Hierarchical Microgrid Research Articles

Delay‐dependent small‐signal stability analysis of inverter‐based islanded microgrids

AbstractThe stability of an inverter‐based hierarchical microgrid is challenged by inverter controls, network dynamics, load models and communication delays among the interacting agents. Moreover, the adoption of non‐dispatchable grid‐following (GFL) inverters further lowers the system inertia in islanded Microgrids (MGs). Therefore, both grid‐forming (GFM) and GFL inverters shall coordinate to perform frequency/voltage regulation through their grid‐support function. This paper investigates the role of GFM‐GFL coordination on the operational stability of real‐world Microgrids (MGs) with communication delays. A complete non‐linear state‐space model is derived for the MG. The time‐delayed system model is discretised in a system of ordinary differential equations to determine delay margins using spectral analysis. Later, the same model is linearised around an operating point to determine delay margins using time‐domain analysis. The equilibrium point is disturbed by load perturbation and the system response is observed for different values of communication delays and control topology. The delay margins estimated theoretically are validated using the time‐domain simulations in MATLAB/SIMULINK. Simulation results are compared to determine the efficacy of grid‐support function in improving operational stability of islanded MGs. The developed evaluation scheme is tested on a multi‐inverter islanded power system.

Distributed Energy Resource Control and Utility Interconnection Management: Implementation in a Distributed Microgrid

This article describes the implementation of a brownfield microgrid with the upgrade of existing distributed energy resources (DERs), design of the medium-voltage (MV) interconnection, and implementation of a microgrid control system to meet the U.S. Department of Defense (DOD) needs at Fort Custer, Michigan. Brownfield microgrids involve complexities in design, integration, and commissioning but offer better economic viability due to reduced capital cost upfront. This work tries to identify the challenges associated with brownfield microgrids and provides systematic practical solutions to mitigate them. As part of the upgrades, backup diesel generators were retrofitted to support grid-parallel operation. A battery energy storage system (BESS) was newly installed to manage intermittencies for the existing solar generation and to support the microgrid. The individual DER assets were integrated in a two-level hierarchical microgrid control architecture and controlled in a coordinated manner to achieve seamless islanding and reconnection with the utility grid at the point of common coupling (PCC). The article provides the field data demonstrating the improvement in resiliency through islanded operation as well as maintaining power quality when operating in parallel with the utility. The economic operation of the assets and participation in utility ancillary services were also key goals achieved through the microgrid. This ability allows the military to reduce its total cost of ownership to achieve its mandated energy security needs.

Distributed Deep Reinforcement Learning for Renewable Energy Accommodation Assessment With Communication Uncertainty in Internet of Energy

Nowadays, microgrids (MG) have attracted much attention, as a key technology of the Internet of Energy (IoE). A great deal of research have shown that the hierarchical microgrid is a more novel structure of IoE. Although the hierarchical microgrid model solves the problem of weak power scheduling capability across microgrids, it suffers from severe communications uncertainty, which can lead to communication delay and fluctuation. To obtain the accurate result of the renewable energy accommodation assessment capacity, a hierarchical microgrid model considering communication uncertainty is proposed in this article. The solution to solve the problem of the assessment renewable energy accommodation capacity for hierarchical MG is a hybrid control based on distribution deep reinforcement learning. The temporal difference (TD) generation adversarial network (TD-GAN) is proposed as a value-based method. Compared with the policy-based method, it can better solve the distributed problem in hybrid control with a generation adversarial network (GAN). Moreover, the challenge that the method cannot handle a continuous action space is solved by using a normalized advantage function (NAF). The method similar with the TD error method is employed to train the GAN network. Simulation results using real power grid data demonstrate the effectiveness and accuracy of the proposed method.

Hierarchical Multiobjective Dispatching Strategy for the Microgrid System Using Modified MOEA/D

The large-scale electric vehicles connected to the microgrid have brought various challenges to the safe and economic operation of the microgrid. In this paper, a hierarchical microgrid dispatching strategy considering the user-side demand is proposed. According to the operation characteristics of each dispatch unit, the strategy divides the microgrid system into two levels: source-load level and source-grid-load level. At the source-load level, priority should be given to the use of the renewable energy output. On the basis of considering the user demand, energy storage, electric vehicles, and dispatchable loads should be utilized to maximize the consumption of the renewable energy and minimize the user’s electricity cost. The source-grid-load level can smooth the tie-line power fluctuation through dispatching of the power grid and diesel generators. Furthermore, the study presents a modified MOEA/D algorithm to solve the hierarchical scheduling problem. In the proposed algorithm, a modified Tchebycheff decomposition method is introduced to obtain uniformly distributed solutions. In addition, initialization and replacement strategies are introduced to enhance the convergence and diversity. A wind-photovoltaic-diesel-storage hybrid power system is considered to verify the performance of the proposed dispatching strategy and the modified algorithm. The obtained results are compared with other dispatching approaches, and the comparisons confirm the effectiveness and scientificity of the proposed strategy and algorithm.

Three-level hierarchical microgrid control—model development and laboratory implementation

Three-level hierarchical microgrid control—model development and laboratory implementation

Coordination of operational planning and real-time optimization in microgrids

Coordination of operational planning and real-time optimization in microgrids

Probabilistic Microgrid Energy Management with Interval Predictions

In this paper, we consider a probabilistic microgrid dispatch problem where the predictions of the load and the Renewable Energy Source (RES) generation are given in the form of intervals. A hybrid method combining scenario-selected optimization and reserve strategy using the Model Predictive Control (MPC) framework is proposed. Specifically, first of all, an appropriate scenario is selected by the optimizer at each optimization stage, and then the optimal scheduling and reservation of system capacity are determined based on the selected scenario and possible variations in the future as provided by the predictors. In addition, a new reserve strategy is introduced to adaptively maintain system reliability and respond to variations in the hierarchical microgrid control. Simulations are conducted to compare our proposed method with the existing robust method and the deterministic dispatch with perfect information. Results show that our proposed method significantly improves the system efficiency while maintaining system reliability.

Multiobjective Joint Economic Dispatching of a Microgrid with Multiple Distributed Generation

Based on the operation characteristics of each dispatch unit, a multi-objective hierarchical Microgrid (MG) economic dispatch strategy with load level, source-load level, and source-grid-load level is proposed in this paper. The objective functions considered are to minimize each dispatching unit’s comprehensive operating cost (COC), reduce the power fluctuation between the MG and the main grid connect line, and decrease the remaining net load of the MG after dispatch by way of energy storage (ES) and clean energy. Firstly, the load level takes electric vehicles (EVs) as a means of controlling load to regulate the MG’s load fluctuation using its energy storage characteristics under time-of-use (TOU) price. Then, in order to minimize the remaining net load of the MG and the COC of the ES unit through Multiobjective Particle Swarm Optimization (MPSO), the source-load level adopts clean energy and ES units to absorb the optimized load from the load level. Finally, the remaining net load is absorbed by the main grid and diesel engines (DE), and the remaining clean energy is sold to the main grid to gain benefits at the source-grid-load level. Ultimately, the proposed strategy is simulated and analyzed with a specific example and compared with the EVs’ disorderly charging operation and MG isolated grid operation, which verifies the strategy’s scientificity and effectiveness.

Development and Application of a Real-Time Testbed for Multiagent System Interoperability: A Case Study on Hierarchical Microgrid Control

This paper presents the development and application of a real-time testbed for multiagent system interoperability. As utility independent private microgrids are installed constantly, standardized interoperability frameworks are required to define behavioral models of the individual agents for expandability and plug-and-play operation. In this paper, we propose a comprehensive hybrid agent framework combining the foundation for intelligent physical agents (FIPAs), IEC 61850, and data distribution service (DDS) standards. The IEC 61850 logical node concept is extended using FIPA-based agent communication language with application specific attributes and deliberative behavior modeling capability. The DDS middleware is adopted to enable a real-time publisher-subscriber interoperability mechanism between platforms. The proposed multi-agent framework was validated in a laboratory-based testbed involving developed intelligent electronic device prototypes and actual microgrid setups. Experimental results were demonstrated for both decentralized and distributed control approaches. Secondary and tertiary control levels of a microgrid were demonstrated for decentralized hierarchical control case study. A consensus-based economic dispatch case study was demonstrated as a distributed control example. It was shown that the developed agent platform is industrially applicable for actual smart grid field deployment.

Hierarchical microgrid energy management in an office building

A two-stage hierarchical Microgrid energy management method in an office building is proposed, which considers uncertainties from renewable generation, electric load demand, outdoor temperature and solar radiation. In stage 1, a day-ahead optimal economic dispatch method is proposed to minimize the daily Microgrid operating cost, with the virtual energy storage system being dispatched as a flexible resource. In stage 2, a two-layer intra-hour adjustment methodology is proposed to smooth the power exchanges at the point of common coupling by coordinating the virtual energy storage system and the electric vehicles at two different time scales. A Vehicle-to-Building control strategy was developed to dispatch the electric vehicles as a flexible resource. Numerical studies demonstrated that the proposed method is able to reduce the daily operating cost at the day-ahead dispatch stage and smooth the fluctuations of the electric power exchanges at the intra-hour adjustment stage.

Population Games Methods for Distributed Control of Microgrids

We illustrate the potential of applying population games in two key related problems in microgrids management: 1) economic dispatch of active and reactive power; and 2) demand response. For the dynamic economic dispatch problem, we present a hierarchical microgrid energy management algorithm able to dispatch active and reactive power dynamically. In the second case, we use an opinion dynamics model, including the market in which the network agents interact. Opinion dynamics considers individuals who shape their beliefs based on information they receive from a subset of the society and offers tools to analyze the outcome of collective decision processes, which apparently can be considered arbitrary. We propose an opinion dynamics model including some desired characteristics, such as prominent agents and environmental incentives.

Hierarchical energy management mechanisms for an electricity market with microgrids

This study addresses a micro-grid electricity market (MGEM) with day-ahead (DA) and real-time market mechanisms integrated. The bidding mechanisms for the market are described in this study, considering the generation cost of different distributed energy resources (DERs), like distributed generator, energy storage system and demand response. Including load and renewable generation forecasting systems and a fuzzy decision supporting system, a hierarchical micro-grid energy management system (MG-EMS) is then proposed to ensure the benefits of involved micro-grid central controller, DER owners and customers. To verify the feasibility of the proposed system, the whole-year historical pricing and load data for New England independent system operator are employed. The numerical results show that the proposed MG-EMS is promising and effective for the operations of MGEM.