In the future Smart Power Grid, decentralized energy management mechanisms such as EPOS, can make the power network more robust to oscillations and peaks in the energy consumption.
EPOS, the Energy Plan Overlay Stabilization system, is an energy demand-side management mechanism. EPOS coordinates the consumption of thermostatically controlled devices, such as refrigerators, water heaters, air conditioners, etc., that are interconnected in a decentralized overlay network. The goal of EPOS is to achieve a stabilization pattern in the total aggregated consumption of all the interconnected devices within a period of time. Two such patterns have been studied so far: (i) Minimization of the deviations in the total consumption and (ii) a recent total consumption pattern with reversed deviations from an earlier total consumption pattern. In this way, the two total consumption patterns with reversed deviations form an averaged stabilized pattern.

A thermostatically controlled device in EPOS is able to generate a number of possible plans. These are possible energy consumption patterns that the operation of this device can support for the next period of time. A device selects the possible plan that contributes best to the stabilization of the total energy consumption. This can be achieved by a brute force computation of the combinations between all the possible plans for every device in the overlay network. Each combination is evaluated by summing all of the possible plans and computing the deviations of the total energy consumption. This approach guarantees the optimum selection of possible plans and therefore provides a maximally stabilized total energy consumption given certain possible plans.

Deriving possible plans from the temperature configuration of thermostatically controlled devices.

However, the number of these combinations is very large and therefore the optimum combination cannot be computed efficiently using a brute force operation. Instead, EPOS first computes combinations between a small group of devices and then selects a combination based on the selections that other groups of devices have already performed. Technically, this adaptation is realized by summing the energy pattern consumption that a group of devices selected to each combination of possible plans that another group of devices computes. Therefore, the evaluation of deviations in each combination is more informative as the selections of possible plans by other devices are considered.

Combining the possible plans of two thermostatically controlled devices.

Aggregation is performed in a decentralized fashion based on peer-to-peer interactions between the devices. Two possible aggregation schemes are considered in EPOS:

  • Structured aggregation using an overlay network organized in a tree topology. AETOS, the Adaptive Epidemic Tree Overlay Service, is an example of how tree topologies can be self-organized. EPOS is evaluated experimentally using this aggregation scheme.
  • Unstructured aggregation using a dynamic overlay network that is able to (i) detect new information for aggregation, (ii) exclude duplicate information and (iii) update information that changes during runtime. Unstructured aggregation in EPOS  is part of ongoing work.

The evaluation of EPOS shows that the stabilization of the total energy consumption improves compared to a system in which devices randomly select a possible plan or simply select their most stabilized possible plan. In the future Smart Power Grid, decentralized energy management mechanisms such as EPOS, can make the power network more robust to oscillations and peaks in energy consumption.

EPOS started as part of my PhD studies resulted in several publications and collaborations. It is still evolving work.

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