An integrated framework of agent-based modelling and robust optimization for microgrid energy management

To handle significant variability in loads, renewable energy generation, as well as various contingencies, (two-stage) robust optimization method has been adopted to construct unit commitment models and to ensure reliable solutions. In this paper, we further explore and extend the modeling capacity of two-stage robust optimization and present two new robust unit commitment variants: the expanded robust unit commitment and the risk constrained robust unit commitment model. We derive structural properties, demonstrate the connection to the popular scenario based stochastic unit commitment models, and present a customized column-and-constraint generation method. Numerical experiments on those models are performed using practical data sets, which illustrate their modeling strength, economic outcomes, and the algorithm performance in solving those models.

Exploring the Modeling Capacity of Two-Stage Robust Optimization: Variants of Robust Unit Commitment Model

Decision and coordination in the dual-channel supply chain considering cap-and-trade regulation

Energy is crucial in supporting people's daily lives and the continual quest for human development. Due to the associated complexities and uncertainties, decision makers and planners are facing increased pressure to respond more effectively to a number of energy-related issues and conflicts, as well as GHG emission mitigation within the multiple scales of energy management systems (EMSs). This quandary requires a focused effort to resolve a wide range of issues related to EMSs, as well as the associated economic and environmental implications. Effective systems analysis approaches under uncertainty to successfully address interactions, complexities, uncertainties, and changing conditions associated with EMSs is desired, which require a systematic investigation of the current studies on energy systems. Systems analysis and optimization modeling for low-carbon energy systems planning with the consideration of GHG emission reduction under uncertainty is thus comprehensively reviewed in this paper. A number of related methodologies and applications related to: (a) optimization modeling of GHG emission mitigation; (b) optimization modeling of energy systems planning under uncertainty; and (c) model-based decision support tools are examined. Perspectives of effective management schemes are investigated, demonstrating many demanding areas for enhanced research efforts, which include issues of data availability and reliability, concerns in

/pdf/a-review-on-optimization-modeling-of-energy-systems-planning-41eimugtfq.pdf

A Review on Optimization Modeling of Energy Systems Planning and GHG Emission Mitigation under Uncertainty

With sustained growth of intermittent power supplies connected to grid, the randomness and volatility of intermittent power supplies will bring new challenges to power system optimization dispatch. For dealing with the uncertainty of large-scale intermittent power supply, this paper introduces robust optimization theory into optimization dispatch for hybrid wind/photovoltaic/hydro/thermal power systems. Meanwhile in order to make an ideal compromise between reliability and economy of system dispatch, this paper also introduces concept of uncertainty to cover shortcoming of conventional robust optimization which is conservative. A flexible robust optimization with adjustable uncertainty budget dispatch model is built for hybrid power system to achieve coordination between reliability and economy, and then the uncertainty budget decision methods are studied to reduce blindness of uncertainty budget decisions. Moreover, a new compound differential evolution (CDE) algorithm is designed in this paper. The diversity of individuals and convergence speed are taken into account in process of differential evolution (DE) calculation by introducing a series of operations into DE algorithm, such as individual ranking, population dividing, CDE, and population restructuring. Finally, a sample application is carried out to verify the validity and practicability of the model and the method.

Y.F. Li

Papers

An inexact chance-constrained programming model for water quality management in Binhai New Area of Tianjin, China

A robust optimization method for planning regional-scale electric power systems and managing carbon dioxide

Planning carbon dioxide mitigation of Qingdao's electric power systems under dual uncertainties

Research on the influence factors of ubiquitous power Internet of things for promoting consumption of wind power based on fuzzy G1-ISM in China

Study on coupling optimization model of node enterprises for energy storage-involved photovoltaic value chain in China