A series multi-step approach for operation Co-optimization of integrated power and natural gas systems

Abstract

Power to gas units and gas turbines have provided considerable opportunities for bidirectional interdependency between electric power and natural gas infrastructures. This paper proposes a series of multi-step strategy with surrogate Lagrange relaxation for operation co-optimization of an integrated power and natural gas system. At first, the value of coordination capacity is considered as a contract to avoid dysfunction in each system. Then, the uncertainties and risks analysis associated with wind speed, solar radiation, and load fluctuation are implemented by generating stochastic scenarios. Finally, before employing surrogate Lagrange relaxation, the non-linear and non-convex gas flow constraint is linearized by two-dimension piecewise linearization. In the proposed procedure, constraints for energy storages and renewable energy sources are included. Two case studies are employed to verify the effectiveness of the proposed method. The surrogate Lagrange relaxation approach with coordination branch & cut method enhances the accuracy of convergence and can effectively reduce the decisionmaking time.

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“…1,2 Accordingly, establishing a natural gas network model that can be effectively applied to the comprehensive analysis of the electricity−gas system is of great significance to maintain the stable operation of such a system. 3 The coupling between electricity systems and natural gas networks makes the electricity systems more dependent on the natural gas networks. Previous studies have focused on analyzing the impacts of a steady-state natural gas system on an electricity system.…”

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“…Kim et al ( 2020) developed an optimization system for CHP based on a neural network where the computation speed of their method is more than 7000 times faster than the physics-based model. [27] proposed a series of multi-step schemes to minimize the cost of an integrated power and gas system, in which surrogate Lagrangian relaxation is applied to accelerate the optimization process. Zhou et al (2020) [28] performed a tiered gas tariff model to optimize the cost of energy between residential and other regions, potentially decreasing the overall operation cost of multi-region gas and power complementary systems.…”

Edge‐cloud collaborative architecture based multi‐time scales rolling optimization of regional integrated electrical and natural gas energy system considering wind power uncertainty

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“…Existing researches generally assumed that a vertically centralized cloud operator monitors and controls the entire IEGES, all the decisions were made within the cloud server, which would increase the computational complexity and the burden of data exchange. Faridpak et al [7] proposed a multi-step strategy with surrogate Lagrange relaxation for co-optimization of IEGES, although relaxation method was utilized to consider the constraints of both networks at the same time, the optimization process was still conducted under a cloud architecture. Wang et al [8] studied the optimal operation of IEGES with consideration of renewable energy uncertainties, also cloud architecture was assumed in the optimization and problem was constructed and solved as a mixed-integer linear programming (MILP) model.…”