Distributed robust synergistic scheduling of electricity, natural gas, heating and cooling systems via alternating direction method of multipliers

Abstract

Due to the development of multi-energy conversion technologies and the higher public awareness of the sustainability, the energy system has begun a transition toward the energy hub (EH) based integrated energy system (IES). This paper proposes a distributed robust synergistic scheduling method for multi-EH-based IES coupled with electricity, natural gas, heating and cooling, to minimize the operating cost of the IES while considering the uncertainties of renewable generations (RGs) and multi-energy load demands. The nonconvexity of energy network constraints, uncertainties in multiple EHs as well as limitation of information exchange lead to significant challenges for IES scheduling. To deal these issues, the original robust scheduling model of IES is first reformulated as a mixed integer second-order cone programming (MISOCP) by convex relaxation method. Furthermore, to improve the economy and flexibility of robust scheduling solution, a light robust model is developed to address the multiple uncertainties from EHs. Finally, a consensusbased alternating direction method of multipliers (ADMM) approach is developed to tackle the robust scheduling model in MISOCP form, where EH operators parallelly solve their respective EH subproblems with limited information exchange, and subsequently the EH subproblem in MISOCP form is solved by a nonconvex ADMM (NC-ADMM) approach to guarantee the convergence of the consensus-based ADMM. Simulation results in a three-EH IES are presented to illustrate the effectiveness of proposed method for optimal synergy of multiple EHs with uncertainties. K E Y W O R D S integrated energy system, energy hub, multiuncertainty, distributed robust scheduling, ADMM | INTRODUCTIONNowadays, the climate change, fossil resource depletion, as well as policy incentives have made the integrated utilization of multiple energies become a trend. 1,2 Furthermore, the widespread use of multi-energy converters, such as combined heat and power (CHP) unit, gas furnace (GF) unit, and so on, leads to the tight energy interaction among electricity, natural gas, heating, and cooling. 3,4,5 The above facts promote the traditional energy structure transition toward the integrated energy system (IES). Different from the traditional independent

Distributed Collaborative Optimization of a Multi-Region Integrated Energy System Based on Edge Computing Unit

“…In order to ensure the convergence of the optimization problems with discrete variables, previous work improved the C-ADMM algorithm, such as solving the MISOCP nonconvergence caused by the ring natural gas network through the nested computation of the discrete variables relaxation (Wen et al, 2017). The nonconvergence problem was solved by embedding the NC-ADMM method into the subproblem (Chen et al, 2020).…”

Completely Distributed Optimal Scheduling of Multi-region Integrated Energy System Based on ADMM Algorithm

“…2) The output constraint of unit is described as (11). (11) where Pe,t represents the output of generator unit; Pe,min, Pe,max represent the lower limit and upper limit of the output at the time t; generator unit e is the combination of thermal power units and gas turbines.…”

“…In addition, the alternating direction multiplier method (ADMM) [8][9][10][11][12][13][14] is also widely used in distributed optimization problems. In [8], ADMM algorithm is applied to the economic scheduling of micro grid.…”

“…In this scheme, each microgrid only solves its local problems and exchanges a little information with neighboring microgrids. Literature [10] and [11] proposed the distributed robust collaborative scheduling model and used ADMM algorithm to decouple the connection of each region. In order to improve energy efficiency, a distributed optimization strategy of IES that analysed the uncertainties of source side and load side is proposed in [12].…”

Economic Optimization Scheduling Based on Load Demand in Microgrids Considering Source Network Load Storage

“…So far, optimal scheduling models of microgrids are solved by mathematical programming methods and intelligent optimization algorithms [19][20][21]. Chen F. et al simplified converted a scheduling model into a mixed integer linear programming problem for solving [22].…”