Integrated energy system planning for a heavy equipment manufacturing industrial park

Abstract

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This paper intends to provide key insights to the manufacturing industrial park designers for selecting the typical days of electric load and planning the resources for energy-producing infrastructure. First, a hybrid time-series model of energy-consuming equipment based on the autoregressive integral moving average model (ARIMA) and temporal convolutional network (TCN) is generated. According to this model, the energy consumption (EC) curve of large equipment in the industrial park can be depicted. Moreover, the present study designed a TLSM-IPML (typical load stratification method for industrial parks with manufacturing load) algorithm based on the typical day-selected method. The data clustering method is utilized to analyze the energy usage characteristics. Furthermore, an energy usage-based planning model is proposed, network constraints are considered, and a multi-optional method is designed to solve the problem. Finally, case studies validate the superior performance of TLSM-IPML in analyzing the characteristics of energy consumption and planning the model in reducing MES (manufacturing industrial factory integrated energy system) economic costs.

Keywords

• manufacturing industrial integrated energy system
• load data clustering
• typical day
• load clustering
• equipment modeling
• integrated energy system planning