Virtual inertia for suppressing voltage oscillations and stability mechanisms in DC microgrids

Renewable energy sources (RES) are gradually penetrating power systems through power electronic converters (PECs), which greatly change the structure and operation characteristics of traditional power systems. The maturation of PECs has also laid a technical foundation for the development of DC microgrids (DC-MGs). The advantages of DC-MGs over AC systems make them an important access target for RES. Due to the multi-timescale characteristics and fast response of power electronics, the dynamic coupling of PEC control systems and the transient interaction between the PEC and the passive network are inevitable, which threatens the stable operation of DC-MGs. Therefore, this dissertation focuses on the study of stabilization control methods, the low-frequency oscillation (LFO) mechanism analysis of DC-MGs and the state-of-charge (SoC) imbalance problem of multi-parallel energy storage systems (ESS). Firstly, a virtual inertia and damping control (VIDC) strategy is proposed to enable bidirectional DC converters (BiCs) to damp voltage oscillations by using the energy stored in ESS to emulate inertia without modifications to system hardware. Both the inertia part and the damping part are modeled in the VIDC controller by analogy with DC machines. Simulation results verify that the proposed VIDC can improve the dynamic characteristics and stability in islanded DC-MG. Then, inertia droop control (IDC) strategies are proposed for BiC of ESS based on the comparison between conventional droop control and VIDC. A feedback analytical method is presented to comprehend stability mechanisms from multi-viewpoints and observe the interaction between variables intuitively. A hardware in the loop (HIL) experiment verifies that IDC can simplify the control structure of VIDC in the promise of ensuring similar control performances. Subsequently, a multi-timescale impedance model is established to clarify the control principle of VIDC and the LFO mechanisms of VIDC-controlled DC-MG. Control loops of different timescales are visualized as independent loop virtual impedances (LVIs) to form an impedance circuit. The instability factors are revealed and a dynamic stability enhancement method is proposed to compensate for the negative damping caused by VIDC and CPL. Experimental results have validated the LFO mechanism analysis and stability enhancement method. Finally, an inertia-emulation-based cooperative control strategy for multi-parallel ESS is proposed to address the SoC imbalance and voltage deviation problem in steady-state operation and the voltage stability problem. The contradiction between SoC balancing speed and maintaining system stability is solved by a redefined SoC-based droop resistance function. HIL experiments prove that the proposed control performs better dynamics and static characteristics without modifying the hardware and can balance the SoC in both charge and discharge modes

Extended MANA formulation for time-domain simulations of combined power and gas networks

ABSTRACT: The promise of improved system efficiency, reliability, and higher renewable energy hosting capability of the Integrated Energy System concept has driven the development of innovative network coupling technologies and energy system integration methods. Co-ordinated design and operation of the traditionally separate energy systems, including electric power, gas, and heat will lead to the optimal use of synergies between energy networks and bring forth numerous benefits to the energy sector. To fully understand the potential and quantitatively assess the operation performance of the combined energy networks, a unified modeling and simulation framework using an extended MANA formulation is proposed in this paper, which is capable of incorporating arbitrary gas network configurations and unbalanced power networks in a systematic manner needed. A case study with combined power and gas networks via EnergyHubs is implemented to demonstrate the application of the proposed method. (C) 2019 The Authors. Published by Elsevier Ltd

Renewable Energy Integration in Distribution System with Artificial Intelligence

With the increasing attention of renewable energy development in distribution power system, artificial intelligence (AI) can play an indispensiable role. In this thesis, a series of artificial intelligence based methods are studied and implemented to further enhance the performance of power system operation and control. Due to the large volume of heterogeneous data provided by both the customer and the grid side, a big data visualization platform is built to feature out the hidden useful knowledge for smart grid (SG) operation, control and situation awareness. An open source cluster calculation framework with Apache Spark is used to discover big data hidden information. The data is transmitted with an Open System Interconnection (OSI) model to the data visualization platform with a high-speed communication architecture. Google Earth and Global Geographic Information System (GIS) are used to design the visualization platform and realize the results. Based on the data visualization platform above, the external manifestation of the data is studied. In the following work, I try to understand the internal hidden information of the data. A short-term load forecasting approach is designed based on support vector regression (SVR) to provide a higher accuracy load forecasting for the network reconfiguration. The nonconvexity of three-phase balanced optimal power flow is relaxed to an optimal power flow (OPF) problem with the second-order cone program (SOCP). The alternating direction method of multipliers (ADMM) is used to compute the optimal power flow in distributed manner. Considering the reality of distribution systems, a three-phase unbalanced distribtion system is built, which consists of the hourly operation scheduling at substation level and the minutes power flow operation at feeder level. The operaion cost of system with renewable generation is minimized at substation level. The stochastoc distribution model of renewable generation is simulated with a chance constraint, and the derived deterministic form is modeled with Gaussian Mixture Model (GMM) with genetic algorithm-based expectationmaximization (GAEM). The system cost is further reduced with OPF in real-time (RT) scheduling. The semidefinite programming (SDP) is used to relax the nonconvexity of the three-phase unbalanced distribution system into a convex problem, which helps to achieve the global optimal result. In the parallel manner, the ADMM is realizing getting the results in a short time. Clouds have a big impact on solar energy forecasting. Firstly, a convolutional neural network based mathod is used to estimate the solar irradiance, Secondly, the regression results are collected to predict the renewable generation. After that, a novel approach is proposed to capture the Global horizontal irradiance (GHI) conveniently and accurately. Considering the nonstationary property of the GHI on cloudy days, the GHI capturing is cast as an image regression problem. In traditional approaches, the image regression problem is treated as two parts, feature extraction and regression, which are optimized separately and no interconnections. Considering the nonlinear regression capability, a convolutional neural network (CNN) based image regression approach is proposed to provide an End-to- End solution for the cloudy day GHI capturing problem in this paper. For data cleaning, the Gaussian mixture model with Bayesian inference is employed to detect and eliminate the anomaly data in a nonparametric manner. The purified data are used as input data for the proposed image regression approach. The numerical results demonstrate the feasibility and effectiveness of the proposed approach

Deep Reinforcement Learning for Distribution Network Operation and Electricity Market

The conventional distribution network and electricity market operation have become challenging under complicated network operating conditions, due to emerging distributed electricity generations, coupled energy networks, and new market behaviours. These challenges include increasing dynamics and stochastics, and vast problem dimensions such as control points, measurements, and multiple objectives, etc. Previously the optimization models were often formulated as conventional programming problems and then solved mathematically, which could now become highly time-consuming or sometimes infeasible. On the other hand, with the recent advancement of artificial intelligence technologies, deep reinforcement learning (DRL) algorithms have demonstrated their excellent performances in various control and optimization fields. This indicates a potential alternative to address these challenges. In this thesis, DRL-based solutions for distribution network operation and electricity market have been investigated and proposed. Firstly, a DRL-based methodology is proposed for Volt/Var Control (VVC) optimization in a large distribution network, to effectively control bus voltages and reduce network power losses. Further, this thesis proposes a multi-agent (MA)DRL-based methodology under a complex regional coordinated VVC framework, and it can address spatial and temporal uncertainties. The DRL algorithm is also improved to adapt to the applications. Then, an integrated energy and heating systems (IEHS) optimization problem is solved by a MADRL-based methodology, where conventionally this could only be solved by simplifications or iterations. Beyond the applications in distribution network operation, a new electricity market service pricing method based on a DRL algorithm is also proposed. This DRL-based method has demonstrated good performance in this virtual storage rental service pricing problem, whereas this bi-level problem could hardly be solved directly due to a non-convex and non-continuous lower-level problem. These proposed methods have demonstrated advantageous performances under comprehensive case studies, and numerical simulation results have validated the effectiveness and high efficiency under different sophisticated operation conditions, solution robustness against temporal and spatial uncertainties, and optimality under large problem dimensions

Power System Stability With a High Penetration of Inverter-Based Resources

Inverter-based resources (IBRs) possess dynamics that are significantly different from those of synchronous-generator-based sources and as IBR penetrations grow the dynamics of power systems are changing. This article discusses the characteristics of the new dynamics and examines how they can be accommodated into the long-standing categorizations of power system stability in terms of angle, frequency, and voltage stability. It is argued that inverters are causing the frequency range over which angle, frequency, and voltage dynamics act to extend such that the previously partitioned categories are now coupled and further coupled to new electromagnetic modes. While grid-forming (GFM) inverters share many characteristics with generators, grid-following (GFL) inverters are different. This is explored in terms of similarities and differences in synchronization, inertia, and voltage control. The concept of duality is used to unify the synchronization principles of GFM and GFL inverters and, thus, established the generalized angle dynamics. This enables the analytical study of GFM-GFL interaction, which is particularly important to guide the placement of GFM apparatuses and is even more important if GFM inverters are allowed to fall back to the GFL mode during faults to avoid oversizing to support short-term overload. Both GFL and GFM inverters contribute to voltage strength but with marked differences, which implies new features of voltage stability. Several directions for further research are identified, including: 1) extensions of nonlinear stability analysis to accommodate new inverter behaviors with cross-coupled time frames; 2) establishment of spatial–temporal indices of system strength and stability margin to guide the provision of new stability services; and 3) data-driven approaches to combat increased system complexity and confidentiality of inverter models