Abstract
Rapid improvement in efficiency of renewable energy sources (RESs), increased concerns about carbon emission, substantial power-flow losses, and the emergence of smart grid have all contributed to increased interest toward customers’ contribution in electricity generation in RES-based prosumer nano-grids (PNGs). Such contributions within a PNG can be effectively controlled using dynamic pricing tariffs (DPTs) that hold customers accountable for their electric power exchange behaviors. Prosumer customers optimize their internal power grid based on utility-provided DPT and expect to surpass the break-even point within an anticipated period of time. However, their expected payback period may not be met, as the DPT changes over time as more customers join the market. Therefore, the joint design of PNG and DPT is necessary to give the customers the opportunity to revisit their investment plans, and, at the same time, the utility the opportunity to adjust the pricing scheme. We propose an iterative decentralized three-level optimization scheme to achieve both objectives. The proposed scheme can be applied to a large system for planning of optimal PNGs, while providing a reliable payback period on investment for customers that are involved. The effectiveness of the proposed scheme is verified numerically using a large historical data set.
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