Guangchao Geng

Energy Management of Microgrid in Grid-Connected and Stand-Alone Modes

There are two operation modes of microgrids: grid-connected mode and stand-alone mode. Normally, a microgrid will be connected to the main grid for the majority of time, i.e., operates in the grid-connected mode. In the stand-alone mode, a microgrid is isolated from the main grid; the highest priority for microgrids is to keep a reliable power supply to customers instead of economic benefits. So, the objectives and energy management strategies are different in two modes. In this paper, a novel double-layer coordinated control approach for microgrid energy management is proposed, which consists of two layers: the schedule layer and the dispatch layer. The schedule layer obtains an economic operation scheme based on forecasting data, while the dispatch layer provides power of controllable units based on real-time data. Errors between the forecasting and real-time data are resolved through coordination control of the two layers by reserving adequate active power in the schedule layer, then allocating that reserve in the dispatch layer to deal with the indeterminacy of uncontrollable units. A typical-structure microgrid is studied as an example, and simulation results are presented to demonstrate the performance of the proposed double-layer coordination control method in both grid-connected mode and stand-alone mode.

A Reduced-Space Interior Point Method for Transient Stability Constrained Optimal Power Flow

Transient stability constrained optimal power flow (TSOPF) is a big challenge in the field of power system operation because of its computational complexity. At present, the combination of numerical discretization method with interior point method (IPM) is considered as one of the best algorithms for large-scale TSOPF problems. However, it still suffers from the curse of dimensionality as well as unacceptable computational time and memory consumption. Although the TSOPF problem after numerical discretization is a very large nonlinear programming problem (NLP), its degrees of freedom is relatively small, which makes it very suitable for solution by a reduced-space technique. Considering this characteristic of TSOPF problems, the combination of a reduced-space technique with predictor-corrector IPM is presented in this paper as a way of relieving the computational burden of numerical discretization-based TSOPF algorithms. Several key steps of the reduced-space approach, including building basis matrices, partition strategy, computation of cross term and reduced Hessian, are discussed in detail. Detailed case studies indicate that the proposed reduced-space approach can remarkably reduce both CPU time and memory usage compared to the full-space approach, and therefore is a very promising method for solving large-scale TSOPF problems.

A hybrid dynamic optimization approach for stability constrained optimal power flow

Stability-constrained optimal power flow (SOPF) is an effective and economic tool to enhance stability performance by adjusting initial steady-state operating conditions, with the consideration of rotor angle and short-term voltage performance criteria. SOPF belongs to the category of dynamic optimization problems which are computationally expensive. In order to reduce its computational complexity, a hybrid dynamic optimization approach is proposed for efficient and robust solving SOPF problems. Based on the direct multiple shooting method, this approach combines the algorithmic advantages from existing direct sequential and simultaneous approaches. Coarse-grained parallelism among multiple shooting intervals is explored. A modular-based implementation architecture is designed to take advantage of the loose coupling between time-domain simulation and optimization. Case studies on various test systems indicate that the proposed approach is able to reduce computation time compared with other direct approaches for dynamic optimization. Also, the investigated parallelizations are effective to achieve acceleration on a symmetric multiprocessing platform.

An Efficient Implementation of Automatic Differentiation in Interior Point Optimal Power Flow

This paper presents an improved implementation of automatic differentiation (AD) technique in rectangular interior point optimal power flow (OPF). Distinguished from the existing implementation of AD, the proposed implementation adds a subroutine to identify all constant first-order and second-order derivates by AD and form a list of constant derivates before the processing of iterations. At every iteration of interior point OPF algorithm, only the changing derivates are updated by AD tool. An excellent AD software-ADC-is used as a basic AD tool to finish the proposed implementation. A user-defined model interface is provided with AD technique to enhance performance and flexibility. Numerical studies on several large-scale power systems indicate that the proposed implementation of AD can compete with hand code in execution speed without loss of maintainability and flexibility of AD codes. This paper demonstrates that AD technique has an application potential in online operating environments of power systems instead of hand-coded derivates, and greatly relieves the burdens of software developers.

A Two-Level Parallel Decomposition Approach for Transient Stability Constrained Optimal Power Flow

Transient stability constrained optimal power flow (TSCOPF) is able to reduce costs while keeping the operation point away from the stability boundary. While especially useful in modern power system operations, TSCOPF problems are practically very hard to solve; unacceptable computational time is considered to be one of the largest barriers in applying TSCOPF-based solutions. Based on the reduced-space interior point method (RIPM)-which takes advantage of the relatively few degrees of freedom and shows promising potential for solving large-scale TSCOPF problems-this paper introduces a parallel RIPM algorithm with high computing efficiency for multi-contingency TSCOPF problems. A two-level parallelism is developed to fully utilize the computing power of a Beowulf cluster equipped with multi-core CPUs. First, several compute-intensive steps of the TSCOPF algorithm are decomposed according to different contingencies with mathematical equivalent transformations, the corresponding computing tasks are assigned, stored, and processed on different nodes. Second, the distributed computing task is accelerated using elemental decomposition on Jacobian matrices, and then high performance multithreaded mathematical libraries are employed to fully exploit the multi-core CPUs on each node. The effectiveness of the proposed parallel algorithm is benchmarked on a Beowulf cluster with 16 computing nodes with 128 CPU cores using a series of test cases including up to 2746 buses and 16 contingencies. The results of the case studies indicate that the proposed parallel decomposition approach inherits the optimal solution and convergence properties of the original serial interior point method (IPM) approach and shows great capacity in accelerating TSCOPF solution.

Hierarchical Unit Commitment With Uncertain Wind Power Generation

A hierarchical unit commitment (HUC) model is presented with the objective of maintaining system security by scheduling reserves in power systems with high wind penetration. The reserves in the HUC model include generation reserve, ramping reserve, and transmission reserve. These three reserves are proposed to guarantee system security with uncertain wind power. Moreover, the simplifications for the transmission reserve are applied to reduce computational burden. The hierarchical scheduling strategy, which considers both conventional and emergency operations, divides the wind power output into two intervals based on confidence levels, and uses different scheduling strategies for different intervals of wind power output. Finally, case studies compare HUC with RUC and SUC, which shows the effectiveness in system safety enhancement and the effectiveness in practical systems of the proposed HUC model.

Hydro-Thermal-Wind Coordination in Day-Ahead Unit Commitment

With large-scale wind power integration, more reserve has to be scheduled to ensure power supply safety. Proper hydro-thermal coordination is able to provide essential reserve. This paper constructs a unit commitment (UC) model that coordinates hydro and thermal power generation to support secure and economic wind power integration. Several reserves, including an online generation reserve, a ramping reserve and a transmission capacity reserve, are identified and considered in this UC model to counteract the variability and uncertainty inherent in wind power. The reserves are determined based on interval programming and provided through hydro-thermal coordination. By scheduling reserves, this UC model guarantees a reliable power supply while properly maintaining hydropower station reservoir water levels. Finally, numerical results on an eight-bus system and a real-world large-scale power system show the effectiveness of this UC model for guaranteeing system security with high wind power penetration.

Power System Dynamic Model Reduction Based on Extended Krylov Subspace Method

Modern power systems have experienced a significant increase in its complexity, and extremely large-scale system models have to be addressed in the study of stability analysis and control. Model reduction is a technique for developing an approximate system model with lower dimensions that shares similar properties to the original system. This work proposes a computationally efficient approach for linear system model reduction in large-scale power systems that is based on the balanced truncation method using an extended Krylov subspace technique. Key algorithm improvements, including sparsity handling of the linearized power system models and efficient computational techniques for solving dual Lyapunov equations, are discussed in detail. In addition, application of the proposed model reduction is extended to unstable systems, and a power system stabilizer (PSS) parameter optimization method based on reduced system models is used to validate the effectiveness of the proposed method. Case studies show that the resulting reduced models preserve the unique characteristics of the original high-dimensional models, including time and frequency domain responses as well as eigenvalues. Numerical results based on test systems with up to 12 \thinspace685 buses demonstrate the computational efficiency and validity of the proposed approach.

A Parallel Reduced-Space Interior Point Method With Orthogonal Collocation for First-Swing Stability Constrained Emergency Control

First-swing stability constrained emergency control (FSCEC) enhances power system transient stability during large disturbances, but is difficult to solve for larger systems because of its computational complexity. The method proposed in this work guarantees first swing transient stability by using a parallel reduced-space interior point method (IPM) with orthogonal collocation to solve FSCEC problems. This novel algorithm discretizes differential-algebraic equations using orthogonal collocation, which leads to a relatively low problem dimension, and accelerates the optimization process through a reduced-space technique by utilizing the property of small degrees of freedom after numerical discretization. Furthermore, a two-level parallelism is explored in reduced-space IPM (RIPM) algorithm and implemented with state-of-the-art parallelization techniques. The proposed approach was benchmarked on a Beowulf cluster with 64 CPU cores to show its excellent computational efficiency.

An Efficient Parallel Krylov-Schur Method for Eigen-Analysis of Large-Scale Power Systems

An efficient parallel Krylov-Schur method is proposed for computing eigenvalues and eigenvectors related with oscillating modes of low damping for large-scale power systems. Improved restarting techniques are explained and demonstrated in details, which focus on a refined selection of kept subspace in contraction and a reliable mechanism of no missing target eigenvalues. Cayley and shift-invert transforms are used to decouple the computation of eigen-analysis and enable the proposed parallelization with a master-slave scheme. Based on the improved restarting techniques, the strategy for adaptive allocation of shifts and the coordination of parallel computing tasks, the proposed method is capable of computing a large number of eigen-pairs with satisfactory accuracy, convergence rate and parallel acceleration. The computational efficiency is validated by three test cases from real-world large-scale power systems on a symmetric multi-processing (SMP) computer.