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With the increasing demand of refined products and growing concern about carbon emissions, the planned multi-product pipelines, serving as the primary way of refined products transportation, should be designed reasonably, economically, and low carbon-oriented so that the sustainable and environmentally friendly production and the rational construction of downstream oil supply chain can be achieved. Aiming at this issue, a multi-objective mixed-integer linear programming (MILP) model is proposed to minimize the total economic costs and CO 2 emission simultaneously. Various actual process and technical constraints including pipeline construction, pump stations layout, pipeline hydraulic and pumps configurations are considered in the model. Based on the augmented ε-constraint method, the conflicting objectives are dealt with and the Pareto front can be get. Finally, based on a predicted future scenario of regional refined products demand, the proposed model was successfully applied to a real-word planned multi-product pipeline in China. Two cases are given to demonstrate the model's applicability and the influence of CO 2 emission objective on the pipeline design scheme determination. Meanwhile, the trade-off between the two objectives was analyzed in detail. [ABSTRACT FROM AUTHOR]

• A discrete-time MOMILP model for pump scheduling of products pipelines is built. • Various factors about pump operation are considered comprehensively in the model. • A two-stage strategy is adopted to deal with the proposed model. • Two cases from a products pipeline system in China illuminate model's practicability. As one of the major means to link refineries to local markets, pipelines are crucial for refined oil supply chains. Pump scheduling is vital to optimally operate the refined products pipelines. Existing studies mostly quantified the number of pump stop/restart in the form of cost, whereas the cost coefficient was often subjectively selected. The present study built a multi-objective mixed-integer linear programming (MOMILP) model for refined products pipelines to minimize the number of pump stop/restart and reduce the pump running cost simultaneously. In this study, the minimum continuous running time of pumps, changing electricity price (i.e., time-changing electricity price and local electricity) and the pressure limits of special points were considered meticulously, e.g. high-elevation points, low-elevation points and pump stations. In the first stage, the improved augmented ε-constraint method (AUGMECON) was adopted to deal with this model, and obtained a seris of pump operating schemes, acting as a Pareto set. Then the results of the AUGMECON were evaluated by an established evaluation model based on neural network. Finally, two cases from a refined products pipeline system in China were employed to verify the practicability and accuracy of the proposed model. The results of this study can effectively guide the pump scheduling of refined products pipelines. [ABSTRACT FROM AUTHOR]

• A hybrid method for the design and optimal siting of regional heat-gas-renewable energy system is proposed. • The applicability of the proposed methodology is verified through a real instance. • The stability and convergence of the hybrid method are tested and analyzed. Distributed energy systems (DESs) are considered to be the future of energy systems because of their inherent high efficiency, cost-effectiveness, energy-saving capability, and environmentally friendly characteristics. Although there are many reports on the design optimization of DESs, few of them focus on the classification and siting optimization of building clusters, which are of critical importance to district-scale DESs. This study proposes a cost-effective hybrid method to optimize the design and siting of multiple DESs for district building clusters. First, k-means clustering is performed to initially divide the district into building clusters. A mixed-integer linear programming (MILP) model is then established to optimize the DES configuration and operation strategies for building clusters in consideration of the design and operation constraints. Genetic algorithm is subsequently implemented to optimize the cluster configurations for the district such that the annual total cost is minimized. The proposed method was applied to a twenty-building district-scale energy system located in Guangzhou, China as a case study. The results show that the optimized design and siting scheme obtained via the hybrid method can reduce the annual total cost and carbon emissions by 23.65% and 75.32%, respectively, relative to the conventional energy system; the stability and convergence rate of the hybrid method were also tested and analyzed. In addition, a sensitivity analysis was carried out to analyze the effect of energy price on the economic and environmental performance of DESs. [ABSTRACT FROM AUTHOR]