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.

可再生能源在配电系统中的高渗透率对电网的电压调节提出了挑战。电压不稳定可能引发电网的严重破坏。当前电力系统控制的趋势是使用分布式、灵活和鲁棒的结构。应用基于智能体的控制系统是并行解决复杂问题的有效途径。乘子交替方向法（ADMM）可以作为设计分布式计算系统的理论框架。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为多个子问题来求解。实时仿真是一种有效的方法，使研究人员能够在安全的环境中测试复杂设计的控制器。然而，在实时（RT）模拟器的计算时间增加的模型的复杂性。近年来，尽管多智能体控制技术在电力系统中的应用的优点，只有少数的工作已经做了修改ADMM算法，以充分利用并行化的硬件结构的现场可编程门阵列（FPGA）的实时。基于FPGA的平台用于硬件在环（HIL）测试，作为在更短时间内运行更逼真模型的解决方案。FPGA以更少的时间步长和更高的灵活性并行提供高性能仿真。该方案是一个创新的应用，在实时模拟并网光伏系统集成到基于多智能体系统的最优电压控制。不同代理之间的协调将使用FPGA实现。该计划的主要目标是设计一种创新的，简单的，强大的FPGA应用程序，用于连接到魁北克水电网络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。该方案的近期目标是：1.设计并仿真一种基于ADMM的智能Agent控制算法。2.分析所提出的技术在PV的高穿透下的操作。 3.开发基于分布式优化的电压控制算法，加速算法的收敛。4.在大规模的真实的网络上实现了该方法。5.使用实时FPGA设计并行仿真方法，确保整个系统分区的延迟最小。该项目的长期目标是：1。实现了一种低成本、高效率的配电网和输电网不确定性下系统电压稳定的解决方案。2.提出了一种基于神经网络的智能Agent训练方法，以减少分布式ADMM算法的运行时间。3.在管理和控制本地微型网络方面发展独特的专业知识。