# A Small-Sample Scenario Optimization Scheduling Method Based on Multidimensional Data Expansion
## 5 Case Studies
### Optimal Dispatching Results of the Integrated Energy System  
For the optimal scheduling task of an integrated energy system (IES), a rolling time domain optimization strategy is used to dynamically regulate the output allocation of each energy unit in collaboration with the energy storage system every hour based on the historical operating status of the system, the PV output prediction for the next hour (generated by the prediction model proposed in this paper), and the fine-tuned load demand (determined by the equipment operation plan) to achieve the minimization of the total system operating cost. This paper adopts the offline reinforcement learning approach and conducts learning from the expanded large-scale dataset. In the experiment, the simulation step size is taken as 1 h, the batch size is set to 512, the experience playback capacity is 50,000, the reward discount factor is 0.995, the number of neurons in the hidden layer of the Actor and Critic networks is 256, the learning rate of the Actor network is 0.0001, the learning rate of the Critic network is 0.001, and the soft updating parameter of the target network is 0.01. The hyperparameters \(\sigma\)1 and \(\sigma\)2 in Equation (29) work together to affect the average operating cost and power imbalance to better understand the effect of the hyperparameter \(\sigma\)2. In this paper, \(\sigma\)1 is set to 1, and the value of \(\sigma\)2 is tested in the range of [20, 50, 100, 150]. Figure 9 shows the algorithm's average running cost and power imbalance during the training period, and it can be seen that, when \(\sigma\)1 is fixed, the larger the value of \(\sigma\)2 is, the degree of power imbalance will be reduced rapidly, and the algorithm will converge faster. At the same time, a lower value of \(\sigma\)2 will speed up the convergence of the operating cost and slow down the convergence of the power imbalance. Overall, the deep reinforcement learning algorithm can converge gradually within 300 rounds in the tests conducted. Reference [32] was used for some parameter settings. Three DG units are defined using the parameters shown in Table 4. These parameter settings are based on the typical configurations of existing diesel generator sets in the literature and are obtained through simulation verification based on actual simulation scenarios. They can effectively reflect the operating cost characteristics of the generator sets. The charging/discharging limit of the EESs is set to 100 kW, the nominal capacity is set to 500 kW, and the energy efficiency is set to \(\eta_{B}\) = 0.9. We assume the grid's maximum output/input limit is 100 kW. To encourage the use of renewable energy, the sales price is set to half of the current electricity price, i.e., \(\beta\) = 0.5. Using data at one-hour resolution, the complete dataset after expansion is divided into a training set and a test set, including PV power generation, electrical load demand, and electricity prices for one year. The training set is only 3 weeks of data in the scenario without data expansion. The training set contains the first 3 weeks of each month in the  
Figure 9: The total cost and power imbalance situations under different \(\sigma_{2}\): (**a**) Total cost curves under different parameters. (**b**) Power imbalance curves under different parameters. scenario that includes data expansion. The test sets are all scheduled for the future week, and the process is also experimented with in the summer, winter, and transition seasons. The initial SOC of the EESs was randomly set during training. All algorithms were implemented using Python via PyTorch and trained on 1000 episodes. The training time averaged about 1.5 h per 1000 episodes. Default settings were used unless otherwise stated. The hyperparameters \(\sigma_{1}\) and \(\sigma_{2}\) were defined as 1 and 100 as default values, respectively. The total running cost and power imbalance were used as metrics to evaluate the performance of the DRL algorithm.