The energy markets are emerging slowly towards a more decentralized form, e.g. technologies are allowing prosumers to trade energy from their solar house rooftops. But as these markets are governed by people and depend on the behavior of people directly, they are susceptible to challenges from the social, economic and cultural norms around the people. One of the example is the cultural discrimination in energy trading noted by some researchers and practitioners in rural India, where people do not prefer to trade energy and goods with different castes, especially lower castes. This creates an unfair market for the poor and lower caste producers, and thus this thesis aimed at modeling such markets through agent based modeling, and introduce different automation mechanisms, namely local mediation, and bid splitting to prevent the effects of discrimination in energy trading. Local mediation allows introduction of a mediator who prevents from direct trade between people and thus prevents any discrimination, bringing in some sort of anonymity. The other mechanism of bid split allows splitting the bids by producer and consumer into small chunks, such that 2 producers in market with 1 and 100 units of energy to trade would look like 101 producers in market trading 101 units of energy. Evaluating the results of these mechanisms on increase of social welfare, efficiency, market access and reduction of inequality has shown that the bid split helps in reducing the discrimination, and so does the introduction of mediation (where the mediator does not discriminate). The former has much more significant effect on the market access, and the validation of the model has helped derive some policy recommendations for implementer in the energy industry and governance field e.g. on the load management for the devices.