Harnessing demand flexibility to minimize cost, facilitate renewable integration, and provide ancillary services

textRenewable energy is key to a sustainable future. However, the intermittency of most renewable sources and lack of sufficient storage in the current power grid means that reliable integration of significantly more renewables will be a challenging task. Moreover, increased integration of renewables not only increases uncertainty, but also reduces the fraction of traditional controllable generation capacity that is available to cope with supply-demand imbalances and uncertainties. Less traditional generation also means less rotating mass that provides very short term, yet very important, kinetic energy storage to the system and enables mitigation of the frequency drop subsequent to major contingencies but before controllable generation can increase production. Demand, on the other side, has been largely regarded as non-controllable and inelastic in the current setting. However, there is strong evidence that a considerable portion of the current and future demand, such as electric vehicle load, is flexible. That is, the instantaneous power delivered to it needs not to be bound to a specific trajectory. In this thesis, we focus on harnessing demand flexibility as a key to enabling more renewable integration and cost reduction. We start with a data driven analysis of the potential of flexible demands, particularly plug-in electric vehicle (PEV) load. We first show that, if left unmanaged, these loads can jeopardize grid reliability by exacerbating the peaks in the load profile and increasing the negative correlation of demand with wind energy production. Then, we propose a simple local policy with very limited information and minimal coordination that besides avoiding undesired effects, has the positive side-effect of substantially increasing the correlation of flexible demand with wind energy production. Such local policies could be readily implemented as modifications to existing "grid friendly" charging modes of plug-in electric vehicles. We then propose improved localized charging policies that counter balance intermittency by autonomously responding to frequency deviations from the nominal frequency and show that PEV load can offer a substantial amount of such ancillary services. Next, we consider the case where real-time prices are employed to provide incentives for demand response. We consider a flexible load under such a pricing scheme and obtain the optimal policy for responding to stochastic price signals to minimize the expected cost of energy. We show that this optimal policy follows a multi-threshold form and propose a recursive method to obtain these thresholds. We then extend our results to obtain optimal policies for simultaneous energy consumption and ancillary service provision by flexible loads as well as optimal policies for operation of storage assets under similar real-time stochastic prices. We prove that the optimal policy in all these cases admits a computationally efficient form. Moreover, we show that while optimal response to prices reduces energy costs, it will result in increased volatility in the aggregate demand which is undesirable. We then discuss how aggregation of flexible loads can take us a step further by transforming the loads to controllable assets that help maintain grid reliability by counterbalancing the intermittency due to renewables. We explore the value of load flexibility in the context of a restructured electricity market. To this end, we introduce a model that economically incentivizes the load to reveal its flexibility and provides cost-comfort trade-offs to the consumers. We establish the performance of our proposed model through evaluation of the price reductions that can be provided to the users compared to uncontrolled and uncoordinated consumption. We show that a key advantage of aggregation and coordination is provision of "regulation" to the system by load, which can account for a considerable price reduction. The proposed scheme is also capable of preventing distribution network overloads. Finally, we extend our flexible load coordination problem to a multi-settlement market setup and propose a stochastic programming approach in obtaining day-ahead market energy purchases and ancillary service sales. Our work demonstrates the potential of flexible loads in harnessing renewables by affecting the load patterns and providing mechanisms to mitigate the inherent intermittency of renewables in an economically efficient manner.Electrical and Computer Engineerin

Numerical solution of MHD slip flow of a nanofluid past a radiating plate with Newtonian heating : a lie group approach

In this paper, we have examined the magnetohydrodynamic flow of a nanofluid past a radiating sheet. The Navier velocity slip, Newtonian heating and passively controlled wall boundary conditions are considered. The governing equations are reduced into similarity equations with the help of Lie group. A collocation method is used for simulation. The influence of emerging parameters on velocity, temperature, nanoparticle volumetric fraction profiles, as well as on local skin friction factor and local Nusselt number are illustrated in detail. It is found that the friction (heat transfer rate) is lower (higher) for passively controlled boundary conditions as compared to the case of an actively controlled boundary condition. The magnetic field decreases both the skin friction and the rate of heat transfer. The findings are validated with existing results and found an excellent agreement. The model explores new applications in solar collectors with direct solar radiative input using magnetic nanofluids

Secure Consensus Averaging in Sensor Networks Using Random Offsets

Abstract — In this work, we have examined the distributed consensus averaging problem from a novel point of view considering the need for privacy and anonymity. We have proposed a method for incorporating security into the scalable average consensus mechanisms proposed in the literature. Random Offsets Method (ROM) is lightweight, transparent and flexible since it is not based on cryptography, does not require any change in the fusion system and can be used optionally by some nodes who care about their privacy. In this method, which is based on noisification of nodes ’ information, we achieve robustness against n − 1 colluding adversaries in a network of n nodes, which is maximum level of robustness against collusions. Convergence and collusion robustness of ROM are analyzed mathematically and through simulation. I

Adaptive Consensus Averaging for Information Fusion over Sensor

Abstract — This paper introduces adaptive consensus, a spatio-temporal adaptive method to improve convergence behavior of the current consensus fusion schemes. This is achieved by introducing a time adaptive weighting method for updating each sensor data at each iteration. Adaptive consensus method will improve node convergence rate, average convergence rate and the variance of error over the network. A mathematical formulation of the method according to the adaptive filter theory as well as derivation of the time adaptive weights and convergence conditions are presented. The analytical results are verified by simulation as well. I