Maximizing Flexible Electricity Use by Load Balancing of Smart Grids

by Stephanie Oehler

The electricity supply has traditionally been dictated by consumers. Consumers demand varying amounts of energy depending on their instantaneous needs and suppliers are left to use whatever resources are necessary to meet their demands. As populations grow and electricity demands per capita increase, the discrepancy between demand and sustainable supply levels continues to widen. The smart grid may have the potential to mediate the conflicting objectives of consumers, who prefer supply levels that correspond with high levels of convenience for them according to their preferences, and suppliers, who would benefit from producing at a more constant rate. Hassan et al. (2013) explore the plausibility of load balancing, which has been enabled by smart grid technologies, as a method of balancing demand to more closely align with reasonable supply levels.

Electricity load balancing enables consumers to designate essential devices, meaning that they provide services that users cannot live comfortably without, and non-essential devices, which have more flexible usage requirements. Energy suppliers can take advantage of non-essential devices that consumers declare are flexible and provide the necessary electricity to run them when it is most convenient given the current supply. The authors constructed two models, the Grid Convenient (GC) and User Convenient (UC) Schedules, which served as the two extremes for how loads can be distributed over time. They proceeded to evaluate the degrees of deviation from grid objectives and the impacts on inconvenience levels imposed on consumers for a variety of levels of flexible devices within the total load. Hassan et al. concluded that increasing the number of flexible loads in the smart grid fulfills producer objectives and only minimally increases user inconvenience to a certain flexible load level, beyond which inconvenience levels continue to increase significantly and utility gains only improve mildly. The authors hoped to shed light on the benefits and potential of load balancing to more predictably and sustainably utilize and manage the smart grid so utility companies can create incentives that will encourage consumers to expand their electricity use flexibility in the future.

While prior studies have explored the potential energy supply savings from load balancing, Naveed Hassan and colleagues were determined to consider these benefits in conjunction with the inconvenience levels subsequently imposed on consumers by exerting more control over their electricity use patterns. The authors began by defining the variables, which included: essential and flexible load types, which were based on the necessity of timely usage of devices, levels of inconvenience for users based on deviation of loads from their preferred time slots, and the GC and UC schedules. The GC Schedule grouped together all essential and flexible loads and distributed them evenly to construct a flat electricity demand curve which was optimal for energy suppliers. The UC Schedule, on the other hand, allowed consumers to distribute their loads according to their preferences and thus represented the current day energy consumption distribution. The schedules served as the extremes in this study, and the authors sought to discover the point at which there were enough flexible loads to flatten the demand curve without significantly inconveniencing consumers, which was somewhere between the models. They did this by creating new load combinations under the assumptions that only flexible loads could be shifted and changing time slots would increase inconvenience levels. The simulation consisted of one hour time slots, baseline consumption household appliances, loads that varied between one and five kilowatt hours (kwh), and 100 total appliances that required between one and five time slots each. The authors created a sub-optimal algorithm using Multi-Processor Scheduling to assign loads to time slots. They simplified this algorithm so they could manipulate the variables more easily by assuming that all flexible devices had equal power consumption, varying time slot requirements, and deeming task order irrelevant. Hassan and colleagues proceeded to run trials of the algorithm with varying numbers of flexible devices and measured the deviation from the GC and UC schedules, as well as the corresponding inconvenience levels.

Trials with varying numbers of flexible devices within the 100 device load revealed that higher percentages of flexible devices did result in the flattening of the electricity demand curve. Subsequently, however, there were significant increases in inconvenience levels as the share of flexible devices increased, because consumers had less control over electricity usage for those appliances and their preferred time slots were not always available. Between 40 and 100 flexible devices, the authors noticed that there was only a slight 5.1% improvement in terms of load balancing, while there was a significant 52.7% increase in inconvenience levels. The authors concluded that increasing the share of flexible devices in the load profile was beneficial to the utilities and relatively harmless to customers until a certain level, at which point the load balancing began to cost more per unit in terms of increases in consumer inconvenience. Thus, Hassan and colleagues recommended incentivizing a relatively small number of flexible devices so that producer benefits were observed but inconvenience to customers was minimized.

Hassan, N., Wang, X., Huang S., Yuen, C., 2013. Demand shaping to achieve steady electricity consumption with load balancing in a smart grid. Innovative Smart Grid Technologies  (ISGT), 1–6.

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