Yi Guo

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Guo

First Name

Yi

ORCID

0000-0001-9589-3806

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Publications 1 - 10 of 26

An Online Joint Optimization-Estimation Architecture for Distribution Networks
Guo, Yi; Zhou, Xinyang; Zhao, Changhong; et al. (2023)
IEEE Transactions on Control Systems Technology
In this article, we propose an optimal joint optimization-estimation architecture for distribution networks, which jointly solves the optimal power flow (OPF) problem and static state estimation (SE) problem through an online gradient-based feedback algorithm. The main objective is to enable a fast and timely interaction between the OPF decisions and state estimators with limited sensor measurements. First, convergence and optimality of the proposed algorithm are analytically established. Then, the proposed gradient-based algorithm is modified by introducing statistical information of the inherent estimation and linearization errors for an improved and robust performance of the online OPF decisions. Overall, the proposed method eliminates the traditional separation of operation and monitoring, where optimization and estimation usually operate at distinct layers and different time scales. Hence, it enables a computationally affordable, efficient, and robust online operational framework for distribution networks under time-varying settings.
Sparse Structure Design for Stochastic Linear Systems via a Linear Matrix Inequality Approach
Guo, Yi; Stanojev, Ognjen; Hug, Gabriela; et al. (2024)
IEEE Transactions on Control Systems Technology
We propose a sparsity-promoting feedback control design for stochastic linear systems with multiplicative noise. The objective is to identify an optimal sparse control architecture and optimize the closed-loop performance while stabilizing the system in the mean-square sense. Our approach approximates the nonconvex combinatorial optimization problem by minimizing various matrix norms subject to the linear matrix inequality (LMI) stability condition. We present two design problems to reduce the number of actuators and the number of sensors via a low-dimensional output. A regularized linear quadratic regulator with multiplicative (LQRm) noise optimal control problem and its convex relaxation are presented to demonstrate the tradeoff between the suboptimal closed-loop performance and the sparsity degree of control structure. Case studies on power grids for wide-area frequency control show that the proposed sparsity-promoting control can considerably reduce the number of sensors and actuators without significant loss in system performance. The sparse control architecture is robust to substantial system-level disturbances while achieving mean-square stability.
Optimal Power Flow With State Estimationin the Loop for Distribution Networks
Guo, Yi; Zhou, Xinyang; Zhao, Changhong; et al. (2023)
IEEE Systems Journal, Special Issue on RFID Technology : Opportunities and Challenges
In this article, we propose a framework for running optimal control-estimation synthesis in distribution networks. Our approach combines a primal-dual gradient-based optimal power flow solver with a state estimation feedback loop based on a limited set of sensors for system monitoring, instead of assuming exact knowledge of all states. The estimation algorithm reduces uncertainty on unmeasured grid states based on certain online state measurements and noisy "pseudomeasurements." We analyze the convergence of the proposed algorithm and quantify the statistical estimation errors based on a weighted least-squares estimator. The numerical results on a 4521-node network demonstrate that this approach can scale to extremely large networks and provide robustness to both large pseudomeasurement variability and inherent sensor measurement noise.
Editorial: Advances in flexibility exploration and utilization for low carbon power systems
Liu, Yu; Zang, Haixiang; Guo, Yi; et al. (2023)
Frontiers in Energy Research
Multiple Ancillary Services Provision by Distributed Energy Resources in Active Distribution Networks
Stanojev, Ognjen; Guo, Yi; Aristidou, Petros; et al. (2022)
arXiv
The electric power system is currently experiencing radical changes stemming from the increasing share of renewable energy resources and the consequent decommissioning of conventional power plants based on synchronous generators. Since the principal providers of ancillary services are being phased out, new flexibility and reserve providers are needed. The proliferation of Distributed Energy Resources (DERs) in modern distribution networks has opened new possibilities for distribution system operators, enabling them to fill the market gap by harnessing the DER flexibility. This paper introduces a novel centralized MPC-based controller that enables the concurrent provision of voltage support, primary and secondary frequency control by adjusting the setpoints of a heterogeneous group of DERs in active distribution grids. The input-multirate control framework is used to accommodate the distinct timescales and provision requirements of each ancillary service and to ensure that the available resources are properly allocated. Furthermore, an efficient way for incorporating network constraints in the formulation is proposed, where network decomposition is applied to a linear power flow formulation together with network reduction. In addition, different timescale dynamics of the employed DERs and their capability curves are included. The performance of the proposed controller is evaluated on several case studies via dynamic simulations of the IEEE 33-bus system.
Gradient-Based Multi-Area Distribution System State Estimation
Zhou, Xinyang; Liu, Zhiyuan; Guo, Yi; et al. (2020)
IEEE Transactions on Smart Grid
The increasing distributed and renewable energy resources and controllable devices in distribution systems make fast distribution system state estimation (DSSE) crucial in system monitoring and control. We consider a large multi-phase distribution system and formulate DSSE as a weighted least squares (WLS) problem. We divide the large distribution system into smaller areas of subtree structure, and by jointly exploring the linearized power flow model and the network topology, we propose a gradient-based multi-area algorithm to exactly and efficiently solve the WLS problem. The proposed algorithm enables distributed and parallel computation of the state estimation problem without compromising any performance. Numerical results on a 4,521-node test feeder show that the designed algorithm features fast convergence and accurate estimation results. Comparison with traditional Gauss-Newton method shows that the proposed method has much better performance in distribution systems with a limited amount of reliable measurement. The real-time implementation of the algorithm tracks time-varying system states with high accuracy. © 2020 IEEE
Contactless Islanding Detection Method Using Electric Field Sensors
Chen, Kun-Long; Guo, Yi; Wang, Juncheng; et al. (2021)
IEEE Transactions on Instrumentation and Measurement
An accurate and cost-effective islanding detection is in a great need for distributed generation (DG) integration. An onsite contactless islanding detection scheme is proposed in this article, based on a small number of tiny and portable electric field (EF) sensors. In contrast to the existing mainstream detection techniques, which are heavily depended on the instrument transformer measurement in substations, the proposed contactless scheme is able to be deployed on the ground below the overhead feeders, enabled by a compact EF sensor and the associated accurate frequency measurement algorithm. The effectiveness, robustness, efficiency, and security advantages of the proposed method are investigated and verified by the simulations using various feeder configurations. It is further supported by the laboratory experiments based on a scale-down overhead feeder testbed.
Sparse optimal control of networks with multiplicative noise via policy gradient
Gravell, Benjamin; Guo, Yi; Summers, Tyler (2019)
IFAC-PapersOnLine ~ 8th IFAC Workshop on Distributed Estimation and Control in Networked Systems (NECSYS 2019)
We give algorithms for designing near-optimal sparse controllers using policy gradient with applications to control of systems corrupted by multiplicative noise, which is increasingly important in emerging complex dynamical networks. Various regularization schemes are examined and incorporated into the optimization by the use of gradient, subgradient, and proximal gradient methods. Numerical experiments on a large networked system show that the algorithms converge to performant sparse mean-square stabilizing controllers.
Stochastic modeling for the aggregated flexibility of distributed energy resources
Wen, Yilin; Guo, Yi; Hu, Zechun; et al. (2024)
Electric Power Systems Research
This paper proposes an uncertainty modeling method for the aggregated power flexibility of DERs. Basically, both outer and inner approximated power-energy boundary models are utilized to describe the aggregated flexibility of controllable DERs. These power and energy boundary parameters are uncertain because the availability of controllable devices, such as electric vehicles and thermostatically controlled loads, cannot be precisely predicted. The optimal operation problem of the aggregator is thus formulated as chance-constrained programming (CCP). Then, a flexibility envelope searching algorithm based on the ALSO-X+ method is proposed to solve the CCP, the result of which is a conservative approximation of the original CCP but not as conservative as the Conditional Value-at-Risk approximation. After optimizing the aggregated power of the group of DERs, the decision at the aggregator level is disaggregated into the flexibility regions of individual DERs. Finally, the numerical test demonstrates the effectiveness and robustness of the proposed method.
Real-time coordination of integrated transmission and distribution systems: Flexibility modeling and distributed NMPC scheduling
Dai, Xinliang; Guo, Yi; Jiang, Yuning; et al. (2024)
Electric Power Systems Research
This paper proposes a real-time distributed operational architecture to coordinate integrated transmission and distribution systems (ITD). At the distribution system level, the distribution system operator (DSO) calculates the aggregated flexibility of all controllable devices by power-energy envelopes and provides them to the transmission system operator (TSO). At the transmission system level, a distributed nonlinear model predictive control (NMPC) approach is proposed to coordinate the economic dispatch of multiple TSOs, considering the aggregated flexibility of all distribution systems. The subproblems of the proposed approach are associated with different TSOs and individual time periods. In addition, the aggregated flexibility of controllable devices in distribution networks is encapsulated, re-calculated, and communicated through the power-energy envelopes, facilitating a reduction in computational complexity and eliminating redundant information exchanges between TSOs and DSOs, thereby enhancing privacy and security. The framework's effectiveness and applicability in real-world scenarios are validated through simulated operational scenarios on a summer day in Germany, highlighting its robustness in the face of significant prediction mismatches due to severe weather conditions.

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