of HVAC systems. This article presents a sensitivity and uncertainty analysis method for HVAC optimal control strategies. The critical measurements in a control strategy are identified by using sensitivity analysis. The uncertainty analysis provides information for further risk analysis. A modified exponentially weighted moving average filter is developed and adopted to reduce the uncertainty of control strategies. The sensitivity and uncertainty analysis is conducted on demand-controlled ventilation strategies for multi-zone office buildings. Improvements of the control strategies based on the analysis results are also conducted. Results demonstrate the sensitivity and uncertainty analysis are effective in evaluating control strategies. The accuracy of the control strategy is significantly improved, and the uncertainty is reduced.

Sensitivity and uncertainty analysis of cooling water control strategies

Many studies have been focusing on optimal control of HVAC systems. Various optimal control methods were developed by researchers. However, the uncertainties in the optimal control strategies were rarely considered. The uncertainties commonly exist in the measurements of real plants. These measurement uncertainties impact the outputs of optimal control strategies. As a result, the risk of implementing the optimal control strategies in real systems should be analyzed. This article presents a method for analyzing the impacts of measurement uncertainties on control strategies. The method is used to evaluate control strategies for a typical cooling water system. Results show that the method applied in the uncertainty analysis is effective in estimating the uncertainty propagation in control strategies. Furthermore, energy wastes of a central cooling plant resulting from measurement uncertainties in different seasons using different control strategies are estimated.