Renewable and Low-Carbon Electric Power and Grid Integration

Led by Michael McELROY (Chair, Harvard-China Project), LU Xi (Tsinghua School of Environment), and postdoc CHEN Xinyu (Harvard-China Project), Project researchers have explored the status and prospects for renewable and low carbon electric power in China, including the challenges of and solutions to integration of variable renewable sources into an inflexible, coal-dominated power system. 

Click on "More Publications" below for a full list of publications supported by the Harvard-China Project in this research area.
 
From assessing wind power potentials using meteorological data and the geophysical constraints, to exploring energy storage and other strategies to ease grid integration of variable power sources, this research has deepened understanding of the role that expanding renewable power capacities can play in reducing emissions of air pollutants and carbon dioxide in China.

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Acknowledgment: Some of the papers cited here are based on work supported by the National Science Foundation under Grants No. ATM-1019134 or ATM-0635548 (indicated by acknowledgments in the papers themselves). Any opinions, findings and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation (NSF).

 

Related Publications

Shaojun Huang, Yuanzhang Sun, and Qiuwei Wu. In Press. “Stochastic Economic Dispatch with Wind using Versatile Probability Distribution and L-BFGSB Based Dual Decomposition.” IEEE Transactions on Power Systems.Abstract
This paper focuses on economic dispatch (ED) in power systems with intermittent wind power, which is a very critical issue in future power systems. A stochastic ED problem is formed based on the recently proposed versatile probability dis-tribution (VPD) of wind power. The problem is then analyzed and proved to be strictly convex. Although such convex optimiza-tion is tractable in many cases, it may take a long time to solve due to its large scale. This paper proposes a dual decomposition method to decompose the large problem. Then two methods are employed to solve the decomposed problem, namely, the subgra-dient method and a faster method, limited-memory BFGS with box constraints (L-BFGS-B, a quasi-Newton method). Case stud-ies were conducted to verify the efficiency of the dual decomposi-tion and L-BFGS-B method for solving the stochastic ED problem.
Xie Yunyun, Chen Xi, Wu Qiuwei, and Zhou Qian. In Press. “Second-Order Conic Programming Model for Load Restoration Considering Uncertainty of Load Increment based on Information Gap Decision Theory.” International Journal of Electrical Power and Energy Systems.Abstract

Load restoration is an important issue for power system restoration after a blackout. A second order conic programming (SOCP) model is proposed based on the information gap decision theory (IGDT) to maximize load pickup considering the uncertainty of load increment. Because distribution functions of load increment are difficult to obtain, the optimization of load pickup is transformed to maximize the fluctuation range of load increment by the IGDT. The derived optimal fluctuation range can ensure that the reenergized system is secure, and the amount of load pickup is always better than the specified expectation. Moreover, because the optimization model of the fluctuation range is a mixed-integer nonlinear model which is challenging to solve accurately and efficiently, the nonlinear model is transformed into a SOCP model that can be efficiently solved using CPLEX. The efficiency of the IGDT-based SOCP model is validated using the New England (10-machine 39-bus) system. The simulation results show that the derived load pickup shows expected robustness with respect to the load increment uncertainty.

Benjamin Si Hao Chew, Yan Xu, and Qiuwei Wu. In Press. “Voltage Balancing for Bipolar DC Distribution Grids: A Power Flow based Binary Integer Multi-Objective Optimization Approach.” IEEE Transactions on Power Systems.Abstract

The re-emergence of 2-phase bipolar DC distribution network, which utilizes the neutral wire for efficient distribution, has spurred research interest in recent years. In practice, system efficiency (power loss) and voltage unbalance are major concerns for the planning and design of the 2-phase DC bipolar network. While most of the existing methodologies are power electronics solutions, there are very few works on resolving the problem from the power system perspective. This paper proposes a model based optimization method by firstly formulating the power flow model for 2-phase DC bipolar network using the single line modeling technique and nodal analysis. Secondly, a binary integer load distribution model is proposed to consider the redistribution of unipolar loads across the two unipolar distribution poles. Together with the power flow model, the system power loss and system voltage unbalance indices are formulated as a binary integer quadratic model. Thirdly, a multi-objective optimization model is formulated and solved using the weighted sum approach. The proposed method is applied to a DC LED lighting system design which considers both voltage unbalance and power loss. Using a 15 bus single source and a 33 bus multi-source network as case studies, the developed power flow model is validated with very high accuracy. Compared to existing iterative methods, the proposed model-based approach is able to significantly improve the voltage balancing across the distribution system.

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