Title

Incentive Mechanism Design for Integrated Microgrids in Peak Ramp Minimization Problem

Publication Date

11-2018

Document Type

Article

Organizational Units

Daniel Felix Ritchie School of Engineering and Computer Science, Electrical and Computer Engineering

Keywords

Microgrids, Minimization, NIST, Distributed algorithms, Uncertainty, Energy storage

Abstract

Due to the high levels of renewable integration into the future power system, matching supply to demand becomes much more challenging, which requires system operators to have sufficient ramping flexibility to respond to the huge demand-supply gap when renewable generation suddenly drops off. To overcome this challenge, microgrids have been advocated as a viable solution to reduce variability and uncertainty associated with renewable generation by rescheduling their energy generation and storage resources based on grid flexibility requirements. In this paper, we propose an incentive mechanism design to motivate microgrids to participate in the peak ramp minimization problem for the system. By offering reimbursement for each microgrid to deviate from the original optimal operation point, the ramping capability requirement to match supply demand can be significantly reduced. We model and analyze the economic interaction between the distribution system operator (DSO) and microgrids using the Nash bargaining theory. The Nash bargaining solution (NBS) can be obtained by solving the centralized social welfare maximization problem. However, due to the distributed topology of the power network as well as independent decision-making nature of microgrids, the centralized design is not suitable for practical implementation. Therefore, we propose two distributed algorithms to achieve the NBS using the alternating direction method of multipliers decomposition technique, which can execute in either synchronous fashion or asynchronous fashion. The simulation results demonstrate the convergence performance of the proposed distributed algorithms as well as the efficacy of our model in achieving benefits for both microgrids and the DSO.

Publication Statement

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