## 3 Results
### The Micro-Solar CHP Plant Performance
& \(9.12\times 10^{3}\) & \(6.09\times 10^{3}\) & \(4.78\times 10^{2}\) & \(4.41\times 10^{3}\) & \(7.85\) & \(72.49\) & \(2.50\times 10^{3}\) & \(-1.66\times 10^{3}\) \\ Apr. & \(9.85\times 10^{3}\) & \(6.73\times 10^{3}\) & \(5.29\times 10^{2}\) & \(4.88\times 10^{3}\) & \(7.86\) & \(72.49\) & \(2.43\times 10^{3}\) & \(-1.71\times 10^{3}\) \\ May & \(1.09\times 10^{4}\) & \(7.68\times 10^{3}\) & \(6.07\times 10^{2}\) & \(5.57\times 10^{3}\) & \(7.90\) & \(72.46\) & \(2.28\times 10^{3}\) & \(-1.49\times 10^{3}\) \\ Jun. & \(1.17\times 10^{4}\) & \(8.21\times 10^{3}\) & \(6.51\times 10^{2}\) & \(controller), although the conversion efficiencies of the ORC unit are almost the same. On the contrary, during winter, the fuzzy logic controller is able to improve the integration with the TES unit time, thus supporting the ORC operation also in OM5 and OM6, as reported in Table 9. On an annual basis, in the case of a fuzzy logic controller, the operation of the plant in OM3 increases (about 844 h/year compared to 262 h/year in the case of a baseline controller), the operating hours of the ORC system reduce (about 3220 h/year in the case of a baseline controller and about 2830 h/year with a fuzzy logic controller) and the solar field is subjected to a longer period of defocusing (OM\(-\)1). To better appreciate the peculiarities of the fuzzy logic controller in case the plant is coupled with a school building load profile, the daily trends of the most important performance parameters are reported in Figure 8a-d for a sunny winter day and a partially cloudy mid-season day by comparing the impact of both controllers. Independent from the controller, when the solar radiation is high, and, as a consequence, the collected thermal energy, the diathermic oil reaches a temperature level high enough to run the ORC under nominal operating conditions with almost constant electrical and thermal power outputs. On the contrary, when the TES also supplies energy to the ORC unit, the inlet temperature of the diathermic oil to the evaporator may reduce with time and the power outputs from the ORC as well. As can be clearly noted, the fuzzy logic controller is able to extend the operation of the ORC unit for a longer period when a thermal load is required, thus better exploiting the collected thermal energy by the solar field. This is obtained by reducing the number of switches between the different OMs when the thermal energy demand is absent or low (i.e., in the afternoon), while storing the collected thermal energy in the latent heat TES (on a typical winter day, the plant works in OM3 for a significantly longer period). The benefits of the fuzzy logic controller are even higher during the mid-season days, when the TES is also able to provide thermal energy to the ORC unit in the early morning, thus covering most of the energy demand while operating in OM5 and OM6. Figure 8: Daily trend of plant performance coupled with a school building load profile: (**a**) sunny winter day in the case of a baseline controller, (**b**) sunny winter day in the case of a fuzzy logic controller, (**c**) partially cloudy mid-season day in the case of a baseline controller, and (**d**) partially cloudy mid-season day in the case of a fuzzy logic controller. This is possible thanks to the higher temperatures reached by the TES unit in the case of a fuzzy logic controller, as reported in Figure 9a-d. Hence, during a typical winter day, the system can also prolong its operation when the collected thermal energy decreases by working in OM6, while during the mid-season days, the collected thermal energy by the solar field allows full melting of the PCM material and thus the operation of the plant also in OM5. Hence, it is evident that the fuzzy logic controller permits better usage of the TES unit, in accordance with the information embedded in the Time variable, which accounts for the periods of time when the load is expected to be higher.