Power System Operation

Power system operation is a classic research field, however, the development of renewables and other emerging technologies, such as the energy router and the big data technology, brings challenges to the traditional operation of power systems. We concentrate on developing novel analysis methodology which is suitable for the operation of modern power systems. Our works on power system operation include stochastic unit commitment, probabilistic load flow analysis, power system operation with energy router, and power system operation under big data perspective.

 

Stochastic Unit Commitment

From the operation perspective, 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. In this area, the stochastic unit commitment (SUC) approach attracts significant attentions and has become one of the most commonly used methods. In the power system SUC model, the uncertainty and variability of the renewable energy generation, such as wind power, are usually expressed by multiple scenarios. The performance of SUC is driven by how well the selected scenarios represent the stochastic nature. 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 as much as possible. We also propose a look-ahead SUC model to operate power systems with concentrating solar power (CSP) under high renewable energy penetration. Besides, we propose a risk-based unit commitment model that can identify the underlying risks of the scheduling imposed by renewable energy uncertainty, including loss of load, wind curtailment and branch overflow.

Probabilistic Load Flow Analysis

Probabilistic load flow (PLF) is proposed to evaluate how the uncertainties of renewable energy and load influence the power flow and the underlying operational risks of the system. Earlier methods can only be used to handle the independent uncertainties, but research shows that the uncertainties of loads and renewable energy are correlated, which makes it necessary to take the correlations into consideration. In addition, the PLF algorithms mentioned above are mainly implemented for transmission systems. To fill this gap, we propose a discrete convolution methodology for PLF of ADS considering correlated uncertainties.

Power System Operation with Energy Router

The energy router is an emerging device concept that is based on an advanced power electronic technique. It is able to realize flexible and dynamic electric power distribution in power systems analogous to the function of information routers in the Internet. It is of great interest to investigate how the energy router can be used to optimize power system operation. We formulate the steady-state power flow model of the energy router embedded system network and the related optimal power flow formulation.

Power System Operation from Big Data Perspective

Traditional model-based methods derive linearized power flow (PF) models by making approximations in the analytical PF model according to the physical characteristics of the power system. Today, more measurements of the power system are available and thus facilitate data-driven approaches beyond model-driven approaches. We study a linearized PF model through a data-driven approach.

Economics electricity market involving RE

With the rapidly increasing penetration of RE, RE producers are becoming increasingly responsible for the deviation of the wind power output from the forecast. Such uncertainty results in revenue losses to the RE power producers (RPPs) due to penalties in ex-post imbalance settlements. The revenue for RPPs under such mechanism is modeled. The optimal strategy for managing the uncertainty of RE power by purchasing reserves to maximize the RPPs revenue is analytically derived with rigorous optimality conditions.

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, 2018. accepted, in press. “Link

  2. 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

  3. Yuxiao Liu, Ning Zhang, Yi Wang, Jingwei Yang and Chongqing Kang. Data-Driven Power Flow Linearization: A Regression Approach, IEEE Transactions on Smart Grid, 2018. accepted, in press. “Link

  4. 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

  5. Jianqiang Miao, Ning Zhang, Chongqing Kang, Jianxiao Wang, Yi Wang and Qing Xia. Steady-state power flow model of energy router embedded AC network and its application in optimizing power system operation, IEEE Transactions on Smart Grid, 2018, 9(5): 4828-4837. “Link

  6. 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

  7. Jingwei Yang, Ning Zhang, Chongqing Kang and Qing Xia. A State-Independent Linear Power Flow Model with Accurate Estimation of Voltage Magnitude, IEEE Transactions on Power Systems, 2017, 32(5): 3607-3617. “Link

  8. Ning Zhang, Chongqing Kang, Qing Xia, Yi Ding, Yuehui Huang, Rongfu Sun, Junhui Huang and Jianhua Bai. A Convex Model of Risk-Based Unit Commitment for Day-Ahead Market Clearing Considering Wind Power Uncertainty, IEEE Transactions on Power Systems, 2015, 30(3): 1582-1592. “Link