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Effective demand responsiveness (DR) is crucial to the stability of the electrical grid. With increasing penetration of renewable energy sources demands higher load variation adaptability. Therefore, consumer-side flexibility is required for responding to abrupt DR signals. Real-time pricing (RTP) offer a direct approach for continually communicating DR signals. RTP has shown effectiveness in residential applications, however, its implications are impaired in industrial buildings which are less price-elastic due to stresses imposed by just in time (JIT) manufacturing and market competition. In this paper, we propose an instantaneous demand control methodology for industrial and commercial buildings, where the DR action is continually updated as new DR signals are received. We utilize the hour-ahead RTP (RTP-HA) tariffs and the demand shifting concept. The instantaneous approach is independent of price prediction uncertainty and scheduling approaches. The controller algorithm is converted to a linear optimization problem which is solved optimally and saves computational time, making it practical for real-time use. The method is robust and verified using MATLAB/SIMULINK with actual, one week, data from eight industrial and commercial buildings in Florida. Results show modest reductions in consumers' electricity bills while maintaining required comfort standards. Results also address the load synchronization problem associated with RTP.

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Smart Grids require a clear understanding of consumer demand patterns. Classification of consumers according to their demand pattern is required for the effective planning of tariffs, eligibility for demand-side management (DSM) programs, energy production and transmission, as well as for security purposes. We propose a framework for classification of consumer load patterns using a hybrid system with a parameter estimation model, a clustering model and an artificial neural network (ANN). The proposed model provides an effective unbiased classification method. The process starts with generating a training data set from existing consumers without a priori classification. The raw load data is processed through a parameter estimation model and a clustering algorithm to generate a training data set with distinct impartial classification clusters. The training data is fed to an ANN for learning. Once the load patterns are learned, the model can be used to further classify new consumers into a demand pattern. The ANN provides fast and accurate clustering performance without underlying assumptions about shape or class. An analysis of the optimal number of clusters is presented. Results indicate that clusters with distinguishable characteristics are achieved and we demonstrate how regulators can make use of this method in demand curtailment planning.