Electrical Load Distribution Research Articles

Measured end-use electric load profiles for 12 Canadian houses at high temporal resolution

The electricity drawn by appliances and lighting represents a significant fraction of the total energy used in residential buildings. As construction techniques improve and as building regulations become more stringent, it is expected that the relative significance of these non-HVAC electrical loads will grow. Not only is the annual energy consumption of non-HVAC electrical loads of growing importance, their temporal distribution influences the operation of energy conversion equipment within the building and in the electrical supply network. This work provides new measured data on the electrical consumption of a sample of 12 Canadian houses. Measurements at 1-min intervals were taken of whole-house electricity draws, as well as the draws of individual devices, including the furnace, air-conditioner, range/cooker, clothes dryer, dishwasher, and domestic hot water heater. The annual consumption patterns of the 12 houses were found to range widely. Furthermore, daily variations in the temporal distribution of electrical loads of each house were found to be significant. The probability distribution of some of the measured data was compared with previously developed synthetically derived non-HVAC electric load profiles, and it was found that the latter do not adequately capture either the temporal variability nor the variation between households observed in the measurements.

Approximating electrical distribution networks via mixed-integer nonlinear programming

Given urban data derived from a geographical information system (GIS), we consider the problem of constructing an estimate of the electrical distribution system of an urban area. We employ the image data to obtain an approximate electrical load distribution over a network of a prespecificed discretization. Together with partial information about existing substations, we determine the optimal placement of electrical substations to sustain such a load that minimizes the cost of capital and losses. This requires solving large-scale quadratic programs with discrete variables for which we present a novel penalization-smoothing scheme. The choice of locations allows one to determine the optimal flows in this network, as required by physical requirements which provide us with an approximation of the distribution network. Furthermore, the scheme allows for approximating systems in the presence of no-go areas, such as lakes and fields. We examine the performance of our algorithm on the solution of a set of location problems and observe that the scheme is capable of solving large-scale instances, well beyond the realm of existing mixed-integer nonlinear programming solvers. We conclude with a case study in which a stage-wise extension of this scheme is developed to reflect the temporal evolution of load.

Optimum generation capacities of micro combined heat and power systems in apartment complexes with varying numbers of apartment units

Abstract A dynamic simulation of micro combined heat and power (micro-CHP) systems that includes the transient behavior of the system was developed by modeling the generation of electricity and recovery of heat separately. Residential load profiles were calculated based on statistical reports from a Korean government agency, and were used as input data to select the optimum capacities of micro-CHP systems based on the number of apartment units being served, focusing on both economic and energetic criteria. The capacity of internal combustion engine (ICE) based micro-CHP was assumed to be in the range 1–500 kW, and the dependence of the efficiency of the generator unit on the capacity was included. It was found that the configuration (i.e., the capacity and number of generator units) that maximized the annual savings also had favorable energetic performance. Additionally, the statistical mode calculated from the annual electrical load distribution was verified as a suitable indicator when deciding the optimum capacity of a micro-CHP system.