SmartGrid

Scenario generation is an important step in the operation and planning of power systems. In this talk, we present a data-driven approach for scenario generation using the popular generative adversarial networks, where to deep neural networks are used in tandem. Compared with existing methods that are often hard to scale or sample from, our method is easy to train, robust, and captures both spatial and temporal patterns in renewable generation. In addition, we show that different conditional information can be embedded in the framework. Because of the feedforward nature of the neural networks, scenarios can be generated extremely efficiently.

System resiliency is the number 1 concern for electrical utilities in 2018 according to the CEO of the PJM, the nation's largest independent system operator. This talk will offer insights and practical answers through examples, of how power grids can be affected by weather and how countermeasures, such microgrids, can be applied to mitigate them. It will focus on two major events; Super Storm Sandy and Hurricane Maria, and the role of renewable energy resources and microgrids in these two natural disasters. It will discuss the role of microgrids in blackstarting the power grid after a blackout.

Transmission-distribution coordinated energy management (TDCEM) is recognized as a promising solution to the challenge of high DER penetration, but lack of a distributed computation method that universally and effectively works for TDCEM. To bridge this gap, a generalized master-slave-splitting (G-MSS) method is proposed based on a general-purpose transmission-distribution coordination model (G-TDCM), enabling G-MSS to be applicable to most central functions of TDCEM. In G-MSS, a basic heterogeneous decomposition (HGD) algorithm is first derived from the heterogeneous decomposition of the coupling constraints in the KKT system regarding G-TDCM. Optimality and convergence properties of this algorithm are proved. Furthermore, a modified HGD algorithm is developed by utilizing subsystem's response function, resulting in faster convergence. The distributed G-MSS method is then demonstrated to successfully solve central functions of TDCEM including power flow, contingency analysis, voltage stability assessment, economic dispatch and optimal power flow. Severe issues of over-voltage and erroneous assessment of the system security that are caused by DERs are thus resolved by G-MSS with modest computation cost. A real-world demonstration project in China will be presented.

The speakers are renowned scholars or industry experts in power and energy systems. We believe they will bring novel insights and fruitful discussions to Stanford. This seminar is offered as a 1 unit seminar course, CEE 272T/EE292T. Interested students can take this seminar course for credit by completing a project based on the topics presented in this course.

While the cost of battery energy storage systems is decreasing, justifying their deployment beyond pilot or subsidized projects remains challenging. In this talk, we will discuss how to optimize the size and location of batteries used for spatio-temporal arbitrage by either vertically-integrated utilities or merchant storage developers. We will also consider other applications of battery energy storage, such as reserve and frequency regulation and how battery degradation can be taken into account in optimal dispatch decisions.

The speakers are renowned scholars or industry experts in power and energy systems. We believe they will bring novel insights and fruitful discussions to Stanford. This seminar is offered as a 1 unit seminar course, CEE 272T/EE292T. Interested students can take this seminar course for credit by completing a project based on the topics presented in this course.

Sensor-enabled embedded systems are redefining how future communities sense, reason about and manage utilities (water, electric, gas, sewage), roads, traffic lights, bridges, parking complexes, agriculture, waterways and the broader environment. With advances in low-power wide area networks (LP-WANs), we are seeing radios able to transmit small payloads at low data rates (a few kilobits per second) over long distances (several kilometers) with minimal power consumption. As such, LP-WANs have become both a target of study as well as an enabler for a variety of research projects. In this talk, I will describe our experiences in developing and deploying wireless sensing systems for energy-efficient building and smart-grid applications. I will start-off by discussing a number of hardware platforms and sensing techniques developed to improve visibility into buildings and their occupants. This includes new devices for occupancy estimation, demand-side management using electric water heaters and an assortment of low-cost and easy-to-install sub-metering devices. I then show how these devices can be easily integrated using an open-source platform called OpenChirp that provides data context, storage and visualization for sensing systems. Finally, I will go over a case-study where we electrified over 500 homes in rural Haiti with wireless smart-meters that now no longer require expensive and toxic kerosene for lighting.

The speakers are renowned scholars or industry experts in power and energy systems. We believe they will bring novel insights and fruitful discussions to Stanford. This seminar is offered as a 1 unit seminar course, CEE 272T/EE292T. Interested students can take this seminar course for credit by completing a project based on the topics presented in this course.

The speakers are renowned scholars or industry experts in power and energy systems. We believe they will bring novel insights and fruitful discussions to Stanford. This seminar is offered as a 1 unit seminar course, CEE 272T/EE292T. Interested students can take this seminar course for credit by completing a project based on the topics presented in this course.

The seminars are scheduled for 1:30 pm on the dates listed above. The speakers are renowned scholars or industry experts in power and energy systems. We believe they will bring novel insights and fruitful discussions
to Stanford. This seminar is offered as a 1 unit seminar course, CEE 272T/EE292T. Interested students can take this seminar course for credit by completing a project based on the topics presented in this course.

We discuss how Optimization, Inference and Learning (OIL) methodology is expected to re-shape future demand-response technologies acting across interdependent energy, i.e. power, natural gas andheating/cooling, infrastructures at the district/metropolitan/distribution level. We describe hierarchy ofdeterministic and stochastic planning and operational problems emerging in the context of physical flows over networks associated with the laws of electricity, gas-, fluid- and heat-mechanics. We proceed to illustratedevelopment and challenges of the physics-informed OIL methodology on examples of: a) Graphical Models approach applied to a broad spectrum of the energy flow problems, including online reconstruction of the grid(s) topology from measurements; b) Direct and inverse dynamical problems for timely delivery of services in the district heating/cooling systems; c) Ensemble Control of the phase-space cycling energy loads via Markov Decision Process (MDP) and related reinforcement learning approaches.