Optimal supplementary frequency controller design using the wind farm frequency model and controller parameters stability region

In most of the existing studies, the frequency response in the variable speed wind turbines (VSWTs) is simply realized by changing the torque set-point via appropriate inputs such as frequency deviations signal. However, effective dynamics and systematic process design have not been comprehensively discussed yet. Accordingly, this paper proposes a proportional-derivative frequency controller and investigates its performance in a wind farm consisting of several VSWTs. A band-pass filter is deployed before the proposed controller to avoid responding to either steady state frequency deviations or high rate of change of frequency. To design the controller, the frequency model of the wind farm is first characterized. The proposed controller is then designed based on the obtained open loop system. The stability region associated with the controller parameters is analytically determined by decomposing the closed-loop system's characteristic polynomial into the odd and even parts. The performance of the proposed controller is evaluated through extensive simulations in MATLAB/Simulink environment in a power system comprising a high penetration of VSWTs equipped with the proposed controller. Finally, based on the obtained feasible area and appropriate objective function, the optimal values associated with the controller parameters are determined using the genetic algorithm (GA).     

Qualitative analysis of fluid dynamics and heat transfer characteristics in porous structures towards aqueous phase reforming hydrogen production

Porous structure is commonly used as a catalytic support in the field of fixed-bed reactor for hydrogen production from methanol aqueous phase reforming. The fluid dynamics and heat transfer characteristics of the fluid in a specific porous structure are considered profound influences for catalytic reactions. This study presented a qualitative investigation on the flow and heat transfer characteristics of fluids in variable porous structures. Specifically, the discrete average interpolation method was employed to obtain the value of a continuous structure. From the results, using the area-average temperature equation was 11% (±1.5%) larger and precise than that from the previous work using traditional hydrodynamic equation. The proposed variable porous structure could not only increase the turbulence intensity of the fluid which could further enhance reaction mass transfer but also enhance the convective heat transfer of the fluidic reactants. Some of these findings could inspire the reactor design for improving catalytic efficiency of hydrogen production.     

Improved CCM for variable causality detection in complex systems

Convergent cross-mapping (CCM), has been largely implemented for variable causality detection in complex systems like chemical process. However, this method is susceptible to problems regarding parameter selection and threshold determination. The synchronization phenomenon and the Moran effect, which are two interference terms in causality detection, must also be addressed. Therefore, an improved CCM is proposed to overcome these limitations in this paper. In the improved CCM, the optimal embedding dimension is selected based on the pseudo-nearest-neighbor theory. Also, Monte Carlo simulation is adopted to evaluate the convergence threshold. Next, by using the defined time delay detection function, the synchronization phenomenon and the Moran effect are identified to reduce the interference terms and further improve the accuracy. Finally, the improved CCM method is applied in a numerical example and a hydrocracking process to demonstrate its feasibility and superior performance than other methods.     

Two-echelon multi-product multi-constraint product returns inventory model with permissible delay in payments and variable lead time

Supply chain is not limited to delivering products to the end-costumers since the defective products that are returned back to the producers by the consumers. The producers should be superior knowledge to utilize the return products effectively so as to maintain our natural resources and to provide better service to customers. In this paper, a distributor and a warehouse consisting of a serviceable part and a recoverable part supply chain problem is considered in which there are several products, the distributor has limited space capacity and budget to purchase all products. In this supply chain, the defective products are returned back to the warehouse by the distributor and the warehouse recovered those defective products into perfect products having the same value as the procured products. The lead-time of receiving products from a warehouse to a distributor is a variable which is controllable by adding extra crashing cost. For each product, a fraction of the shortage is backordered and the rest are lost. A mathematical model is employed in this study for optimizing the order quantity, lead time and total number of deliveries with the objective of minimizing system total cost. We show that the model of this problem is a constrained non-linear programme and present a simple Lagrangian multiplier technique to solve it. Numerical and sensitivity analysis are given to show the applicability of the proposed model in real-world product returns inventory problems.     

Constructing General Orthogonal Fractional Factorial Split-Plot Designs

While the orthogonal design of split-plot fractional factorial experiments has received much attention already, there are still major voids in the literature. First, designs with one or more factors acting at more than two levels have not yet been considered. Second, published work on nonregular fractional factorial split-plot designs was either based only on Plackett–Burman designs, or on small nonregular designs with limited numbers of factors. In this article, we present a novel approach to designing general orthogonal fractional factorial split-plot designs. One key feature of our approach is that it can be used to construct two-level designs as well as designs involving one or more factors with more than two levels. Moreover, the approach can be used to create two-level designs that match or outperform alternative designs in the literature, and to create two-level designs that cannot be constructed using existing methodology. Our new approach involves the use of integer linear programming and mixed integer linear programming, and, for large design problems, it combines integer linear programming with variable neighborhood search. We demonstrate the usefulness of our approach by constructing two-level split-plot designs of 16–96 runs, an 81-run three-level split-plot design, and a 48-run mixed-level split-plot design. Supplementary materials for this article are available online.     

Event-triggered variable horizon Supervisory Predictive control of hybrid power plants

The supervision of a hybrid power plant, including solar panels, a gas microturbine and a storage unit operating under varying solar power profiles is considered. The Economic Supervisory Predictive controller assigns the power references to the controlled subsystems of the hybrid cell using a financial criterion. A prediction of the renewable sources power is embedded into the supervisor. Results deteriorate when the solar power is unsteady, owing to the inaccuracy of the predictions for a long-range horizon of 10 s. The receding horizon is switched between an upper and a lower value according to the amplitude of the solar power trend. Theoretical results show the relevance of horizon switching, according to a tradeoff between performance and prediction accuracy. Experimental results, obtained in a Hardware In the Loop (HIL) framework, show the relevance of the variable horizon approach. Power amplifiers allow us to simulate virtual components, such as a gas microturbine, and to blend their powers with that of real devices (storage unit, real solar panels). In this case, fuel savings, reaching 15%, obtained under unsteady operating conditions lead to a better overall performance of the hybrid cell. The overall savings obtained in the experiments amount to 12%.     

Dynamic analysis and split range control for maximization of operating range of continuous microbial fuel cell

Human development is inherently connected with availability of water and energy. Energy production requires water, whereas water treatment needs energy. On the other hand, microbial fuel cell has capability to produce energy and water simultaneously from waste water or organic matter. In this paper, first principle-based model of variable volume microbial fuel cell is simulated. Hydraulic retention time is selected as the manipulated variable using the study of steady state and dynamic responses. Classical PI and model predictive control strategies are developed for controlling the produced power from the cell, and its performance is tested for servo problem. Settling time for positive and negative set points is found to be 126 and 889 h in case of classical PI and 120 and 750 h in case of linear MPC, respectively along with large increase (three times order of magnitude) in working volume for negative set point. These control challenges are overcome by using split range controller with variable and constant volume microbial fuel cells. The settling time for negative set point is found to be 49 and 21 h for classical PI and linear MPC schemes, respectively, which is significantly lower than using only variable volume microbial fuel cell. Also, there is no increase in the working volume of the constant volume microbial fuel cell. Hence, operating range of the microbial fuel cell is enhanced using split range controller.     

Efficient recursive least squares parameter estimation algorithm for accurate nanoCMOS variable gain amplifier performances

ABSTRACT

With the aggressive scaling of integrated circuit technology, parametric estimation is a critical task for designers who looked for solutions to the challenges of some Nanoscale CMOS parameters. This paper presented the prediction of primary parameters of CMOS transistor for 16 nm to 10 nm process nodes using both of Bisquare Weights (BW) method and a novel recursive least squares (RLS) parameter estimation algorithm. The proposed RLS algorithm consists of the minimisation of a quadratic criterion relating to the prediction error in order to attain the best estimated parameters of the developed mathematical model. The obtained results thanks to the proposed RLS algorithm were better than those reached using the BW method. Comparisons between Predictive Technology Model (PTM) data and parameters estimated with RLS algorithm were made to check the validity and the consistency of the proposed algorithm. These predicted primary parameters were helpful to estimate and to optimise the performances of the Variable Gain Amplifier (VGA) which was a basic circuit element with a key role in the design of new upcoming receivers.     

The cannibalization effect of wind and solar in the California wholesale electricity market

Increasing penetration of zero marginal cost variable renewable technologies cause the decline of wholesale electricity prices due to the merit-order effect. This causes a “cannibalization effect” through which increasing renewable technologies’ penetration undermines their own value. We calculate solar and wind daily unit revenues (generation weighted electricity prices) and value factors (unit revenues divided by average electricity prices) from hourly data of the day-ahead California wholesale electricity market (CAISO) for the period January 2013 to June 2017. We then perform a time series econometric analysis to test the absolute (unit revenues) and relative (value factors) cannibalization effect of solar and wind technologies, as well as the cross-cannibalization effects between technologies. We find both absolute and relative cannibalization effect for both solar and wind, but while wind penetration reduces the value factor of solar, solar penetration increases wind value factor, at least at high penetration and low consumption levels. We explore non-linearities and also find that the cannibalization effect is stronger at low consumption and high wind/solar penetration levels. This entails that wind and (mainly) solar competitiveness could be jeopardized unless additional mitigation measures such as storage, demand management or intercontinental interconnections are taken.