Power networks and the smart grid

Risk and opportunity. The power network, from generation to transmission and distribution to consumption, will undergo the same kind of architectural transformation in the next decades that computing and the communication network has gone through in the last two. We envision a future network with hundreds of millions of active endpoints. These are not merely passive loads as are most endpoints today, but endpoints that may generate, sense, compute, communicate, and actuate. They will create both a severe risk and a tremendous opportunity: an interconnected system of hundreds of millions of distributed energy resources (DERs) introducing rapid, large, and random fluctuations in power supply and demand, voltage and frequency, and our increased capability to coordinate and optimize their operation.

The transformed power network will be defined by four characteristics and their associated control and economic challenges:

  • Renewable energy sources: The generation from renewable resources such as wind and solar is intermittent and less predictable. How do we design the control architectures and algorithms, market mechanisms, data analytics, and communication protocols to balance supply and demand, support voltage, and regulate frequency in the presence of volatile supply?
  • Hundreds of millions of active endpoints: Sensors, actuators, and communication devices will be deployed at generators, along power lines, at substations, in transformers, in DERs such as photovoltaics, wind turbines, inverters, storage devices, electric vehicles, and smart appliances in distribution networks, microgrids, and premises. How should we jointly optimize control applications and information and communication technologies (ICT) to manage these hundreds of millions of active endpoints?
  • Millions of individual and institutional agents: Stakeholders in the operation of the new power-ICT grid include industrial, commercial and residential customers, together with micro grids, utilities, power generators, energy services companies and regulators, with diverse and often conflicting objectives. How should we design economic mechanisms and business models to align incentives and market forces to engender desired global outcomes?
  • Integration of information technologies and advanced power electronics: ICT-enabled smart components such as DC/AC inverters, smart batteries, flexible AC transmission systems (FACTS) devices, and storage devices will be increasingly deployed. How should we jointly design ICT-enabled power electronics and the control applications to maximize reliability, security, and efficiency?

Our approach. As infrastructure deployment progresses, the new bottleneck will be the need for overarching frameworks, foundational theories, and practical algorithms to manage a fully ICT-enabled power network. We will explore one end of the spectrum of control architectures from fully centralized control (closer to today's system) to fully distributed control (our focus). Our approach has the following four characteristics:

  • Endpoint-based control: Each DER self-manages through local sensing, computing, communication, and control. Intelligence will be embedded everywhere, from EVs and smart appliances to inverters and storage devices, from homes to microgrids to substations.
  • Local algorithms with global perspective: Algorithm design, be it for Volt/VAR control or real-time pricing, will start with a mathematical model with global objectives. These objectives will then be decomposed into local algorithms for implementation locally at each control point.
  • Joint design of control and economic mechanisms: Power flow is determined not just by Kirchhoff Laws but also market mechanisms and tariff structures. Its optimization therefore must integrate control and economics to incentivize and organize large distributed control policies.
  • Mathematical foundation: The theories of control, optimization, and stochastic processes will provide the foundation for a holistic framework that integrates engineering and economics and facilitates systematic algorithm design.

Our approach will achieve three benefits: the ability to control active endpoints at scale, a framework to understand their global behavior, and improved reliability and efficiency. One of the biggest challenges with large-scale systems is the difficulty in understanding its structural properties. The interactions between large numbers of local algorithms can often be fragile and cryptic. The control and optimization framework will not only lead to local algorithms with high efficiency, more importantly, it also provides a means to understand their interactions and coordinate their global behavior. Both the power and the risk of a network originate from the interconnection of local algorithms that are distributed across protocol layers in a network device and at different locations. Often, interesting and counterintuitive behaviors arise when local algorithms interact in intricate and surprising ways. Given the scale and diversity of the system, such behaviors will be impossible to discover or explain without a fundamental understanding of the underlying structure. New mathematical framework must be developed to explore structures, clarify ideas, and suggest directions, to achieve efficient and robust design.

This project is part of a broad focus on energy and sustainability at Caltech through the Resnick Institute and NSF AitF project.