Modeling and supervisory control design for a combined cycle power plant

The traditional control strategy based on PID controllers may be unsatisfactory when dealing with processes with large time delay and constraints. This paper presents a supervisory model based constrained predictive controller (MPC) for a combined cycle power plant (CCPP). First, a non-linear dynamic model of CCPP using the laws of physics was proposed. Then, the supervisory control using the linear constrained MPC method was designed to tune the performance of the PID controllers by including output constraints and manipulating the set points. This scheme showed excellent tracking and disturbance rejection results and improved performance compared with a stand-alone PID controller’s scheme

Comparing optimization schemes for solving case studies with multiple heat exchangers using high-order pinch point temperature difference methods

Heat exchangers (HEs) are often modeled using pinch point temperature difference (ΔTpinch) methods when optimizing systems with HEs. However, even small inaccuracies in model predictions of HEs will introduce numerical noise that can cause optimization algorithms to fail. A recent study of single HEs suggests that highorder interpolation methods can compute ΔTpinch much faster than conventional methods. However, the performance of such methods when optimizing HE systems have not previously been tested. Heat pumps with 2 and 3 HEs, with and without an ejector are optimized using different schemes. Results from these case studies show that non-linear constrained gradient-based optimization algorithms are more than 5 times faster than particle swarm (PS), and that the conventional genetic algorithm (GA) should not be used. However, the main conclusion is that the case study optimizations are solved 5–10 times faster if ΔTpinch is calculated using hybrid high and low-order interpolation methods

Application of mathematical programming techniques to power system optimization

Imperial Users onl

Estimating flow and transport parameters in the unsaturated zone with pore water stable isotopes

The first author was funded by the DFG Research Group: From Catchments as Organised Systems to Models based on Functional Units (FOR 1598). The second author was funded by the DFG project Coupled soil-plant water dynamics – Environmental drivers and species effects (contract numbers: GE 1090/10-1 and WE 4598/2-1). The isotope data in the precipitation for Roodt were provided by FNR/CORE/SOWAT, project of the Luxembourg Institute of Science and Technology – LIST. Sampling of the isotope profiles was made possible by the support of the CAOS Team and Begona Lorente Sistiaga, Benjamin Gralher, Andre Böker, Marvin Reich and Andrea Popp. Special thanks to Britta Kattenstroth and Jean Francois Iffly for their technical support in the field and Barbara Herbstritt for her support in the laboratory. For Roodt, soil texture and hydraulic parameter information were provided by Conrad Jackisch and Christoph Messer (KIT, Karlsruhe, Germany) and hydraulic conductivity data were provided by Christophe Hissler and Jérôme Juilleret (LIST). Pore water isotope and soil moisture data for Hartheim were provided by Steffen Holzkämper and Paul Königer. Temperature and precipitation data for Hartheim were provided by the Chair of Meteorology and Climatology, University of Freiburg.Peer reviewedPublisher PD

Photovoltaic Thermal hybrid collectors for a district CCHP system: modelling and optimization

Modellazione e simulazione di un sistema basato sulla tecnologia dei pannelli solari ibridi per la trigenerazione di energia destinata ad un uso residenziale. I carichi e le condizioni ambientali sono valori orari annuali tratti da banche dati per le città di Atene in Grecia e Vicenza in Italia. L'ottimizzazione sfrutta un algoritmo evolutivo per implementare un problema di ricerca del minimo. Le funzioni obiettivo usate sono parametri economici ed energetici.openEmbargo temporaneo per motivi di segretezza e/o di proprietà dei risultati e/o informazioni sensibil

Computational methods for solving optimal industrial process control problems

In this thesis, we develop new computational methods for three classes of dynamic optimization problems: (i) A parameter identification problem for a general nonlinear time-delay system; (ii) an optimal control problem involving systems with both input and output delays, and subject to continuous inequality state constraints; and (iii) a max-min optimal control problem arising in gradient elution chromatography.In the first problem, we consider a parameter identification problem involving a general nonlinear time-delay system, where the unknown time delays and system parameters are to be identified. This problem is posed as a dynamic optimization problem, where its cost function is to measure the discrepancy between predicted output and observed system output. The aim is to find unknown time-delays and system parameters such that the cost function is minimized. We develop a gradient-based computational method for solving this dynamic optimization problem. We show that the gradients of the cost function with respect to these unknown parameters can be obtained via solving a set of auxiliary time-delay differential systems from t = 0 to t = T. On this basis, the parameter identification problem can be solved as a nonlinear optimization problem and existing optimization techniques can be used. Two numerical examples are solved using the proposed computational method. Simulation results show that the proposed computational method is highly effective. In particular, the convergence is very fast even when the initial guess of the parameter values is far away from the optimal values.Unlike the first problem, in the second problem, we consider a time delay identification problem, where the input function for the nonlinear time-delay system is piecewise-constant. We assume that the time-delays—one involving the state variables and the other involving the input variables—are unknown and need to be estimated using experimental data. We also formulate the problem of estimating the unknown delays as a nonlinear optimization problem in which the cost function measures the least-squares error between predicted output and measured system output. This estimation problem can be viewed as a switched system optimal control problem with time-delays. We show that the gradient of the cost function with respect to the unknown state delay can be obtained via solving a auxiliary time-delay differential system. Furthermore, the gradient of the cost function with respect to the unknown input delay can be obtained via solving an auxiliary time-delay differential system with jump conditions at the delayed control switching time points. On this basis, we develop a heuristic computational algorithm for solving this problem using gradient based optimization algorithms. Time-delays in two industrial processes are estimated using the proposed computational method. Simulation results show that the proposed computational method is highly effective.For the third problem, we consider a general optimal control problem governed by a system with input and output delays, and subject to continuous inequality constraints on the state and control. We focus on developing an effective computational method for solving this constrained time delay optimal control problem. For this, the control parameterization technique is used to approximate the time planning horizon [0, T] into N subintervals. Then, the control is approximated by a piecewise constant function with possible discontinuities at the pre-assigned partition points, which are also called the switching time points. The heights of the piecewise constant function are decision variables which are to be chosen such that a given cost function is minimized. For the continuous inequality constraints on the state, we construct approximating smooth functions in integral form. Then, the summation of these approximating smooth functions in integral form, which is called the constraint violation, is appended to the cost function to form a new augmented cost function. In this way, we obtain a sequence of approximate optimization problems subject to only boundedness constraints on the decision variables. Then, the gradient of the augmented cost function is derived. On this basis, we develop an effective computational method for solving the time-delay optimal control problem with continuous inequality constraints on the state and control via solving a sequence of approximate optimization problems, each of which can be solved as a nonlinear optimization problem by using existing gradient-based optimization techniques. This proposed method is then used to solve a practical optimal control problem arising in the study of a real evaporation process. The results obtained are highly satisfactory, showing that the proposed method is highly effective.The fourth problem that we consider is a max-min optimal control problem arising in the study of gradient elution chromatography, where the manipulative variables in the chromatographic process are to be chosen such that the separation efficiency is maximized. This problem has three non-standard characteristics: (i) The objective function is nonsmooth; (ii) each state variable is defined over a different time horizon; and (iii) the order of the final times for the state variable, the so-called retention times, are not fixed. To solve this problem, we first introduce a set of auxiliary decision variables to govern the ordering of the retention times. The integer constraints on these auxiliary decision variables are approximated by continuous boundedness constraints. Then, we approximate the control by a piecewise constant function, and apply a novel time-scaling transformation to map the retention times and control switching times to fixed points in a new time horizon. The retention times and control switching times become decision variables in the new time horizon. In addition, the max-min objective function is approximated by a minimization problem subject to an additional constraint. On this basis, the optimal control problem is reduced to an approximate nonlinear optimization problem subject to smooth constraints, which is then solved using a recently developed exact penalty function method. Numerical results obtained show that this approach is highly effective.Finally, some concluding remarks and suggestions for further study are made in the conclusion chapter

Optimal scheduling of microgrid with distributed power based on water cycle algorithm

Microgrid, taking advantage of distributed power generation technology, plays an important role in maximizing the utilization of renewable energy. Based on the problems of the energy crisis, environmental contamination and the high operating cost of the microgrid, the microgrid model can effectively ease energy pressure.We can dispatch the output of each part in the microgrid to obtain the optimal economy. Since many traditional optimization algorithms have limitations of local optimization, multiple iterations, and slow convergence speed, this paper proposes a method that applies the Water Cycle Algorithm (WCA) to optimize the dispatch of the microgrid to minimize the operating cost. The mathematical model of each distributed power is established. The interactive power between the microgrid and large grid is also considered. The lowest generation cost considering environmental benefits is taken as the objective function. Water cycle algorithm is implemented to obtain the optimal solution under various constraints. Some optimization algorithms such as Genetic Algorithm (GA), Interior Search Algorithm (ISA), and Differential Search Algorithm (DSA) were used for results evaluation. By comparing the results obtained from four different algorithms, a case study shows the WCA possesses the advancements of better convergence performance, faster calculation and higher precision compared to the other algorithms. The results demonstrate that the WCA applied to determine the optimal scheduling of the microgrid can achieve a better result than some other algorithms with an acceptable accuracy and efficiency

Smart green charging scheme of centralized electric vehicle stations

This paper presses a smart charging decision-making criterion that significantly contributes in enhancing the scheduling of the electric vehicles (EVs) during the charging process. The proposed criterion aims to optimize the charging time, select the charging methodology either DC constant current constant voltage (DC-CCCV) or DC multi-stage constant currents (DC-MSCC), maximize the charging capacity as well as minimize the queuing delay per EV, especially during peak hours. The decision-making algorithms have been developed by utilizing metaheuristic algorithms including the Genetic Algorithm (GA) and Water Cycle Optimization Algorithm (WCOA). The utility of the proposed models has been investigated while considering the Mixed Integer Linear Programming (MILP) as a benchmark. Furthermore, the proposed models are seeded using the Monte Carlo simulation technique by estimating the EVs arriving density to the EVS across the day. WCOA has shown an overall reduction of 13% and 8.5% in the total charging time while referring to MILP and GA respectively