Rise in distributed energy resources has prompted a shift in the way electricity market operators function. Traditionally, centralized optimization techniques are used by such operators to plan for economic dispatches of power tominimize the overall operational costs and increase system social welfare. Due to the dispersed nature of renewable energy sources as scattered nodes in a system, it can get difficult to accommodate them in a centralized optimization problem. Also, sharing of complete information for such nodes can create privacy issues. This motivates research in the field of distributed optimization techniques. This thesis aims to develop a distributed optimization algorithm for a DC distribution system which would take into account network congestion and line losses and ultimately provide a more precise optimal solution. Based on past research for distributed optimization approaches for AC systems, the Consensus and Innovations approach was used to model an algorithm and provide nodal optimization for a DC system with minimal data exchange. The developed model was implemented on various DC network topologies like meshed grids, single line networks, T Shaped networks, etc. and a converged output of system variables was accomplished. The results were also compared with a centralized optimization approach to check for deviations. The decision variables for the developed approach were found to be well within the deviation range of 2 percent. The algorithm managed to provide distributed optimization within a DC system while minimizing power generator operational costs and cost associated with network losses.