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Energy dispatch controllers for a photovoltaic system

机译：光伏系统的能量分配控制器

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In this paper two energy dispatch controllers for use in a grid-independent photovoltaic (PV) system are presented. The first, an optimal energy dispatch controller, is based on a class of Adaptive Critic Designs (ACDs) called Action Dependent Heuristic Dynamic Programming (ADHDP). This class of ACDs uses two neural networks to evolve an optimal control strategy over time. The first neural network or "Action" network dispenses the actual control signals while the second network or "Critic" network uses these control signals along with the system states to provide feedback to the action network, measuring performance using a utility function. This feedback loop allows the action network to improve behavior over time. The optimal energy dispatcher places emphasis on always meeting the critical load, followed by keeping the charge of the battery as high as possible so as to be able to power the critical load in cases of extended low output from the PV array, and lastly to power the non-critical load in so far as to not interfere with the first two objectives. The second energy dispatch controller is a smart energy dispatch controller and is built using knowledge from an expert, codified into a series of static rules. This smart energy dispatch controller is called the "PV-priority 2" controller. These energy dispatchers are compared with a static scheme called the "PV-priority 1". The PV-priority 1 controller represents the standard control strategy. Results show that the ADHDP-based optimal energy dispatcher (or controller) outperforms the standard PV-priority 1 energy dispatcher in meeting the stated objectives, but trails the PV-priority 2 energy dispatcher. However, the major advantage of the ADHDP controller is that no expert is required for designing the controller, whereas for a rule-based controller such as the PV-priority 2 controller, an expert is always required.
机译：本文介绍了两种用于与电网无关的光伏（PV）系统的能量分配控制器。第一个是最佳的能量分配控制器，它基于一类称为动作相关启发式动态规划（ADHDP）的自适应关键设计（ACD）。此类ACD使用两个神经网络来逐步发展最优控制策略。第一个神经网络或“动作”网络分配实际的控制信号，而第二个神经网络或“关键”网络使用这些控制信号以及系统状态向动作网络提供反馈，从而使用效用函数来测量性能。该反馈回路允许动作网络随着时间的推移改善行为。最佳能量分配器着重于始终满足临界负载，然后保持电池电量尽可能高，以便在PV阵列输出持续低功率的情况下能够为临界负载供电，最后为电池供电在不干扰前两个目标的前提下达到非关键负载。第二个能源调度控制器是一个智能能源调度控制器，它使用专家的知识构建而成，并被编码为一系列静态规则。这种智能能源调度控制器称为“ PV优先级2”控制器。将这些能量分配器与称为“ PV优先级1”的静态方案进行比较。 PV优先级1控制器代表标准控制策略。结果表明，基于ADHDP的最佳能源分配器（或控制器）在满足既定目标方面优于标准PV优先级1的能源分配器，但落后于PV优先级2的能源分配器。但是，ADHDP控制器的主要优点是设计该控制器不需要专家，而对于基于规则的控制器（例如PV优先级2控制器），则始终需要专家。

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