As the ingress of Renewable Systems and Energy Storage is gaining pace, the concept of local markets is emerging as an attractive alternative to utility grid services. Although, local markets are naive and at emerging stage, their advantages are being realized at both technical and financial aspects. The local market for trading electricity includes prosumers who own Distributed Energy Resources (like PV, Battery Storage) and sell their surplus generation of energy to their existing peers in the community. The local market is based on co-operative sharing economy where all users can participate to meet their demands at a chance of lower prices than that offered by the utility companies. The Thesis looks forward to develop an effective model containing PV and Battery combination in residential community based on two sets of historical demand data and PV generation, and compare the results through performance indices to see how PV and energy storage contribute to savings and help reduce overall grid dependency. The local market has been set up for a community of houses in New South Wales, Australia and local prices have been considered according to the grid prices and feed-in tariff prices prevailing in the market. The overall aim of the research is to optimize the electricity dispatch for these particular demand data sets with an appropriate pricing strategy to achieve cost minimization in terms of energy purchase, increase Self sufficiency, Self consumption and Social Welfare. The simulation of the electricity trading has been carried out using Python programming and Gurobi 9.0 (academic license) and SciPy library as the solver.