This paper presents the behavior of bidding above marginal cost, called economic withholding, for price-maker generating companies under wind uncertainty. Economic withholding prevents some lower cost resources from supplying the demand, and compels the ISO to buy more costly resources to meet the load, therefore result in higher market clearing prices than what the competitive outcome would have been. We formulate the underlying economic withholding problem under uncertainty as a robust bilevel optimization problem, where the generating company makes a robust decision in the presence of continuous uncertain variables in order to maximize its overall profit and guarantee the revenue security in the worst-case scenario. This formulation provides an alternative distribution-free equilibrium concept. A practical solution methodology is proposed to drive the exact reformulation of the robust bilevel optimization resulted in Mixed Integer Linear Programming (MILP) problem which is scalable and can be easily implemented in practical portfolio optimization and operation. Simulation results confirm the effectiveness of the proposed framework to provide optimum economic withholding decision.

The aim of this study was to develop an intelligent, glucose-responsive hydrogel composed of methylated N-(4-N,N-dimethylaminobenzyl) chitosan as a quaternized and aromatized derivative of chitosan. In this study, a cross-linked hydrogel, incorporating boric acid (BA), was prepared from the synthesized chitosan derivative. The preparation of hydrogel was statistically optimized using box-behnken response surface methodology. The chemical structure and morphology of the optimized hydrogel were examined using FT-IR spectroscopy and scanning electron microscopy, respectively. Furthermore, the glucose-responsive behavior of the prepared hdyrogel was evaluated by in vitro release studies. Finally, the efficacy of the prepared hydrogel in glucose-responsive insulin delivery was studied in vivo by implementation of the hydrogels in male wistar rats and their blood glucose levels were evaluated in the pre-determined time intervals. The optimized hydrogel with proper characteristics was prepared. Following 480 min of incubation, the in vitro release study demonstrated 26.1±1.84%, 52.7±3.11% and 78.9±3.85% of cumulative insulin release in glucose-free-, glucose 3%- and glucose 5%- phosphate buffer, respectively. Finally, the obtained in vivo results demonstrated 1.5-fold reduction in blood glucose level in diabetic animals in comparison with non-diabetic ones.

Wind turbine interference - the reduction in output power of a turbine downwind of any others - is a major problem for wind farm optimization and control. With interference, it is well-known for specific cases that co-operative optimization of power output yields more power than does the selfish optimization of individual turbines. This paper develops explicit solutions for simple models of interference for the general case of any number of turbines in line with the wind. For the simplest case of no wake recovery, analytical solutions for co-operative and selfish optimization are derived. They show that co-operative optimization nearly always yields more power and never yields less. Adding a simple form of wake recovery for equally spaced turbines precludes analytical solutions, but numerical solutions are developed to find the power to any required level of accuracy. Again, co-operative optimization is superior in nearly all cases. These simple solutions should be useful for demonstrating the importance of interference and for testing methods for optimizing wind farm layout and operation. It is shown that the maximum benefit from co-operative operation occurs at turbine spacing comparable to that commonly used in wind farms. The analysis is then extended to include topographical effects modeled as changes in wind speed along the line. Explicit solutions are obtained for two turbines in line. Co-operative optimization remains the best strategy. In particular, when the wind speed increases, it quickly becomes optimal to shut down the first turbine.

In this paper, we propose a dynamic market mechanism that converges to the desired market equilibrium. Both locational marginal prices and the schedules for generation and consumption are determined through a negotiation process between the key market players. In addition to incorporating renewables, this mechanism accommodates both consumers with a shiftable Demand Response and an adjustable Demand Response. The overall market mechanism is evaluated in a Day Ahead Market and is shown in a numerical example to result in a reduction of the cost of electricity for the consumer, as well as an increase in the Social Welfare.

In this paper, a dynamic model of the wholesale energy market that captures the effect of uncertainties of renewable energy sources and real-time pricing with demand response is derived. Beginning with a framework that includes real-time pricing as an underlying state, an attempt is made in this model to capture the dynamic interactions between generation, demand, locational marginal price (LMP), and congestion price near the equilibrium of the optimal dispatch. Conditions under which stability of the market can be guaranteed are derived. Modeling the effect of renewable energy resources (RERs) and demand response as perturbations, robust stability of the energy market model in the presence of such perturbations is discussed. Numerical studies of an IEEE 30-bus are reported to illustrate the effect of transmission lines constraints on the wholesale market stability in the presence of wind power.

In this paper, we propose a hierarchical transactive control architecture that combines market transactions at the higher levels with inter-area and unit-level control at the lower levels. A model of the overall grid is introduced, with dynamics at primary, secondary, and tertiary levels. With a goal of ensuring frequency regulation using optimal allocation of resources in the presence of uncertainties in renewables and load, a hierarchical control methodology is presented.

The increasing demand for electricity and the emergence of smart grids have presented new opportunities for residential energy management systems (REMS) in demand response market. Several techniques are available for optimizing the operation schedules and decisions of REMS. However, it can be challenging for REMS to capture sufficient resolution and horizon to make good short-term and long-term decisions, respectively, under limited computing resources. We propose a two-horizon algorithm, which can achieve high resolution schedules with reasonable value of energy services while limiting computation time. Simulation results are provided to confirm the validity of the proposed approach.

The notion of observability, is a measure of how well internal states of a system can be reconstructed using a given set of measurements. In this paper, we derive necessary and sufficient conditions for observability in a power system. Deriving sufficient conditions for observability is quite difficult and algebraic observability is often used as a surrogate tool for observability. We show that algebraic observability is necessary but not sufficient for observability. It is also shown that standard measurement sets of at least one voltage measurement, and paired active and reactive power measurements may lead to unobservability for certain measurement configurations. Using a nonlinear transformation and properties of graph theory, a set of sufficient conditions are derived for observability. These conditions are shown to be dependent on the topological properties as well as the type of available measurements. The efficiency and robustness of the proposed approach is also discussed. All results are validated using an IEEE-14 bus system. The proposed method can be utilized off-line as a planning tool during the initial stages of measurement system design as well as on-line prior to state estimation.

The main foundations of the emerging Smart Grid are (1) Distributed Energy Resources (DER) enabled primarily by intermittent, nondispatchable renewable energy sources such as wind and solar, and independent microgrids and (2) Demand Response (DR), the concept of controlling loads via cyber-based communication and control and economic signals. While smart grid communication technologies offer dynamic information provide real-time signals to utilities, they inevitably introduce delays in the energy real-time market. In this article, a dynamic, discrete-time model of the wholesale energy market that captures these interactions is derived. Beginning with a framework that includes optimal power flow and real-time pricing, this model is shown to capture the dynamic interactions between generation, demand, and locational marginal price near the equilibrium of the optimal dispatch. It is shown that the resulting dynamic real-time market has stability properties that are dependent on the delay due to the measurement and communication. Numerical studies are reported to illustrate the dynamic model, and a suitable communication topology is suggested.

The Smart Grid paradigm is influenced primarily by the need to integrate renewable energy from wind and solar resources. Two main tools that have been proposed to carry out integration are (i) decision and control that makes use of all available information via a cyber-physical infrastructure that includes communication, and computation, (ii) Demand Response (DR), the concept of controlling loads using smart meters and devices as well as economic signals. Given that the pertinent information is available at multiple time-scales and from multiple sources, decision and control algorithms need to necessarily have a distributed, hierarchical structure. In this paper, we propose a distributed cyber-physical control architecture to match energy supply to energy load at the sub-transmission and distribution levels. A hierarchical model of the overall cyber-physical energy system is introduced, and includes the dynamics of the grid at the primary, secondary, and tertiary levels. With a goal of ensuring frequency regulation using optimal allocation of resources including renewable energy resources (RER), a distributed control methodology is presented and numerically evaluated in the presence of intermittency in the RERs.

The design and analysis of oscillator networks raise numbers of fundamental questions in systems and control. The stability and robustness of oscillator networks are the most significant challenges that must be addressed in control and communication design of such networks. In this paper we investigate the problem of synchronization in an oscillator network modeled by a non-uniform Kuramoto model. Conditions under which the network is synchronized, are studied and the procedure for designing distributed controllers, where information exchange between controllers occurs through communication network, is proposed. The guideline for designing communication topology for the overall distributed controllers is presented. Numerical studies are reported to validate the theoretical results and performance of distributed controllers.

The recent paradigm shift in the architecture of a smart grid is driven by the need to integrate renewable energy sources, the availability of information via advanced metering and communication, and an emerging policy of a demand structure that is intertwined with pricing. By using smart grid communication technologies that offer dynamic information, the ability to use electricity more efficiently and provide real-time information to utilities is expected to be significantly improved. The introduction of both renewable energy sources as well as efforts to integrate them through an information processing layer brings in dynamic interactions between the major components of a smart grid. In this paper, a dynamic model of the wholesale energy market due to the network constraints is derived. This dynamic model is fundamentally linked to one of the central features of the energy market, of optimal power flow. Beginning with a framework that includes real-time pricing, an attempt is made in this model to capture the dynamic interactions between generation, demand, locational marginal price, and congestion price near the equilibrium of the optimal dispatch. Conditions under which stability of the market can be guaranteed are derived. Numerical studies are reported to illustrate the dynamic model, and its stability properties.

One of the main challenges in the emerging smart grid is the integration of renewable energy resources (RER). The latter introduces both intermittency and uncertainty into the grid, both of which can affect the underlying energy market. An interesting concept that is being explored for mitigating the integration cost of RERs is Demand Response. Implemented as a time-varying retail electricity price in real-time, Demand Response has a direct impact on the underlying energy market as well. In this paper, beginning with an overall model of the major market participants together with the constraints of transmission and generation, the energy market is analyzed in the presence of both RERs and Demand Response. The effect of uncertainties in the RER on the market equilibrium is quantified, with and without real-time pricing. Standard KKT criteria are used to derive optimality conditions. Perturbation analysis methods are used to compare the equilibria in the nominal and perturbed cases. Sufficient conditions are derived for the existence of a unique equilibrium for the perturbed market. Numerical studies are reported using a 4-node IEEE bus to validate the theoretical results.

The efficiency, safety, and reliability of the electricity transmission and distribution system of a power grid is expected to be significantly improved via a cyber-enabled energy management. By using smart grid communication technologies that offer dynamic information, the ability to use electricity more efficiently and provide real-time information to utilities is expected to be significantly improved. The specific metering infrastructure that we study in this paper is a smart meter located in suitable places in a power grid offering two way communication regarding various data. In particular, we study the effect of a smart meter with real time monitoring of consumption on energy imbalance, congestion due to constraints on transmission capacity, and the overall stability of the dynamic power market. A market with an elastic consumer is used to evaluate the results. Numerical studies of an IEEE 30-bus are reported to illustrate the overall impact of a smart meter.

In recent years chaotic secure communication and chaos synchronization have received ever increasing attention. In this paper, for the first time, a fractional chaotic communication method using an extended fractional Kalman filter is presented. The chaotic synchronization is implemented by the EFKF design in the presence of channel additive noise and processing noise. Encoding chaotic communication achieves a satisfactory, typical secure communication scheme. In the proposed system, security is enhanced based on spreading the signal in frequency and encrypting it in time domain. In this paper, the main advantages of using fractional order systems, increasing nonlinearity and spreading the power spectrum are highlighted. To illustrate the effectiveness of the proposed scheme, a numerical example based on the fractional Lorenz dynamical system is presented and the results are compared to the integer Lorenz system.

This paper deals with the novel fractional order Linear Quadratic Gaussian (FLQG) controller and study of the robustness of this proposed controller in comparison with classical LQG. The significance of fractional order control is that it is a generalization and interpolation of the classical integer order control theory, which can achieve more adequate modeling and clear-cut design of robust control system. In this paper, LQR controller with fractional derivatives and Fractional Kalman filters are proposed. In addition fractional LQG is used to control the aircraft system. To demonstrate the enhancement in using fractional LQG, robustness of the control design is compared with the integer order LQG in the presence of coprime factor uncertainty. Simulations confirm much more robustness of the fractional order LQG than classical LQG.

This paper deals with the speed control of two-inertia system by fractional order PI D lm controller design with evolutionary algorithms. Fractional controller means the order of I, D controllers will not only be integer but also can be any real number. The significance of frictional order control is that it is a generalization and "interpolation" of the classical integer order control theory, which can achieve more adequate modeling and clear-cut design of robust control system. However, most of fractional order control researches were originated and concentrated on the control of chemical processes, while in motion control the research is still in a primitive stage. In this paper, we tune the controller parameters with EA to control of the two-inertia system, which is a basic control problem in motion control. The novelty of the proposed paper is using EA to tune the Fractional PI D lm parameters and implementing this controller as a robust controller in comparison with classical PID.

This paper deals with the speed control of two-inertia system by fractional order PI D l m controller design with evolutionary algorithms. Fractional controller means the order of I, D controllers will not only be integer but also can be any real number. The significance of frictional order control is that it is a generalization and interpolation of the classical integer order control theory, which can achieve more adequate modeling and clear-cut design of robust control system. However, most of fractional order control researches were originated and concentrated on the control of chemical processes, while in motion control the research is still in a primitive stage. In this paper, we tune the controller parameters with EA to control of the two-inertia system, which is a basic control problem in motion control. The novelty of the proposed paper is using EA to tune the Fractional PI D l m parameters and implementing this controller as a robust controller in comparison with classical PID.

In this paper a novel approach for cardiac arrhythmias detection is proposed. The proposed method is based on using Independent Component Analysis (ICA) and wavelet transform to extract important features. Using the extracted features different machine learning classification schemas, MLP and RBF neural networks and K- nearest neighbor, are used to classify 274 instance signals from the MIT-BIH database. Simulations show that multilayer neural networks with Levenberg-Marquardt (LM) back propagation algorithm provide the optimal learning system. We were able to obtain 98.5% accuracy, which is an improvement in comparison with the similar works.