11 research outputs found
A review on the charging station planning and fleet operation for electric freight vehicles
Freight electrification introduces new opportunities and challenges for
planning and operation. Although research on charging infrastructure planning
and operation is widely available for general electric vehicles, unique
physical and operational characteristics of EFVs coupled with specific patterns
of logistics require dedicated research. This paper presents a comprehensive
literature review to gain a better understanding of the state-of-the-art
research efforts related to planning (charging station siting and sizing) and
operation (routing, charge scheduling, platoon scheduling, and fleet sizing)
for EFVs. We classified the existing literature based on the research topics,
innovations, methodologies, and solution approaches, and future research
directions are identified. Different types of methodologies, such as heuristic,
simulation, and mathematical programming approaches, were applied in the
reviewed literature where mathematical models account for the majority. We
further narrated the specific modeling considerations for different logistic
patterns and research goals with proper reasoning. To solve the proposed
models, different solution approaches, including exact algorithms,
metaheuristic algorithms, and software simulation, were evaluated in terms of
applicability, advantages, and disadvantages. This paper helps to draw more
attention to the planning and operation issues and solutions for freight
electrification and facilitates future studies on EFV to ensure a smooth
transition to a clean freight system.Comment: 43 pages, 4 figures, 2 table
Contingency-Constrained Unit Commitment With Intervening Time for System Adjustments
The N-1-1 contingency criterion considers the con- secutive loss of two
components in a power system, with intervening time for system adjustments. In
this paper, we consider the problem of optimizing generation unit commitment
(UC) while ensuring N-1-1 security. Due to the coupling of time periods
associated with consecutive component losses, the resulting problem is a very
large-scale mixed-integer linear optimization model. For efficient solution, we
introduce a novel branch-and-cut algorithm using a temporally decomposed
bilevel separation oracle. The model and algorithm are assessed using multiple
IEEE test systems, and a comprehensive analysis is performed to compare system
performances across different contingency criteria. Computational results
demonstrate the value of considering intervening time for system adjustments in
terms of total cost and system robustness.Comment: 8 pages, 5 figure
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Critical Infrastructure Systems: Distributed Decision Processes over Network and Uncertainties
Critical infrastructure systems (CISs) provide the essential services that are vital for a nation's economy, security, and health, but the analysis of CISs are challenged due to their inherent complexity. This dissertation focuses primarily on the system analysis of critical infrastructure systems, with a particular interest to address the modeling and computational challenges brought by uncertainties, interdependencies and distributed decision making of various components and stakeholders involved in CISs, so that a secure, reliable, efficient and resilient system can be further pursued. Through two examples, the first one is on electric vehicle charging infrastructure planning in a competitive market, and the second one is on power generators planning in a restructured electricity market, we illustrate how our general modeling framework, N-SMOPEC, can be adapted to formulate the specific problems in transportation and energy system. Each example is solved by decomposition based approach with convergence properties developed based on recent theoretical advances of variational convergence. Median size numerical experiments are implemented to study the performance of proposed method and draw practical insights. In addition, we have shown some knowledge from different domains, such as microeconomics, energy and transportation, can be shared to facilitate the formulation and solution process of seemingly unrelated problems of each other, which could possibly foster the communication between different fields and open up new research opportunities from both theoretical and practical perspectives
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A Stochastic Multi-Agent Optimization Model for Energy Infrastructure Planning Under Uncertainty and Competition
This paper presents a stochastic multi-agent optimization model that supports energy infrastructure planning under uncertainty. The interdependence between different decision entities in the system is captured in an energy supply chain network, where new entrants of investors compete among themselves and with existing generators for natural resources, transmission capacities, and demand markets. Directly solving the stochastic energy supply chain planning problem is challenging. Through decomposition and reformulation, we convert the original problem to many traffic network equilibrium problems, which enables efficient solution algorithm design.View the NCST Project Webpag
Recommended from our members
A Stochastic Multi-Agent Optimization Model for Energy Infrastructure Planning Under Uncertainty and Competition
This paper presents a stochastic multi-agent optimization model that supports energy infrastructure planning under uncertainty. The interdependence between different decision entities in the system is captured in an energy supply chain network, where new entrants of investors compete among themselves and with existing generators for natural resources, transmission capacities, and demand markets. Directly solving the stochastic energy supply chain planning problem is challenging. Through decomposition and reformulation, we convert the original problem to many traffic network equilibrium problems, which enables efficient solution algorithm design.View the NCST Project Webpag
Recommended from our members
Contingency-Constrained Unit Commitment With Intervening Time for System Adjustments
The N-1-1 contingency criterion considers the con- secutive loss of two components
in a power system, with intervening time for system adjustments. In this paper, we consider
the problem of optimizing generation unit commitment (UC) while ensuring N-1-1 security.
Due to the coupling of time periods associated with consecutive component losses, the
resulting problem is a very large-scale mixed-integer linear optimization model. For
efficient solution, we introduce a novel branch-and-cut algorithm using a temporally
decomposed bilevel separation oracle. The model and algorithm are assessed using multiple
IEEE test systems, and a comprehensive analysis is performed to compare system performances
across different contingency criteria. Computational results demonstrate the value of
considering intervening time for system adjustments in terms of total cost and system
robustness