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.

可再生能源在分配功率系统中的高渗透正在引起电网电压调节的挑战。电压不稳定性可能会触发电力网络中的严重损坏。电源系统控制的当前趋势正在朝着分布式，灵活和健壮结构的使用趋向。应用基于智能代理的控制系统是解决复杂问题的有效方法。乘数的交替方向方法（ADMM）可以用作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，能够通过以完全分布的方式将它们分解为多个子问题来解决复杂问题。实时仿真是一种有效的方法，它允许研究人员在安全环境中测试复杂设计的控制器。但是，实时模拟器中的计算时间随模型的复杂性而增加。近年来，尽管在电力系统中应用多代理控制技术的应用有了确定的优势，但仅进行了几项工作来修改ADMM算法以实时充分利用现场可编程门阵列（FPGAS）的并行硬件结构。基于FPGA的平台用于硬件式测试（HIL）测试，作为在更少的时间内运行更现实的模型的解决方案。 FPGA并行提供高性能模拟，并且时间步长较小，灵活性更高。该程序是对基于多代理系统的最佳电压控制集成的网格连接的PV系统实时模拟的创新应用。不同代理之间的协调将使用FPGA实施。该计划的主要目的是设计FPGA在与Hydro-Québec网络相关的PV系统中的创新，简单且可靠的应用，以确保对当地能源的最佳管理以及安全注入清洁能源。该程序的短期目标是：1。设计和模拟基于智能代理的控制算法基于ADMM。 2.在PV的高渗透率下，分析提出的技术的运行。 3.开发基于分布式优化的电压控制算法，以加速算法的收敛性。 4.通过在大规模的真实网络上实现该方法来验证该方法。 5.设计使用实时FPGA的并行模拟方法确保整个系统部门的最小延迟。该项目的长期目标是：1。在分配和传输级别期间在不确定性下实施低成本和有效的解决方案。 2.开发一种使用神经网络训练代理的智能方法旨在减少分布式ADMM算法的运行时间。 3.开发本地微型网络管理和控制方面的独特专家。