Pirathayini Srikantha

An analysis of peak demand reductions due to elasticity of domestic appliances

Unlike prior work on demand management, which typically requires industrial loads to be turned off during peak times, this paper studies the potential to carry out demand response by modifying the elastic load components of common household appliances. Such a component can decrease its instantaneous power draw at the expense of increasing its duration of operation with no impact on the appliances lifetime. We identify the elastic components of ten common household appliances. Assuming separate control of an appliances elastic component, we quantify the relationship between the potential reduction in aggregate peak and the duration required to complete the operation of appliances in four geographic regions: Ontario, Quebec, France and India. We find that even with a small extension to the operation duration of appliances, peak demand can be significantly reduced in all four regions both during winter and summer. For example, during winter in Quebec, a nearly 125 MW reduction in peak demand can be obtained with just a 10% increase in appliance operation duration. We conclude that exploiting appliance elasticity to reduce peak power demand should be an important consideration for appliance manufacturers. From a policy perspective, our study gives regulators the ability to quantitatively assess the impact of requiring manufacturers to conform to “smart appliance” standards.

Distributed Optimization of Dispatch in Sustainable Generation Systems via Dual Decomposition

Distributed generators (DGs) are being widely deployed in todays power grid. These energy sources are highly variable posing practical challenges for deployment and grid management. In this paper, a novel scalable distributed power dispatch strategy is proposed to effectively manage DGs at the distribution substation level, capitalizing on the recent push to cyber-enable power grid operations. We demonstrate how the inherent separability of the power dispatch problem allows the use of dual decomposition that enables every participating DG to locally compute its dispatch strategy based on simple broadcast data by the utility. Results and comparisons indicate that the DGs are able to rapidly converge to an optimal economical dispatch vector with significantly less concentrated computational effort and communication overhead, promoting security and privacy.

A DER Attack-Mitigation Differential Game for Smart Grid Security Analysis

Information and communication infrastructure will be extensively deployed to monitor and control electric power delivery components of todays power grid. While these cyber elements enhance a utilitys ability to maintain physical stability, if a subset are compromised by adversaries, disruption may occur. In this paper, a novel framework based on the principles of differential games is proposed that demonstrates stealthy worst-case strategies for attackers to disrupt transient stability by leveraging control over distributed energy resources. We demonstrate that if the electric power utility is able to identify uncompromised components, countermeasures can exist that effectively reduce the impact of attack for a fixed time interval. Based on our results, we develop insights to construct safety margin recommendations for cyber-physical smart grid actuation elements that promote system resilience during a cyber attack.

Distributed control for reducing carbon footprint in the residential sector

The current power grid is conservatively provisioned for rarely-occurring peaks. Expensive, quickly-ramping generators, typically with high carbon emissions, provide peak power. Therefore, it is possible to reduce both capital cost and carbon footprint by reducing the peak load. We address this issue by proposing to intelligently reduce loads from household appliances during peak times. Our scheme capitalizes on the fact that the power consumed by resistive loads can be reduced, at the cost of a small increase in appliance operation duration, with little impact on perceived user comfort. Specifically, on the receipt of congestion signals from the grid, appliance-based controllers intelligently reduce their load while ensuring that user comfort does not degrade below a pre-specified level. Simulations show that significant gains in energy reduction can be obtained with our scheme. For example, in Quebec, an estimated 12.9 MWh of peak power reduction can be obtained for a maximum of 10% increase in appliance operation duration.

Denial of service attacks and mitigation for stability in cyber-enabled power grid

Monitoring and actuation represent critical tasks for electric power utilities to maintain system stability and reliability. As such, the utility is highly dependent on a low latency communication infrastructure for receiving and transmitting measurement and control data to make accurate decisions. This dependency, however, can be exploited by an adversary to disrupt the integrity of the grid. We demonstrate that Denial of Service (DoS) attacks, even if perpetrated on a subset of cyber communication nodes, has the potential to succeed in disrupting the overall grid. One countermeasure to DoS attacks is enabling cyber elements to distributively reconfigure the systems routing topology so that malicious nodes are isolated. We propose a collaborative reputation-based topology configuration scheme and through game theoretic principles we prove that a low-latency Nash Equilibrium routing topology always exists for the system. Numerical results indicate that during an attack on a subset of cyber nodes, the proposed algorithm effectively enables the remaining nodes to converge quickly to an equilibrium topology and maintain dynamical stability in the specific instance of an islanded microgrid system.

A novel evolutionary game theoretic approach to real-time distributed Demand response

Todays electric power grid aims to provide reliable power service that meets (even the most rarely occurring) demand peaks. Consequently, this necessitates the grid be equipped with ample generation sources that can be costly and unsustainable. Demand response (DR) is one popular approach to reduce peak demand that requires communications and coordination amongst DR participants. In this paper, we propose a novel distributed DR strategy that employs simplistic cost signals broadcasted by the electric power utility for coordination and convergence to a desired aggregate peak demand reduction. We make use of evolutionary game theoretic concepts to analytically and empirically establish important convergence properties. Our results indicate that our strategy is real-time and highly scalable demonstrating promise for practical deployment.

Resilient Distributed Real-Time Demand Response via Population Games

The proliferation of high powered electric devices is a driving force in the rising of peak power demand from electric power utilities. One way to accommodate these rising consumption patterns involves the deployment of high capacity dispatchable, but largely unsustainable peak generation systems. To avert these extravagant costs and the likelihood of grid overload, demand response (DR) strategies can be employed to curtail overall consumption, thus reducing peak patterns. In this paper, we propose a distributed real-time DR approach. The proposed method fosters seamless cooperation between DR participants for rapid convergence to expected aggregate load curtailment, while accounting for individual consumer satisfaction needs. We assess this paper through theoretical analysis based on population game theory and simulations to demonstrate its inherent flexibility, scalability, and resilience making it attractive for practical widespread deployment.

Distributed sustainable generation dispatch via evolutionary games

Todays power grid is provisioned conservatively for rarely occurring demand peaks. These peaks are served by flexible generation systems that are typically costly and have significant carbon footprint. Distributed power sources such as wind turbines and solar panels are sustainable but unreliable as these have inherently variable generation capacities. An effective power dispatch management system is necessary to harness the significant generation potential of these intermittent systems. In this work, a novel scheme is proposed which leverages upon the recent cyber-enablement in the power grid to distributively dispatch a large number of strategically interacting small-scale variable generators. We incorporate evolutionary game theoretic techniques into the formulation of the dispatch strategy as it provides an opportunity to model the aggregate behaviour of tactical agents making inter-dependent decisions and aids with establishing deterministic steady state predictions of the system state. Numerical and theoretical results presented in this work show that the proposed strategy is highly scalable and enables real-time power dispatch of intermittent systems while maintaining low computational overhead.

Real-Time Integration of Intermittent Generation With Voltage Rise Considerations

In the modern electric power grid, a commonly observable recent phenomenon is the increasing penetration of renewable generation sources especially at the distribution network (DN) level. The traditional DN is not designed for bidirectional power flow induced by these volatile sources and, therefore voltage rise is a major concern. In order to enable mass renewable integration into any type of existing radial DN without requiring expensive line/bus upgrades and avoiding adverse effects of voltage rise, these generation sources (with possible nonconvex discrete output levels) must be dispatched in real-time while taking into account nonconvex voltage constraints. Ubiquitous connectivity between power components is available in todays grid due to the cyber-physical nature of these devices. We leverage this to propose a distributed algorithm based on principles of population games for efficient dispatch that minimizes dependence of the DN on the main grid for sustainable system operation. Theoretical and simulation studies show that the proposed algorithm allows for the seamless coexistence of a large number of renewables that are highly responsive to fluctuations in demand and supply with strong convergence properties while successfully mitigating voltage rise issues.

A Game Theoretic Approach to Real-Time Robust Distributed Generation Dispatch

Power demands are rising at an exponential pace due to the increasing proliferation of high-energy consuming devices such as plug-in hybrid electric vehicles. It is well known that scaling traditional power generation systems to accommodate these soaring demands will be excessively costly and may lead to negative environmental ramifications. One approach to supplement increasing energy needs involves diversifying the generation mix to incorporate a large number of local distributed generators (DGs) for economical and sustainable operation. However, such an approach remains an open challenge due to the inherent generation variability of DGs. In this paper, we propose a distributed generation dispatch strategy that can effectively coordinate a large number of DGs to meet consumer demand in real time. Through theoretical analysis based on population games and simulation studies, we demonstrate that our dispatch strategy is scalable and allows for the seamless integration of alternative energy resources into the grid in a robust and an optimally cost-effective manner.