Neda Nasiriani

Design, Modeling, and Simulation of On-Demand Communication Mechanisms for Cyber-Physical Energy Systems

Advanced communication technology is the enabling factor for distributed sensing and control in smart grid. The performance of communication has a significant effect on the performance of the controllers that manage a power system. This effect is more profound when transient level behavior and critical applications are concerned. In these cases, an important issue is to design control-aware communication strategies for utilizing available communication technologies. Such strategies should describe what needs to be communicated when and between which nodes. In this paper, an “on-demand” strategy is presented that describes how communication subsystems should be configured, almost agnostically to the underlying technologies, to achieve significant performance improvement for the application. The on-demand method relies on the concept of error-dependent communication for tracking dynamical systems over communication networks. The paper also introduces the design of an embedded communication simulator integrated with PSCAD for cosimulation of communication strategies/protocols and power system components.

Stable and Fair Power Control in Vehicle Safety Networks

Cooperative vehicle safety (CVS) systems operate based on frequent broadcast of vehicle state (e.g., position, heading, and speed) and safety information to other vehicles over a wireless local area network. Such networks are formed using dedicated short-range communications (DSRC) technology. In crowded networks, the shared wireless channel may become congested and fail to deliver the broadcast information, potentially affecting the reliability of CVS applications. This issue, which is known as a scalability issue, has been identified as one of the challenges to be addressed with high-scale deployment of vehicle safety networks. Several scalability algorithms have been recently introduced and studied in the literature; currently, a select number of algorithms are under study by industry for possible adoption and standardization. One method used by some algorithms is congestion control through power or range adaptation. In this paper, we analyze two existing range control algorithms in terms of time stability and spatial fairness. We derive stability conditions for the main range control scheme that is currently under study. An improved algorithm that relaxes these conditions is then proposed, and its convergence properties are studied in detail. We show that the algorithm is stable and can be controlled to quickly converge to desired operation points. Finally, fairness properties of several power control schemes are studied, and an enhancement method is presented that ensures that spatial fairness is achieved by the presented power control schemes.

Stability analysis of congestion control schemes in vehicular ad-hoc networks

Cooperative vehicle safety (CVS) systems operate based on broadcast of vehicle position and safety information to neighboring cars. The communication medium of CVS is a vehicular ad-hoc network. One of the main challenges in large scale deployment of CVS systems is the issue of scalability. To address the scalability problem, several congestion control methods have been proposed and are currently under field study. These algorithms adapt transmission rate and power based on network measures such as channel busy ratio. We examine two such algorithms and study their dynamic behavior in time and space to evaluate stability (in time) and fairness (in space) properties of these algorithms. We present stability conditions and evaluate stability and fairness of the algorithms through simulation experiments. Results show that there is a trade-off between fast convergence, temporal stability and spatial fairness. The proper ranges of parameters for achieving stability are presented for the discussed algorithms. Stability is verified for all typical highway density cases for static traffic as well as real scenarios. Fairness is shown to be naturally achieved for some algorithms and its analysis is under study in another work of us. Under the same conditions other algorithms may have problem to maintain fairness in space. We have shown that this can be resolved by a distributed measurement of CBR and is verified.

An embedded communication network simulator for power systems simulations in PSCAD

The emergence of distributed communication based control schemes in power systems emphasizes the need for a realistic power systems simulation tool that allows inclusion of communication components. Communication performance parameters like delay and loss in transfer of measurements and commands can affect the result of control and should be taken into account while studying a control mechanism over power system dynamics. In this paper we present the method and tools that we have developed to allow simulation of communication networks inside off-the-shelf product for power system transient simulation, PSCAD. In particular we present the structural designs and interfaces of modules that are needed for implementing an embedded communication network simulator in PSCAD, and provide a brief guide on how power system engineers could use these modules in their designs. In addition, we present an example of a control scheme using communication for stabilizing a power system that incorporates integration of a renewable energy source and energy storage. We use the IEEE 13-node test feeder as our case study. A wind generator and battery are connected to the system to add more dynamic behavior; a control for the system has been designed which works with communication interface and modules implemented in PSCAD. The test system is used to validate and verify the embedded communication simulator and its implementation.

Performance and fairness analysis of range control algorithms in cooperative vehicle safety networks at intersections

Cooperative vehicle safety (CVS) systems rely on broadcast of vehicle position to other neighboring vehicles. Scalability is one of the main challenges of CVS, and many congestion control algorithms have been proposed to resolve the issue. These algorithms need to be robust, stable and fair in especially both highways and urban areas. Many of the proposed algorithms have been examined in the past mostly for highway scenarios. In particular, a study of properties such as stability in time and fairness in space for intersection scenarios has not yet been presented in literature for some important scalability solutions. In this paper we study two congestion control algorithms in intersection scenarios and evaluate their fairness and stability properties. We show that the stability and fairness in different typical intersection scenarios can be achieved. The robustness of the algorithms to the high variability of density in intersection scenarios is also verified. While unfairness does not happen in most of the scenarios naturally, a previously proposed method for resolving possible unfairness is evaluated for completeness of the study.

Optimal Peak Shaving Using Batteries at Datacenters: Characterizing the Risks and Benefits

A datacenters power consumption is a major contributor to its operational expenditures (op-ex) and one-time capital expenditures (cap-ex). The recurring electricity cost is often in large determined by datacenter peak-demand under peak-based pricing which is employed by major electric utility providers. There is a growing interest in reducing a datacenters electricity costs by using throttling techniques and/or energy storage devices (batteries) which are readily available at most datacenters as a backup energy source. A datacenters power-demand uncertainty makes this a challenging problem, which is largely neglected in existing work, by assuming perfect predictability of power demand. We model this inherent uncertainty as a Markov chain and also evaluate the risk of over/under charging batteries as a result of the randomness in power demand. We design an online optimization framework for peak shaving which considers Conditional Value at Risk and allows for navigating cost-risk trade-offs of datacenters based on their energy infrastructure and workload characteristics. We show that this framework offers significantly higher (up to 2X) cost-savings with small risks of over/under charging batteries, compared to existing stochastic optimization techniques. This framework leverages Markov Decision Processes to perform online dynamic peak shaving, considering battery degradation costs under peak-based pricing.

Multicommodity games in public-cloud markets considering subadditive resource demands

In still developing, public cloud-computing markets, prices for virtual machine (VM) offerings fluctuate, and not just for spot/preemptible instances. Moreover, some (particularly derivative) providers allow for fine-grain initial resource provisioning and dynamic reprovisioning of VMs. In this preliminary study, we consider long-lived tenants of a public cloud under resource-based service-level agreements. For noncooperative aggregative multicommodity (plural IT resource) games among them, conditions are established for an interior Nash equilibrium and an exact potential (giving convergence to Nash equilibrium). Also discussed are extensions to plural IT resource demands that are subadditive in workload intensity.