Emerging smart grid technologies and increased penetration of renewable energy sources (RESs) direct the power sector to focus on RESs as an alternative to meet both baseload and peak load demands in a cost-efficient way. A key issue in such schemes is the design and analysis of energy trading techniques involving complex interactions between an aggregator and multiple electricity suppliers (ESs) with RESs fulfilling a certain demand. This is challenging because ESs can be of various categories, such as small/medium/large scale, and they are self-interested and generally have different preferences toward trading based on their types and constraints. This article introduces a new contract theoretic framework to tackle this challenge by designing optimal contracts for ESs. To this end, a dynamic pricing scheme is developed such that the aggregator can utilize to incentivize the ESs to contribute to both baseload and peak load demands according to their categories. An algorithm is proposed that can be implemented in a distributed manner by trading partners to enable energy trading. It is shown that the trading strategy under a baseload scenario is feasible, and the aggregator only needs to consider the per unit generation cost of ESs to decide on its strategy. The trading strategy for a peak load scenario, however, is complex and requires consideration of different factors, such as variations in the wholesale price and its effect on the selling price of ESs, and the uncertainty of energy generation from RESs. Simulation results demonstrate the effectiveness of the proposed scheme for energy trading in the local electricity market.