Optimal design of distributed energy systems for industrial parks under gas shortage based on augmented e-constraint method

摘要

Natural gas distributed energy systems have developed rapidly owing to their high efficiency, low environmental impact, high energy supply reliability, and good economic returns. As the main users of natural gas distributed energy, industrial parks account for 67.7% of the total installed capacity of the industry. Therefore, disrupted gas supply to industrial parks during gas shortage periods results in decreased production and consequently huge economic losses. This study addresses this issue by attempting to develop an optimized design for distributed energy systems and natural gas allocation in multiple industrial parks under gas shortage conditions. A multi-objective mathematical programming (MOMP) model is established to minimize the total construction investment and operation costs of each industrial park considering emission, energy balance and other technical constraints. Using the augmented e-constraint method, optimal configurations of distributed energy systems, operation strategy, and economic and emission performance of each industrial park are determined. The distributed energy system design for three industrial parks in Jinan, China, is taken as an example to verify the model. Compared with the non-cooperation case, the annual total cost of full-cooperation can be reduced by 8.29%, and the annual total carbon emission of full-cooperation can be reduced by 427.55 t. Additionally, sensitivity analyses of energy price and total input natural gas are conducted to provide further insights into the optimization of distributed energy system for each industrial park. The results show that electricity price has the strongest impact on the economic performance of the industrial parks, followed by natural gas price and biomass price, but natural gas price does not affect the gas allocation ratio. Moreover, when the total input natural gas is increased by 10-90%, the total cost can be reduced by 0.69% -7.83%. (C) 2019 Elsevier Ltd. All rights reserved.