An Regional Market Manager (RMM) is supposed to take into account a variety of items including the participants in the market, technical constraint, price variation/reaction, electricity-price uncertainty, and types of the applied demand response program, to name a few. One of the demand response programs is Emergency Demand Response Program (EDRP) which is employed in this paper. In the present study, the objective function of the RMM is formulated in a market environment in order to determine the optimal demand, incentive, and power purchased with considering some of technical constraints such as incentive limits, demand limits, power purchased, and power balance. Co-evolutionary Improved Teaching Learning-Based Optimization (C-ITLBO) is applied to maximize the RMM's pro t. In addition, the demand level in the EDRP is determined based on a logarithmic model that includes Price Elasticity Matrix (PEM). The reserve supplied due to Aggregators (AGGs) is also prioritized using Reserve Margin Factor (RMF). Further, Information-gap decision theory (IGDT) is applied to model uncertainty in the initial electricity price. The above-mentioned items are modeled in a multi-level formulation