Integrated demand response (IDR) is a cost-effective approach to promoting renewable energy consumption and energy saving in multi-energy systems. The efficient use of the flexibility of demand-side energy sources as well as the elasticity of energy demands increases the tendency for the development of IDR. This article proposes a bilevel optimization framework that allows a large multi-energy consumer (LMEC) equipped with self-provided energy resources to manipulate electricity market prices using optimum execution of the IDR to fulfill electricity, heating, and cooling needs, simultaneously. At the upper level, the LMEC operator seeks to minimize energy procurement costs associated with electricity, heating, and cooling, whereas at the lower level, the aim is to maximize social welfare from the market operator's perspective in order to establish the market-clearing price. The Karush–Kuhn–Tucker (KKT) conditions and strong duality theory are utilized to convert the model into a single-level mixed-integer linear programming framework. In addition, a developed information gap decision theory technique is adopted to cope with the uncertainty of power generation from wind turbines owned by the LMEC operator under a risk-averse scheme. The numerical findings demonstrate the potential of IDR in influencing power market prices to the benefit of LMEC and lowering its energy procurement costs.