Daniel Georgiev

Probabilistic Extension of the Backward/Forward Load Flow Analysis Method

The environmental need to curb distribution network losses and utilize renewable energy sources has created new challenges in estimation. High fidelity estimates are required even in the presence of significant uncertainty. Herein, we develop a new analytical probabilistic load flow method that, unlike existing analytical methods, is not based on a Taylor series approximation of the power equations. The method is exact for a set of distributions that includes the multivariate normal distribution. The method implementation is made scalable by casting all formulas into the framework of the popular backward/forward algorithm. The advantages of this approach are illustrated on a radial IEEE 32-bus test system. Significant improvements are observed in the presence of large power uncertainties and near the network power limits. Uniformly better estimation of power losses is achieved.

Risk limiting dispatch with optimal curtailing in active distribution networks

Renewable energy sources often provide intermittent power at distributed locations in transmission and distribution networks. Efficient utilization of these sources must consider economics, computation, and reliability in managing network resources. A new computational tool for efficient dispatch of intermittent sources is developed using the risk limiting operational paradigm. Optimal dispatch of regulation and load following ancillary services is computed using current estimates of future random energy production. Substitution of intermittent power with firm power through a curtailment strategy is used to avoid regulation costs in excess of current firm power prices. The underlying mathematical framework is a non-convex optimal power flow problem, which is shown to have an exact convex relaxation under a set of realistic assumptions. The methodology is successfully tested on an IEEE 30-bus test system. Several operational effects of source uncertainty are captured. For instance, optimal solutions are shown to create power flows that mutually compensate intermittencies from different sources to minimize regulation requirements.

Model discrimination of polynomial systems via stochastic inputs

Systems biologists are often faced with competing models for a given experimental system. Unfortunately, performing experiments can be time-consuming and expensive. Therefore, a method for designing experiments that, with high probability, discriminate between competing models is desired. In particular, biologists often employ models comprised of polynomial ordinary differential equations that arise from biochemical networks. Within this setting, the discrimination problem is cast as a finite-horizon, dynamic, zero-sum game in which parameter uncertainties in the model oppose the effort of the experimental conditions. The resulting problem, including some of its known relaxations, is intractable in general. Here, a new scalable relaxation method that yields sufficient conditions for discrimination is developed. If the conditions are met, the method also computes the associated random experiment that can discriminate between competing models with high probability, regardless of the actual system behavior. The method is illustrated on a biochemical network with an unknown structure.

Computing Intervals of Secure Power Injection

Abstract Existing tools used in many power system operations evaluate individual scenarios for power injection and network configuration but fail to consider nearby regions in the operating space. Such tools may lead to market transactions, preventive actions, or corrective actions that are nominally efficient but poor in general. Herein the foundations of an interval method are introduced. The presented results include an algorithm defined within a tractable optimisation framework that computes maximal power injection sets containing power injection profiles that are necessarily secure. The method is demonstrated on a simple 4 bus test system as well as on a medium sized IEEE 30 bus test system.

Backward/forward probabilistic network state estimation tool and its real world validation

Increasing penetration of renewable energy sources into conventional power networks causes increase of the uncertainty level which reveals new research and technological challenges. High fidelity network state estimation in environment with significant share of intermittent energy sources is required for planning and operation activities performed by system operators. Probabilistic load flow methods stand for promising estimation techniques which can be applicable for environment with strong presence of uncertainty. Paper describes an estimation tool based on probabilistic backward/forward method. Special emphasis is given to the description of particular components of the estimation tool. Reliability and robustness of the tool is demonstrated on the results from real world validation.

Modular algorithms for computing Intervals of Secure Power Injection

New market tools and distributed energy sources in present day transmission systems necessitate innovation in supporting information technologies. Existing tools used for power system operation evaluate individual power system snapshots but do not fully consider nearby regions in the operating space. Such tools may lead to market transactions, preventive actions, or corrective actions that are nominally efficient but poor in general. The Intervals of Secure Power Injection (ISI) method provides an alternative set based approach. Herein the ISI method is reformulated to allow fast recomputation of the interval sets under marginal changes in network topology. This method can be potentially applied in short term reconfiguration planning and in contingency analysis. Computation efficiency of proposed modular algorithms was tested on IEEE and real transmission system models.

Optimal donor selection during verification process: which factors are worth to know?

BACKGROUND The selection of optimal donor is crucial for successful hematopoietic stem cell transplantation (HSCT). Thereby, it is appropriate to know, in addition to basic human leukocyte antigen (HLA) gene matches, other immunogenic or nonimmunogenic parameters predicting the outcome of transplant. OBJECTIVE A unified approach is necessary to provide a comprehensive view of the patient-donor compatibility characterization outside of standard HLA genes. The approach should be applicable as a tool for optimizing procedures for extended donor typing and/or verification typing of a donor. METHODS The study used the summary, unification, and innovation of existing practical knowledge and experience of the Czech National Marrow Donor Registry of various factors beyond HLA matching with impact on transplant outcome. RESULTS An information technology system-implemented procedure (a verification algorithm) is presented as the decision support approach for prematurely discarding less suitable donors from the transplantation process. It is intended primarily for the transplant specialist to help establish optimal procedures for verifying and determining donor critical factors. CONCLUSIONS A process defining HLAs, killer cell immunoglobulin-like receptors, and cytokine typing strategies was proposed to provide support to a transplant specialist in refining the choice of a suitable donor.

2DEP cytometry: distributed dielectrophoretic cytometry for live cell dielectric signature measurement on population level

In this work, a novel force equilibrium method called distributed dielectrophoretic cytometry (2DEP cytometry) was developed. It uses a dielectrophoresis (DEP)-induced vertical translation of live cells in conjunction with particle image velocimetry (PIV) in order to measure probabilistic distribution of DEP forces acting on an entire cell population. The method is integrated in a microfluidic device. The bottom of the microfluidic channel is lined with an interdigitated electrode array. Cells passing through the micro-channel are acted on by sedimentation forces, while DEP forces either oppose sedimentation, support sedimentation, or neither, depending on the dielectric (DE) signatures of the cells. The heights at which cells stabilize correspond to their DE signature and are measured indirectly using PIV, which enables simultaneous and high-throughput collection of hundreds of single-cell responses in a single PIV frame. The system was validated using polystyrene micro-particles. Preliminary experimental data quantify the DE signatures of immortalized myelogenous leukemia cell lines K562 and KG1. We show DEP-induced cell translation along the parabolic velocity profile can be measured by PIV with sub-micron precision, enabling identification of individual cell DE signatures. DE signatures of the selected cell lines are distinguishable. Throughput of the method enables measurement of DE signatures at 10 different frequencies in almost real time.

A dynamic riboregulator design with a programmable all-or-nothing response

Riboregulators represent an important class of genetic regulatory devices whose behavior is programmable by specification of the underlying nucleic acid sequence. Both natural and synthetic riboregulators are often modeled as static devices that activate or repress potential RNA binding sites. Individual riboregulators are thereby functionally similar to linear gains found in electrical systems. While in silico design tools are developed to maximize their in vivo performance, design of dynamic behavior has yet to be considered. Herein a simple riboregulator design is proposed that appends programmable dynamic behavior to existing riboswitches. The resulting device is shown to produce an all-or-nothing response. Simple design rules for this device are derived.

Effects of loss models on locational reserve policies in uncertain power systems

Renewable energy sources (RES) often provide intermittent power at distributed locations in transmission and distribution networks. Storage devices distributed across the network are recognized as an important tool for compensating such power fluctuations. Operational policies of storage devices considered in literature focus solely on network balance. Here, the consumed network capacity is also considered. A linear network model with uncertain power and storage elements is supplemented with three possible models of line losses. Policy computation is formulated in each case as a convex optimization problem. The different solutions are compared in terms of computational complexity and policy structure. A single solution shows preference for local energy storage and remains computationally efficient. Other solutions either do not respond to uncertainty or attempt to reduce uncertainty using unnecessary power transmissions. Hence, the results yield a useful loss model as well as characterize undesirable behaviors that should be avoided with future models.