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.

Synergy among renewable energy sources (RESs), energy network (EN) expansion and electrified transport provides a potential pathway toward low-carbon energy systems. However, the ambition of deep decarbonization may not be achieved by connecting all stakeholders through single market-wise effort. To this end, this article presents a coordinative planning framework of public transport electrification (PTE), RESs and ENs for decarbonization of urban multi-energy systems (UMESs). The framework is formulated as a three-level programming and driven by both government incentives and market signals. In the upper-level, the investment decision models of RESs, ENs and PTE are developed. Particularly, public transport hubs (PTHs) are viewed as flexible multi-energy demands whose potential of implementing demand response is incorporated in PTE planning model. In the middle-level, a carbon emission-security assessment subproblem is proposed, where market equilibriums are captured by Karush-Kuhn-Tucker optimality conditions; then the nested Benders decomposition and Lagrangian relaxation approach are utilized to synthetize capacity incentives paid by governments. In the lower-level, a hybrid energy pricing mechanism is designed to obtain integrated energy-carbon prices. The above hierarchical model is solved by an iterative algorithm where all investors update their planning decisions in a decentralized manner based on customized government incentives and market prices. Numerical studies prove that our proposed approach can effectively coordinate all stakeholders and promote the low-carbon transition of UMESs.