Because of the need to keep balancing between electricity supply and demand continuously, a demand analysis is necessary as it can provide essential information to manage operation of a power system. This study presents a seasonal peak demand characteristics investigation for commercial area in Japan by developing hourly demand regression models for each season. Meteorological parameters and existing holidays are considered as demand drivers. Many standard statistical tests are applied to the models. From results, typical forms of link between commercial demand and key drivers at peak hours (daytime and evening) in each season can be revealed through the models. Knowing more detail characteristic of a demand particularly during high load hours can help utilities to maintain expected optimal operation of power systems and service to consumers all the time.

As meteorological conditions can be unique in different countries and may have influence on electricity demand, providing demand model to analyze characteristic of demand is useful as obtained information can be used to manage related power systems better. This paper proposes regression based demand model to identify typical characteristic of demand in Indonesia more detail. Three different demand areas (Racing Area, Poltek-Area, and Paropo Area) in Makassar, Indonesia including their total demand (Total-Area) are analyzed by creating demand model. The demands are correlated with meteorological parameters (temperature functions, relative humidity, and wind speed) and holidays. Individual characteristics are firstly observed to obtain main drivers and their typical effect on each demand area. Furthermore, general characteristics are analyzed to find common characteristic of demand such as what variables influence electricity demand generally. Several options for model are calculated and assessed by statistical tests to get best model. Results indicate more information concerning characteristic of demands can be revealed by models which are well validated. Each demand area has individual characteristic as demand drivers and their effect are relatively different between areas. Other results concerning general characteristic confirm temperature functions, relative humidiy, and holidays are important driver for demand. The variables are quite good to explain electricity demand generally as adjusted coefficient of determination of model (R 2 , ) is 76.42%.An electric power system is expected can effectively service load demand in all time during its operation. To achieve such expected condition, knowledge about characteristics of connected demand in the system is an important thing, as it can be used by power utilities to manage their power systems better. By performing electricity demand analysis particularly characteristic analysis, typical information such as key drivers for demand and how far their effect under a certain condition are possibly known. One of the method that can be used for the task above is regression approach. However, providing a good demand model as analysis tool is not an easy task as load 978·1·4799-6432-1114/$31.00 mOl41EEE 383

This research proposes a control method in the Single Machine Infinite Bus (SMIB) system by using energy storage based on Capacitive Energy Storage (CES) and Proportional Integral Derivative (PID). The CES-PID parameter is optimized using the Ant Colony Optimization (ACO) algorithm. From the results, the ACO algorithm can quickly converge, the ACO obtained the fitness function value of 2.5054e-07, with 50 iterations. ACO found the most optimal value at the 19th iteration. With elapsed time is 59.179148 seconds. To investigate the stability of the SMIB system with CES-PID control, two case studies are applied, namely increasing and load shedding. The system analysis reviewed is time domain simulation frequency response and rotor angle, Eigenvalue analysis, and damping system. From the simulation result, it can be obtained optimal SMIB system performance with a control method based on Capacitive Energy Storage-PID.

As driver variables may influence more than one area of electricity demand, knowing typical effect of the variables on certain demand areas is important. Utilities can use the information of managing power systems to meet electricity demand for different areas more effective. Based on the regression analysis approach, this study presents a peak demand characteristics comparison between Japanese residential and commercial areas in seasonal level by composing hourly electricity demand model for each area. Besides, a representative hour for off-peak demands is also analyzed. Similar variables (temperature, humidity, wind speed, and holidays) are applied to explain peak and off-peak of summer, autumn, winter, and spring in both areas. Results indicate key drivers for peak and off-peak demands are not same in the certain seasons for both areas. Obtained key variables tend to affect stronger peaks and off-peaks for residential than for commercial area in four observed seasons.

This paper presents seasonal regression models of demand to investigate electricity consumption characteristics. Electricity consumption in commercial areas in Japan is analyzed by using meteorological variables, namely temperature and relative humidity. A dummy variable for holidays is also considered. We have developed models for two levels of period to analyze demand characteristics, that is, half year models and seasonal models. Some options for each model are calculated and validated by statistical tests to obtain better models. As results, half year and seasonal models present explicit information about how the variables affect the demand differently for each period. These specific information help in analyzing characteristics of studied commercial demand.