Power System Planning & Operation

In this classic research field, we conduct our research in two aspects: 1) We address the newly emerged issues in power system planning and operation using advanced optimization methodology or business model. Especially, we focus on the planning and operation issues brought by new participators are integrated into the power system such as high penetrated renewable energy (RE), electrical vehicles, demand response. Advanced algorithms are proposed to address the uncertainty issues in the stochastic generation and transmission planning and unit commitment. Furthermore, the concept of cloud energy storage is proposed as a solution for storage sharing among different consumers. 2) We develop practical methods in power system planning and operation that is capable of being used real-life power systems. We have developed software kits of power system operation simulation and power system risk assessment. The tools have been used in more than ten provincial and regional power systems in China. We have also conducted some interesting research using these tools.


Generation & transmission expansion planning model and algorithms


The increasing complexity of power systems rises the necessity of incorporating detailed power system operation models into long-term planning studies. We propose novel planning models and algorithms to obtain practical optimal results and lighten computational burden. To integrate new participators, planning of pumped storage for wind power integration are discussed in our work. Considering the interacting of storage equipment and invested transmission lines, coordinated planning model between transmission line and storage is also proposed.

To incorporate operational flexibility in power system planning studies, a high-efficiency and simplified network-constrained unit commitment model for system planning is studied. To tackle the calculation difficulties in the stochastic planning model, we propose an effective algorithm based on Benders decomposition and multi parametric linear planning so that massive RE scenario could be considered.

Power system operation simulation


Power system operation simulation is a basic tool for evaluating the performance of power system planning scheme. We have developed a software package named “GOPT”, a platform integrating related research achievement in our lab. This software package was designed to simulate chronologically the operation of a power system on a daily basis with a resolution of one hour. It thus makes possible a fine-grained assessment of the feasibility, reliability, and economics of generation and transmission expansion planning. Several case studies on real situation in China have been done to show the effectiveness and provided practical advices such as the impacts on RE curtailment by employing electrical boiler.

Power system risk assessment


The power system risk assessment acts as an important role in the power system to identify the weakness of power system against blackout. We propose a mid-short-term risk assessment model considering the impact of external environment on transmission lines. The relationship model between natural disasters and transmission lines is presented. The conditional outage rate model and the sampling technique are then proposed considering the correlated outage of multiple transmission lines when a disaster happens.

To accelerate the calculation of reliability evaluation, we propose a multi-parametric linear planning based fast power system reliability evaluation using transmission line status dictionary. Our proposed method improves the efficiency for 30 times on IEEE Reliability Test Systems and a provincial power system in China.

Stochastic unit commitment


Stochastic optimization methods are introduced in power system operations to strategically schedule the flexible resources to accommodate the uncertainty and variability of the renewable power generation. Among the massive research in stochastic unit commitment (SUC), we propose a novel scenario representation method referred to as scenario mapping technology (SMT), which is able to compact large amount of scenarios while preserving the uncertainty and variability features of wind power. The method is able to present large amount of scenarios while keeping the SUC tractable. We also propose a risk-based unit commitment model that can identify the underlying risks of the scheduling imposed by renewable energy uncertainty while keeping the SUC model as compact as the deterministic unit commitment model, including loss of load, wind curtailment and branch overflow.

Stochastic unit commitment


We develop a novel way of using energy storage - cloud energy storage - a grid-based storage service that enables ubiquitous and on-demand access to a shared pool of grid-scale energy storage resources. We firstly propose the concept of cloud energy storage which utilized central energy storage facilities to provide distributed storage services to residential and small commercial users. We then develop the architecture, enabling technologies and operation mechanisms that would facilitate the cloud energy storage. We also design the business model of cloud energy storage and demonstrated its profitability using real-life residential load and electricity data.

Related Publications:

  1. Ershun Du, Ning Zhang, Bri-Mathias Hodge, Qin Wang, Zongxiang Lu, Chongqing Kang, Benjamin Kroposki and Qing Xia. Operation of a High Renewable Penetrated Power System with CSP plants: A Look-ahead Stochastic Unit Commitment Model, IEEE Transactions on Power Systems, 2019, 34(1): 140-151. “Link

  2. Zhenyu Zhuo, Ershun Du, Ning Zhang, Chongqing Kang, Qing Xia and Zhidong Wang, Incorporating Massive Scenarios in Transmission Expansion Planning with High Renewable Energy Penetration. IEEE Transactions on Power Systems. accepted, in press, “Link”.

  3. Ershun Du, Ning Zhang, Chongqing Kang and Qing Xia. A High-efficiency Network-constrained Clustered Unit Commitment Model for Power System Planning Studies, IEEE Transactions on Power Systems, 2018. accepted, in press. “Link

  4. Antonio J. Conejo, Yaohua Cheng, Ning Zhang and Chongqing Kang. Long-term coordination of transmission and storage to integrate wind power, CSEE Journal of Power and Energy Systems, 2017, 3(1): 36-43. “Link

  5. Ning Zhang, Chongqing Kang, Daniel S. Kirschen, Qing Xia, Weimin Xi, Junhui Huang and Qian Zhang. Planning pumped storage capacity for wind power integration, IEEE Transactions on Sustainable Energy, 2013, 4(2): 393-401. “Link

  6. Ning Zhang, Xi Lu, Michael B. McElroy, Chris P. Nielsen, Xinyu Chen, Yu Deng and Chongqing Kang. Reducing curtailment of wind electricity in China by employing electric boilers for heat and pumped hydro for energy storage, Applied Energy, 2016, 184: 987-994. “Link

  7. Qianyao Xu, Chongqing Kang, Ning Zhang, Yi Ding, Qing Xia, Rongfu Sun and Jianfei Xu. A probabilistic method for determining grid-accommodable wind power capacity based on multiscenario system operation simulation, IEEE Transactions on Smart Grid, 2016, 7(1): 400-409. “Link

  8. Ning Zhang, Chongqing Kang, Jingkun Liu, Jianbo Xin, Junbiao Wan, Jing Hu and Wenxiao Wei. Mid-short-term risk assessment of power systems considering impact of external environment, Journal of Modern Power Systems and Clean Energy, 2013, 1(2): 118-126. “Link

  9. Pei Yong, Ning Zhang, Chongqing Kang, Qing Xia and Dan Lu. MPLP Based Fast Power System Reliability Evaluation Using Transmission Line Status Dictionary, IEEE Transactions on Power Systems, 2018. accepted, in press. “Link

  10. Ershun Du, Ning Zhang, Chongqing Kang and Qing Xia. Scenario Map Based Stochastic Unit Commitment, IEEE Transactions on Power Systems, 2018, 33(5): 4694-4705. “Link

  11. Jingkun Liu, Ning Zhang, Chongqing Kang, Daniel S. Kirschen and Qing Xia. Decision-making models for the participants in cloud energy storage, IEEE Transactions on Smart Grid, 2018, 9(6): 5512-5521. “Link

  12. Jingkun Liu, Ning Zhang, Chongqing Kang, Daniel Kirschen and Qing Xia. Cloud energy storage for residential and small commercial consumers: A business case study, Applied Energy, 2017, 188: 226-236. “Link