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This research describes a real-time optimization model for multi-agent demand response (DR) from a Load Serving Entity (LSE) perspective. Three major categories of customers and five types of energy resources are considered simultaneously to achieve efficient DR decision making in highly stochastic future energy markets. Two infinite horizon stochastic optimization models are formulated; specifically, an LSE model and a dynamic pricing customer model. The objective of these models is to minimize long-term cost and discomfort penalty of the LSE and dynamic pricing customers. Because preferences of these two agents are different, they are inseparable and difficult to solve. A deterministic finite horizon linear program is solved as an approximation of the suggested stochastic model, and computational experiments are provided.