Cyber-physical systems are self-adaptive, changing their behavior at run time to adapt to their context. Hence, their simulations must also handle variability at run time. The lack of support for variability in industrial equation-based modeling languages, such as Modelica, causes problems when simulating self-adaptive systems, e.g., they only limitedly support structural variability, and state transitions are based on if-then-else conditions that can cause conflicts, especially for complex control mechanisms. We present a modeling technique for equation-based models containing variability by implementing concise and dedicated language constructs to express state space and transitions via contextual modeling. Contextual modeling abstracts the modeled world and allows the definition of constraints that reduce the risk of reaching conflicting states. We demonstrate the feasibility of our approach on a case study, presenting the advantages of our modeling technique regarding the definition of state control and the reduction of risk for reaching conflicting states.