We develop a bi-level model, which captures strategic decision making by PEV owners, to optimize the design of a plug-in electric vehicle (PEV) charging station with distributed energy resources. The upper level of the model determines the optimal configuration of the station and pricing schemes whereas the lower level captures charging decisions by PEV owners. A robust formulation is employed to capture uncertain wholesale energy prices, renewable-resource availability, and PEV flows. The resulting bi-level robust-optimization model is transformed into an equivalent single-level optimization problem by replacing the lower-level problem with Karush-Kuhn-Tucker optimality conditions. A column-and-constraint-generation algorithm is used to solve the resultant single-level problem. Results from a realistic case study and a parameter analysis demonstrate the effectiveness of the proposed model in capturing the impacts of uncertainty and self-interested behavior by PEV owners.

Various real-world problems can be attributed to constrained multi-objective optimization problems. Although there are various solution methods, it is still very challenging to automatically select efficient solving strategies for constrained multi-objective optimization problems. Given this, a process knowledge-guided constrained multi-objective autonomous evolutionary optimization method is proposed. Firstly, the effects of different solving strategies on population states are evaluated in the early evolutionary stage. Then, the mapping model of population states and solving strategies is established. Finally, the model recommends subsequent solving strategies based on the current population state. This method can be embedded into existing evolutionary algorithms, which can improve their performances to different degrees. The proposed method is applied to 41 benchmarks and 30 dispatch optimization problems of the integrated coal mine energy system. Experimental results verify the effectiveness and superiority of the proposed method in solving constrained multi-objective optimization problems.

This paper proposes a new planning framework for optimal allocation of parking-lot (PL)-based charging infrastructures to facilitate the efficient integration of plug-in electric vehicles (PEVs). Unlike existing works, the present study explicitly considers the uncertain implications of incentive policy on PEV owners charging behaviors and its effects in PL planning. For this aim, a regret-matching technique is introduced to model the bounded rationality of PEV owners in deciding the choice to use different charging options for recharging their vehicle, as a dependency with respect to the incentive value and the accessibility of the charging service in long-term horizon. Such endogenous uncertainties are considered simultaneously with the inherent exogenous randomness of PEV demand and captured by the proposed PL planning model using a proper scenario generation method. The resulting model turns out to be a two-stage stochastic programming with decision-dependent uncertainties and it is solved by using the genetic algorithm. Numerical studies based on a modified IEEE 12-bus distribution network verify the effectiveness of the proposed model and the approach.