Optimization control system analysis

For a typical trigeneration in a mature energy market, system size, system performance, energy price (subject to high variability and volatility) and the quantities of energy

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services demand (daily and seasonal alterations) determine the profitability of the operation. Most literature studies in the optimal control strategy of the trigeneration system always utilize a set of variable loads depicted by employing the temporal evolution of thermal, electrical and cooling demands [100]. Steady-state and set-point are the most important issues for the methods of continuously operating processes by using linear control models, such as Real-time optimization and Model Predictive Control (MPC). However, the application of nonlinear MPC is very limited for the transient processes (e.g. batch processes, continuous processes) [101].

Most studies or practices have proved that input, output and state constraints are the most important elements which have been diffusely employed to develop a series of control design methods in linear systems. The stability, desirable performance and constrains of these systems have been identified by a nonlinear control law. In recent years, there is the most popular standard for constrained multivariable control method in related linear systems, which is MPC or simply MPC in the process industries.

During the MPC working period at every sampling time, it can gather feedback of the present measurement or system state and resolve the open-loop optimal control problem in a limited time horizon to identify the operating sequence of values for control system in the future. Therefore, control procedure will be a reiteration after the first sequence is identified at each sampling instant.

Control emphasis is disparate on each element for every application, indeed, for the same application, the control emphasis is usually diverse for operations from time to time. Lu in [102] developed an explicit triangle coordinates to represent the control emphasis as shown in Fig. 2.9.

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Fig. 2.9. Control emphasis description [102]

In Fig.2.9, every vertex of the triangle presents a “pure” form of control, and each side defines a combination of two controls. Moreover, each point inside the triangle

indicates a combination of three types of controls with independent relative emphases.

For example, because of the unique set of objectives under particular different

operations, the control strategy of the application X may not suitable for the application Y in the triangle. However, in a typical integrated system, the various control strategies will be chaotic, so there is a significant demand for developing a method to conclude all three control emphasis into one control procedure; therefore, MPC is introduced and proposed before each “pure” control is clarified the problem [102].

Before developing the MPC framework for a particular system, the structure of the control system should be analysed and discussed. Tatjewski in [103] describes a functional multiplayer control structure implemented by distributed control systems (DCS), which is presented in Fig. 2.10.

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Fig. 2.10. The hierarchical control structure [103]

Safety is the most important issue for the control system, same as the quality of a product. The fundamental safety of dynamic control procedure in the production is determined by the direct control layer or basic control layer. The direct control layer composes the various controllers or valves, which is the only connection to the production, also with the input variables.

Many literatures have presented the robust and auto-tuning control strategies, such as [104]. PID controller as its advantage in acceptable stable performance is still dominant in the basic control layer. Also, modern DSC systems can provide a series of

computational advanced approaches comparing with the limitation of PID performance, advanced control algorithms have been designed and implemented by modified PID in the particular MPC algorithms.

Constraint control or set-point control in the higher layer is depicted by advanced feedback controllers with the set-point values. For the multivariable and non-linear processes system, the set-point control algorithms should manipulate with high control performance. The impact of the constraint control layer is directly connected with the development of basic control algorithms and local optimization control algorithms.

However, seldom literature has mentioned the distinction between the direct control (basic control) layer and constraint control (set-point control) level. With the

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development and widely usage of DMC algorithm and other MPC type algorithms in current industries, there is a potential demand for separating the advanced control and basic control, which has been minutely presented in [105].

In addition, it should also be noticed that the constraint control layer cannot be always operated if there is no demand for the set-point control layer. Moreover, this layer cannot fully isolate the direct control layer from the optimization layer—as is depicted in the structure of Fig. 2.10. Meantime, optimization of set-points for feedback

controllers is responsible for the performance of the next level of the hierarchy, the local optimization layer, which is directly above the regulatory control.

In [106], the author details a networked linear MPC approach based on neighbourhood optimization for a set of continuously linked processes. By utilizing the predictive state observer, the flexible errors of the structure of the sub-process can be corrected

primarily during the modelling. Another study has utilized genetic and sequential quadratic programming algorithms to tackle the constrained optimization air

temperature control issue [107]. For each sampling period (a prediction horizon of 1h with 1-min), set-point value is tracked, and effort of control is minimized by calculating the controller outputs [107]. Therefore, the future behaviour of a greenhouse

environment in the north of Portugal is optimized. The greenhouse climate is described by the mathematical models, and also the controller should be responsible for predicting the greenhouse environmental conditions over a specified time interval.

A Optimization of Thermal control system

A multi-objective optimization problem is presented by Fares et al. in [108] to identify the optimal layer thickness and optimal closed loop control function for a symmetric cross-ply laminate subjected to thermo-mechanical loadings. An optimization procedure is designed to maximize the critical combination of the implemented boundary load and temperature levels and to minimize the laminate dynamic response subject to constraints on the thickness and control energy.

A novel formulation of multi-objective corresponding power and voltage control for power system is specified in [109]. In this study, the load boundaries and operational boundaries are considered to obtain the power loss, voltage deviation and the voltage stability index of the system. Mathematic formulation is detailed to deal with the

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objective formulation of the problem. Therefore, a pseudo-goal function is employed to eliminate the use of weighing coefficients. The inequality boundaries are applied into the fitness function by pseudo goal feature which guarantees that the selected optimal approach is suitable. An optimization approach, which aims to maintain a flexible and efficient method for the optimization and coordination of power system controls, is applied in a power system simulation software package is presented in [110].

B Optimization of Energy Storage control system

A composite framework for the optimal design and control of metal-hydride storage under hydrogen desorption operation is described in [111]. The features of this framework include a specified two-dimension dynamic process model, a design and operational dynamic optimization approach and a multi-parametric model predictive controller method. In this case, the mathematical model, optimization and dynamic simulation are also implemented by designing a robust MPC controllers and employing an optimized PI controller.

A novel control method, optimized by genetic algorithm, is presented in [112] to operate stand-alone hybrid renewable electrical systems with hydrogen storage. In this study, it is an aim to optimize the control of the hybrid system, minimize the whole consumption and optimize the usage of the spare energy. When the amount of energy load is higher than the demand which generated by the renewable sources (wind, PV and hydro), the control strategy will provide the most economical approach to satisfying the energy deficit.

C Optimization of cooling control system

For the cooling or refrigerating system, there is an enormous potential demand to depress the operating cost and increase the energy efficiency with advanced control method. An adaptive optimal control model for building cooling and heating sources is presented in [113], optimization control strategy is implemented for the building

cooling source system in a commercial building in Changsha, China. This optimization control strategy can reduce the energy consumption of the cooling source system by 7%. Yao Y et al. in [114] develop a mathematical model for the optimization operation of the cooling system, which based on the energy analysis of the primary dynamic appliances that consists of chillers and pumps. In this case, a term named “System Coefficient of Performance (SCOP)” is introduced to analyse the efficiency of energy usage of one central cooling system. Optimization issue is depicted minutely as the

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maximization of SCOP while providing the comfort conditions in the air-conditioning zones during the operation for the whole cooling system [114]. The optimal approaches deal with the condenser water flow rates and the chilled water temperature to ensure the desirable system energy efficiency, and the compatible optimal control model is

consists of the optimal control model, parameter identification and optimal algorithm.

The optimal control model contains the objective function and constraints, and the model for parameter identification describes each component in the optimal control model with fuzzy self-tuning oblivious factor method; genetic algorithm is employed to explorer the optimum values for the discrete and continuous variables [114].