We consider a future self-organized energy community that is composed of “prosumer” households that can autonomously generate, store, import and export power, and also selfishly strive to minimize their cost by adjusting their load profiles using the flexibly of their distributed storage. In such scenario, the aggregate load profile of the self-organized community is likely to be volatile due to the flexibility of the uncoordinated selfish households and the intermittence of the distributed generations. Previously, either centralized solutions or cooperation based decentralized solutions were proposed to manage the aggregate load, or the load of an individual selfish household was considered. We study the interplay between selfish households and community behavior by proposing a novel dynamic pricing model that provides an optimal price vector to the households to flatten the overall community load profile. Our dynamic pricing model intelligently adapts to the intermittence of the DGs and the closed-loop feedback that might result from price-responsiveness of the selfish households using its learning mechanism. Our dynamic pricing scheme has distinct import and export tariff components. Based on our dynamic pricing model, we propose a polynomial-time distributed DS scheduling algorithm that runs at each household to solve a cost minimization problem that complies with the selfish nature of the households. Our simulation results reveal that our distributed algorithm achieves up to
72.5% reduction in standard deviation of the overall net demand of the community compared to a distributed scheduling with two-level pricing scheme, and also gives comparable performance with a reference centralized scheduling algorithm.
- Schedule Algorithm
- Dynamic Price
- Iteration Cycle
- Learning Factor
- Energy Community
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