Control and economics of power systems with renewables

非发电机资源（NGR），如能量存储和需求响应，是可再生能源不稳定性的概念性解决方案。天然气发电具有提高电力系统效率和可靠性的巨大潜力，但也将通过规模、不确定性和经济性增加相当多的新的复杂性。实际上，一个需求响应程序可能包含10^5个以上的负载，每个负载都受到建模不准确、通信受限以及人类行为和天气等随机因素的不确定性的影响。NGR通常具有与传统发电机截然不同的特性，例如更快的斜坡能力，硬能量容量约束，以及因此的动态充电状态。这些因素意味着NGR不能像传统的发电资源那样被对待。特别是，电力系统运营商必须（i）使用大型负荷聚合，就好像他们是单独的NGR，（ii）支付NGR关于他们独特的物理特性，以鼓励适当参与当前的市场和未来的投资。事实上，2001年加州电力危机和最近的摩根大通交易丑闻等电力市场滥用现象的持续存在，表明了基于物理模型的健全经济机制的重要性。 该研究计划将通过开发有效利用NGRs的基本算法和经济框架来应对这些挑战。管理不确定负载群体的新方法将建立在在线学习理论和基于多面体理论的负载聚合技术的基础上。例如，多臂强盗指数的政策将同时利用和改进估计模型的负载。由于NGRs将是电力系统调节的重要来源，市场必须考虑NGRs和电力系统的动态。为此，从最优控制和动态博弈论的工具将被用来设计合同和定价机制的基础上的物理模型的NGRs和电力系统监管，并确定潜在的脆弱性，游戏和市场滥用。 该研究将产生一套广泛的工具，用于优化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。由此产生的好处将包括（一）新的理论见解和研究方向在电力系统运行和经济，（二）高素质的人才在加拿大电力工业和学术界的职业培训，以及（三）减少对环境的影响。

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.