In this article, a multiobjective optimization methodology is tailored for energy producers and sellers (Prosumers) and electric vehicle charging service providers (EVCS) in IES. The primary objective is to harmonize economic and safety aspects of system, while addressing uncertainties associated with renewable energy and involvement of electric vehicles (EVs) in integrated demand response (IDR). Initially, an orderly charging model for EVs is developed by driving behavior patterns, taking into account the influence of EVCS aggregating EVs in IDR via photovoltaic-energy storage charging stations. Subsequently, a stochastic programming method with conditional value at risk is utilized to alleviate potential risks from stochastic and intermittent nature of renewable energy outputs. Furthermore, the optimization framework incorporates operating costs of Prosumers and EVCS, and peak-to-valley difference of IES load as multiobjective functions. The optimal solution is determined by technique for order of preference by similarity to ideal solution. Ultimately, case studies confirm that the proposed approach effectively balances economic performance for multiple stakeholders and operational safety, and enhances system robustness under uncertain conditions.

Location-based mobile services, while improving user daily life, also raise significant privacy concerns in the sharing of location data. These trajectories indicate users’ traveling behavioural traces with rich semantics derived from open-source information. Behavioral-semantic analysis reveals users’ travelling motivations and underlying behavioral patterns. It contributes to attackers launching inferential attacks for behavior prediction, identity identification, or other privacy invasions, even when the location data is protected. It remains open to the issues of behavioral-semantic privacy-risk quantification and privacy-protection evaluation. This paper aims to reveal such semantic privacy risks of user behaviors arising from the publication of location trajectories in mobile scenarios. We formalize user semantic-mobility process to analyze his underlying behavior patterns. Then, we design semantic inference algorithms conditional on the released trajectory to reason about the observation-based likelihood of the user’s actual staying and transfer behaviours and behavioural-trace tracking. Extensive experiments with real-world data demonstrate their performance on inference accuracy and semantic similarity, offering a quantification criterion for deploying mobile privacy protection.