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At least 19 records

Flexible Operation of Microgrids Through Operator-Configurable Microgrid Controllers

Microgrids are becoming critical assets in power distribution systems. This is partly due to the resiliency and reliability challenges faced by power distribution systems in the last decade and partly due to the increased integration of distributed energy resource assets, such as solar photovoltaic. This increased need for microgrids has created a multitude of needs for microgrid controllers. Most microgrid controllers in the past decade have been uniquely programmed or custom programmed to meet the needs of a specific microgrid. Currently, IEEE standards aim to integrate the needs and capabilities of microgrid controllers under a unified umbrella. But the standards do not recommend necessary or appropriate software development practices for microgrid controllers. In addition, the programmable nature and maintenance of controllers are also key characteristics that fall outside the coverage of the standards. In this paper, we present the best software practices that utility engineers and microgrid operators should consider in the microgrid software platform. This paper can be used as an information document for developing documentation of controller requirements and assessing microgrid controller requirements.

Banshee microgrid↗

Laboratory Evaluation of Commercial Utility Microgrid Controller Test Results

The functional requirements of many microgrid controllers (MGCs) are expanding and evolving to meet growing utility and community needs. At a high level, the utility microgrid controller serves resilience and reliability use cases by coordinating transitions between grid-connected and islanded states and by managing the system during island operations. This includes control scenarios that require the microgrid controller to use flexible microgrid boundaries, maintain energy balance, coordinate with peer systems, and manage grid-forming (GFM) and grid-following (GFL) distributed energy resources (DER). In order to evaluate these functional enhancements, microgrid controller test plans must also be developed to ensure that the implemented controllers provide adequate performance. This report provides MGC test plans for both island operation and transition functions. The functions covered in this report include feeder level energy management, island constraint management, secondary voltage and frequency control, black start, and synchronized reconnection. This second edition update also includes results from applying the tests to a commercial utility microgrid controller. These results evaluate the performance and reliability of the controller under various operational scenarios. It identifies specific areas where the controller excels and highlights gaps that need to be addressed for future enhancements. The application of these test plans on real-world system behavior provides insights on commercial equipment readiness for field deployment. These test cases can be applied to utility-managed microgrid controllers that exclusively manage utility-owned equipment; the tests also apply to third-party managed microgrid controllers that coordinate with utility- and customer-owned equipment. The report can also be used by technology developers and project developers in industry to evaluate control strategies and performance characteristics for community microgrid controllers.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Utility Microgrid Controller Test Plan

The functional requirements of many microgrid controllers (MGCs) are expanding and evolving to meet growing utility and community needs. At a high level, the utility microgrid controller serves resilience and reliability use cases by coordinating transitions between grid-connected and islanded states and by managing the system during island operations. This includes control scenarios that require the microgrid controller to use flexible microgrid boundaries, maintain energy balance, coordinate with peer systems, and manage grid-forming (GFM) and grid-following (GFL) DER. In order to evaluate these functional enhancements, microgrid controller test plans must also be developed to ensure that the implemented controllers provide adequate performance. This report provides MGC test plans for both island operation and transition functions. The functions covered in this first edition report include feeder level energy management, island constraint management, secondary voltage and frequency control, black start, and synchronized reconnection. These test cases can be applied to utility-managed microgrid controllers that exclusively manage utility-owned equipment; the tests also apply to third-party managed microgrid controllers that coordinate with utility- and customer-owned equipment. The report can also be used by technology developers and project developers in industry to evaluate control strategies and performance characteristics for community microgrid controllers.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Microgrid Controller Survey Report: 2024 Update

In the past decade, the number of microgrids deployed by electric utilities, end-use customers, and third parties has been increasing significantly both in the US and worldwide. The increase is driven by various reasons, such as carbon emission reduction, energy efficiency improvement, reliability and resilience enhancement, energy infrastructure modernization, and more. Microgrid controllers, as the most important components in these deployed microgrids, optimally coordinate the operation and control of the microgrid assets (e.g., distributed energy resources [DERs], energy storage systems, demand response, and even electric vehicles) and ensure that the microgrid is being operated in an efficient, clean, and resilient manner. To that end, the market for microgrid controllers has been growing rapidly in recent years. Various microgrid controllers have been released and continuously updated by vendors to satisfy the different needs of microgrid customers. However, the functionalities demonstrated by microgrid controllers released by different vendors show a huge difference because of a lack of widely known and accepted industrial standards, and various microgrid customers have specific requirements for different applications and scenarios. To obtain a clear view of the current state of the commercial microgrid controllers’ functionalities and identify potential research gaps, a survey of the functionalities of commercial microgrid controllers and the Advanced Distribution Management System (ADMS) developed by vendors and national laboratories was conducted in 2023. The results of the survey are presented in this report with the current status of commercial microgrid controllers analyzed, potential research gaps identified, and future research trends revealed.

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Controller-Hardware-in-the-Loop Evaluation of a Microgrid Controller for a Microgrid System With Multiple Grid-Forming Inverters

This paper presents the laboratory evaluation of a commercial Microgrid Management System (MGMS) implemented in the real-world Bronzeville Microgrid which features a futuristic scenario with high renewable energy integration and the use of multiple Grid-Forming (GFM) inverters. The primary objective of the performance evaluation for the MGMS is to assess the MGMS's capability to dispatch GFM units, including a GFM PV unit and two GFM battery units, to maintain the system stability and ensure economic operation, thus guaranteeing the microgrid's resilience during prolonged outages and dynamic events. The laboratory controller hardware-in-the-loop provides realistic testing environment through detailed electromagnetic transient modeling of the microgrid system, hardware MGMS, and standard communication protocols (DNP3). This CHIL evaluation shows how the MGMS effectively manages the GFM inverters, highlighting its performance in maintaining stability, reliability, and survivability in a microgrid environment with a high penetration of renewable energy sources.

controller hardware-in-the-loop↗

Controller Hardware-in-the-Loop Evaluation of a Microgrid Controller for a Microgrid System with Multiple Grid-Forming Inverters

This paper presents the laboratory evaluation of a commercial Microgrid Management System (MGMS) implemented in the real-world Bronzeville Microgrid which features a futuristic scenario with high renewable energy integration and the use of multiple Grid-Forming (GFM) inverters. The primary objective of the performance evaluation for the MGMS is to assess the MGMS's capability to dispatch GFM units, including a GFM PV unit and two GFM battery units, to maintain the system stability and ensure economic operation, thus guaranteeing the microgrid's resilience during prolonged outages and dynamic events. The laboratory controller hardware-in-the-loop provides realistic testing environment through detailed electromagnetic transient modeling of the microgrid system, hardware MGMS, and standard communication protocols (DNP3). The CHIL evaluation shows how the MGMS effectively manages the GFM inverters, highlighting its performance in maintaining stability, reliability, and survivability in a microgrid environment with a high penetration of renewable energy sources.

controller hardware-in-the-loop↗

Controller Hardware-in-the-Loop Evaluation of a Microgrid Controller for a Microgrid System with Multiple Grid-Forming Inverters: Preprint

This paper presents the laboratory evaluation of a commercial Microgrid Management System (MGMS) implemented in the real-world Bronzeville Microgrid which features a futuristic scenario with high renewable energy integration and the use of multiple Grid-Forming (GFM) inverters. The primary objective of the performance evaluation for the MGMS is to assess the MGMS's capability to dispatch GFM units, including a GFM PV unit and two GFM battery units, to maintain the system stability and ensure economic operation, thus guaranteeing the microgrid's resilience during prolonged outages and dynamic events. The laboratory controller hardware-in-the-loop provides realistic testing environment through detailed electromagnetic transient modeling of the microgrid system, hardware MGMS, and standard communication protocols (DNP3). The CHIL evaluation shows how the MGMS effectively manages the GFM inverters, highlighting its performance in maintaining stability, reliability, and survivability in a microgrid environment with a high penetration of renewable energy sources.

controller hardware-in-the-loop↗

Integrating a Microgrid Controller with a Local OpenADR Server

When military microgrids isolate themselves from the main electrical grid, they must locally balance electricity supply and demand. Since local generation may be limited, the current strategy is to shed all but the most critical loads by tripping smart circuit breakers, which must then be reset manually (e.g., ESTCP project EW-201350). This strategy is typically applied at the building level, meaning that entire buildings housing mission critical activities must be excluded from any load management, while those considered non-critical may lose service entirely. The remotely controlled switchgear needed to manage load in this way is very expensive ($\$30,000$-$\$50,000$ per building). While effective at shedding load, this strategy disrupts installation operation and risks damaging equipment during both disconnection and re-energization. With the goals of lowering costs, protecting equipment, and enhancing the agility of DoD microgrids, this report demonstrates the use of cybersecure automated demand response (ADR) technology to manage microgrid loads during grid-independent (a.k.a. "islanded") operation. This automated approach achieves load shedding and shifting through communication signals sent to equipment controllers rather than by cutting off the flow of electricity within the microgrid itself. Because it operates only on the base network, with no connection to external entities, this strategy avoids the main cybersecurity concern raised by past applications of ADR on military bases.

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HHL algorithm with mapping function and enhanced sampling for model predictive control in microgrids

Here, this paper presents a refined quantum Harrow Hassidim Lloyd (HHL) algorithm for microgrid control. The first novelty of the developed method is that a mapping shift function enables the original HHL algorithm to handle general linear equations with non-singular and indefinite matrix. Second, a method of Matrix Extension for Amplifying Sampling Probabilities of Intended Solution (ME-ASPI) is proposed to design the reformulated linear algebraic equations, allowing for improved sampling efficiency of the quantum tomography in the refined HHL algorithm. Then, we applied the method to solve the model predictive control (MPC) problem in nonlinear dynamical microgrids. Specifically, with the ME-ASPI method, the refined HHL algorithm can effectively obtain the intended partial optimal control inputs for MPC. The optimization of quadratic programming problem in each time step of MPC is transformed into a linear system problem, which is addressed by the proposed quantum solver through using only partial information, with the time complexity improved from $\mathscr{O}(\mathscr{N}^{2.37286})$ classically to $\mathscr{O}(\mathscr{N}^{2} log \mathscr{N}$ x $p$ log $p)$ in quantum. Numerical examples have validated the effectiveness of the refined HHL algorithm with the proposed mapping function and the ME-ASPI method. By leveraging quantum properties, the proposed method provides a hybrid quantum–classical framework for microgrid control. This generic method can also potentially tackle many other challenges in analyzing and controlling general complex engineered systems.

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Reactive Power Control Methods for Islanded Operation

As feeder-level microgrid controls begin to incorporate multiple GFM DER and voltage regulation equipment, island reactive power dispatch strategies can be improved. Although Volt-Var Optimization (VVO) control is typical in an Advanced Distribution Management System (ADMS), most microgrid controllers do not apply VVO during island operations. This report investigates reactive power control strategies for feeder-level microgrid operations. It also proposes evaluation metrics that can be applied to analyze the performance improvements of the new microgrid control strategy.

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Enhancing Power Resilience for Remote Communities: A Comprehensive Renewable Energy Solution for Itbayat Island

The island of Itbayat, Philippines, faces significant challenges in maintaining a reliable and resilient power supply due to its current reliance on a vulnerable power distribution system managed by a local electric cooperative. The existing infrastructure, which includes diesel generators and a radial network configuration with some above-ground lines, is highly susceptible to frequent typhoons and adverse weather conditions. These factors, combined with inadequate staffing and high operational costs, result in frequent power outages that disrupt daily life and hinder economic development. This white paper proposes a comprehensive solution to enhance the resilience and reliability of Itbayat's power system by integrating renewable energy sources, specifically solar photovoltaic (PV) systems, battery storage, and a microgrid controller. The proposed solution aims to reduce dependency on diesel fuel, optimize energy use, and provide a sustainable and robust power supply for the island. Key components of the solution include: 1. Solar PV Installation: Deploying solar PV panels to harness abundant solar energy, reducing reliance on diesel fuel. 2. Battery Storage Systems: Installing battery storage to store excess solar energy and ensure a continuous power supply during low solar generation periods. 3. Microgrid Controller: Implementing a microgrid controller to manage and optimize the integration of solar PV, battery storage, and existing diesel generators. The proposed solution addresses several critical issues, including system vulnerability, generator dependency, and operational inefficiencies. By adopting this innovative approach, Itbayat Island can achieve a more resilient, efficient, and sustainable energy infrastructure, ensuring a stable power supply for its residents and enhancing overall energy security.

14 SOLAR ENERGY↗

Grid Resiliency with a 100% Renewable Microgrid

San Diego Gas & Electric Company (SDG&E) installed America’s first and largest utility-scale microgrid in Borrego Springs in 2013. The first generation Borrego Springs Microgrid utilized diesel generators to form and stabilize the microgrid island, with support from grid-scale batteries and local solar photovoltaic (PV) generation. In this project, SDG&E in partnership with National Renewable Energy Laboratory (NREL) demonstrated through modeling, simulation and utility field testing that blackstart and islanding of the microgrid can be led with 100% renewable, inverter based resources (IBRs), to help reduce community reliance on conventional generation resources. Through equipment upgrades, grid-forming island leader capability was transitioned to a battery IBR instead of the Borrego Springs Microgrid diesel generators. A new microgrid controller was integrated to the microgrid and programmed to control and manage multiple energy storage systems. Synchrophasor and other power quality data verified autonomous, high-speed response of the IBRs through blackstart, islanding, and load step testing. Results of project field evaluations provide distribution systems operators (DSO) with increased confidence that renewable, IBR can replace traditional generators to blackstart and island microgrids and rapidly establish stable island frequency with rapid changes in peak power demand. Importantly, the project validated the integration feasibility of a distributed energy resource management system (DERMS) controller that manages multiple grid-forming and grid-following IBRs, establishing a standard design interface to reduce the complexity of integrating new DERs in the future and supporting replication by the industry. As a result of learnings in this project, SDG&E has implemented the microgrid controller strategy at multiple other microgrid sites, thereby validating the replicability of the solution. Hardware-in-the-loop (HIL) simulations including power and controller HIL hardware — along with electromagnetic transient (EMT) simulations of Borrego Springs Microgrid —informed adjustments to inverter parameters and were important to characterize the performance of the IBRs in relevant operating conditions before deployment. The EMT and HIL simulations of islanding the entire community are important contributions in providing confidence in IBR performance prior to future islanding of the community in the field. High-fidelity EMT and/or HIL simulation of IBRs can de-risk field operations, and its relevance and importance as a tool is increasing as distribution grids and microgrids become more complex and dynamic with an increasing proportion of renewable generation, distributed energy storage, and two-way power and energy flows.

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Droop-Free Distributed Control for AC Microgrids

In this chapter, droop-free distributed control for AC microgrids is discussed. The hierarchical structure of microgrid control is first introduced. Secondary level controls both with and without droop are discussed. Then two droop-free distributed controls are presented: one achieves average voltage regulation and perfect power sharing, and the other one achieves average voltage regulation, voltage variance regulation, and relaxed reactive power sharing. For the latter droop-free control, the relaxed reactive power sharing is achieved through a special distributed generator (DG) that will not participate in reactive power sharing.

Mohiuddin, Sheik Mohammad↗

Artificial Neural Network-based State Estimation for Low Observable, Unbalanced Microgrids for Microgrid Building Blocks

The microgrid building blocks (MBB) were proposed as microgrid components with combined sub-components with power conversion, communication, and microgrid control capability, or a subset of such sub-components. This work addresses the microgrid controller, present in an MBB, which requires accurate state estimation to perform its tasks, including for monitoring, power flow (dispatch), fault detection, etc. In this paper, an artificial neural network (ANN)-based framework for state estimation is proposed for an MBB, especially for unbalanced and low observable microgrids. To overcome the challenge of low observability in unbalanced systems, a concept of extended adjacent matrix is introduced to reduce the required number of measurements for state estimation. Addressing the challenges, a feed forward neural network (FNN) is utilized to enhance estimation accuracy and reliability with the reduced number of measurements. The proposed state estimation is validated through extensive simulations on a microgrid, which was achieved from the modified IEEE 34-bus distribution test feeder with multiple distributed energy resources (DERs) and demonstrated superior performance in estimation accuracy and low observability.

Choi, Jongchan↗