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    随着过去几十年里世界人口的快速增长和社会的不断发展进步，人类对于能源的需求不断增随着过去几十年世界人口的快速增长和社会的不断发展进步，人类对于能源的需求不断增加，全球的能源短缺问题日趋严重。节能减排、提高能源的利用率受到各国科学工作者的广泛关注。在电力领域，各国开始投入大量的资源研究智能电网，以期提高电能利用率、降低用电成本。随着电力技术的发展，各国电力公司开始逐步采用实时电价机制指导用户优化用电。蓄电池等储能器件的成本降低使普通用户可以便利地使用储能设备存储与释放电能。此外，太阳能等可再生能源发电技术的进步降低了用户的用电经济成本，同时减少了传统发电方式温室气体及污染物排放量，保护了环境。

    神经网络是一种基于计算的智能控制算法，它模拟人脑的信息处理机制，将系统的特性赋值于神经网络的连接权值之中, 然后利用大量数据进行自我学习，在复杂系统控制、机器学习、模式识别等领域有广泛的应用。自适应动态规划(Adaptive dynamic programming)是一种集成了动态规划、强化学习和神经网络三者优势的智能控制方法，解决了传统动态规划的“维数灾”问题。自适应动态规划基于系统的大量已知数据进行计算实验，在求解复杂非线性系统的优化控制问题方面有很好的应用效果。因此，本文将研究自适应动态规划方法在智能微网中的电能优化协调控制问题。

    本文的主要工作和贡献体现在以下几个方面：

    1.针对智能微网系统中双智能住宅用户的电能协调利用问题，提出了一种基于自适应动态规划方法的电能优化协调控制算法。在实时电价情况下，每个智能住宅用户都拥有一个储能设备，两个智能住宅用户(及外界公共电网)间的电能传输规划问题十分复杂，难以直接求解。提出的基于自适应动态规划的电能优化协调控制算法能够有效解决双智能住宅用户系统的电能传输规划问题，利用惩罚成本来提高储能设备的利用效率，实现了储能设备的使用成本控制。该算法利用储能设备对外界公共电网采购的电能进行了时间维度上和空间维度上的转移，节省了用户的经济成本，同时降低了外界公共电网负载的峰谷差，提高了外界公共电网总负载均衡性。

    2. 针对实时电价情况下，存在储能设备的智能微网双住宅用户(及外界公共电网)间的电能优化协调控制问题，太阳能发电的加入使得两个用户间的电能优化问题更加复杂，尤其是考虑到太阳能发电的波动性特点。本文设计了基于自适应动态规划的考虑温度因素和变函数因子的电能优化协调控制方法，将环境温度作为一个系统状态量，利用可变惩罚因子函数降低外界气温对太阳能发电电量的影响，加快训练速度。实验证明该方法有效节省了用户的经济成本，提高了电能利用率。

    3. 考虑到太阳能发电对双住宅用户(及外界公共电网)间电能优化协调控制的影响，提出了一种新的基于天气分类预测的电能优化协调控制算法。基于成熟的天气预报系统，将天气分为三种类型(晴天、局部阴天和阴天)，每一天划归为一种天气类型，并根据天气类型用相应的网络进行训练，以尽量减少天气因素对太阳能发电及整个住宅用户电能优化协调控制问题的影响，提高了相应网络的精准度，提高了算法的有效性和针对性。此外本文设计了相应的电能传输优先级方案，尽可能提高太阳能的利用率，降低电能转换过程中的能量损耗，并根据储能设备的控制问题提出了相应的充放电控制策略调整方案。实验分析表明该方法有效降低了用户的经济成本，提高了太阳能发电电能的利用率。

  With the rapid growth of the world’s population and the continuous development of the society in the past decades, the demand for energy has continuously increased, and the global energy shortage problem is becoming increasingly serious. Energy saving, emission reduction and improving the utilization of energy have attracted extensive attention from the worldwide scientists. In the electricity filed, in order to achieve the goal of improving the utilization of electricity and reducing the cost of electricity, countries have begun investing significant resources in the research of smart grids. With the development of power-related technologies, real-time pricing mechanisms are gradually being adopted to guide users in optimizing power usage. The reduction of the energy storage equipment storage could enable the ordinary users to use energy storage equipment to achieve the storage and release of electrical energy. In addition, advances in renewable energy power generation technologies such as solar power generation have reduced the amount of pollutants emitted by conventional power generation methods and protected the environment. At the same time, renewable energy generation has also reduced the economic cost of electricity for users.

  Artificial neural network is a computation-based intelligent control algorithm. It simulates the information processing mechanism of human brain, assigns the characteristics of the system to the connection weights of the artificial neural network, and then uses a large amount of data for self-learning, which has been widely used in different areas such as complex system control, machine learning, and pattern recognition. Adaptive dynamic programming (ADP) is an intelligent control method that integrates the advantages of dynamic programming, reinforcement learning, and artificial neural network, and solves the "dimensional disaster" problem of traditional dynamic programming. ADP performs computational experiments based on a large amount of known data from the system, and has a good application effect in solving optimization control problems for complex nonlinear systems. Therefore, this paper will explore the application of ADP in the problem of adaptive power planning and coordination in the intelligent microgrid.

  The main work and contributions of this article are presented in the following areas:

  1. Aiming at the problem of power coordination between two smart residential users in an intelligent micro-grid system, an adaptive energy management algorithm based on ADP is proposed. In the case of real-time electricity prices, when all the single intelligent residential users have energy storage devices, the power transmission scheme between any two smart residential users (and external public power grids) is very complicated and difficult to solve. To solve this problem, an ADP-based energy optimization transmission management algorithm between two smart homes based on was proposed for the first time. Second, this work also proposed that the efficiency of energy storage equipment could be increased by the usage of penalty costs, which could control the cost of the usage of store equipment. Finally, re-planning the power of the external public power grid using energy storage equipment, which could improve the balance of the total user load, reduce the peak-valley load difference of the external power grid, and save the user's economic costs at the same time.

  2. For the real-time electricity price situation, there is a problem of optimal coordination and control between two residential users (and external public grids) of the smart microgrid who have energy storage equipment. The incorporation of solar power makes the optimization of the energy between the two users more complicated, especially considering the volatility of solar power generation. A ADP-based coordinated power control optimization method was designed to prioritize the power generated by solar power generation. In the penalty function, a variable weight function was proposed to regulate the influence of the weather on solar power generation. Secondly, the outside air temperature was also used as an input variable of the neural network to increase the influence of solar power generation as much as possible. The results of the experiments show that this method effectively saves the user's economic costs and improves the utilization rate of the electricity energy.

  3. Taking into account the influence of solar power generation on the optimal control of power coordination between two residential users (and outside public power grids), a new power optimization coordination and control algorithm based on weather classification prediction was proposed. Based on the mature weather forecasting system, the weather is divided into three types (sunny, partially cloudy, and cloudy). Each day is divided into a type of weather, and the corresponding network is trained according to the type of weather, which could minimize the influence of weather factor on solar power generation and the coordinated optimization of energy control for the entire residential users. Then the accuracy of the corresponding network and the effectiveness and pertinence of the algorithm could be improved. At the same time, the corresponding power transmission priority scheme has been designed to maximize the utilization of solar energy and reduce the energy loss during power conversion. According to the control problem of energy storage equipment, the corresponding energy storage equipment charging and discharging control strategy adjustment plan was proposed. The results of the experiment show that this method could reduce the user's economic costs and improve the utilization of solar energy effectively.