HubNet: Research Leadership and Networking for Energy Networks (Extension)

英国电力中可再生能源使用量的增长速度令人印象深刻，2014年19%的电力来自可再生能源(杜克斯2015年)，其中一半来自风力涡轮机。大量可变发电源的使用已经改变了电网的设计和运行方式，但随着普及率进一步提高到30%，继而达到50%，保持人们所期望的稳定性和可用性变得更加具有挑战性。人们普遍认为，这个问题的解决是由智能电网提供的，但尽管对智能电网进行了大量讨论，但仍然缺乏对未来网络运行所需的智能和自动化功能的详细规范。例如，智能电网的特征是，在比今天更大的程度上，需求需要受到影响，以跟随可用发电。随着在智能电表的推动下每半小时解决电费账单的到来，有一种机制可以奖励那些使其消费灵活并对电力供应和价格变化做出反应的客户。在这个“智能”技术的例子中，有很多问题，比如这种技术到底是如何工作的，人们将如何使用这种技术，以及数百万个人的行动如何协调起来才能使电网稳定运行。我们开始回答两个广泛的问题。首先，新的计算技术究竟应该如何帮助管理未来的电网？具体地说，我们如何使用分散控制来实现供需平衡(和电压控制等)在地方一级，并促进分散的控制员之间的协调和协作。此外，作为其中的一部分，数据分析的进步如何帮助从系统中产生的海量数据中获得有用的控制信息。其次，在这个更加复杂的系统中，我们如何分析系统故障的风险，并在弹性设计中降低风险。传统上，我们有很好的模型来计算变压器发生故障的概率。我们如何分析和设计客户对短期电价以及光伏和风能的短期波动的高度可变的行为。需要新的设计原则来确保持续的高可靠性供应。明智地使用分散控制和利用新获得的数据，对于确保我们提供一个能够整合大量可再生能源的电力系统，而不会给我们带来不必要的成本或性能下降的负担，至关重要。除了这些具体的技术雄心，HubNet保持其作用，将许多可以做出贡献的学术团体聚集在一起，不仅是电力工程，还包括计算科学、能源经济学和行为建模。此外，我们的角色是作为学术界与网络运营商和设备供应商进行对话的渠道，使我们的研究与该行业的主要挑战保持相关性，并提供一种途径来展示和部署我们的研究结果。我们将继续帮助社会各界确定需要解决的关键研究问题，并将继续利用我们的发现作为证据，支持能源政策和监管。

The pace of increase in renewable energy use in UK electricity is impressive with 19% of electricity sourced from renewables in 2014 (DUKES 2015) and half of that being produced by wind turbines. Use of large quantities of variable generation sources has already caused changes to the way electricity networks are designed and operated but as penetration pushes onward to 30% and thence to 50%, it becomes significantly more challenging to maintain the stability and availability that people have come to expect. It is widely said that the resolution of this problem is offered by a smart grid but despite much discussion of smart grids there is still a lack for detailed specification of the intelligent and automated functions required to operate network of the future..An example smart grid feature is that, to a much greater extent than today, demand needs to be influenced to follow available generation. With the advent of half-hourly resolution of electricity bills, facilitated by smart meters, there is a mechanism in place to reward customers who make their consumption flexible and responsive to changes in electricity availability and price. In this example of "smart" technology there are very many questions over how exactly this might work, how people would engage with this technology and how coordination of millions of individual actions will add up to stable operation of the grid.We set out to answer two broad questions. First, how exactly should new computational techniques help mange the future grid? Specifically how can we use decentralised control to perform demand-supply balancing (and voltage control, etc.) at a local level and facilitate coordination and collaboration between decentralised controllers. Also, as part of that, how do advances in data analytics help gain useful control information from the masses of data being produced in the system. Second, in this more complex system, how do we analyse the risk of a system failure and design in resilience to mitigate risk. Traditionally we have had good models of the probability of the failure of, say, a transformer. How can we analyse and design for the highly variable behaviour of customers' responses to short term electricity prices and the short term fluctuation of PV and wind energy. New design principles are needed to ensure continued high reliability of supply. Being smart about the use of decentralised control and the harnessing of newly available data is crucial to ensuring we provide an electricity system able to incorporate large amounts of renewable energy without burdening us with undue cost or degrading performance.Alongside these specific technical ambitions, HubNet maintains its role in bringing together the many academic groups who can contribute, not just electrical power engineering but also computational sciences, energy economics and behavioural modelling. Further, our role is to be the conduit for the academic community to converse with the network operators and equipment vendors so as to keep our research relevant to the sector's major challenges and provide a route to demonstrate and deploy our findings. We will continue to help identify the key research questions for the community to address and we will continue to use our findings as evidence to support energy policy and regulation.