Coordination optimisation of energy and manufacturing flow for industry integrated energy system

Abstract

With the breaking down of information barriers between energy flow with manufacturing flow, the coordination between them is an effective way to relieve the dual pressure from energy and environment industrial integrated energy system. The authors develop a scenario‐based coordination model for energy flow and manufacturing flow to make full use of the flexibilities of energy supply and production process to reduce energy cost. To capture the flexibility in the energy flow, the authors enhance an electricity‐steam‐product gas–gas storage coupling energy flow model considering multi‐uncertainties. The authors also develop a batch process model to formulate the flexibility in the production process. Based on the batch energy consumption constraints, the energy flow model and the batch process model are integrated as a coordination model. To ensure the feasibility of hard budget constraints under all possible random realisations, we add all‐scenario‐feasibility robust constraints which are infinite‐dimensional constraints into the model. To solve the model, a vertex scenario set based on the characteristics of convex optimisation is constructed to equivalently convert infinite‐dimensional constraints to finite‐dimensional constraints. In this way, the coordination model is transformed to a mixed integer linear programming and can be solved using CPLEX. Finally, numerical test based on a real iron and steel plant is analysed. The results show that coordination between energy with manufacturing flow is effective to reduce the energy cost and carbon emission. Compare with only optimising energy flow, the coordination model can reduce total cost about 221.6 thousand RMB and 304.44t coal every day.

Optimization of iron and steel manufacturing plant considering electricity price tariff and electric arc furnace control

“…Upon activation, the EAF should be maintained for a duration longer than the minimum activation time ($$\text{UT}_{\text{min}}$$) to supply the theoretical minimum energy required for iron dissolution. In addition, when the EAF is shut down, it must be maintained for a duration longer than the minimum turn‐off time ($$\text{DT}_{\text{min}}$$) to ensure a reliable restart of the EAF [15, 27]. These constraints can be expressed using $$v_{k}^{e}$$ and $$w_{k}^{e}$$ as follows: $$$$\begin{equation} \sum _{i=k-\text{UT}_{\text{min}}\cdot K+1}^{k}v_i^{e} \le I_k^{e}.$$…”

“…Upon activation, the EAF should be maintained for a duration longer than the minimum activation time (UT min ) to supply the theoretical minimum energy required for iron dissolution. In addition, when the EAF is shut down, it must be maintained for a duration longer than the minimum turn-off time (DT min ) to ensure a reliable restart of the EAF [15,27]. These constraints can be expressed using v e k and w e k as follows:…”

“…Several load-modelling methods have been proposed for the optimization problem to address the variability and flexibility of electrical loads, including EAFs [15][16][17]. In one study [15], a batch process model was developed to formulate the flexibility of the iron and steel production processes.…”

“…Several load-modelling methods have been proposed for the optimization problem to address the variability and flexibility of electrical loads, including EAFs [15][16][17]. In one study [15], a batch process model was developed to formulate the flexibility of the iron and steel production processes. Through coordination with other multi-energy systems and the proposed batch process model, the overall energy cost of the process was significantly reduced by controlling both the energy demand and supply resources.…”

“…This approach also considers the penalties for deviations from precontracted load curves. However, the approaches proposed in these studies [15][16][17] may not be suitable for realistic situations because they do not consider electricity tariffs related to peak demand. In practice, electricity tariffs are influenced by not only total energy consumption but also peak demand.…”