The demand side management in the smart grid has recently been investigated extensively. By developing the concept of the smart geid, demand side management techniques have also been developed and automated algorithms for this purpose have been proposed. In automated algorithms, load management is performed by users themselves, and in these methods competition between users to maintain their interests is modeled with game theory. A significant point in the design of load management algorithms is that their success depends on the collaboration and participation of the users of the power grid in these load management programs. Therefore, some key features should be considered in designing these algorithms to achieve their purpose in load planning, and reduce the total power grid load peak. Among these features can be mentioned optimality of the algorithms, fairness among consumers who participate in the demand side management programs, and protecting the privacy of consumers against their neighbors. The most important feature in designing demand side algorithms is how these algorithms can cope with dishonest users. Sometimes users give false information about their load profiles to other users in order to increase their profits, in other words some users announce more than their actual consumption to other users in order to increase their profit or reduce their electricity bill. Therefore, the absence of this feature means that the algorithm is not resistant to dishonest users which may discourage the other users from cooperating in the system. In this thesis, we describe the robustness of the DSM algorithms against dishonest users which is neglected in some load management algorithms, and we will provide a billing mechanism that prevents cheating. The proposed electricity bill consists of two parts: 1) The cost of the user's actual consumption. 2) The cost of cheating proportional to the amount of the load profile exceeding this actual consumption. The proposed punishment mechanism encourages the consumers to announce load profiles that are close to their actual consumption. The simulation results show that the proposed billing function results in the reduction of the peak load and the convergence diagram of the system total cost. Keywords: Smart Grid, Demand Side Management Algorithms, Billing Mechanism, Game Theory, Punishment Function