Identification of alternating renewal electric load models from energy measurements

In statistical load modeling methodologies, aggregate electric load behavior is derived by propagating the ensemble statistics of an individual load process which is representative of the loads in the aggregate. Such a modeling philosophy tends to yield models whereby if physical meaning is present at the elemental level, it is preserved at the aggregate level. This property is essential for applications involving direct control of power system loads. The potential applicability of statistical load models is a strong function of one's ability to limit the volume of unusual data required to build those. An identification algorithm for a previously proposed stochastic hybrid-state Markov model of individual heating-cooling loads is presented. It relies only on data routinely gathered in power systems (device energy consumption over constant time intervals). It exploits an alternating renewal viewpoint of the load dynamics. After deriving some general results on the occupation statistics of time homogeneous alternating renewal processes, the analysis is focused on the specific model. In the process, however, some intriguing features likely to be shared by a wide class of alternating renewal processes are revealed. >

[1]  R. Malhamé,et al.  On the statistical properties of a cyclic diffusion process arising in the modeling of thermostat-controlled electric power system loads , 1988 .

[2]  Hilary A. Priestley,et al.  Introduction to Complex Analysis , 1985 .

[3]  Mokhtar S. Bazaraa,et al.  Nonlinear Programming: Theory and Algorithms , 1993 .

[4]  R. E. Mortensen.  Alternating renewal process models for electric power system loads , 1990 .

[5]  Keigo Yamada,et al.  Adaptive Short-Term Forecasting of Hourly Loads Using Weather Information , 1972 .

[6]  Fred Schweppe,et al.  Physically Based Modeling of Cold Load Pickup , 1981, IEEE Transactions on Power Apparatus and Systems.

[7]  R. Malhamé,et al.  Electric load model synthesis by diffusion approximation of a high-order hybrid-state stochastic system , 1985 .

[8]  John Douglas Birdwell,et al.  Residential air conditioner dynamic model for direct load control , 1988 .

[9]  K. Pöysti.  BOX-JENKINS -METHOD IN SHORT-TERM FORECASTING OF GRID LOAD IN FINLAND , 1984 .

[10]  H. D. Miller,et al.  The Theory Of Stochastic Processes , 1977, The Mathematical Gazette.

[11]  Roland P. Malhamé,et al.  Convergence characteristics of a maximum likelihood load model identification scheme , 1992, Autom..

[12]  R. E. Mortensen,et al.  Dynamics of heating and cooling loads: models, simulation, and actual utility data , 1990 .

[13]  Roland Malhamé,et al.  A jump-driven Markovian electric load model , 1990, Advances in Applied Probability.

[14]  A. Debs,et al.  Statistical synthesis of power system functional load models , 1979, 1979 18th IEEE Conference on Decision and Control including the Symposium on Adaptive Processes.