# Integrated Energy System Optimization Based on Standardized Matrix Modeling Method
## 4 Results and Analysis
### Simulation Case Study for Structural Optimization  
The structural optimization is to decide on the system components and their interconnections (i.e., power flow structure and device type) and the key is to determine the power flow structure and device type with long-term economics, energy savings, and environmental protection, to build an energy-efficient system, according to the total annual cooling, heat, and electricity demand of regional end users. Before implementing system structural optimization, the following assumptions are made. 1. The ratio of the total cooling demand by end users to the total electricity demand by end users is \(y=0.3\). 2. The ratio of the total heat demand by end users to the total power demand by end users is \(z=0.4\). 3. From Equation (24), we can see that, the relationship between local renewable energy utilization and comprehensive energy efficiency is relatively determined, and local renewable energy  
Figure 7: The schematic diagram of the operation optimization of integrated energy system. utilization is usually limited by local natural environmental resources and energy policy conditions, and here assume \(u=0.2\). * The current technical level of energy conversion equipment is shown in Table 1.  
The technical level reference value of the remaining common energy conversion equipment is as follows \(\eta_{\mathrm{H}}=3\), \(\eta_{\mathrm{R}}=1\), \(\eta_{\mathrm{C}}=3.5\), \(\eta_{\mathrm{AB}}=0.90\). According to the above assumptions, the optimization objective function of the system can be simplified as \(\eta=f(\alpha,\beta,x)\). The genetic algorithm (GA) [15] and particle swarm optimization (PSO) are chosen as optimization algorithms to search for the optimal annual comprehensive energy efficiency, respectively. The related parameters of the PSO algorithm and GA algorithm are set as described previously [16; 17], seen in Table 2. The system optimization results are shown in Table 3, in which the integrated energy system includes a CHP system based on a fuel cell, an internal combustion engine, a gas turbine, and a steam turbine marked as system 1, system 2, system 3, and system 4, respectively. From Table 3, it can be seen that the PSO algorithm can search for the optimization target more effectively than the GA algorithm under the same population size and number of iterations. When the regional end user's cooling/heat/electricity requirements, renewable energy resource utilization, and energy conversion equipment technology level are determined (as described in the above assumptions), the integrated energy system using only the CHP system with a fuel cell as the prime mover and the absorption refrigeration equipment to supply energy will have the best comprehensive energy efficiency; the comprehensive energy efficiency can reach 83.5%. The optimal comprehensive energy efficiency of the integrated energy system varies with the demands for cooing, heat, and electricity on the user side, as shown in Figures 8-10. \begin{table}
\begin{tabular}{c c c c} \hline
**Type** & **Power Generation Efficiency** & **Heat Generation Efficiency** & **Total Efficiency** \\ \hline Fuel cell & 0.45 & 0.30 & 0.75 \\ Internal combustion engine & 0.40 & 0.40 & 0.80 \\ Gas turbine & 0.35 & 0.45 & 0.80 \\ Steam turbine & 0.30 & 0.50 & 0.80 \\ \hline \end{tabular}
\end{table} 
Table 1: Typical combined heat and power (CHP) system power generation and heat generation characteristics. \begin{table}
\begin{tabular}{c c c} \hline
**Parameter** & **Value** \\ \hline Population size (GA \& PSO) & 80 \\ Number of iterations (GA \& PSO) & 60 \\ Acceleration constant \(c1\) and \(c2\) (PSO) & 1.2 \\ Cross probability (GA) & 0.35 \\ Mutation probability (GA) & 0.30 \\ \hline \end{tabular}
\end{table} 
Table 2: Related parameters of the PSO algorithm and GA algorithm.