## 4 Case Study  
The applicability of the proposed user interface has been investigated on a real smart home including various appliances with smart plugs, residential renewable energy generation units, and different communication and control technologies [18]. As a case study, two different daily load scenarios representing the weekday load profiles of a four-people family in two seasons, winter and summer, have been considered in the application. The working periods and hourly energy consumption values of the appliances are shown in Figure 16. For simplicity, the working periods are assumed to be one hour for all the appliances. In the simulation studies, it is aimed to improve the cost savings by shifting the working periods of certain appliances. It should be noted that the summer data is considered only for brevity and that the price values are based on the time-of-use (ToU) rates adopted in Turkey electricity marketing, which is a three tariff system for domestic end-users, namely, the day rate (06:00 to 17:00), peak (17:00 to 22:00) and off-peak (22:00 to 06:00). In order to provide a cost saving compared to a usual consumption profile, it is assumed that the working period of certain appliances such as washing machine and dishwasher is deferred to the off-peak times based on electricity price and the preferences of the end-user. For the thermostatically controllable loads (TCLs), the smart thermostats are controlled for the changes in the set temperature, resulting in a reduction in the energy consumed. Also, the consumption of some loads such as electric vehicles (EVs) are controlled partially by limiting their power for a certain period. The cost of the energy consumed by the appliances with and without the proposed management algorithm is shown on the upper side of Figure 17, where the blue bar and the green bar represent the hourly costs before and after applying the management algorithm, respectively. The graphic on the lower side in Figure 11 illustrates the total daily costs for each case for the user-defined preferences shown at the bottom. It is also noted that threshold energy limit and comfort preference can be changed easily by end-users regarding their preferences and priorities. Moreover, it should be stated that the mentioned improvement is the result of benefiting from a HEM unit and the role of the end-user interface here is just to create an easy-to-use access point to the HEM system for non-expert end-users. It should be noted that an energy threshold value, which is 492 Wh for this case, as seen from Figure 17, has been employed for deciding the deferring actions. Based on their preferences, the end-users can easily change this value using the developed interface. It is also possible to monitor the historical energy consumption and the related costs on a history screen with the mobile application, as shown in Figure 18.  
As such smart grid vision enabling solutions requiring a high amount of bi-directional data flow become more widespread, it is evident that the electric power system in general will have possible vulnerabilities for any kind of possible cyber-attacks. Therefore, the cybersecurity concept becomes prominent for the continuity of effective electric power system operation by overcoming the mentioned possible vulnerabilities. Here, the objectives of cybersecurity as confidentiality, integrity, and availability (the so-called CIA triad) are taken into account for the assessment of vulnerabilities in different parts of electric power systems and then to find solutions also by prioritizing the vulnerabilities by their possible risky outcomes [19]. Several useful real-world examples of cybersecurity threats in smarter electric power system structures are given in [20]. Specifically for the end-users enrolled in such DR programs, the cybersecurity as well as the privacy of their own data (consumption habits, personal information such as identity number, address details, monetary status, etc.) are very likely to prevent wider participation in such DR programs.