scholarly journals Multi-Objective Load Dispatch Control of Biomass Heat and Power Cogeneration Based on Economic Model Predictive Control

Energies ◽  
2021 ◽  
Vol 14 (3) ◽  
pp. 762
Author(s):  
Lianming Li ◽  
Defeng He ◽  
Jianrong Jin ◽  
Baoyun Yu ◽  
Xiang Gao
Keyword(s):  
Objective Function ◽  
Real Time ◽  
Predictive Control ◽  
Economic Performance ◽  
Control Method ◽  
Pollutant Emission ◽  
Emission Performance ◽  
Multi Objective ◽  
Economic Model Predictive Control ◽  
Fuel Rate

This paper proposes a multi-objective load dispatch algorithm based on economic predictive control to solve the real-time multi-objective load dispatch problem of biomass heat and power cogeneration. According to the energy conservation law and production process, a real-time multi-objective load dispatch optimization model for heat and power units is established. Then, the concept of multi-objective utopia points is introduced, and the multi-objective load comprehensive objective function is defined to coordinate the conflict between the economic performance and pollutant emission performance of the units. Furthermore, using the online receding optimization characteristics of economic predictive control, the comprehensive objective function of multi-objective load dispatching is optimized online. Then, the fuel rate satisfying the economic performance and pollutant emission performance of the units is calculated to realize the economic performance and environmental protection operation of biomass heat and power cogeneration. Finally, the proposed multi-objective load dispatch control method is compared to traditional dispatch strategies by using industrial data. The results show that the method presented here can well balance the production cost and pollutant emission objective under the fluctuation of the thermoelectric load demand, and provides a feasible scheme for real-time dispatching of the multi-objective load dispatch problem of biomass heat and power cogeneration.

scholarly journals Freeway Traffic Congestion Reduction and Environment Regulation via Model Predictive Control

Algorithms ◽  
2019 ◽  
Vol 12 (10) ◽  
pp. 220 ◽  
Author(s):  
Juan Chen ◽  
Yuxuan Yu ◽  
Qi Guo
Keyword(s):  
Model Predictive Control ◽  
Fuel Consumption ◽  
Predictive Control ◽  
Traffic Congestion ◽  
Control Method ◽  
Optimization Method ◽  
Speed Limit ◽  
Multi Objective Optimization ◽  
Nsga Ii ◽  
Multi Objective

This paper proposes a model predictive control method based on dynamic multi-objective optimization algorithms (MPC_CPDMO-NSGA-II) for reducing freeway congestion and relieving environment impact simultaneously. A new dynamic multi-objective optimization algorithm based on clustering and prediction with NSGA-II (CPDMO-NSGA-II) is proposed. The proposed CPDMO-NSGA-II algorithm is used to realize on-line optimization at each control step in model predictive control. The performance indicators considered in model predictive control consists of total time spent, total travel distance, total emissions and total fuel consumption. Then TOPSIS method is adopted to select an optimal solution from Pareto front obtained from MPC_CPDMO-NSGA-II algorithm and is applied to the VISSIM environment. The control strategies are variable speed limit (VSL) and ramp metering (RM). In order to verify the performance of the proposed algorithm, the proposed algorithm is tested under the simulation environment originated from a real freeway network in Shanghai with one on-ramp. The result is compared with fixed speed limit strategy and single optimization method respectively. Simulation results show that it can effectively alleviate traffic congestion, reduce emissions and fuel consumption, as compared with fixed speed limit strategy and classical model predictive control method based on single optimization method.

scholarly journals Improving Flexibility and Energy Efficiency of Post-Combustion CO2 Capture Plants Using Economic Model Predictive Control

Processes ◽  
2018 ◽  
Vol 6 (9) ◽  
pp. 135 ◽  
Author(s):  
Benjamin Decardi-Nelson ◽  
Su Liu ◽  
Jinfeng Liu
Keyword(s):  
Energy Efficiency ◽  
Power Plant ◽  
Model Predictive Control ◽  
Predictive Control ◽  
Economic Performance ◽  
Economic Model ◽  
Power Plants ◽  
Gas Flow ◽  
Renewable Energy Sources ◽  
Economic Model Predictive Control

To reduce CO 2 emissions from power plants, electricity companies have diversified their generation sources. Fossil fuels, however, still remain an integral energy generation source as they are more reliable compared to the renewable energy sources. This diversification as well as changing electricity demand could hinder effective economical operation of an amine-based post-combustion CO 2 capture (PCC) plant attached to the power plant to reduce CO 2 emissions. This is as a result of large fluctuations in the flue gas flow rate and unavailability of steam from the power plant. To tackle this problem, efficient control algorithms are necessary. In this work, tracking and economic model predictive controllers are applied to a PCC plant and their economic performance is compared under different scenarios. The results show that economic model predictive control has a potential to improve the economic performance and energy efficiency of the amine-based PCC process up to 6% and 7%, respectively, over conventional model predictive control.

A Real-Time Multi-Objective Predictive Control Strategy for Wheelchair Ergometer Platform

2019 ◽  
Author(s):  
Toufik Bentaleb ◽  
Viet Thuan Nguyen ◽  
Chouki Sentouh ◽  
Gerald Conreur ◽  
Thierry Poulain ◽  
...  
Keyword(s):  
Real Time ◽  
Predictive Control ◽  
Control Strategy ◽  
Multi Objective

scholarly journals Real-Time Distributed Economic Model Predictive Control for Complete Vehicle Energy Management

Energies ◽  
2017 ◽  
Vol 10 (8) ◽  
pp. 1096 ◽  
Author(s):  
Constantijn Romijn ◽  
Tijs Donkers ◽  
John Kessels ◽  
Siep Weiland
Keyword(s):  
Model Predictive Control ◽  
Real Time ◽  
Energy Management ◽  
Predictive Control ◽  
Economic Model ◽  
Complete Vehicle ◽  
Economic Model Predictive Control

scholarly journals A Cooperative Intermodal Transportation Network Flow Control Method Based on Model Predictive Control

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Yuankai Huang ◽  
Qicai Zhou ◽  
Xiaolei Xiong ◽  
Jiong Zhao
Keyword(s):  
Model Predictive Control ◽  
Flow Control ◽  
Real Time ◽  
Predictive Control ◽  
Dynamic Optimization ◽  
Network Flow ◽  
Control Method ◽  
Discrete Event ◽  
Transport Systems ◽  
Intermodal Transport

With the development of information technology, intermodal transport research pays more attention to dynamic optimization and multi-role cooperation. The core issue of this paper was to realize container routing with dynamic adjustment, real-time optimization, and multi-role cooperation characteristics in the intermodal transport network. This paper first introduces the Intermodal Transport Cooperation Protocol (ITCP) that describes the operation and analysis of intermodal transport systems with the concept of encapsulation and layering. Then, a new network flow control method was built based on Model Predictive Control (MPC) in the ITCP framework. The method takes real-time information from all ITCP layers as input and generates flow control decisions for containers. To evaluate the method’s effectiveness, a discrete event simulation experiment is applied. The results show that the proposed method outperforms the all-or-nothing method in scenarios with high freight volume, which means the method proposed in this paper can effectively balance the network transport load and reduce network operating costs. The research of this paper may throw some new light on intermodal transport research from the perspectives of digitization, multi-role cooperation, dynamic optimization, and system standardization.

scholarly journals Predictive Control for Small Unmanned Ground Vehicles via a Multi-Dimensional Taylor Network

2022 ◽  
Vol 12 (2) ◽  
pp. 682
Author(s):  
Yuzhan Wu ◽  
Chenlong Li ◽  
Changshun Yuan ◽  
Meng Li ◽  
Hao Li
Keyword(s):  
Objective Function ◽  
Real Time ◽  
Predictive Control ◽  
Tracking Control ◽  
Learning Algorithm ◽  
Back Propagation ◽  
Unmanned Ground Vehicles ◽  
Ground Vehicles ◽  
Velocity Signal ◽  
The Stability

Tracking control of Small Unmanned Ground Vehicles (SUGVs) is easily affected by the nonlinearity and time-varying characteristics. An improved predictive control scheme based on the multi-dimensional Taylor network (MTN) is proposed for tracking control of SUGVs. First, a MTN model is used as a predictive model to construct a SUGV model and back propagation (BP) is taken as its learning algorithm. Second, the predictive control law is designed and the traditional objective function is improved to obtain a predictive objective function with a differential term. The optimal control quantity is given in real time through iterative optimization. Meanwhile, the stability of the closed-loop system is proved by the Lyapunov stability theorem. Finally, a tracking control experiment on the SUGV model is used to verify the effectiveness of the proposed scheme. For comparison, traditional MTN and Radial Basis Function (RBF) predictive control schemes are introduced. Moreover, a noise disturbance is considered. Experimental results show that the proposed scheme is effective, which ensures that the vehicle can quickly and accurately track the desired yaw velocity signal with good real-time, robustness, and convergence performance, and is superior to other comparison schemes.

Sign in / Sign up

Export Citation Format

Share Document