# Effect of Demand Side Management on the Operation of PV-Integrated Distribution Systems
## 1 Introduction
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Reference** & **Method/Technique** & **Outcome** \\ \hline
[15] & Heuristic-based load shifting technique & Reduced of peak-to-average ratio and minimized the energy cost \\ \hline
[16] & Parallel autonomous optimization scheme with DR framework & Reduced of peak-to-average ratio and minimized the energy cost \\ \hline
[17] & DR program with model predictive control method & Minimized the electricity bill on the customer side \\ \hline
[18] & DSM mechanism along with artificial neural network & Minimized the operation cost \\ \hline
[19] & Load scheduling method based on online event-triggered energy management algorithm & Reduced the electricity bill as well as ensured the user comfort level \\ \hline
[20] & DR scheme & Achieved an optimal power generation and peak load dispatch \\ \hline
[21] & Load scheduling with game theory algorithm & Reduced peak load as well as energy payment for the consumers \\ \hline
[22] & DR method with mixed-integer linear programming & Reduced operational cost and peak load \\ \hline
[23] & Autonomous and distributed DSM scheme & Reduced of peak-to-average ratio and minimized the energy cost \\ \hline
[24] & Load shifting technique using genetic algorithm & Reduced of peak-to-average ratio \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of some results of algorithms used in DSM. Several studies have presented algorithms in the field of DLC and CVR. [PERSON] et al. [25] proposed monitoring load curves of different houses in the same area to come up with a schedule for managing the utility by employing an artificial neural network and then using DLC. [PERSON] et al. [26] proposed maintaining low energy consumption and peak load reduction by using smart metering and automatically controlling home appliances. In this scheme, users' control units share information regarding energy consumption; the final results show a reduction in peak demand and energy costs. [PERSON] et al. [27] proposed a model to optimize energy consumption using load shifting by means of electric vehicles and other storage systems, as well as with the help of hourly pricing. [PERSON] et al. [28] proposed the integration of users to provide information relating to their energy demand patterns, which leads to building utility functions for each user. They then used a mechanism to maximize these utility functions while minimizing costs with the implementation of time-of-use pricing. The authors of [29] proposed to minimize the peak demand on an hourly basis to obtain a balanced daily schedule. They shifted shiftable appliances using the individual power consumption patterns of users and real-time-pricing with integer linear programming. [PERSON] et al. [30] discussed smart home appliances usage scheduled according to an optimization algorithm that includes PV panels acting as power supply while using DLC. [PERSON] et al. [23] discussed a scheduling game, where the players are the consumers and their strategies are daily usage of house appliances. The technique uses the Nash equilibrium. [PERSON] et al. [31] emphasized the positive effects of PV panels at consumer side on peak demand hours and proposed a direct load control over air conditions on a radial residential network. [PERSON] et al. [32] investigated the implementation of DSM in South Africa by controlling clothes washers and dryers considering 100%, 50% and 30% consumer participation. Using a modified genetic algorithm, they showed how electricity bills can be reduced in all three cases by using DLC. [PERSON] et al. [33] investigated a control scheme for implementing demand response in a system with three affecting sides: distribution system operators, aggregators and demand response resources. Three control levels were proposed that change frequency and voltage depending on the threshold-controllable loads. [PERSON] et al. [34] developed load grouping based on power color algorithm of stochastic-constraint satisfaction.