Quanyuan Jiang

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 Battery Energy Storage System Dual-Layer Control Strategy for Mitigating Wind Farm Fluctuations

Summary form only given. The intermittent power output of a wind farm is the main challenge behind increasing wind power penetration of power systems. This paper proposes a battery energy storage system (BESS) dual-layer control strategy-consisting of a fluctuation mitigation control layer and a power allocation control layer-to mitigate wind farm power output fluctuations. The fluctuation mitigation control layer calculates the power instructions for the BESS so that the combined wind farm and BESS power output meets fluctuation mitigation requirements (FMR). The layer implements a flexible first-order low-pass filter (FLF), updating the time constant of the FLF through use of a particle swarm optimization (PSO) algorithm. The power allocation control layer optimizes power instructions allocation among the battery energy storage units of the BESS. The layers energy management of the BESS uses a mixed-integer quadratic programming (MIQP) model, which improves reliability. Moreover, the paper compares two types of charge/discharge switching constraints to reduce the number of charge/discharge cycles, which can prolong the operational lifetime of the BESS. Finally, the effectiveness of the proposed dual-layer control strategy is verified through case studies.

Wavelet-Based Capacity Configuration and Coordinated Control of Hybrid Energy Storage System for Smoothing Out Wind Power Fluctuations

Stochastically fluctuating wind power has a negative impact on power grid operations. This paper presents a wind power filtering approach to mitigate short- and long-term fluctuations using a hybrid energy storage system (HESS), and a novel wavelet-based capacity configuration algorithm to properly size the HESS. A frequency distribution allocates wind power fluctuations to the different HESS components to more easily satisfy 1-min and 30-min fluctuation mitigation requirements (FMR). An ultra-capacitor bank (UC) mitigates short-term fluctuations. In the HESS, and a lithium-ion battery bank (LB) minimizes long-term fluctuations. This paper also proposes a novel online-wavelet based coordination control scheme for the HESS, consisting of primary filtering (PF) and secondary filtering (SF) stages. The PF stage obtains a combined power output that fully satisfies the FMRs, while the SF stage provides additional smoothing of the wind power output fluctuations after the PF stage. A remaining energy level (REL) feedback control maintains the REL of the battery bank within its proper range. Case studies demonstrate that the proposed wavelet-based algorithm is more efficient than other published algorithms, and needs a lower energy storage capacity to satisfy 1-min and 30-min FMRs.

PMU-Based Fault Location Using Voltage Measurements in Large Transmission Networks

This paper presents a general fault-location method for large transmission networks which uses phasor measurement unit (PMU) voltage measurements where the injected current at a fault point can be calculated by using the voltage change and its relevant transfer impedance on any bus. A two-stage fault-location optimization model is proposed, along with defining a matching degree index. The first stage is the fault region identification stage, which uses the matching degree index to determine the suspicious fault region in order to reduce the search area. The second stage is used to identify the exact fault line and fault distance. A method to determine optimal PMU placement is also proposed in this paper. Case studies verify that the proposed fault-location algorithm and optimal PMU placement scheme can locate faults in large transmission networks quickly and accurately without requiring fault-type classification or fault phase selection.

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.

An Enhanced Numerical Discretization Method for Transient Stability Constrained Optimal Power Flow

Although many efforts have been made in past years, transient stability constrained optimal power flow (TSOPF) remains one of the most difficult problems in power system. A popular approach to deal with transient stability constraints is the numerical discretization method, in which TSOPF is converted to a generalized large-scale nonlinear programming problem, and interior point method is preferred to solve it. In the numerical discretization and interior point method based TSOPF, more than 80%-90% CPU seconds are used in solving the primal-dual linear system. In order to improve the computational efficiency of numerical discretization TSOPF, an enhanced numerical discretization method is proposed in this paper. The key enhancement of the proposed approach is: considering the truncation error of specific numerical integration algorithm, the transient differential equations are discretized to inequality constraints instead of equality constraints. This key enhancement reduces nearly 50% of the primal-dual linear systems dimension and greatly improves the computational efficiency of interior point based TSOPF algorithm. Case studies on several test cases up to 678-bus system indicate that the enhanced approach is much more computationally efficient than the conventional numerical discretization method and is promising to solve larger 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.

Parallel augment Lagrangian relaxation method for transient stability constrained unit commitment

This paper presents a transient stability constrained unit commitment (TSCUC) model which achieves the objective of maintaining both transient stability and economical operation. In the TSCUC model, transient stability constraints are incorporated into the framework of unit commitment. In order to solve TSCUC problem, augmented Lagrangian relaxation (ALR) combined with variable duplication techniques and the auxiliary problem principle (APP) is used to decompose the TSCUC problem into two sub-problems: one sub-problem is a traditional unit commitment (UC) problem with prevailing constraints; another sub-problem is modeled as a transient stability constrained optimal power flow (TSCOPF) problem. The first sub-problem is solved by dynamic programming, while the second sub-problem is solved using a reduced-space interior point method. In ALR, an efficient hybrid sub-gradient method is developed to update all Lagrangian multipliers. The iterative process continues until the duality gap is sufficiently small. Finally, case studies show that the proposed methodology is very efficient for solving TSCUC problems within a parallel computing framework.

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 genetic approach to design a HVDC supplementary subsynchronous damping controller

A novel approach based on genetic search is presented for the design of a supplementary subsynchronous damping controller (SSDC) that is capable of damping out subsynchronous oscillation (SSO) in a parallel AC/DC transmission system. The problem of selecting the parameters of the SSDC is converted to a minimax optimization problem, which is solved by genetic algorithms with an eigenvalue-based objective function. The aim of the proposed control strategy is to choose the best controller parameters in such a way that the dominant eigenvalues of the closed-loop system are shifted to the left-hand side of s-plane as far as possible. Both the eigenvalue analysis and the detailed simulation results demonstrate the effectiveness of the proposed SSDC.