Theoretical and Practical Foundations of Large-Scale Agent-Based Micro-Storage in the Smart Grid
Theoretical and Practical Foundations of Large-Scale Agent-Based Micro-Storage in the Smart Grid
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DOI：
10.1613/jair.3446
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发表时间：
2011-09
期刊：
影响因子：
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通讯作者：
Perukrishnen Vytelingum;T. Voice;S. Ramchurn;A. Rogers;N. Jennings
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文献类型：
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作者：
Perukrishnen Vytelingum;T. Voice;S. Ramchurn;A. Rogers;N. Jennings
在本文中，我们提出了一种新的分散式管理技术，允许电力微型存储设备，部署在个人家庭作为智能电网的一部分，以收敛到有利可图的和有效的行为。具体而言，我们建议使用软件代理，驻留在用户的智能电表，自动化和优化的充电周期的微型存储设备在家里，以尽量减少其成本，我们提出了一个研究的理论基础和实际解决方案的影响，使用软件代理这样的微存储管理。首先，通过形式化的战略选择，每个代理人在决定何时充电电池，我们开发了一个博弈论的框架内，我们可以分析由这些代理人填充的电网的竞争均衡，从而预测最好的消费配置文件，人口给定他们的电池属性和个人负载配置文件。我们的框架还允许我们计算人口将采用的存储量的理论界限。其次，分析微存储部署在电网中的实际影响，我们提出了一种新的算法，每个代理可以用来优化其电池存储配置文件，以尽量减少其所有者的成本。该算法使用的学习策略，使其能够适应实时的电价变化，我们表明，采用这些策略的结果在系统收敛到理论均衡。最后，我们根据经验评估采用我们的微存储管理技术在一个复杂的设置，基于英国电力市场，代理可能有很大的不同的负载配置文件，电池类型和学习率。在这种情况下，我们的方法为使用容量小于4.5 kWh的存储设备的普通消费者节省了高达14%的能源成本，并减少了高达7%的发电碳排放量（只有家庭消费者采用微存储，商业和工业消费者不会改变他们的需求）。此外，证实了我们的理论界限，存在一个均衡，不超过48%的家庭希望拥有存储设备，社会福利也将得到改善（每年节省近15亿英镑）。
In this paper, we present a novel decentralised management technique that allows electricity micro-storage devices, deployed within individual homes as part of a smart electricity grid, to converge to profitable and efficient behaviours. Specifically, we propose the use of software agents, residing on the users' smart meters, to automate and optimise the charging cycle of micro-storage devices in the home to minimise its costs, and we present a study of both the theoretical underpinnings and the implications of a practical solution, of using software agents for such micro-storage management. First, by formalising the strategic choice each agent makes in deciding when to charge its battery, we develop a game-theoretic framework within which we can analyse the competitive equilibria of an electricity grid populated by such agents and hence predict the best consumption profile for that population given their battery properties and individual load profiles. Our framework also allows us to compute theoretical bounds on the amount of storage that will be adopted by the population. Second, to analyse the practical implications of micro-storage deployments in the grid, we present a novel algorithm that each agent can use to optimise its battery storage profile in order to minimise its owner's costs. This algorithm uses a learning strategy that allows it to adapt as the price of electricity changes in real-time, and we show that the adoption of these strategies results in the system converging to the theoretical equilibria. Finally, we empirically evaluate the adoption of our micro-storage management technique within a complex setting, based on the UK electricity market, where agents may have widely varying load profiles, battery types, and learning rates. In this case, our approach yields savings of up to 14% in energy cost for an average consumer using a storage device with a capacity of less than 4.5 kWh and up to a 7% reduction in carbon emissions resulting from electricity generation (with only domestic consumers adopting micro-storage and, commercial and industrial consumers not changing their demand). Moreover, corroborating our theoretical bound, an equilibrium is shown to exist where no more than 48% of households would wish to own storage devices and where social welfare would also be improved (yielding overall annual savings of nearly £1.5B).