The smart grid model is developed with some changes to help in implementing a demand response program which was initially developed for a Pecan Street project. Correspondingly, the real-time solar and load data are collected from the data port for the city of Austin. A single day is selected for our analysis of all four seasons of the year. The flat rate, and real-time and day-ahead pricing information are collected from ComEd. The key challenge for addressing business problems is the flexibility of consumption. However, without considering the properties of loss aversion, the system would not be a practical solution. So, in this article, a dynamic demand response program based on price elasticity that integrates loss aversion characteristics is proposed. The proposed system is compared for all pricing schemes and all seasons. Four scenarios are created for peak time rebate with different combinations of loss aversion factor values and all the possible combinations of rebates. This article directs how these combinations could change the demand curve and how the utility can make a decision about the specific importance of the criteria, such as the total demand carrying capacity, peak demand reduction, and in obtaining optimum profit for utility and the consumer.

There is a huge requirement for power systems to reduce power losses. Adding distributed generators (DGs) is the most common approach to achieving lower power losses. However, several challenges arise, such as determining the ideal size as well as location of the utilized distributed generators. Most of the existing methods do not consider the variety of load types, the variety and size of the utilized DGs besides reducing the convergence time and enhancing the optimization results. The paper performed an optimization algorithm that integrated a golden search-based flower pollination algorithm and fitness-distance balance (FDB) to find out the optimal size as well as the location of the distributed generators. It was then compared with different optimization methods to determine the best optimization technique, and it was determined to be the best technique. In addition, different types of DGs are considered, including solar energy, wind energy, and biogas, along with optimizing the size of the utilized DGs to reduce the system cost. Testing with different types of bus systems, and different types of DGs in a radial distribution system was done to reveal that the modified flower pollination with golden section search was superior in comparison to others with regards to convergence and power loss reduction.