arXiv subjects

Yuting Ji

Publications and source records attributed to Yuting Ji.

7 recordsLinked to original sources

arXiv2025

Exploring the potential resource integration under passenger-freight shared mobility: collaborative optimization of multi-type bus scheduling and dynamic vehicle capacity allocation for urban-rural bus routes

Under the global background of developing urban-rural travel patterns, traditional urban-rural public transport systems are generally faced with the serious challenges of passenger loss and operating deficit, leading to a reduction in the bus frequency and service reliability. In order to break the vicious circle of demand decline-supply shrinkage, passenger-freight shared mobility (PFSM), an innovative operation mode, can achieve synergies between urban-rural logistics and public transport services by integrating public transit network resources and vehicle spare capacity. However, PFSM has changed the operating characteristics of urban-rural bus systems, posing some new challenges. To expand the relevant theory and find the solutions to those challenges, this study proposes an economy-efficiency-low-carbon -oriented resource reconfiguration strategy by formulating the collaborative bilevel optimization of multi-type bus scheduling and dynamic vehicle capacity allocation for urban-rural bus routes. The improved jellyfish search algorithm is developed to solve the premature convergence problem of the traditional algorithms in solving a high-dimensional hybrid discrete-continuous optimization. The results of a case of two urban-rural bus lines in Shanxi Province, China, indicate that the proposed scheme can improve operating revenue by 328.45% and reduce freight carbon emissions by 19.12 tons/year within the increase of 19.46% in average passenger travel time. The sensitivity analysis explicates key parameters selected for PFSM in terms of economic, efficiency and environmental dimensions. The proposed method provides some novel insights and solutions for the sustainable development of urban-rural public transport systems and the last kilometer problem of rural logistics, with significant values of both economic growth and environmental carbon reduction.

math.OC↗

arXiv2018

Data-Driven Load Modeling and Forecasting of Residential Appliances

The expansion of residential demand response programs and increased deployment of controllable loads will require accurate appliance-level load modeling and forecasting. This paper proposes a conditional hidden semi-Markov model to describe the probabilistic nature of residential appliance demand, and an algorithm for short-term load forecasting. Model parameters are estimated directly from power consumption data using scalable statistical learning methods. Case studies performed using sub-metered 1-minute power consumption data from several types of appliances demonstrate the effectiveness of the model for load forecasting and anomaly detection.

stat.AP↗

arXiv2017

Generalized Coordinated Transaction Scheduling: A Market Approach to Seamless Interfaces

A generalization of the coordinated transaction scheduling (CTS)---the state-of-the-art interchange scheduling---is proposed. Referred to as generalized coordinated transaction scheduling (GCTS), the proposed approach addresses major seams issues of CTS: the ad hoc use of proxy buses, the presence of loop flow as a result of proxy bus approximation, and difficulties in dealing with multiple interfaces. By allowing market participants to submit bids across market boundaries, GCTS also generalizes the joint economic dispatch that achieves seamless interchange without market participants. It is shown that GCTS asymptotically achieves seamless interface under certain conditions. GCTS is also shown to be revenue adequate in that each regional market has a non-negative net revenue that is equal to its congestion rent. Numerical examples are presented to illustrate the quantitative improvement of the proposed approach.

math.OC↗

arXiv2016

Multi-Area Interchange Scheduling under Uncertainty

The problem of multi-area interchange scheduling under system uncertainty is considered. A new scheduling technique is proposed for a multi-proxy bus system based on stochastic optimization that captures uncertainty in renewable generation and stochastic load. In particular, the proposed algorithm iteratively optimizes the interface flows using a multidimensional demand and supply functions. Optimality and convergence are guaranteed for both synchronous and asynchronous scheduling under nominal assumptions.

math.OC↗

arXiv2016

Probabilistic Forecasting and Simulation of Electricity Markets via Online Dictionary Learning

The problem of probabilistic forecasting and online simulation of real-time electricity market with stochastic generation and demand is considered. By exploiting the parametric structure of the direct current optimal power flow, a new technique based on online dictionary learning (ODL) is proposed. The ODL approach incorporates real-time measurements and historical traces to produce forecasts of joint and marginal probability distributions of future locational marginal prices, power flows, and dispatch levels, conditional on the system state at the time of forecasting. Compared with standard Monte Carlo simulation techniques, the ODL approach offers several orders of magnitude improvement in computation time, making it feasible for online forecasting of market operations. Numerical simulations on large and moderate size power systems illustrate its performance and complexity features and its potential as a tool for system operators.

stat.AP↗

arXiv2016

Stochastic Interchange Scheduling in the Real-Time Electricity Market

The problem of multi-area interchange scheduling in the presence of stochastic generation and load is considered. A new interchange scheduling technique based on a two-stage stochastic minimization of overall expected operating cost is proposed. Because directly solving the stochastic optimization is intractable, an equivalent problem that maximizes the expected social welfare is formulated. The proposed technique leverages the operator's capability of forecasting locational marginal prices (LMPs) and obtains the optimal interchange schedule without iterations among operators.

eess.SY↗

arXiv2015

Probabilistic Forecast of Real-Time LMP and Network Congestion

The short-term forecasting of real-time locational marginal price (LMP) and network congestion is considered from a system operator perspective. A new probabilistic forecasting technique is proposed based on a multiparametric programming formulation that partitions the uncertainty parameter space into critical regions from which the conditional probability distribution of the real-time LMP/congestion is obtained. The proposed method incorporates load/generation forecast, time varying operation constraints, and contingency models. By shifting the computation cost associated with multiparametric programs offline, the online computation cost is significantly reduced. An online simulation technique by generating critical regions dynamically is also proposed, which results in several orders of magnitude improvement in the computational cost over standard Monte Carlo methods.

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