Collaborative Autonomous Optimization of Interconnected Multi-Energy Systems with Two-Stage Transactive Control Framework

Yizhi Cheng,Peichao Zhang,Xuezhi Liu

doi:10.3390/en13010171

Abstract

Motivated by the benefits of multi-energy integration, this paper establishes a bi-level two-stage framework based on transactive control, to achieve the optimal energy provision among interconnected multi-energy systems (MESs). At the lower level, each MES autonomously determines the optimal setpoints of its controllable assets by solving a cost minimization problem, in which rolling horizon optimization is adopted to deal with the load and renewable energies’ stochastic features. A technique is further implemented for optimization model convexification by relaxing storages’ complementarity constraints, and its mathematical proof verifies the exactness of the relaxation. At the upper level, a coordinator is responsible for minimizing total costs of interconnected MESs while preventing transformer overloading. This collaborative problem is solved iteratively in a proposed two-stage transactive control framework that is compatible with operational time requirement while retaining scalability, information privacy and operation authority of each MES. The effectiveness of the proposed framework is verified by simulation cases that conduct a detailed analysis of the collaborative autonomous optimization mechanism.

Highlights

Continuous environment deterioration and energy depletion have necessitated the comprehensive utilization of various forms of energy
Power losses that include the loss in the connecting line and the transformer are not considered for simplicity, which may have impacts on the local accommodation of renewable energy sources (RES)
This paper proposes a two-stage transactive control (TC) framework for coordinating multiple interconnected multi-energy systems (MESs)

Summary

Introduction

Continuous environment deterioration and energy depletion have necessitated the comprehensive utilization of various forms of energy. Based on significant research works on networked microgrids [7], recent years have witnessed a research re-orientation from energy optimization of a standalone MES to the collaborative optimization among multiple interconnected MESs (IMESs) [6]. The centralized models are established in [5,6,8,9] and subsequently solved with traditional mathematical algorithms [6,8] or modern intelligent optimization techniques [5,9]. While such optimizations guarantee a global optimal utilization of resources, they fail to meet privacy protection, scalability and openness requirements. MESs may have different owners and schedule resources based on their own economic rules and policies [6,10]

Methods

Findings

Discussion

Conclusion