Manikandan Padmanaban

Situational awareness for the electrical power grid

With deployment of high-throughput, low-latency sensors such as PMUs (phasor measurement units), utilities have an opportunity to achieve high-resolution “visibility” into the state of the electrical power grid at any time. In this paper, we revisit situational awareness—a term with origins in reconnaissance and mission planning—and develop a knowledge-based approach for monitoring the electrical power grid that combines both static and dynamic sources of information to enable better comprehension and decision support. At the core of this approach is an abstraction layer for representing and interpreting the granular sensor data reported by PMUs as power system events. We describe how this abstraction layer can be used to develop a cognitive model of the grid operator, engineer, or analyst, and, ultimately, to filter, interpret, and efficiently summarize grid behaviors. We also describe interfaces that we developed and discuss some actual utility use cases implemented in partnership with Hydro-Quebec, Canadas largest electricity generator by a utility and one of the worlds largest producers of clean energy with the largest transmission system in North America.

Congestion relief using grid scale batteries

Sustained load growth in electric power industry has resulted in intensive usage of grid assets including transmission system. Over the years, the transmission infrastructure is unable to evolve at the same proportionate which leads to diminishing reserve capacity in a transmission system. This leads to frequent congestions in the grid. Therefore, congestion management has become vital to maintain the required level of reliability of the supply of power. In contemporary literature, congestion is usually managed by generation rescheduling and/or load shedding. This incurs huge additional costs as generation/distribution companies need to provide huge incentives for changing their pre-committed schedules. In order to minimize the congestion relief cost, this paper proposes a cost efficient congestion management scheme using distributed battery energy storages (BESs) with minimum adjustment to the pre-committed schedules of loads and generators. Simulation results on a 30-bus IEEE benchmark system has validated the potential of BES for congestion management in electric grids.

Photonic Energy Harvesting: Boosting Energy Yield of Commodity Solar Photovoltaic Systems via Software Defined IoT Controls

Solar photovoltaic (PV) systems have a utilization (or capacity) factor of 15-20% worldwide. We propose to enhance the energy yield in a software-defined manner by complementing commodity solar PV systems with cloud-based IoT-controlled reflectors. We also propose designs for brownfield and greenfield settings in solar farms. We study a number of practical engineering issues including effect of solar azimuth, shadowing effects, ground coverage ratio (GCR) tradeoff, constraints on angular control etc. Our designs can raise solar PV energy yield between 50-100% with modest tradeoffs on operational complexity, land requirements (ground coverage ratio) etc. The software-defined IoT control allows a variety of current and future operational or business constraints to be flexibly factored in to tradeoff these factors versus economic gain (eg: levelized cost of energy, LCOE). The paper presents both simulation and experimental evidence for our system. We are actively piloting this technology with solar PV developers and engineering, procurement, construction (EPC) companies in emerging markets.

Association rule mining to understand GMDs and their effects on power systems

Control room operators play an important role in mitigating the effects of disturbances on the power grid. We focus on solar storms and geomagnetic disturbances in this paper. Providing operators with advanced tools that reveal relationships between variables that characterize events in real-time enables faster response. For complex events such as GIC (geomagnetically induced currents), the output of the tool should validate domain knowledge to build trust with the operator. In this paper, we apply association rule mining to discover relationships between physical variables from multiple sources of data relevant to GMDs. We aligned features extracted from ACE (Advanced Composition Explorer) satellite measurements with features extracted from terrestrial magnetometer measurements. We mapped these features during solar storms in 2015 to GIC grid event data processed from synchrophasor streams. Then, we discovered relationships or rules between features relevant for predicting the effects of solar storms on the grid and evaluated our results on the 2015 data. By looking at the predictive value of selected features, we find that features most relevant to GIC vary depending on the prediction latency, reflecting the complex, physical dynamics of GMDs.

Combining multiple sources of data for situational awareness of geomagnetic disturbances

With the increasing complexity of the grid and increasing vulnerability to large-scale, natural events, control room operators need tools to enable them to react to events faster. This is especially true in the case of high impact events such as geomagnetic disturbances (GMDs). In this paper, we present a data-driven approach to building a predictive model of GMDs that combines information from multiple sources such as synchrophasors, magnetometers, etc. We evaluate the utility of our model on real GMD events and discuss some interesting results.