energies
Article
Collaborative Autonomous Optimization
of Interconnected Multi-Energy Systems
with Two-Stage Transactive Control Framework
Yizhi Cheng, Peichao Zhang * and Xuezhi Liu
Department of Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China;
tabdel@sjtu.edu.cn (Y.C.); liuxz@sjtu.edu.cn (X.L.)
* Correspondence: pczhang@sjtu.edu.cn
Received: 9 November 2019; Accepted: 24 December 2019; Published: 30 December 2019
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.
Keywords:
multi-energy system; transactive control; two-stage bi-level optimization; constraint relaxation
1. Introduction
Continuous environment deterioration and energy depletion have necessitated the comprehensive
utilization of various forms of energy. Accordingly, multi-energy systems (MESs) have gained
significant attention for their ability to improve comprehensive energy efficiency, as well as to benefit
system economy and environment [
1
,
2
]. For one thing, an MES can shift supply and demand across
energy vectors and networks, as well as handle the uncertain and volatile generation outputs of
renewable energy sources (RES) [
3
,
4
]. Besides, owing to the energy complementarity, MES has the
untapped potentials of jointly minimizing the operational cost [
5
], enhancing the overall operational
efficiency and to increasing system flexibility [6].
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
]. Collaboration methods of interconnected subsystems
generally fall into centralized and distributed mechanisms. 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. Besides, MESs
may have different owners and schedule resources based on their own economic rules and policies [
6
,
10
].
This indicates that a direct decision authority or compulsory dispatch orders can be unpractical.
Energies 2020, 13, 171; doi:10.3390/en13010171 www.mdpi.com/journal/energies
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