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This paper investigates the feasibility of using a Deep Learning Convolutional Neural Network (DLCNN) in inspection of concrete decks and buildings using small Unmanned Aerial Systems (sUAS). The training dataset consists of images of lab-made bridge decks taken with a point-and-shoot high resolution camera. The network is trained on this dataset in two modes: fully trained (94.7% validation accuracy) and transfer learning (97.1% validation accuracy). The testing datasets consist of 1620 sub-images from bridge decks with the same cracks, 2340 sub-images from bridge decks with similar cracks, and 3600 sub-images from a building with different cracks, all taken by sUAS. The sUAS used in the first dataset has a low-resolution camera whereas the sUAS used in the second and third datasets has a camera comparable to the point-and-shoot camera. In this study it has been shown that it is feasible to apply DLCNNs in autonomous civil structural inspections with comparable results to human inspectors when using off-the-shelf sUAS and training datasets collected with point-and-shoot handheld cameras.

Validation is a critical component in the development of synthetic models, which aims to convince a user that a model achieves its claims of realism. While users of power system test cases are primarily interested in operational results, which could be considered outputs, it is more convenient and feasible to control distributions of data inputs, both structural and otherwise. Validation metrics, are therefore generally focused on input rather than operation features. This paper investigates the link between input and output statistics in power system. Better understanding of this relationship allows validation to continue to focus largely on input statistics, while at the same time offering some assurance about operational behavior.

In this paper we review and expand our previous work on developing random topology networks as a way to generate synthetic power grid data. We first present an algorithm that is able to generate the small-world “eletric” topology appropriate for a transmission network. Next we re-examine the definitions of the proposed numerical measure called “Bus Type Entropy”, to characterize the correlated assignment of generation, load, and connection buses in a real power grid, using the IEEE test cases and newly obtained realistic grid data. In order to study the scaling property of bus type assignment versus network work, we revise the entropy definition for better statistical property and improved numerical stability. We then develop an more efficient search algorithm for the best bus type assignments with the help of the derived scaling function. Finally we introduce some most recent work to generate other electrical parameters such as generation capacities and load settings.

In Figure 2(a) ASE spectra are plotted as function of optical pump stripe length at a fixed pump power density. The modal gain, G, and un-calibrated spontaneous emission, CIbspon, are extracted from this data as fitting parameters to a one dimensional amplifier model. The spontaneous emission is measured in arbitrary units at this point as C, the calibration factor that accounts for the fraction of the ASE generated within the device that is measured at the spectrograph, is not known. However, because we are able to measure gain in real units and because the fraction of gain to spontaneous emission is governed by the level of inversion, C can be determined with a known value of the population inversion factor, Fig. 3(a). This allows measured spontaneous emission spectra to be calibrated in real units, Fig. 3(b), and by adding the spectrally integrated emission rates for the TE and TM polarizations and multiplying by the electronic charge the total radiative recombination current, J spon can be found. For the InP dot material optically pumped to a quasi-Fermi level separation of 1.81 eV we calculate that J spon = (209 ± 49) A cm−2. We validate the experiment by comparisonwith electrically pumped measurements on same device.

The potential of the device is demonstrated through the results of a micro-bead counting experiment. A 0.5 µl sample volume containing 10 µm polystyrene micro-beads in Dl water is deposited into an on-chip inlet reservoir from where it flows, under capillary action, through a buried flow cell. A laser on one side of the cell is forward biased and the opposing laser is operated as a photodiode (Fig. 2(a)). Both are pulsed to provide sub-µs time-resolution of bead transit ‘events’ with a 30 mV noise floor that affords a large dynamic range of over 1.2 V (Fig. 2(b)). After passing through the flow cell the sample fluid is drawn into a spiral patterned exit reservoir that provides sufficient pull through to sustain a continuous flow for over 30 s with flow rate of > 4mm/s.

Electric power engineers and researchers need appropriate randomly generated grid network topologies for Monte Carlo experiments to test and demonstrate new concepts and methods. Our previous work proposed a random topology power grid model, called RT-nested-smallworld, based on a comprehensive study of the real-world grid topologies and electrical properties. The proposed model can be used to produce a sufficiently large number of power grid test cases with scalable network size featuring the same kind of small-world topology and electrical characteristics found in realistic grids. However, the proposed RT-power grid model has a shortcoming that is its random assignment of bus types. And our recent study has shown that the bus type assignment of a realistic power grid is not random but a correlated one. Generally speaking, the buses in a power grid can be grouped into three categories: generation buses (G), load buses (L), and connection buses (C). When studying the dynamics of a grid we need to take into account not only its “electrical” topology but also the generation and load settings including their locations, which are equivalent to the bus type assignments in our model.

The problem of short-term probabilistic forecast of real-time locational marginal price (LMP) is considered. A new forecast technique is proposed based on a multiparametric programming formulation that partitions the uncertainty parameter space into critical regions from which the conditional probability mass function of the real-time LMP is estimated using Monte Carlo techniques. The proposed methodology incorporates uncertainty models such as load and stochastic generation forecasts and system contingency models. With the use of offline computation of multiparametric linear programming, online computation cost is significantly reduced.

In this paper, a much more detailed representation of the nation's electricity system than has been traditionally used in policy models is employed. This detailed representation greatly increases the computational difficulty of obtaining optimal solutions, but is necessary to accurately model the location of new investment in generation. Given the proposed regulation of CO2 emissions from US power plants, an examination of economically efficient policies for reducing these emissions is warranted. The model incorporates realistic physical constraints, investment and retirement of generation, and price-responsive load to simulate the effects of policies for limiting CO2 emissions over a twenty-year forecast horizon. Using network reductions for each of the three electric system regions in the U.S. And Canada, an optimal economic dispatch, that satisfies reliability criteria, is assigned for 12 typical hour-types in each year. Three scenarios are modeled that consider subsidies for renewables and either CO2 emissions regulation on new investment or cap-and-trade. High and low gas price trends are also simulated and have large effects on prices of electricity but small impacts on CO2 emissions. Low gas prices with cap-and-trade reduce CO2 emissions the most, large subsidies for renewables alone do not reduce carbon emissions much below existing levels. Extensive retirement of coal-fired power plants occurs in all cases.

In order to demonstrate and test new concepts and methods for the future grids, power engineers and researchers need appropriate randomly generated grid network topologies for Monte Carlo experiments. If the random networks are truly representative and if the concepts or methods test well in this environment they would test well on any instance of such a network as the IEEE model systems or other existing grid models. Our previous work [1] proposed a random topology power grid model, called RT-nested-small world, based on the findings from a comprehensive study of the topology and electrical properties of a number of realistic grids. The proposed model can be utilized to generate a large number of power grid test cases with scalable network size featuring the same small-world topology and electrical characteristics found from realistic power grids. On the other hand, we know that dynamics of a grid not only depend on its electrical topology but also on the generation and load settings, and the latter closely relates with an accurate bus type assignment of the grid. Generally speaking, the buses in a power grid test case can be divided into three categories: the generation buses (G), the load buses (L), and the connection buses (C). In [1] our proposed model simply adopts random assignment of bus types in a resulting grid topology, according to the three bus types' ratios. In this paper we examined the correlation between the three bus types of G/L/C and some network topology metrics such as node degree distribution and clustering coefficient. We also investigated the impacts of different bus type assignments on the grid vulnerability to cascading failures using IEEE 300 bus system as an example. We found that (a) the node degree distribution and clustering characteristic are different for different type of buses (G/L/C) in a realistic grid, (b) the changes in bus type assignment in a grid may cause big differences in system dynamics, and (c) the random assignment of bus types in a random topology power grid model should be improved by using a more accurate assignment which is consistent with that of realistic grids.

A dynamic data attack on a power system aimed at making the real time economic dispatch infeasible is considered. As a man-in-the-middle attack, the attack modifies part of sensor measurements such that the control center is misled with an incorrect system state estimate, which affects the computation of real time economic dispatch. Two attack mechanisms are considered. The first is an opportunistic approach where the attacker waits for a chance of a successful attack and launches an attack in a single state estimation period. The second is a dynamic attack strategy where the attacker gradually drifts the system state toward the infeasible region for real time economic dispatch. The efficacy of the proposed attacks is demonstrated by numerical experiments with the IEEE 14-bus network.

The problem of forecasting the real-time locational marginal price (LMP) by a system operator is considered. A new probabilistic forecasting framework is developed based on a time in-homogeneous Markov chain representation of the realtime LMP calculation. By incorporating real-time measurements and forecasts, the proposed forecasting algorithm generates the posterior probability distribution of future locational marginal prices with forecast horizons of 6–8 hours. Such a short-term forecast provides actionable information for market participants and system operators. A Monte Carlo technique is used to estimate the posterior transition probabilities of the Markov chain, and the real-time LMP forecast is computed by the product of the estimated transition matrices. The proposed forecasting algorithm is tested on the PJM 5-bus system. Simulations show marked improvements over benchmark techniques.

A data framing attack is presented to exploit the bad data detection and identification mechanisms at a typical ISO/RTO control center. In particular, the proposed attack frames normal meters as sources of bad data and causes the control center to remove useful measurements from the framed meters. The proposed attack uses subspace information of power system measurements; neither the network topology nor the network parameters are required for constructing the attack. It is shown that the proposed attack is capable of perturbing the power system state estimate by an arbitrary degree using only half of the critical measurements. Implications of this attack on power system operations are discussed, and the attack performance is evaluated using benchmark systems.

In this paper we discuss the state-of-the-art in storage technology development as well as the use of storage in a wide array of utility and customer applications. We also discuss the value and use of these various technologies, and how they might influence market design and structure should they become technically and economically viable for use in electric power systems.

This track seeks to explore methods at the frontier of understanding the next generation electric energy system. It focuses on topics in smarter electric grids, engineering, economics and resiliency that are at the forefront of current research, development, and demonstration. The track contains four minitracks each having two sessions. The first minitrack is on electric power system monitoring and control. This minitrack focuses on topics related to the ability of complex systems such as power systems to survive disturbances with minimal impact on performance. One session in this minitrack focuses on infrastructure and architecture issues associated the electric power grid. This includes papers on large-scale deployments of electric grid monitoring, new control system designs, proposed changes to the electric grid protection system and renewable generation forecasting. The other session considers aspects of advanced real-time monitoring. Much of the session focuses on the application of phasor measurement units (PMUs), which can be used to provide realtime measurements of power system quantities with data samples of up to 30 times per second. Other papers look at dynamic system simulations and load control. The second minitrack has two sessions dealing with topics related engineering and economic interactions within the electric grid. The first session explores issues related to the modeling of interconnected networks with the electric grid. One such issue is the planning of the electric grid with various environmental constraints. Other issues considered include electric vehicles and the adequacy of the grid. The second session focuses on the design and operation of electric markets. This includes consideration of the impact of wind, electric vehicles and the consumers themselves. The third minitrack focuses on topics associated with the emerging electric infrastructures needed to accommodate a growing number of distributed and renewable electric resources. The minitrack has two sessions with the first looking into the integration of renewable resources into the existing electric grid. Papers in this session primarily consider various aspects associated with wind energy. The second session deals with the modeling and control of smaller resources such as PV and the loads. The last minitrack explores topics related to advanced concepts to enhance the resilience of future electric power infrastructure. Advanced technologies will require advanced methods for understanding how they can be incorporated into increasingly complex and dynamic infrastructure. The first session looks at electric power network modeling and design issues. Papers here include cascading failure analysis, estimating transmission network parameters and the impact of storage. The final session is on power grid computational issues. Papers discuss computational challenges in power grid operations, using transmission system switching for improved grid economics and wind forecasting for improved generation dispatch. 2013 46th Hawaii International Conference on System Sciences

The vehicle-to-grid (V2G) framework proposes integration of battery-powered electric drive vehicles into the grid, enabling them to be recharged as necessary and to act as suppliers in the ancillary service electricity markets. This is expected to create incentives for the production and adoption of electric vehicles in the automotive industry. V2G frameworks require that the utility company or a third party aggregator has access to each vehicle's charging status via a two-way communication network for billing and planning purposes. We establish that there exist consumer privacy risks associated with current concepts for V2G implementation and argue that consumer preferences and behaviors can be inferred from charging information if privacy is not a primary concern from the outset of V2G design. Finally, we outline a privacy-aware architecture for V2G systems.

There has been a growing literature on the malicious data attack (or data injection attack) on power systems. Most existing work focuses on the DC (linear) model with linear state estimators. This paper examines the effects of nonlinearity in the power systems on the effectiveness of malicious data attack on state estimation and real-time market. It is demonstrated that attack algorithms designed for the DC model may not be effective when they are applied to nonlinear system with nonlinear state estimators. Discussion and experiments results about nonlinearity are provided.

This track seeks to explore methods at the frontier of understanding the next generation electric energy system. It focuses on topics in smarter electric grids, engineering, economics and cyber security that are at the forefront of current research, development, and demonstration. The track contains four minitracks each having two sessions. The first minitrack is on electric power system monitoring and control. One session in this minitrack focuses on predictive and evolutionary modeling of large scale networks. This includes papers on determining the vulnerability of the transmission grid, looking at risk metrics associated with the complex grid dynamics, data representation in future grid studies, and the dynamics of electric grid frequency perturbations. The second session considers aspects of advanced real-time monitoring. Most of the session focuses on the application of phasor measurement units (PMUs), which can be used to provide real-time measurements of power system quantities with data samples of up to 30 times per second. One paper in the session looks at integrated smart grid hierarchical control. The second minitrack has two sessions dealing with topics related engineering and economic interactions within the electric grid. The first session explores issues related to economic regulation of electricity markets. Topics considered include the impact of markets on effective electricity planning, the packaging of energy and reserve bids in co-optimized electricity markets, the selling of wind energy, and the role of nodal capacity markets in insuring adequate electric resources. The second session focuses on the economic issues associated with the integration of demand (load) response and renewable resources. The third minitrack focuses on topics associated with the emerging electric infrastructures needed to accommodate a growing number of non-conventional resources such as non-controllable generation, and distributed resources. The mini-track has two sessions with the first looking into the integration of renewable resources into the existing electric grid. Papers in this session consider the impact of wind energy on generator dispatch profiles, and the integration of photovoltaic (PV) generation at the distribution level. The second session deals with the modeling and control of the electric demand. Paper topics include the consideration of thermostatically controlled loads, the measurement of aggregate demand response, and models for residential microgrids. The fourth minitrack explores electric power system cyber security and computational challenges. The first session, which focuses on cyber security and privacy, includes papers on wireless attacks of smart meters, verification and protection of confidential smart grid information, authenticcation protocols for power grid data delivery, and masking of the electric demand to protect privacy. The final session is on power grid computational issues. Papers discuss computational challenges in power grid operations, multi-rate methods for power grid analysis, the importance of bulk grid reliability data, and better power grid contingency screening methods. 2012 45th Hawaii International Conference on System Sciences

Demand Side Management (DSM) and Demand-Response (DR) programs are aimed at exploiting the intrinsic elasticity of electricity demand to make it responsive to the near term cost of supplying generation. Loads that automatically respond to incentives, are referred to as “transactive”. While it is clear that DSM and DR will be indispensable to loosen the control over the generation and decrease the reserves requirements, there is much debate on what is the right architecture for DSM and DR programs. It is possible that one solution will not fit all possible loads. In this talk, we discuss a model to digitize and aggregate smart loads requests that allows to properly account for inconvenience costs, while modulating the load demand time optimally to come as close as possible to the energy dispatch. We envision that, in addition to pricing, rewarding the tolerance to service delays can be considered as additional powerful mechanism to operate the market efficiently, especially when tapping in micro-grid green generation. The solution we propose provides a natural model for Vehicles to Grid service (V2G), that can be easily extended to a large class of transactive loads.

Conventional power systems have been planned with dispatch able generation using deterministic planning and operating methods and tools since the inception of the industry, with little exception. It now appears that the future power system will need to successfully integrate a high share of non-dispatch able renewable energy whose production exhibits much greater variability and uncertainty than that of conventional energy sources. The need for long distance, high capacity transmission is often greater for integrating the best renewable resources than it is for more conventional resources. Current market structures were not designed with the needs of variable generation in mind, nor were the existing wind forecasting tools. Computational capability is currently limited in its ability to handle the requirements of the new tools and methods required. This paper examines the changes in planning and operating methods and tools necessary to successfully integrate a large share of VG into the power system, and identifies the associated research requirements.

Impacts of malicious data data attack on the real-time electricity market are studied. It is assumed that an adversary has access to a limitted number of meters and has the ability to construct data attack based on what it observes. Different observation models are considered. A geometric framework is introduced based on which upper and lower bounds on the optimal data attack are obtained and evaluated in simulations.

The electric power grid is a complex critical infrastructure network. Its inter-connectivity enables long-distance transmission of power for more efficient system operation. The same inter-connectivity, however, also allows the propagation of disturbances. In fact, blackouts due to cascading failures occur because of the intrinsic electrical properties of this propagation and physical mechanisms that are triggered by it. In this paper we propose a stochastic Markov model, whose transition probabilities are derived from a stochastic model for the flow redistribution, that can potentially capture the progression of cascading failures and its time span. We suggest a metric that should be monitored to expose the risk of failure and the time margin that is left to perform corrective action. Finally we experiment with the proposed stochastic model on the IEEE 300 bus system and provide numerical analysis.

In this paper, we outline a specific communication framework to support a novel demand management program on the edge of the power distribution network. We suggest a model where the arrival process of the smart appliances is made visible to control centers, forming a cellular Microgrid infrastructure, through Home Energy Management Systems (HEMS). These appliances then wait to receive an authorization message before starting to function. The cell control center uses this information to choose an optimal departure process that serves the waiting appliances while minimizing its operational costs. The described information exchange strategy gives the cell the ability to better match the load to the available green energy supply and its day ahead energy bid. We show that this model will allow to increase the integration of intermittent resources in the power grid, with modest communication rate requirements.1

Malicious data attacks to the real-time electricity market are studied. In particular, an adversary launches an attack by manipulating data from a set of meters with the goal of influencing revenues of a real-time market. The adversary must deal with the tradeoff between avoiding being detected by the control center and making maximum profit from the real time market. Optimal attacking strategy is obtained through an optimization of a quasi-concave objective function. It is shown that the probability of detection of optimal attack will always be less than 0.5. Attack performance is evaluated using simulations on the IEEE 14-bus system.

Electrical Vehicles are gaining increasing attention, due to the opportunities and challenges they present for the energy market. On the one hand, they will allow to drastically reduce the need for oil; on the other hand they may require a significant shift in the day to day management of the electricity generation. This paper is concerned with finding appropriate models for residential load in light of a widespread penetration of electric vehicles. The analysis is aimed at finding a SmartGrid solution that would enable us to optimize the generation dispatch in real time and allow to plug cars in any SmartGrid enabled plug. The key idea is to discriminate between regular load and the load due to the EVs, gathering in real time aggregate information about the sensed EV arrivals and their associated charging times in a demand matrix, that can be readily used to optimize the dispatch, while updating without real time constraints the billing record for the EV.

Renewable energy sources (RES) are very likely to continue the upward capacity trend witnessed in the past years. The reasons for adoption are varied and respond to both market pressures, influence of government intervention and a raised awareness of the consequences on the environment of the current generation fleet. The change from dispatch able generation to an environment in which Independent System Operators (ISO's), Regional Transmission Operators (RTO's) and consumers, among others, accommodate the demand to the available generation, requires fundamental changes in the way the system is managed. Also, to better harness the energy from renewable sources, both new methods and technologies need to be adopted, counteracting for the sometimes unpredictable behavior of these sources. This study proposes a method with multi-period optimization to help prescribe the optimal placement and usage of RES and Energy Storage Systems (ESS) with full information of the system. Four cases are analyzed in their dispatches, as well as the benefits to the participants in the wholesale market, for a reduced 30-bus network. While the data requirements are high, and the use of a reduced system limits the applications herein proposed, the policy implications from the results obtained provide useful insights into an ongoing debate regarding on how to direct investment in the electrical system.

We explore the privacy issues implicated by the development of demand response systems. We begin by highlighting the invasive nature of fine-granularity power consumption data, showing that the data collected by Advanced Metering Infrastructure (AMI) reveals detailed information about behavior within the home. We then show how privacy-aware design principles lead to novel system architectures that realize the benefits of demand response without requiring that AMI data be centrally collected. The resulting systems avoid both harm to subscribers and the potential need to scrap AMI-based demand response efforts in the face of public outcry. We also show that Trusted Platform Modules can be used to develop privacy-sensitive metering infrastructure.

The Cornell SuperOPF is used to illustrate how the system costs can be determined for a reliable network (the amount of conventional generating capacity needed to maintain System Adequacy is determined endogenously). Eight cases are studied to illustrate the effects of geographical distribution, ramping costs and load response to customers payment in the wholesale market, and the amount of potential wind generation that is dispatched. The proposed regulatory changes for electricity markets are 1) to establish a new market for ramping services, 2) to aggregate the loads of customers on a distribution network so that they can be represented as a single wholesale customer on the bulk-power transmission network and 3) to make use of controllable load to mitigate the variability of wind generation as an alternative to upgrading the capacity of the transmission network. The cost of ramping reduces the amount of potential wind generation that is dispatched because of the inherent variability of wind speeds. The analysis evaluates whether the ability to dispatch some load that is not time-sensitive, such as charging the batteries in electric vehicles over night above the minimum usage requirement, can be an effective way to use more of the potential wind generation without upgrading the transfer capacity of a transmission network. The expectation is that more wind generation can be dispatched at times when load is relatively low and congestion on the network is not a major limitation.

Centrality measures are used in network science to rank the relative importance of nodes and edges of a graph. Here we define new measures of centrality for power grid structure that are based on its functionality. We show that the relative importance analysis based on centrality in graph theory can be generalized to power grid network with its electrical parameters taken into account. In the paper we experiment with the proposed electrical centrality measures on the NYISO-2935 system and the IEEE 300-bus system. We analyze the centrality distribution in order to identify important nodes or branches in the system which are of essential importance in terms of system vulnerability. We also present and discuss a number of interesting discoveries regarding the importance rank of power grid nodes and branches.

The problem of constructing malicious data attack of smart grid state estimation is considered together with countermeasures that detect the presence of such attacks. For the adversary, using a graph theoretic approach, an efficient algorithm with polynomial-time complexity is obtained to find the minimum size unobservable malicious data attacks. When the unobservable attack does not exist due to restrictions of meter access, attacks are constructed to minimize the residue energy of attack while guaranteeing a certain level of increase of mean square error. For the control center, a computationally efficient algorithm is derived to detect and localize attacks using the generalized likelihood ratio test regularized by an L_1 norm penalty on the strength of attack.

In this paper we apply the Singular Value Decomposition (SVD) analysis to examining the coupling structure of an electrical power grid in order to highlight opportunities for reducing the network traffic, by identifying what are the salient data that need to be communicated between parts of the infrastructure to apply a control action. Our main finding is that typical grid admittance matrices have singular values and vectors with only a small number of strong components. The SVD sparsity can be exploited to construct an efficient decentralized system-wide monitoring and control architecture. We also discuss the potential applications of the proposed architecture and its robustness under contingency; and experiment the SVD analysis with the NYISO-2935 system and the IEEE-300 system.

In this paper we numerically study the topology robustness of power grids under random and selective node breakdowns, and analytically estimate the critical node-removal thresholds to disintegrate a system, based on the available US power grid data. We also present an analysis on the node degree distribution in power grids because it closely relates with the topology robustness. It is found that the node degree in a power grid can be well fitted by a mixture distribution coming from the sum of a truncated Geometric random variable and an irregular Discrete random variable. With the findings we obtain better estimates of the threshold under selective node breakdowns which predict the numerical thresholds more correctly.

Malicious attacks against power system state estimation are considered. It has been recently observed that if an adversary is able to manipulate the measurements taken at several meters in a power system, it can sometimes change the state estimate at the control center in a way that will never be detected by classical bad data detectors. However, in cases when the adversary is not able to perform this attack, it was not clear what attacks might look like. An easily computable heuristic is developed to find bad adversarial attacks in all cases. This heuristic recovers the undetectable attacks, but it will also find the most damaging attack in all cases. In addition, a Bayesian formulation of the bad data problem is introduced, which captures the prior information that a control center has about the likely state of the power system. This formulation softens the impact of undetectable attacks. Finally, a new L ∞ norm detector is introduced, and it is demonstrated that it outperforms more standard L 2 norm based detectors by taking advantage of the inherent sparsity of the false data injection.

Earlier research has shown that adding wind capacity to a network can lower the total annual operating cost of meeting a given pattern of loads by displacing conventional generation. At the same time, the variability of wind generation and the need for higher levels of reserve generating capacity to maintain reliability standards impose additional costs on the system that should not be ignored. The important implication for regulators is that the capacity cost of each MW of peak system load is now much higher. Hence, the economic benefits to a network of using storage and controllable load to reduce the peak system load will be higher with high penetrations of wind generation. These potential benefits will be illustrated in a case study using a test network and the SuperOPF. An important feature of the SuperOPF is that the amount of conventional generating capacity needed to maintain Operating Reliability is determined endogenously, and as a result, it is possible to determine the net social benefits of relying more on an intermittent source of generation, such as wind capacity, that lowers operating costs but increases the cost of maintaining System Adequacy. The capabilities of the SuperOPF provide a consistent economic framework for evaluating Operating Reliability in real-time markets and System Adequacy for planning purposes. Basically, a financially viable investment requires that the reductions in the total annual costs of the existing system should be larger than the annualized cost of financing the addition of, for example, wind generation to a network. The scenarios considered make it possible to determine 1) the amount of conventional generating capacity needed to meet the peak system load and maintain System Adequacy, 2) the amount of missing money paid to generators to maintain Financial Adequacy, 3) changes in the congestion rents for transmission that are collected by the system operator, and finally, 4) the total annual system costs paid by customers directly in the Wholesale Market and, indirectly, as missing money. The results show that the benefits (i.e. the reduction in the total annual system costs) from making an investment in wind capacity and/of upgrading a tie line are very sensitive to 1) how much of the inherent variability of wind generation has to be accommodated on the network, and 2) how the missing money paid to conventional generators is determined (e.g. comparing a regulated market and a deregulated market).

This paper describes the optimal power flow (OPF) architecture implemented in MATPOWER, an open-source Mat-lab power system simulation package. It utilizes an extensible architecture that allows the user to easily add new variables, constraints and costs to the standard OPF problem formulation while preserving the structure needed to use pre-compiled solvers. A software object is used to encapsulate the definition of the problem formulation, manage the corresponding named sets of variables, constraints and costs, and handle all of the tedious index maintenance tasks. The software design has the advantage of minimizing the coupling between variables, constraints and costs, making it possible, for example, to add variables to an existing model without having to explicitly modify existing constraints or costs to accommodate them. The example of adding joint co-optimization of reserves to the OPF, based on fixed zonal reserve requirements, is used to illustrate the capabilities of MATPOWER's extensible OPF architecture.

A massively parallel finite difference time domain method was used to compute scattering of a focused optical beam by multiple heterogeneous biological cells. A perfectly matched layer boundary condition and the scattered-field-only method were utilized in the simulation to increase accuracy and computational efficiency. A fifth-order approximation to the focused Gaussian beam was used for the incident field. A parametric study was performed to determine scattering effects of varying cellular fine structure, such as nuclear refractive index, organelle volume density, cellular shape and the cell membrane on the point spread function (PSF) of the beam. It was found that two-photon PSF is largely unaffected by increasing numbers of scatterers within cells, while two-photon excitation signal strength is dependent on both beam focal depth and the density of scatterers in tissue.

This paper presents our study in designing a 700 acre low-energy community on the Island of Hawaii. This study was an interdisciplinary collaboration among engineering, architecture, landscape architecture and business management. We took an integrated approach, which encompasses reducing energy demand, optimizing on-site renewable energy generation, implementing an efficient energy distribution system as well as effective energy management, and conserving natural resources. Technologies and their system integration were modeled and analyzed including solar photovoltaics, battery and compressed air energy storage, plug-in hybrid electric vehicles, demand response, microgrid, energy aggregator, passive and deep source cooling, microclimate and alternative landscaping. Various business models were developed. We showed that, with careful planning, it is feasible for a low energy or net zero energy community to become environmentally friendly and economically profitable at the same time. This community will benefit its residents, developers, investors, the utility company and the rest of the world.

Security constrained optimal power flow programs are important tools for ensuring correct dispatch of supply while respecting the many constraints imposed by the delivery system. In addition to getting the dispatch right, locational prices must be calculated with equal precision in order to infuse market participants with the proper incentives for operation and investment. In this paper we discuss a co-optimization framework in which contingencies, ancillary services, and network constraints are correctly accounted for in determining both dispatch and price.

Raman microspectroscopy is used for the first time to study chemical and structural transformations in RPE cells exposed to cw and short-pulsed high-intensity light irradiation.

Femtosecond pulses derived from a Ti:sapphire oscillator are used to locally modify collagen-rich tissue. Raman and fluorescence microspectroscopy are employed for in situ observations of structural transformations of collagen matrix.

Simulation based on standard models is often used as part of the engineering design process to test theories and exercise new concepts before actually placing them into operation. In order to tackle the problem of likely widespread catastrophic failures of electric power grids, an autonomously reconfigurable power system will have to rely on wide-area communication systems, networked sensors, and restorative strategies for monitoring and control. Standard practice is to use simulation of a small number of certain historical test systems to test the efficacy of any proposed design. We believe this practice has shortcomings when examining new communication system ideas. In this paper we develop the means for producing power grids with scalable size and randomly generated topologies. These ensembles of networks can then be used as a statistical tool to study the scale of communication needs and the performance of the combined electric power control and communication networks. The topological and system features of the randomly generated power grids are compared with those of standard power system test models as a "sanity check" on the method.

This paper proposes a method for choosing the best composite power system expansion plan considering an annual outage cost assessment. The proposed method minimizes the investment budget (economics) for constructing new generating units and transmission lines as well as the annual outage cost subject to deterministic (demand constraint) and probabilistic reliability criterion (LOLER), while considering the uncertainties of system elements. It models the power system expansion problem as an integer programming one. The method solves for the optimal strategy using a probabilistic theory-based branch and bound method that utilizes a network flow approach and the maximum flow-minimum cut set theorem. The proposed method is applied to a 5-bus MRBTS, the test results demonstrate that the proposed method is suitable for solving the composite power system expansion planning problem subject to practical future uncertainties.

This paper proposes three methods for choosing the transmission system expansion plan considering three reliability constraints, which are deterministic reliability criterion, probabilistic reliability criterion and security criterion based on N-alpha contingency in order to give more successful market operation. The proposed method minimizes total investment cost. It models the transmission system expansion problem as an integer programming one. The method solves for the optimal strategy using a branch and bound method that utilizes a network flow approach and the maximum flow-minimum cut set theorem. The 21 bus system case study results demonstrate that the proposed method is practical for solving the power system expansion planning problem subject to practical future uncertainties.

This paper proposes a method for choosing the best composite power system expansion plan considering a outage cost assessment. The proposed method minimizes the investment budget (economics) for constructing new generating units and transmission lines as well as outage cost subject to deterministic (demand constraint) and probabilistic reliability criterion (LOLER), while considering the uncertainties of system elements. It models the power system expansion problem as an integer programming one. The method solves for the optimal strategy using a probabilistic theory-based branch and bound method that utilizes a network flow approach and the maximum flow- minimum cut set theorem. Although the proposed method is applied to a simple sample study, the test results demonstrate that the proposed method is suitable for solving the composite power system expansion planning problem subject to practical future uncertainties.

The exercise of market power is a major concern in deregulating the electric energy business in the U.S. Exploitation of market power can destroy any market efficiencies and award unwarranted profits based solely on the lack of competition rather than on economic merit. There have been several attempts to quantify market power with much of the work focused separately on either real power or reactive power. This paper addresses a new possibility for a market participant to exploit an opportunity to increase revenue by gaming both real and reactive markets.

This paper introduces a multiple-access coding technique that is tailored to solve average consensus problems efficiently in wireless networks. We propose a novel data driven architecture which grants channel access to nodes based on their local data values. We analyze the performance of the scheme in the presence of quantization errors and noise. We show that our scheme is unbiased with respect to quantized consensus algorithms, it achieves good MSE performance, and it can be configured to provide a speedup in the convergence rate. The amount of speedup achieved is a function of |Qk| which indicates the number of quantization bins used to represent the state variables exchanged during the computation.

This track seeks to explore methods at the frontier of understanding the restructuring of electric power system worldwide. It will focus on engineering, economics and policy issues that are at the forefront of current thinking.

Reliable and efficient operation of power networks is of paramount importance. In this paper we explore communication architectures that leverage phasor measurement units (PMUs) and fusion centers to enable robust real-time monitoring of power networks over wide areas. We examine scalability issues in data aggregation for a power network in which PMUs are attached to every bus. We argue that in power networks, the data recorded by the PMUs are spatially correlated and therefore the data admit a sparse representation in a given basis. Furthermore, the number of bits required to reconstruct the PMU data to within a given accuracy at a remote location grows sublinearly with the density of the power network. Our results are: i) the PMUs should not transmit their data to the fusion center independently or asynchronously - an intermediate data aggregation step is beneficial, and ii) if we perform data aggregation then we can add more PMUs to the network to achieve finer network monitoring without causing network congestion

The deregulated electricity market calls for robust OPF tools that can provide deterministic convergence, accurate computation of a variety of nodal prices, support of continuous costs as well as discrete bids and offers, full active and reactive power flow modeling of large-scale systems, and satisfactory worst-case performance that meets the real-time dispatching requirement. For historical reasons, most prior research on OPF has focused on performance issues, without much treatment of requirements. This paper discusses these new challenges and presents two new algorithms for reliable computation of large-scale market-based OPF: trust-region based augmented Lagrangian method (TRALM) and step-controlled primal-dual interior point method (SCIPM). The former is more theoretically rigid while the latter is more effective in practice. The new algorithms, along with several existing ones, are tested and compared using large-scale power system models

This paper proposes a method for choosing the best composite power system expansion plan considering a probabilistic reliability criterion. The proposed method minimizes the investment budget (economics) for constructing new transmission lines subject to deterministic (demand constraint) and probabilistic reliability criteria (LOLER), while considering the uncertainties of system elements. It models the power system expansion problem as an integer programming one. The method solves for the optimal strategy using a probabilistic theory-based branch and bound method that utilizes a network flow approach and the maximum flow-minimum cut set theorem. Although the proposed method is applied to a simple sample study, the test results demonstrate that the proposed method is suitable for solving the power system expansion planning problem subject to practical future uncertainties