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With the increasing penetration of intermittent energy resources such as solar and wind power, the issue of instantaneous mismatch between power generation and power demand has become increasingly severe and difficult to deal with. Adapting to such an energy paradigm, electricity markets have become more and more volatile, jeopardizing the stability of the power grid and potentially causing significant damage to the network. One solution to address such a challenge is to adopt Demand Response (DR) policies, where the consumers of electricity will modify or reduce their electricity usage from their normal consumption patterns responding to the changes in the electricity price or incentives offered. As one of the largest electricity consumers, the process industry could potentially benefit from demand response operation, such as lowering the production capacity when the electricity price is high and raising it when the electricity price is lower. However, such flexible process operation usually depends on two main factors: fast response to the price or incentive signals and large capacity allowing flexible operation, both of which will depend on the underlying process design. While process operation under a volatile electricity market has attracted a lot of attention from researchers, the design of DR-enabled chemical processes has received limited scrutiny.Motivated by above considerations, this dissertation focuses on developing a process design and analysis framework to integrate process design and process operation, with the consideration of DR. The first part of the dissertation will focus on developing the framework to design process networks, allowing varied operating levels, as well as enabling optimal process flow sheet reconfiguration under different DR scenarios. The second part of the dissertation will focus on developing the framework and proposing new metrics to evaluate and quantify the ability and potential of the process to participate in DR programs (i.e., DR-p