Jianhui Wang, Guangqing Bao, Peizhi Wang, et al. A collaborative approach to integrated energy systems that consider direct trading of multiple energy derivatives[J]. Global Energy Interconnection, 2023, 6(4): 418-437.
DOI:
Jianhui Wang, Guangqing Bao, Peizhi Wang, et al. A collaborative approach to integrated energy systems that consider direct trading of multiple energy derivatives[J]. Global Energy Interconnection, 2023, 6(4): 418-437.DOI: 10.1016/j.gloei.2023.08.004.
A collaborative approach to integrated energy systems that consider direct trading of multiple energy derivatives
摘要
Abstract
The cooperative model of a multi-subject Regional Integrated Energy System (RIES) is no longer limited to the trading of traditional energy
but the trading of new energy derivatives such as Green Certificates (GC)
Service Power (SP)
and CO2 will be more involved in the energy allocation of the cooperative model. This study was conducted for the multi- entity RIES cooperative model considering the trading of electronics
GC
SP
and CO2. First
a cooperative framework including wind-photovoltaic generation system (WG)
combined heat and power system (CHP)
and power-carbon-hydrogen load (PCH) is proposed
and the mechanism of energy derivatives trading is also analyzed. Then
the sub-models of each agent in the cooperative model are established separately so that WG has the capability of GC generation
CHP has the capability of GC and CO2 absorption
and PCH can realize the effective utilization of CO2. Then
the WG–CHP–PCH cooperative model is established and equated into two sub-problems of cooperative benefit maximization and transaction payment negotiation
which are solved in a distributed manner by the alternating directed multiplier method (ADMM). Finally
the effectiveness of the proposed cooperative model and distributed solution is verified by simulation. The simulation results show that the WG–CHP–PCH cooperative model can substantially improve the operational efficiency of each agent and realize the efficient redistribution of energy and its derivatives. In addition
the dynamic parameter adjustment algorithm (DP) is further applied in the solving process to improve its convergence speed. By updating the step size during each iteration
the computational cost
the number of iterations
and the apparent oscillations are reduced
and the convergence performance of the algorithm is improved.