Аңдатпа
An integrated mathematical model for the energy network, in which the energy flow from the final consumption level to the primary carriers' level is modelled using matrix relationships, is presented.In this model, the energy network is considered as a multilayer system and the relationship between consumption sectors, energy carriers, conversion units, and power generation plants is expressed in a precise and structured manner.The main innovation is to present a two-level approach to solve the problem of optimal operation of the energy network, in which decisionmaking at the macro and micro levels is carried out simultaneously and in a coordinated manner.To optimize the operation of energy carriers, a dynamic programming method has been used to determine the best combination of energy carriers and system operation modes in a 24-hour time horizon.This method, by considering the time dependence of decisions, determines the optimal path of network operation in such a way that the total cost and consumption of energy carriers are minimized.Next, considering the importance of electrical energy in the structure of the energy network, the problem of electrical energy distribution between power plants has been considered as a key sub-problem.To solve this part, the Teaching-Learning-Based Optimization (TLBO) has been used to determine the economic distribution of production between power plants in such a way that the cost of electrical energy production is minimized.Simulation studies have been conducted on an energy network consisting of four power plants and six energy consumption sectors (domestic, industrial, transportation, agriculture, other and non-energy).The results show that the proposed approach is able to determine the optimal path of energy network operation with high accuracy and lead to a significant reduction in the total system cost.It is also clear that the optimal selection of power plant combinations at different times has a direct impact on the energy carrier consumption pattern.Analysis of the economic distribution results shows that the TLBO algorithm has good convergence and stable performance and is able to provide acceptable optimal responses in a reasonable computational time.The findings of this study indicate that the use of a hybrid framework based on dynamic programming and the TLBO algorithm can effectively optimize the energy network operation and manage the consumption of energy carriers.These results also show that integrating decision-making at the grid level and the power generation level plays a key role in improving efficiency and reducing energy system costs.