5.2.3. Case Study 3

This case study compares the results of the conducted experimentation and the simulation of the RT-EMS and presents the performance of this system compared to previous work.

The experimental validation of the implemented T-Cell based RT-EMS developed in a MAS environment was tested in the Energy Systems Research Laboratory at Florida International University. Three different scenarios were taken into consideration to validate the efficiency and performance of the proposed algorithm:


• Scenario 3: Sudden load variation. This scenario challenges MG operation when there is a sudden load increase. It is reflected in the period 15:00–16:00, where the load increases from 1500 to 2400 W.

Table 1 presents the parameters of the considered system. It presents the interval values of each system variable. The energy storage is considered operating between 0.4 and 0.9 to protect the battery from the deep discharging and overcharging.


**Table 1.** Microgrid parameters.

Figures 11 and 12 illustrate the power profile of the wind and PV systems. As explained in Section 4.1, both wind and PV were emulated by two synchronous generators. It is noticeable from the two profiles that at the beginning of each period, there is a power fluctuation due to system transients. The power measured during the experiment is also slightly below simulated values because of the system losses. The wind generator was supposed shut down during the period 6:00–7:00 and the PV power profile was also assumed to be null before 7:00 and after 20:00; yet due to experimental constraints the generators were kept running at the minimum level. However, in Figures 13–15, the experimental profile of the energy storage, the load and the controllable load matches the simulated values. The wind and PV profiles data used in this experiment are available in [43] and [44], respectively.

**Figure 11.** Comparison between simulated output and testbed measured values of wind power.

**Figure 12.** Comparison between simulated output and testbed measured values of PV power.

**Figure 13.** Comparison between simulated output and testbed measured values of energy storage power.

**Figure 14.** Comparison between simulated output and testbed measured values of load power.

**Figure 15.** Comparison between simulated output and testbed measured values of controllable load.

Figure 16 describes the power exchange with the grid. Unlike the PV and the wind emulators that receive the reference from EMS, the power exchange with the grid is automatically imported or injected depending on the system consumption and generation. For this reason, all system errors are compensated by the grid power which creates, in some cases, a mismatch between the experiment and simulation values. It also should be mentioned that the simulation shows the RMS value without any transient, but the experimental result demonstrates the actual values along with transient of the power system which includes the compensation within the generators, reactions and power grid losses.

**Figure 16.** Comparison between simulated output and testbed measured values of grid power.

Figure 17 illustrates the MG dynamic during a day of operation going through the three scenarios. From 00:00 to 2:00, the MG is supplying the load from the wind and the grid. As soon as the load decreases from 2:00 to 6:00, the proposed algorithm allows charging of the energy storage with the excess power. During the shutdown of the wind generator from 6:00 to 7:00, reflected by a drop of the wind power (scenario 2), the needed power was supported by the energy storage and the controllable load. The following day's hours match more load increase, which is supplied by the grid, wind, controllable load, and the increasing PV power. From 13:00 to 15:00, the PV power is at the top generation, which allows charging the energy storage with excess power. Scenario 3 is tested from 15:00 to 16:00 in which the load suddenly raises from 1500 to 2400 W. The algorithm compensates the deficit power using the energy storage and controllable load. The MG continues its normal operation and uses the available power from the energy storage until it reaches its minimum value; then, the power needed is imported from the grid and the available wind power.

Through the 24 h of operation in different scenarios, the proposed T-cell based RT-EMS has proven his effectiveness and capability of maintaining the stability and balance of the MG.

The proposed T-Cell algorithm that has been implemented for the EMS optimization is coded in Java language using the Eclipse programming environment. The environment integrates the JADE tool kit and the DDS interoperability agents. Before implementing those agents, it is important to define the data model for the MG. In our case, the data model consists of four variables of type double that represents the reference computed by the MGO. These four variables are published through the DDS agents into four topics, as illustrated in Figure 18. In the other side, the LabVIEW SCADA system implements the subscribers for these topics. Once the new data is published, it is automatically intercepted by the subscribers and sent to the appropriate generator or controllable load.

**Figure 17.** Experimental results of the MG powers during 24 h operation.

**Figure 18.** DDS Connext view.

The RT-EMS has been tested using a computer with the following specifications: 4GB of RAM and 2 GHz of CPU. In this configuration, the average execution time of the T-Cell algorithm is 231 ms. However, when executed in a computer of 6G of RAM, the average execution time is 60 ms. In both cases, the T-Cell algorithm presented better performance than mixed integer nonlinear programming (MINLP) and multi-period artificial bee colony algorithm (MABC) that have 8.23 s and 1.14 s respectively [10].

The execution time is very crucial for RT-EMS in MG context and not as important as in day ahead management where the optimization is run a day before. The notion of real time that has been reported in different papers is between 3 to 5 min. The reason behind choosing this time is because it is less than the time needed for the adjustment of protective relay such as over/under voltage relay. Besides, PV systems and wind generators usually need more than 5 s to be affected by atmospheric variations. Although the implemented RT-EMS could be executed every 5 min and still presents better results than previous works, it is executed every 15 min. The main reason is that the system has the MGS, which is continually checking for high load variation and in this case, launches a new optimization cycle. This way, system resources are used optimally. The MG under study has an RS232 serial connection network for the control of the testbed components and the SCADA system, as illustrated in Figure 5. The latency time for sending data from the LabVIEW SCADA system to the generators and controllable loads in the testbed is1s[45].

Furthermore, for a measurement message size of 32 bytes and message rate of 1000 Msg/s, the average latency for DDS middleware is 243 μs with 90% below 269 μs [46]. The T-Cell optimization algorithm executed in a computer of 4GB of RAM and 2 GHz of CPU has an average latency of 231 ms. Therefore, the total latency of the RT-EMS is 1.3 s on average, which is way less than the 3 min presented in [10].

Another positive point could be stated. As the MG size increases and additional components are connected, the system variables will increase. This would be reflected in the execution time that would increase too. However, the proposed immune algorithm was tested along with fast, chaotic particle swarm optimization (PSO) and independent component analysis based PSO in the case of the small and medium power network, and the T-Cell algorithm reached a better optimum in minimum time [15].
