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This paper presents a chance-constrained scheduling (CCS) approach for variable wind generation, in the day-ahead timescale, including energy storage. The day-ahead CCS utilizes the ramping of conventional generation as well as the dispatch of energy storage to enhance the load following and ramping support capabilities, to mitigate the impact of net load ramps. The proposed CCS approach is converted...
This paper proposes a stochastic programming approach based on Conditional Value at Risk (CVaR) to measure the impact of prediction errors brought about by the renewable energy and load on DC-linked multi-microgrids coalition in the distribution network so that the system scheduling operator can achieve the lowest day-ahead scheduling cost at a certain risk. Due to the prediction errors of the renewable...
In conditions of a mass character of higher education, the problems of student identification are relevant because of the differences in the contingent of students in terms of level of training, personal and cognitive characteristics. The learning process is characterized by the presence of uncertainty factors, which requires modeling and control of the application process of methods and tools of...
This paper investigates the mean-square stabilizability of a networked discrete-time linear feedback system with multi-step random delay induced by a communication channel. It is assumed that the probability mass function (PMF) of the random delay is known. According to this model, an averaged channel transfer function and the spectral density of the channel uncertainty caused by the delay are obtained...
The traditional power grid is confronted with great challenges brought by the integration of renewable power sources (such as solar and wind) for their uncertain, volatile, and intermittent characteristics. This paper investigates the unit commitment problem with stochastic solar power integration and makes the following major contributions. First, the scheduling problem is formulated as a two-stage...
The paper proposes a novel validity evaluation method, Improved Feature Selective Validation, in order to evaluate the uncertainty analysis results in Electromagnetic Compatibility simulation. By comparing with the questionnaire results, it is shown that the proposed method can provide more accurate evaluation than a previous method called Mean Equivalent Area Method.
A plug-in electric vehicle (PEV) can be used for load shifting household demand using an optimal control strategy to minimize the overall cost of the owner. The PEV can provide initial charge, final desired charge and charging time data to the charging station when plugged in, and the information can be used in a decision-making model to charge/discharge the PEV storage unit in a cost effective manner...
A novel stochastic finite-difference time-domain (S-FDTD) technique is presented in this paper for the precise and rapid assessment of electromagnetic field statistical properties. The featured algorithm is systematically verified through detailed comparisons with the results obtained via existing, yet rather resource (CPU and memory) consuming, Monte Carlo-FDTD and other stochastic implementations.
The objective of this work is to investigate wind uncertainty within a long term generation and transmission expansion planning framework. Specifically, this work investigates uncertainty associated with wind power within a 2 stage stochastic program and quantifies its importance with the well known quantities “Value of Stochastic Solution” VSS and “Expected Value of Perfect Information” EVPI. Simulations...
This work investigates the incorporation of a risk metric based on time to collision for Adaptive Cruise Control applications on multi-lane motorways. To improve the fuel-efficiency and safety, a stochastic model predictive control approach is suggested that limits the violation probability of the imposed risk metric. For this reason, a Bayesian network is used to predict the probability distributions...
Offering strategy of a price-maker demand response aggregator (DRA) in a two-settlement market is presented in this paper. The aggregator minimizes its cost by offering energy and price bids in the day-ahead market and energy bids in the balancing market. On the other hand, DRA optimally manages the aggregated demands of a large number of electric vehicles and properly distributes them through the...
This paper presents an optimal Day-Ahead Electricity Market (DAM) bidding strategy for an aggregator leveraging a pool of residential prosumers: residential customers with local photovoltaic (PV) production and plug-in electric vehicle (PEV) charging flexibility. The aggregator's point-of-view differs from the social planner angle that is taken in the majority of the existing literature, mainly the...
This paper compares the stochastic convergence of the Uniform Random number generators of two simulation software namely Matlab and Python and establishes the significance in choosing the right random number generator for error propagation studies. It further discusses about the application of Gaussian type of these random number generators to nonlinear cases of Error propagation using the Monte Carlo...
A stochastic electromagnetic-circuit modeling workflow is presented for the computer-aided analysis of electromagnetic interference effects in multi-scale, shielded environments. The multi-scale complexity of the environment is tackled through the selective relaxation of the full electromagnetic coupling between different sub-domains when appropriate. The Stochastic Collocation technique is relied...
Based on the three-terminal interval number and the project risk theory, this paper discusses the problem of risk weighting in uncertain market decision-making, and puts forward the method of combining the grey system theory and the three-terminal interval number to determine the weight of decision-making. The best-selling weight, the unsalable weight and the normal weight are calculated by expert...
One of the important types of decision-making theory is uncertain decision-making. It is critical to express uncertain information effectively and depose them in reasonable and simple way. Firstly this paper makes a brief overview of the existing method for recording uncertainty information, such as stochastic method, fuzzy mathematics and interval method. Then the conception of grey system theory...
Software-defined cellular networks (SDCN) have been recently introduced to enable flexible cellular network design that facilitates fulfilling 5G design requirements. Placement of controllers within the SDCN plays a crucial role in optimizing its performance. In this paper, we study the controller placement problem in SDCN, considering the uncertainty in cellular user locations. Specifically, our...
In this article, we discuss the delay-dependent stochastic stability problem of nonlinear systems with Markovian jumping parameters. Provides a numerical example to verify the efficiency of this method.
This paper studies a class of fuzzy-stochastic resource-allocation (fSRA) problems which involve both subjective and objective uncertainty (i.e., fuzziness and randomness). In the FSRA, the capability of a resource to complete a task is characterized by a probability parameter which is uncertain and stochastic while the reward of a task is expressed as a fuzzy number. The FSRA problem is formulated...
This paper is concerned with the adaptive stabilization problem of high-order stochastic nonlinear systems with time-varying delay. Indeed, there exist lots of deterministic results for the system without uncertainty and time delay. However, owing to the existence of time varying delay, unknown parameters and the relaxed nonlinear terms, it is not easy to choose an appropriate Lyapunov-Krasoviskii...
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