Assessing the need for network-based technical constraints in economic optimization of electric vehicle charging

Charging-cost minimization has been identified as an effective motivation for EV users to relinquish their charging autonomy and participate in centralized energy management programs. It has become increasingly common to include technical constraints in cost-based charging strategies to ensure that the distribution network’s loading limits are not violated. However, the higher operational security entails greater computational complexity as these constraints are based on nonlinear power flow equations. The resultant rise in execution time could hinder real-time implementation and is usually overcome by simplifying the problem formulation with convenient assumptions. These simplifications result in approximation of the solution space and potential loss of the global optimum. A possibility of avoiding such complications exists in systems that employ dynamic tariffs, in which electricity price varies as a function of system demand and cost reduction is equivalent to load regulation. As cost-saving strategies shift EV charging from expensive peak-load hours to the cheaper off-peak hours, they are able to reduce network congestion and thereby the likelihood of unacceptable operating conditions. This paper attempts to analyze the regulative action inherent in cost minimization and develop a method to assess the need for technical constraints in economic optimization of EV charging. In order to deal with the stochastic nature of EV charging and its impact on the network, the proposed method simulates the worst-case charging scenario. The worst-case analysis enables the system operator to quantify the maximum corrective action that can be imparted by the cost-minimizing action and examine its adequacy.