# Period Cycle Optimization of Integrated Energy Systems with Long-Term Scheduling Consideration
## 2 Materials and Methods
### System's Optimization Algorithms
#### 2.1.4 Energy and Component Cycle Optimization Method  
In the optimization design and operation of integrated energy systems, the energy and component cycle (ECC) optimization method will be employed, coupled with scenario simulations. Scenario simulations utilize data related to energy structure and component parameters, combined with functions within neural networks, to perform simulations using data derived from daily scenario models. It enables the calculation of real-time data on component operation, energy consumption, and energy conservation effects. Scenario simulations over a single cycle are used to describe the uncertainty of energy demand and energy resources in an integrated energy system. Additionally, random system disturbances are introduced to simulate special circumstances that may occur in real-world situations. The ECC optimization method uses component capacity, power, and energy attributes as variables, with the ultimate goals of minimizing the total energy consumption (\(Q_{total}\)), total energy cost (\(C_{total}\)), and total carbon emissions (\(E_{total}\)) and ensuring the economic feasibility of the energy structure.  
By generating random scenario data over cycles through simulation, this method is employed to evaluate and optimize system design solutions. The optimization process is continually refined on a per-cycle basis, ultimately yielding real-time simulation data, energy savings, and economic feasibility for the energy structure. This process involves eight main steps:  
1. Define decision variables. The primary decision variables in this approach encompass component parameters and energy attributes.
2. Establish operating cycles. Following the scenario simulation method, determine the total operating duration and operating cycles to assess the initial design solution set periodically.
3. Configure optimization scheme parameters based on the decision variables and operational cycles; set optimization scheme parameters.
4. Simulate real-time operations. Utilizing component capacities, energy attributes, and operational cycles defined in the initial design solutions, simulate the operational states and power profiles of each component to meet the energy demands and constraints of the current real-time scenario.
5. Compute scheme performance. Based on the real-time scenario simulation results, calculate the power profiles of various components, total energy cost, total carbon emissions, and total energy consumption over the operational cycle.
6. Evaluate schemes. Assess the optimization and improvements needed for the current operational cycle based on the performance from the simulation.
7. Determine the termination conditions. If the simulated runtime reaches the predefined operational time, exit the ECC optimization method and output all the result data. Otherwise, continue with step 8.
8. Update optimization schemes. According to the results of the simulation and evaluation, retain certain optimization schemes within the collection and remove others to accommodate new optimization methods. Then, return to step 4 to continue the cycle simulation.