Ride-sharing represents a sustainable transportation paradigm that is beneficial to human, society, and environment. Common ride-sharing pricing approaches determine prices for riders through optimizing one or more figures of merit, e.g., the profit or revenue. However, they overlook an important issue—fairness—which, when perceived by the riders, can critically affect their degree of satisfaction. In this work, we take the initiative to consider an intuitive and appropriate notion of individual fairness called fairness-in-hindsight for riders in ride-sharing pricing. We study the problem of online fair pricing of shared rides (which allow multiple riders to share one ride) with an aim to maximize the profit of the ride-sharing operator/platform. We design an online fair ride-sharing pricing algorithm called OnFairRP, which comprises phases oflearning, transition, and exploitation. We prove that OnFairRP has a sub-linear regret bound and can guarantee the fairness between riders. Our extensive performance evaluations using real-world data traces of ride-sharing demonstrate the advantages of OnFairRP over benchmarking schemes including commonly used methods with or without fairness guarantee.

The escalating electrical demands of large-scale computational models in Internet data centers (IDCs) coupled with their significant carbon footprint underscore the potential synergy with demand response (DR) for promoting sustainable power system operations. Despite its potential, this intersection has been insufficiently investigated in existing studies. To fill the gap, an electricity-carbon collaborative demand response (ECCDR) framework is developed and a privacy-preserving bi-level optimization model is proposed to fulfill this goal. First, the ECCDR framework is designed by combining dynamic carbon emissions from power systems with traditional DR, aiming to concurrently maximize the economic and emission reduction benefits. Second, a privacy-preserving bi-level optimization model is proposed to orchestrate computational task distribution within IDCs, facilitating load shifting in power systems. It is done by exchanging non-sensitive information between power systems and IDCs, ensuring privacy yet paving the way for ECCDR’s pragmatic deployment. Third, distributed photovoltaic (PV) and battery energy storage systems (BESS) are integrated into IDC operations, further amplifying ECCDR’s potential. Simulation results reveal that the bi-level optimization model results in cost-efficient operations for both the power system and IDCs without invading privacy, while the ECCDR paradigm demonstrates superior advantages compared to the conventional DR.

Air pollution problems are attracting increasing attention, especially among the developing countries with frequent haze events. Renewable energy sources such as wind power are expected to help relieve such environmental concerns. However, air pollution issues under such a changing energy structure receive inadequate attention. Mostly, constraints for total pollutant emissions are considered in unit commitment (UC) and economic dispatch problems. In this paper, we propose a UC model with wind power that considers the dispersion of air pollutants. The dispersion process is described by models involving meteorological conditions and the system's geographical distribution, to estimate the spatial distribution of air pollutants, i.e., the concentration of ground-level air pollutants at monitored locations, such as load centers. A penalty cost is introduced based on this estimation. Particulate matter 2.5 μm or less in diameter, the major air pollutant concerning most developing countries, is selected as the focus of this paper. To properly estimate and sufficiently utilize the benefits of wind power for air pollutant dispersion control, robust optimization is applied to accommodate wind power uncertainty. Case studies justify this consideration of air pollutant dispersion, and demonstrate the effectiveness of the proposed model for improving load centers' air pollution control and utilizing wind power benefits.

In this paper, an improved multiperiod risk-limiting dispatch (IMRLD) is proposed as an operational method in power systems with high percentage renewables integration. The basic risk-limiting dispatch (BRLD) is chosen as an operational paradigm to address the uncertainty of renewables in this paper due to its three good features. In this paper, the BRLD is extended to the IMRLD so that it satisfies the fundamental operational requirements in the power industry. In order to solve the IMRLD problem, the convexity of the IMRLD is verified. A theorem is stated and proved to transform the IMRLD into a piece-wise linear optimization problem that can be efficiently solved. In addition, the locational marginal price of the IMRLD is derived to analyze the effect of renewables integration on the marginal operational cost. Finally, two numerical tests are conducted to validate the IMRLD.