# Simulation of Wind-Battery Microgrid Based on Short-Term Wind Power Forecasting
## 3 Simulation Study and Discussion of Results
### Simulation Results and Discussion
#### 3.4.1 Case Study 1--Constant Load Demand  
The first case study considers a constant daily load demand curve of 8 kWh during the 15 days of the simulation. Using scenario (a) as a benchmark and the mean absolute percentage error (MAPE) as a measure of error that is applied to each data point of the simulation, a reduction in the forecasting error of 13.8% is observed for scenario (b), and 14.6% for scenario (c), when compared to the value of error (i.e., 26.62%) in the reference scenario (a). This result shows not only the improvements when using the proposed ANN forecasting method, but also the enhancement of the PLM method over the unadjusted ANN-based forecasts. Examining the distribution of the accuracy of the forecast on a daily basis, it may be observed that 75% of the forecasted values have an absolute error of less than 44.36% in scenario (a), as compared with 16.56% and 15.46% in scenarios (b) and (c), respectively. This result provides an overview of the distribution of forecasting errors during the simulation. Figure 3 depicts the error distribution for the scenarios in case study 1. \begin{table}
\begin{tabular}{c c} \hline
**Description** & **Specification** \\ \hline Configuration & 3 blades, horizontal axis \\ Rotor diameter & \(\Phi\) 8.0 m \\ Rated power & 11 kW @ 11 m/s \\ Cut-in wind speed & 3 m/s \\ Cut-off wind speed & 25 m/s \\ Generator efficiency & \(>\)0.85 \\ Battery bank voltage & 240 Vdc \\ System output voltage & 110/220 Vac \\ Wind energy utilizing ratio (\(C_{p}\)) & 0.40 \\ Battery voltage & 24 V \\ Battery capacity & 150 Ah \\ Minimum discharge level & 30\% \\ Maximum charge level & 100\% \\ Battery discharge efficiency & 0.8 \\ Battery charge efficiency & 0.8 \\ \hline \end{tabular}
\end{table} 
Table 1: Wind turbine and energy storage specifications. \begin{table}
\begin{tabular}{c c} \hline
**Parameter** & **Value** \\ \hline Number of iterations & 10.000 \\ Simulated days & 15 \\ Initial level of battery SoC & 80\% \\ Pre-negotiated electricity price: grid to building (G2B) & 0.2 \(\xi\)/kWh \\ Penalized price G2B & 0.3 \(\xi\)/kWh \\ Electricity price: building to grid (B2G) & 0.15 \(\xi\)/kWh \\ \hline \end{tabular}
\end{table} 
Table 2: Parameters of Monte Carlo simulation. Regarding the amount of electricity purchased and sold during the simulations, a substantial difference is observed between scenarios (a) and (b), with an average of 67.59 kWh purchased and 31.97 kWh sold in the scenario (a) versus 49.44 kWh purchased and 42.89 kWh sold in the scenario (b). When comparing scenarios (b) and (c), it is noted that the results of the latter are slightly inferior with an average of 50.46 kWh purchased and 39.01 kWh sold. This observation can be attributed to the fact that, in the PLM method, the minimization of risk dominates the maximization of profits. The same behavior is also observed in the quantiles of electricity that are sold and purchased in scenarios (b) and (c), as shown in Table 3. Analyzing these variables in detail on a daily basis and given that the initial battery SoC is considered as equal to 80%, the simulation results show that the peaks of electricity sales occur during the first and second day of the data sample (note that the first day of the sample refers to a windy day). Figure 4 shows a pyramid diagram of the electricity purchased versus the electricity sold for scenarios (a) and (b). As far as the costs of electricity purchased and sold are concerned, it is possible to extrapolate the same results that are obtained for the amount of electricity exchanges with few differences revealing the peculiarities of the dataset. In the data sample employed in this work, the wind intensity follows a decreasing pattern during the 15 days of the study, thus rather better results would be expected with a different wind speed pattern. Table 3 also shows the significant improvements in net profit in scenario (a) over (b), as well as the worsening of the results in scenario (c) as compared to (b).