Control and economics of power systems with renewables

可再生能源电力系统的控制和经济性

• 批准号: RGPIN-2014-05344
• 负责人: Taylor, Joshua
• 金额: $ 2.19万
• 依托单位: University of Toronto
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2018
• 资助国家: 加拿大
• 起止时间: 2018-01-01 至 2019-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=666613
• 关键词: Control; economics; power; systems; renewables

Non-generator resources (NGRs) like energy storage and demand response are conceptual solutions to the intermittency of renewable energy sources. NGRs have great potential to enhance the electric power system's efficiency and reliability, but will also add considerable new complexity through scale, uncertainty, and economics. Indeed, a demand response program may contain upwards of 10^5 loads, each subject to uncertainty from modeling inaccuracies, limited communications, and random factors like human behavior and weather. NGRs in general have starkly different characteristics than conventional generators, such as faster ramping capabilities, hard energy capacity constraints, and consequently dynamic states of charge. These factors mean that NGRs cannot be treated like conventional generation resources. In particular, power system operators must (i) use large load aggregations as though they were individual NGRs, and (ii) pay NGRs with regard to their unique physical characteristics so as to encourage proper participation in current markets and future investments. Indeed, the persistence of electricity market abuses like the 2001 California electricity crisis and the more recent JP Morgan trading scandal signifies the importance of sound economic mechanisms based on physical models.**This research program will address these challenges by developing fundamental algorithmic and economic frameworks for effectively utilizing NGRs. New methodologies for managing uncertain load populations will be built on online learning theory and load aggregation techniques based on polytope theory. For instance, multi-armed bandit index policies will be derived for simultaneously utilizing and improving estimated models of loads. Since NGRs will be significant sources of power system regulation, markets must account for both the dynamics of NGRs and the power system. Toward this end, tools from optimal control and dynamic game theory will be employed to design contracts and pricing mechanisms that are based on physical models of NGRs and power system regulation, and to identify potential vulnerabilities to gaming and market abuse.**The research will produce a broad set of tools for optimizing NGR utilization in technical and economic dimensions, in turn facilitating the integration of renewable energy sources into the existing power infrastructure. The resulting benefits will include (i) new theoretical insights and research directions in power system operation and economics, (ii) training of highly qualified personnel for careers in Canadian power industry and academia, and (iii) reduced environmental impacts.

非发电机资源（NGRs）如储能和需求响应是可再生能源间歇性的概念解决方案。核反应堆在提高电力系统的效率和可靠性方面具有巨大的潜力，但也会因规模、不确定性和经济性而增加相当大的新复杂性。事实上，一个需求响应程序可能包含超过10^5个负载，每个负载都受到建模不准确、有限的通信和随机因素（如人类行为和天气）的不确定性的影响。总的来说，核反应堆具有与传统发电机截然不同的特征，例如更快的爬坡能力、硬能量容量限制以及因此产生的动态电荷状态。这些因素意味着ngr不能像传统发电资源那样被对待。特别是，电力系统运营商必须(i)使用大负荷聚合，就像它们是单独的核反应堆一样；（ii）根据核反应堆独特的物理特性向其支付费用，以鼓励其适当参与当前市场和未来投资。事实上，电力市场的持续滥用，如2001年加州电力危机和最近的摩根大通交易丑闻，表明了基于物理模型的健全经济机制的重要性。**本研究项目将通过开发有效利用ngr的基本算法和经济框架来解决这些挑战。在线学习理论和基于多面体理论的负荷聚合技术将成为管理不确定负荷种群的新方法。例如，为同时利用和改进负荷估计模型，将推导出多臂强盗指数政策。由于核反应堆将成为电力系统监管的重要来源，市场必须同时考虑核反应堆和电力系统的动态。为此，将采用最优控制和动态博弈论的工具来设计基于ngr和电力系统监管物理模型的合同和定价机制，并确定游戏和市场滥用的潜在漏洞。**该研究将产生一套广泛的工具，以优化NGR在技术和经济方面的利用，从而促进可再生能源与现有电力基础设施的整合。由此产生的好处包括：(1)电力系统运行和经济方面的新理论见解和研究方向；(2)为加拿大电力工业和学术界培养高素质人才；(3)减少对环境的影响。