Modelling of the whole process of a university campus CHP power plant and dynamic performance study

Combined heat and power (CHP) refers to a process/system designed to utilize the waste or residual heat from a power generation process. Thus, a CHP plant can produce both electricity and heat. The nature of such a combination makes the process more complex than any single power generation process or boiler heating system. The paper focuses on modelling study and analysis of energy efficiency of the University of Warwick micro-CHP power plant. In this CHP modelling study, a gas turbine module is built to provide driving power and methane is used as fuel gas. Heat recovery system and auxiliary boiler modules are developed for thermal power generation. All the sub-systems are validated by comparing the simulation results with the operating data collected from the CHP plant. The dynamic performance of the key CHP process outputs is studied with respect to the variation of the input syngas stream, including electricity generation, thermal power output and water output temperature. Simplified controllers are also applied to the gas engineheat recovery subsystem and auxiliary boiler. Simulation results with/without feedback control are both analyzed. The study has highlighted the key factors which influence the plant performance and suggested the strategy for potential energy efficiency improvement.     

考虑节点边际价格的热电联产机组主从博弈竞价策略

本文构建了以热电联产机组(combined heat and power unit,CHP)、电力市场和热力市场为参与者的主从博弈模型,并基于电力市场中节点边际电价(locational marginal electricity price,LMEP)的概念,提出了节点边际热价(locational marginal heat price,LMHP)的概念.在节点边际电价的求解中,采用了支路潮流(branch power flow,BPF)模型,考虑了配电网中的网络损耗从而可以得到更精确的计算结果.在节点边际热价的求解中,考虑了管道热损耗,并基于管道损耗方程分析了节点边际热价的分布规律.在此基础上,采用变步长迭代寻优算法求解热电联产机组、电力市场、热力市场各自最优出力和最优报价策略.最后,通过一个6节点电网–4节点热网的算例对所构建的主从博弈模型及热电联产机组的竞价策略进行了验证.     

Carbon emission impact on the operation of virtual power plant with combined heat and power system

A virtual power plant (VPP) can realize the aggregation of distributed generation in a certain region, and represent distributed generation to participate in the power market of the main grid. With the expansion of VPPs and ever-growing heat demand of consumers, managing the effect of fluctuations in the amount of available renewable resources on the operation of VPPs and maintaining an economical supply of electric power and heat energy to users have been important issues. This paper proposes the allocation of an electric boiler to realize wind power directly converted for supplying heat, which can not only overcome the limitation of heat output from a combined heat and power (CHP) unit, but also reduce carbon emissions from a VPP. After the electric boiler is considered in the VPP operation model of the combined heat and power system, a multi-objective model is built, which includes the costs of carbon emissions, total operation of the VPP and the electricity traded between the VPP and the main grid. The model is solved by the CPLEX package using the fuzzy membership function in Matlab, and a case study is presented. The power output of each unit in the case study is analyzed under four scenarios. The results show that after carbon emission is taken into account, the output of low carbon units is significantly increased, and the allocation of an electric boiler can facilitate the maximum absorption of renewable energy, which also reduces carbon emissions from the VPP.     

汽包式火电机组直接能量平衡协调控制

为了提高火力发电机组的效率,提出了在火电机组协调控制中直接应用能量平衡原理的解决方案。分别对典型汽包式火电机组的特性、能量需求信号、放热量计算、机前压力控制的相互矛盾关系等方面进行分析,并对直接能量平衡原理加以推理,最后结合大量的现场实际工况,设计了一套完善的套锅炉汽机协调控制算法。经过现场应用表明,该算法取得了良好的效果。     

面向高比例新能源电网的重大耗能企业需求响应调度

随着国家“双碳”重大战略的提出,高比例新能源并网将成为我国电力能源转型的重要态势.针对火电机组、配电网和需求侧关联的系列运行约束制约了电网对高比例新能源的有效消纳这一问题,本文提出重大耗能企业这一主要电力负荷参与网需求响应(Demand response, DR)的研究思路,通过重大耗能企业与电网协调调度促进新能源消纳,并获得经济补偿以减少运行成本.研究首先基于混合需求侧响应机制,提出以重大耗能企业、新能源、火电机组为核心的协调调度方法,并根据新能源预测值-预测误差的信息依存顺序提出了两步调度策略.在此基础上,进行生产过程行为建模以实现重大耗能企业需求侧响应决策描述,并建立高比例新能源并网的重大耗能企业需求响应与电网协调调度优化模型.最后,基于烟台电网实际系统进行算例分析,验证了重大耗能企业通过需求响应参与电网协调调度以及两步调度策略的有效性.     

Optimal control of combined heat and power units under varying thermal loads

This paper describes a model based optimizer that allows a combined heat and power (CHP) unit to supply backup power to a Smart Grid on the one hand and minimize the cost for heat and power supply on the other hand. The model of the CHP unit is lean but nevertheless accurately represents the unit behavior, including thermal behavior of the storage as well as the aging effect of engine starts. Thanks to the small model dimension we can solve the optimal dispatch problem efficiently using dynamic programming. Two selected soft- and hard-ware in the loop tests are discussed to demonstrate the performance of the approach. A re-optimization strategy is discussed that allows reactions to wrong predictions of external influences like weather.     

An adaptive optimization system and point tracking technique for estimation of variable-speed seawater pumped storage

There are few kinds of researches on the capacity optimization of seawater pumped storage with variable-speed units. First, the pumped storage effects are investigated to smoothing the large-scale offshore wind power. From the perspective of energy saving and improving efficiency, a method on maximum efficiency tracking is proposed, based on the synthetic characteristic curve of the pump turbine. Second, under the background of the time-of-use electricity price in the power market, the relevant model is established, taking full account of the characteristics of random seasonal fluctuation of tailwater level. Besides, this model satisfies the constraints of variable-speed units (especially under the pump mode). Finally, the key factors influencing profit growth, such as the fluctuation rate, the unit type, and the optimal capacity, are investigated based on a real case. The results show that the variable-speed unit can give full play to the efficiency advantage when the operating head and power demand of the plant change sharply. Because the output range of the variable-speed unit is extensive and can be continuously adjusted (especially in pump mode), the joint system will actively curtail the offshore wind to seek the maximum profit.

     

Recurrent neural network for combined economic and emission dispatch

Recently, the combined economic and emission dispatch (CEED) problem, which aims to simultaneously decrease fuel cost and reduce environmental emissions of power systems, has been a widespread concern. To improve the utilization efficiency of primary energy, combined heat and power (CHP) units are likely to play an important role in the future. The goal of this study is to propose an approach to solve the CEED problems in a CHP system which consists of eight power generators (PGs), two CHP units and one heat only unit. Owing to the existence of power loss in power transmission line and the non-convex feasible region of CHP units, the proposed problem is a nonlinear, multi-constraints, non-convex multi-objectives (MO) optimization problem. To deal with it, a recurrent neural network (RNN) combined with a novel technique is developed. It means that the feasible region is separated into two convex regions by using two binary variables to search for different regions. In the frame of the neurodynamic optimization, existence and convergence of the dynamic model are analyzed. It shows that the convergence solution obtained by RNN is the optimal solution of CEED problem. Numerical simulation results show that the proposed algorithm can generate solutions efficiently.     

A model-based strategy for quantifying the impact of availability on the energy flow of data centers

The demand for higher computing power increases and, as a result, also leads to an increased demand for services hosted in cloud computing environments. It is known, for example, that in 2018 more than 4 billion people made daily access to these services through the Internet, corresponding to more than half of the world’s population. To support such services, these clouds are made available by large data centers. These systems are responsible for the increasing consumption of electricity, given the increasing number of accesses, increasing the demand for greater communication capacity, processing and high availability. Since electricity is not always obtained from renewable resources, the relentless pursuit of cloud services can have a significant environmental impact. In this context, this paper proposes an integrated and dynamic strategy that demonstrates the impact of the availability of data center architecture equipment on energy consumption. For this, we used the technique of modeling colored Petri nets (CPN), responsible for quantifying the cost, environmental impact and availability of the electricity infrastructure of the data centers under analysis. Such proposed models are supported by the developed tool, where data center designers do not need to know CPN to compute the metrics of interest. A case study was proposed to show the applicability of the proposed strategy. Significant results were obtained, showing an increase in system availability of 100%, with equivalents operating cost and environmental impact.

     

Automatic load change coordinated control of air separation units

An automatic load change coordinated control system is developed for strongly coupled and nonlinear air separation units. Firstly, a loosely coupled model is established to solve the tight coupling problem and a robust control strategy integrated with loosely coupled model and priority setting of controlled variables is formed. Secondly, an incremental load balancing calculation method is proposed to handle the nonlinearity and the external target of backflow valve opening is adjusted in real time to solve the hysteresis characteristic. Finally, the comprehensive energy consumption decreases by 2% through deploying the designed automatic control system in an actual air separation unit.     

700MW超超临界火电机组协调系统全工况多模型预测控制及其工程应用

针对700MW超超临界机组协调控制系统,提出全工况下的协调系统多模型预测控制策略。首先利用Gap metric理论分析机组非线性特征,指出低负荷范围内的局部模型密度应当高于高负荷范围内的局部模型密度,指导了机组运行空间的划分及全工况模型集的选取。提出使用第三方外挂实时控制平台以实现多模型预测控制算法,并给出了相应实施方案。工程应用表明,全工况下的协调系统多模型预测控制策略提升了机组协调控制系统性能,使机组具备了深调至30%Pe的能力。     

A second-order sliding mode and fuzzy logic control to optimal energy management in wind turbine with battery storage

The optimal control of large-scale wind turbine has become a critical issue for the development of renewable energy systems and their integration into the power grid to provide reliable, secure and efficient electricity, despite any possible constraints such as sudden changes in wind speed. This paper deals with the modeling and control of a hybrid system integrating a permanent magnet synchronous generator (PMSG) in variable speed wind turbine (VSWT) and batteries as energy storage system (BESS). Moreover a new supervisory control system for the optimal management and robust operation of a VSWT and a BESS is described and evaluated by simulation under wind speed variation and grid demand changes. In this way, the proposed coordinated controller has three subsystems (generator side, BESS side and grid side converters). The main function of the first one is to extract the maximum wind power through controlling the rotational speed of the PMSG, for this a maximum power point tracking algorithm based on fuzzy logic control and a second-order sliding mode control (SOSMC) theory is designed. The task of the second one is to maintain the required direct current (DC) link voltage level of the PMSG through a bidirectional DC/DC converter, whereas in the last, a (SOSMC) is investigated to achieve smooth regulation of grid active and reactive powers quantities, which provides better results in terms of attenuation of the harmonics present in the grid courant compared with the conventional first-order sliding controller. Extensive simulation studies under different conditions are carried out in MATLAB/Simulink, and the results confirm the effectiveness of the new supervisory control system.

     

Optimal Operation of Distributed Generations Considering Demand Response in a Microgrid Using GWO Algorithm

The widespread penetration of distributed energy sources and the use of load response programs, especially in a microgrid, have caused many power system issues, such as control and operation of these networks, to be affected. The control and operation of many small-distributed generation units with different performance characteristics create another challenge for the safe and efficient operation of the microgrid. In this paper, the optimum operation of distributed generation resources and heat and power storage in a microgrid, was performed based on real-time pricing through the proposed gray wolf optimization (GWO) algorithm to reduce the energy supply cost with the microgrid. Distributed generation resources such as solar panels, diesel generators with battery storage, and boiler thermal resources with thermal storage were used in the studied microgrid. Also, a combined heat and power (CHP) unit was used to produce thermal and electrical energy simultaneously. In the simulations, in addition to the gray wolf algorithm, some optimization algorithms have also been used. Then the results of 20 runs for each algorithm confirmed the high accuracy of the proposed GWO algorithm. The results of the simulations indicated that the CHP energy resources must be managed to have a minimum cost of energy supply in the microgrid, considering the demand response program.     

Optimization and control of a stand-alone hybrid solid oxide fuel cells/gas turbine system coupled with dry reforming of methane

This paper presents the hybrid solid oxide fuel cells (SOFC)/gas turbine (GT) system coupled with dry reforming of methane (DRM). The DRM is a syngas producer by consuming greenhouse gas. The stand-alone (off-the-grid) power system is developed by using a combination of a post-burner, recuperators and pressurized recycles in place of external energy supplies. To address the stand-alone operation and meet the complete combustion condition for the burner, the optimal operating conditions are initially determined by solving a constrained optimization algorithm for maximizing the hybrid power efficiency, and the dynamic control loops are implemented in a plantwide environment. In the proposed plantwide control strategy, the inventory control framework is added to regulate the plant component inventory, an air/fuel cross-limiting combustion control is added to ensure complete combustion and reduce heat loss, and the power and CO₂ emission control configuration is added to achieve the quality control performance. Finally, the simulation shows that the IMC-based multi-loop control scheme can efficiently regulate the total system power and control CO₂ emissions per kWh of electricity as well.     

Efficient energy management of CO2 capture plant using control-based optimization approach under plant and market uncertainties

This paper employs a control-based optimization algorithm encompassing of an intelligence model predictive control (MPC) scheme and mixed integer non-linear programming (MINLP) for coal-fired power plant retrofitted with flexible solvent-based post combustion CO₂ capture (PCC) plant (integrated plant). The agility and robustness of the developed control algorithm (MPC) is demonstrated through the control response time and efficiency of energy requirement including the financial and operational benefits of the plant subjected to plant and market uncertainties. While, the MINLP is utilized to forecast plant operational modes by ensuring the operational fidelity of integrated plant. This involves utilization of historical (2011) and forecast (2020) market conditions (electricity tariff and carbon price) subject to maximum plant net operating revenue. The outcomes show that the future power plant will operate in mixed operation modes, for instance in unit turndown and load following modes, which contribute to a minimum capture energy penalty at 3.13 MJ_th/tonne CO₂. Moreover, under the same year (2020), MPC exhibits superior control performance by satisfactorily obtain 94% actual CO₂ capture from the ideal cumulative CO₂ capture. Additionally, the integrated plant is capable to resume approximately 96% actual revenue from the ideal net operating revenue projected by the control-based optimization algorithm. The algorithm demonstrates that the installation of control system package (MPC) into the flexible PCC plant associated with coal-power generator could contribute to efficient energy management subjects to unprecedented uncertainties.     

Combined heat and power economic dispatch using integrated civilized swarm optimization and Powell’s pattern search method

An integrated technique that embeds civilized swarm optimization (CSO) and Powell’s pattern search (PPS) method is proposed to search economic dispatch of combined heat and power (CHP) dispatch problem. In the proposed technique, CSO is selected as global search technique and PPS is undertaken as a local search technique. Civilized swarm optimization is having attributes of particle swarm optimization (PSO) and society civilization algorithm (SCA). In CSO, mutually interacting societies forms the civilization. The positions of society particles are updated through the guidance of own leader along with their best positions. The best performing particle of CSO is further improved by PPS method based on a certain set criterion. The PPS method is based on the conjugate search direction method and does not require the gradient or Hessian matrix of the function to be optimized. The CHP dispatch problem has a mutual dependency of demand and heat-power capacity of generating units, so it requires an effective constraint handling strategy. In this work, variable reduction strategy with exterior penalty method is applied to satisfy equality constraints. The proposed technique is tested on five CHP test systems considering valve-point loading effect, prohibited operating zones constraint, and transmission losses. The obtained results are compared to the results reported in the literature and found satisfactory. Further, for verification of statistical performance of the proposed technique, t-test and Wilcoxon signed rank test is also performed.     

Statistical process control based fault detection of CHP units

This paper describes a fault diagnosis method that provides early detection of fouling of the heat recovery system of combined heat and power units. Early detection of fouling build-up is difficult from basic data analysis methods due to limited instrumentation, and a unit can operate for many months with a reduced heat transfer rate before an unplanned shut down. This novel application of statistical process control (SPC) using an estimate of the coolant flow rate, provides advanced warning of fouling build-up and allows significantly increased energy recovery and reduced financial losses from unplanned unit shut down and incorrect fault identification.     

基于改进动态规划法的电力系统机组组合问题研究

机组组合问题是在调度周期内满足各种运行约束条件的前提下,通过合理地控制机组的开、停状态以实现总运行成本最低目标的问题。本文以电力系统机组组合为研究对象,对优先顺序法与动态规划法在机组组合中的应用进行了研究和探讨,并根据他们的优缺点,建立了改进的算法—基于优先顺序法的动态规划法。     

Intelligent coordinated controller design for a 600 MW supercritical boiler unit based on expanded-structure neural network inverse models

Under present widespread automatic generation control (AGC) centered on regional power grid, a large-capacity coal-fired supercritical (SC) power unit often operates under wide-range variable load conditions. Since a SC once-through boiler unit is represented by a typical multivariable system with large inertia and non-linear, slow time-variant and time-delay characteristics, it often makes the coordinated control quality deteriorate under wide-range loading conditions, and thus influences the unit load response speed and leads to heavy fluctuation of the main steam pressure. To improve the SC unit’s coordinated control quality with advanced intelligent control strategy, the neural-network (NN) based expanded-structure inverse system models of a 600 MW SC boiler unit were investigated. A feedforward neural network with time-delayed inputs and time-delayed output feedbacks was adopted to establish the inverse models for the load and the main steam pressure characteristics. Based on the model, a neural network inverse coordinated control scheme was designed and tested in a full-scope power plant simulator of the given SC power unit, which showed that the proposed coordinated control scheme can achieve better control results compared to the original PID coordinated control.     

Practical-optimal control of a drum boiler power plant

Multivariable control of power plants has been studied by many researchers and also a number of practical applications have been presented in the last few years with the aim of improving the load following capabilities of thermal units. This paper summarizes the main results of an experimental application of optimal control to a drum boiler power plant at the Italian national electricity board. Referring to a previous work for the process model building, first a model reduction is performed with the main purpose of getting a simplified control structure. Then, an optimal regulator including feedforward and integral control is synthesized and some practical drawbacks of the synthesis are discussed with referrence to traditional control schemes leading to a reformulation of the optimal regulator which, while conserving optimality, exhibits a number of practical advantages and improved robustness against typical plant parameter variations. Since the motivation of the control structure reformulation is mostly derived from experimental results, field trials are reported where the performances of a well tuned coordinated control, of the classical optimal regulator and of the reformulated optimal control structure are compared. Perspectives of future industrial applications are briefly discussed in the conclusions.