Markov-Decision-Process-Assisted Consumer Scheduling in a Networked Smart Grid
Markov-Decision-Process-Assisted Consumer Scheduling in a Networked Smart Grid
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DOI：
10.1109/access.2016.2620341
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发表时间：
2017
期刊：
影响因子：
3.9
通讯作者：
Zhi Liu;Cheng Zhang;M. Dong;Bo Gu;Yusheng Ji;Y. Tanaka
中科院分区：
文献类型：
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作者：
Zhi Liu;Cheng Zhang;M. Dong;Bo Gu;Yusheng Ji;Y. Tanaka
许多新建的住宅和工厂都配备了将太阳能等绿色能源转化为电能的设备。电力消费者可以将他们不消耗的额外电力输入智能电网进行销售，这在日本等国家是法律允许的。为了减少高峰时段的用电量，智能电网通常采用时变定价方案，无论是卖给用户的电量还是从用户购买的电量。由于网络物理系统和先进的通信和计算技术的发展，目前的智能电网通常是网络化的，并且有可能将天气预报等信息集成到这种网络化的智能电网中。因此，我们可以利用这些信息和历史数据，高精度地预测未来的发电水平（例如，太阳能和风能，其发电主要受天气影响）。消费者面临的关键问题是，如何在综合考虑当前储能状态、时变电价以及未来用电量和发电量的情况下，对联网智能电网的购电和售电进行规划，使自己的利益最大化。这个问题不是微不足道的，对于提高智能电网的利用率和吸引消费者投资新能源发电系统以及其他目的至关重要。在本文中，我们的目标是这样一个网络化的智能电网系统，在这个系统中，未来的发电量可以根据天气预报进行合理的预测。我们利用马尔可夫决策过程模型对消费者行为进行调度，以优化消费者的净收益。大量的仿真结果表明，该方案明显优于基准竞争方案。
Many recently built residential houses and factories are equipped with facilities for converting energy from green sources, such as solar energy, into electricity. Electricity consumers may input the extra electricity that they do not consume into the smart grid for sale, which is allowed by law in countries such as Japan. To reduce peak-time electricity usage, time-varying pricing schemes are usually adopted in smart grids, for both the electricity sold to consumers and the electricity purchased from consumers. Thanks to the development of cyber-physical systems and advanced technologies for communication and computation, current smart grids are typically networked, and it is possible to integrate information such as weather forecasts into such a networked smart grid. Thus, we can predict future levels of electricity generation (e.g., the energy from solar and wind sources, whose generation is predominantly affected by the weather) with high accuracy using this information and historical data. The key problem for consumers then becomes how to schedule their purchases from and sales to the networked smart grid to maximize their benefits by jointly considering the current storage status, time-varying pricing, and future electricity consumption and generation. This problem is non-trivial and is vitally important for improving smart grid utilization and attracting consumer investment in new energy generation systems, among other purposes. In this paper, we target such a networked smart grid system, in which future electricity generation is predicted with reasonable accuracy based on weather forecasts. We schedule consumers’ behaviors using a Markov decision process model to optimize the consumers’ net benefits. The results of extensive simulations show that the proposed scheme significantly outperforms the baseline competing scheme.