FPGA-based Real-time Simulation of Grid-Connected Distributed Solar Systems using Multi-Agent Control

基于 FPGA 的多智能体控制并网分布式太阳能系统实时仿真

• 批准号: RGPIN-2022-03004
• 负责人: Chandra, Ambrish
• 金额: $ 4.01万
• 依托单位: École de technologie supérieure
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=750689
• 关键词: FPGA; based; Real; time; Simulation

High penetration of renewable energy sources in distribution power systems is causing challenges to the voltage regulation of grids. Voltage instability may trigger serious damages in power networks. Current trend in control of power systems is moving toward the use of distributed, flexible, and robust structures. Applying an intelligent agent-based control system is an effective way for solving complex problems in parallel. The Alternating Direction Method of Multipliers (ADMM) can be used as a theoretical framework to design distributed computational systems. ADMM-based algorithm is an iterative optimization method able to solve complex problems by decomposing them into multiple sub-problems in a fully distributed way. Real-time simulation is an efficient way that allows researchers to test complex designed controllers in a secure environment. However, the computational time in the real-time (RT) simulators increase with the complexity of the models. In recent years, despite the identified advantages of the application of multi-agent control technique in power systems, only a few works have been done to modify the ADMM algorithm to fully utilize the parallelized hardware structure of the Field Programmable Gate Arrays (FPGAs) in real-time. FPGA-based platforms are used for Hardware-in-the-Loop (HIL) testing as a solution for running more realistic models in less time. FPGAs offer high-performance simulations in parallel with less time step and higher flexibility. This program is an innovative application in real-time simulation of grid-connected PV systems integrated to an optimal voltage control based on multi-agent systems. Coordination between different agents will be implemented using FPGA. The main objective of the program is to design an innovative, simple, and robust application of FPGA in real-time control of PV systems connected to Hydro- Québec's networks to ensure optimal management of local energy as well as the safe injection of clean energy. The short-term objectives of the program are to: 1.Design and simulate an intelligent agent-based control algorithm based on ADMM. 2.Analyze the operation of proposed technique under high penetrations of PVs. 3.Develop the distributed optimization-based voltage control algorithm to accelerate the convergence of the algorithm. 4.Validate the method by implement it on real networks in large-scale. 5.Design a parallel simulation approach using real-time FPGAs ensuring minimum latency in the whole system division. The long-term objectives of the project are to: 1. Implement a low-cost and effective solution for voltage stability of the system under uncertainties during distribution and transmission level.  2.Develop an intelligent method for training the agents using neural networks aims to reduce the running time of distributed ADMM algorithms. 3.Develop a unique expertise in the management and control of local micro-networks.

可再生能源在配电网中的高渗透率给电网的电压调节带来了挑战。电压不稳定可能会对电网造成严重的危害。当前电力系统控制的趋势是使用分布式、灵活和健壮的结构。采用基于智能体的智能控制系统是并行求解复杂问题的有效途径。乘子交替方向法(ADMM)可以作为分布式计算系统设计的理论框架。基于ADMM的算法是一种迭代优化方法，能够以完全分布式的方式将复杂问题分解成多个子问题来求解。实时仿真是允许研究人员在安全环境中测试复杂设计的控制器的有效方法。然而，实时(RT)模拟器的计算时间随着模型的复杂程度而增加。近年来，尽管多智能体控制技术在电力系统中的应用具有许多优点，但对ADMM算法的改进工作很少，以充分利用现场可编程门阵列(现场可编程门阵列)的并行硬件结构。基于FPGA的平台用于硬件在环(HIL)测试，作为一种解决方案，可以在更短的时间内运行更逼真的模型。现场可编程门阵列以更少的时间步长和更高的灵活性提供高性能的并行模拟。该程序是基于多智能体系统的最优电压控制在光伏并网发电系统实时仿真中的创新应用。不同代理之间的协调将使用现场可编程门阵列来实现。该计划的主要目标是设计一种创新、简单和强大的应用程序，将现场可编程门阵列应用于与魁北克水电公司电网相连的光伏系统的实时控制，以确保当地能源的最佳管理以及清洁能源的安全注入。该方案的短期目标是：1.设计并仿真一种基于ADMM的智能智能体控制算法。2.分析了所提出的技术在高渗透率下的运行情况。3.开发了基于分布式优化的电压控制算法，加快了算法的收敛速度。4.通过在实际网络上的大规模实现对该方法进行了验证。5.设计了一种基于实时现场可编程门阵列的并行仿真方法，保证了整个系统划分的最小时延。该项目的长期目标是：1.在配电和输电电平不确定的情况下，实现一种低成本、有效的系统电压稳定解决方案。2.开发一种智能的神经网络训练代理的方法，旨在减少分布式ADMM算法的运行时间。3.在地方微型网络的管理和控制方面发展独特的专门知识。