Optimal operation of coproduction with storage

We discuss the optimal operation of a combined heat and power (CHP) plant with heat storage. The dynamics of the district heating (DH) network are also considered. The decision variables include production of heat and power, supply temperature and operation of heat storage. Heat demand for the DH system and shadow prices for the electrical power system are inputs to the production system. The optimization criterion is minimization of total costs over the planning period. A non-linear optimization model based on real data is formulated and representative case studies are performed.     

An interactive optimization model for energy systems planning associated with clean‐energy development under uncertainty

In this study, an interactive fuzzy chance‐constrained resolution (IFCR) method is developed for supporting energy systems planning (ESP) under uncertainty. IFCR can not only tackle multiple uncertainties presented as fuzzy membership functions and probability distributions using an interactive resolution method, but also enable decision makers to seek optimal solutions between satisfaction degree of objective and feasibility of constraints. Then, an IFCR‐ESP model is formulated for energy systems of Bayingolin Mongol Autonomous Prefecture (abbreviated as Bazhou). Results provide decision makers with a complete view of the relationship between uncertain inputs and solutions. Policy‐based solutions concerning energy consumption, electricity generation, capacity expansion, pollutant emission, and system cost are analyzed, which can help decision makers to identify desired strategies for ESP. Results indicate that transitioning from the conventional energy system to a sustainable one is associated to policy support, resources availability, energy supply security, and capital investment; clean energy policy (e.g. shift from coal to gas) is an effective way to facilitate the local energy system development in a sustainable manner; the solutions under different feasibility degrees can help decision makers to conduct in‐depth analyses of tradeoffs between system cost and constraint‐violation risk in an interactive way. Copyright © 2016 John Wiley & Sons, Ltd.     

Multi-criteria design optimization and thermodynamic analysis of a novel multi-generation energy system for hydrogen,cooling, heating,power, and freshwater

The main focus of this paper is to present thermodynamic and economic analyses and multi-objective optimization of a novel geothermal-solar multigeneration system. The system aims to produce hydrogen, freshwater, electricity, cooling load, and hot water and designed based on geothermal and solar energy. After modeling and thermodynamic and economic analysis, exergy destruction rate, exergy efficiency and, cost rate were calculated for each component of the system. The results showed that the highest amount of exergy destruction was related to parabolic trough collectors (PTCs) and absorption chillers. To select the geothermal fluid of the organic Rankine cycle (ORC), several different fluids were investigated, among which isobutene was selected. By using the Group method of data handling (GMDH) neural network, a mathematical relationship was obtained between the inputs and outputs of the problem and were given as inputs to the non-dominated sorting genetic algorithm II (NSGAII)alg. The final optimal point was obtained applying the technique for order of preference by similarity to ideal solution (TOPSIS) decision criterion at which the exergy efficiency and cost rate were calculated to be 21.63% and 63.89 $/h, respectively. The meteorological data of the Zanjan, Isfahan, and Bandar Abbas cities were used to calculate the performance accurately at the TOPSIS selection point. To provide a comparison between different cities, the performance of the system was evaluated on September 17 as a sample day. On this day, the proposed system produces 26.38 kg of hydrogen and 373.8 m³ of freshwater in Isfahan.     

长江洞庭湖和宜昌至汉口流域洪水过程模拟研究

针对长江洞庭湖和宜昌至汉口流域的特点,应用现代系统工程理论和方法,提出并建立了多输入单输出系统模型.利用历史洪水的实测资料,模拟洞庭湖流域、宜昌至汉口流域的洪水过程,取得了相当高的模拟精度.其成果可为长江中游流域的洪水预报和防洪调度决策提供技术支持和服务.     

A dynamic multi-stage data envelopment analysis model with application to energy consumption in the cotton industry

Data envelopment analysis (DEA) is a non-parametric method for evaluating the relative efficiency of homogenous decision making units (DMUs) with multiple inputs and outputs. In this paper, we present a dynamic multi-stage DEA (DMS-DEA) approach to evaluate the efficiency of cotton production energy consumption. In the proposed model, the farms which consume resources (i.e., fertilizers, seeds, and pesticides) to produce cotton are assumed to be the DMUs. Inputs not consumed during a planning period are carried over to the next period in the planning horizon. Initially, a DMS-DEA model is used to determine the overall efficiency of the DMUs with dynamic inputs. Next, the efficiency score of each DMU is calculated for each time period in the planning horizon. We demonstrate the applicability of the proposed method and exhibit the efficacy of the procedures and algorithms with a real-life case study of energy consumption in the cotton industry.     

PWR-FBR with closed fuel cycle for a sustainable nuclear energy supply in China

From the thermal reactor to the fast reactor and then to the fusion reactor; this is the three-step strategy that has been decided for a sustainable nuclear energy supply in China. As the main thermal reactor type, the commercialized development phase of the pressurized water reactor (PWR) has been stepped up. The development of the fast reactor (FBR) is still in the early stage, marked by China experimental fast reactor (CEFR), which is currently under construction. According to the strategy study on the fast reactor development in China, its engineering development will be divided into three steps: the CEFR with a power of 65 MWt/20 MWe; the China prototype fast reactor (CPFR) with a power of 1 500 MWt/600 MWe; and the China demonstration fast reactor (CDFR) with a power of 2 500–3 750 MWt/1 000–1 500 MWe. With regards to the fuel cycle, a 100 t/a PWR spent fuel reprocessing pilot plant and a 500 kg/ a MOX fabrication plant are under construction. A project involving the construction of an industrial reprocessing plant and an MOX fabrication plant are also under application phase.     

Feasibility-based inexact fuzzy programming for electric power generation systems planning under dual uncertainties

In this study, a feasibility based inexact fuzzy programming approach (FBIFP) was developed and applied to the planning of a regional electric power generation system (REPGS). As an extension of existing interval linear programming and fuzzy linear programming, FBIFP could tackle uncertainties expressed as intervals, fuzzy sets as well as their combination in both of the objective function and constraints. Through introducing the index of feasibility degree, FBIFP could be used to facilitate the efficient reflection of constraint violation, and thus allow decision makers to have a complete view of the relationships between uncertain inputs and the related solutions. A hypothetic regional electric power generation system was provided for demonstrating applicability of the developed model. Useful solutions for the planning of REPGS were generated. Interval solutions under different feasibility degrees were obtained. They could be used for helping decision makers identify desired alternatives under various reliability risks of the system. The solutions could also help identify optimal patterns for energy resources allocation, electricity generation and facility capacity expansion with a minimized system cost. Tradeoffs between system costs and system security could be successfully addressed through the analysis of the feasibility degrees of constraints, i.e., a higher feasibility degree would correspond to a higher system cost, while a lower system cost would run into a higher risk of potential instability of the study system.     

Measuring eco-efficiency based on green indicators and potentials in energy saving and undesirable output abatement

Nowadays, majority of organizations are seeking to achieve sustainable development with respect to “green” concept. One of the main criteria for assessing green performance is eco-efficiency. To identify all aspects of the eco-efficiency, inputs should be divided into energy and non-energy and outputs should be divided into good and bad outputs. To deal with this issue, a data envelopment analysis (DEA) model is developed to divide inputs into both energy and non-energy and outputs into both desirable (good) and undesirable (bad) outputs. Likewise, variables are separated into both discretionary and non-discretionary factors. Accordingly, a bounded adjusted measure (BAM) based on green indicators is developed to calculate the eco-efficiency of decision making units (DMUs). Besides, energy saving potentials and undesirable output abatement potentials are calculated to show correlation coefficient between energy consumption and undesirable output. Finally, proposed model is validated by assessing the eco-efficiency of some selected members of organization for economic cooperation and development (OECD). Australia, Finland, Ireland, New Zealand, and Switzerland are recognized as eco-efficient countries and the rest of countries are inefficient in terms of the eco-efficiency. High and positive Spearman correlation coefficient between energy consumption and undesirable outputs addresses that the more use of energy inputs, the more undesirable outputs.     

农业水资源系统随机多维模糊优选模型及应用

基于多目标模糊优选动态规划理论,结合复杂农业水资源系统决策的复杂性、多目标性以及水文过程的随机性和具有时序性的特点,提出了随机多维模糊优选动态规划模型,并根据所研究问题的性质,给出两种随机多维问题的解法:随机多维决策相对优属度最大法和阶段模糊优选法。将该理论及方法应用于作物非充分灌溉制度的设计,实例证明了解法的可行性和模型的实用性。     

Dynamic modeling of solid oxide fuel cell stack based on local linear model tree algorithm

This paper presents a dynamic model for Solid Oxide Fuel Cell (SOFC) stack using Local Linear Model Tree (LOLIMOT) algorithm which is useful for both dynamic and steady-state studies. Most of conventional SOFC models require a large number of parameters and factors, which are difficult to be determined or estimated. In this paper, a LOLIMOT-based model, which does not require the parameters of fuel cell, is proposed for each operation mode of SOFC. In these models, decision tree-based feature selection approach is exploited to select inputs of the LOLIMOT. The proposed models are trained in a short time and they have little errors.     

Fuzzy model representation of thermosyphon solar water heating system

The aim of this paper is to focus on improvement in prediction accuracy of model for thermosyphon solar water heating (SWH) system. The work employs grey-box modeling approach based on fuzzy system to predict the outlet water temperature of the said system. The prediction performance results are compared with neural network technique, which has been suggested by various researchers in the last one decade. The outlet water temperature prediction by fuzzy modeling technique is analyzed by using 3 models, one with three inputs (inlet water temperature, ambient temperature, solar irradiance), next with two inputs (inlet water temperature, solar irradiance) and last one with single input (solar irradiance/inlet water temperature). An improved prediction performance is observed with three inputs fuzzy model.     

基于数据包络分析的炼油企业经济效益评价方法研究

DEA是根据采集到的原始数据来评价具有多个输入、多个输出的复杂的经济体(定义为决策单元DMU)的评价方法,DEA模型的主要组成部分包括决策单元和输入、输出指标。炼油企业具有多输入、多输出的特性,因此可以采用DEA模型进行评价,其经济效益评价指标体系具体指标包括固定资产净值、单位完全费用、利税额、工业增加值、原油加工能力、人力资源效率、原油加工量和综合商品率。选择某石油公司下属22家炼油企业作为决策单元DMU,以各家企业某年的相关数据为基础,应用C2R模型进行经济效益评价。评价结果表明,选取的22家炼化企业中,达到技术规模有效的有11家,它们无论在技术还是规模上,投入和产出均达到了均衡状态;技术规模相对非有效的也有11家,这表明它们在经营过程中存在浪费和生产能力没有完全有效利用的现象,可利用投影分析法对其进行改进。建议通过炼油装置大型化,形成炼厂规模;改善装置结构,适应原油加工趋势;加强设备管理,提高装置利用率;采取积极措施,降低炼油成本;减员增效,提高人力资源效率等措施来提高企业经济效益。     

Decision support system integrating GIS with simulation and optimisation for a biofuel supply chain

A range of economic and societal issues has resulted from fossil fuel consumption in the transportation sector in the U.S. These include health related air pollution, climate change, dependence on imported oil, and other oil related national security concerns. Biofuels production from various lignocellulosic biomass types, such as wood, forest residues, and agriculture residues, have the potential to replace a portion of the total fossil fuel consumption. This study focused on locating biofuel facilities and designing the biofuel supply chain to minimise the overall cost. For this purpose, an integrated methodology was proposed by combining the Geographic Information System technology with simulation and optimisation modelling methods. The GIS-based method was used as a precursor for selecting biofuel facility locations by employing a series of decision factors. The identified candidate sites for biofuel production served as inputs for simulation and optimisation modelling. The simulation/optimisation model and identified locations provided an integrated decision support system for decision makers to determine the optimal cost, energy consumption, and emissions for candidate locations. This novel methodology development extends prior research.     

Energy efficiency measurement in agriculture with imprecise energy content information

Measuring energy efficiency is crucial when planning energy reduction policies. However, decision makers are understandably reluctant to act in the absence of solid data and results supporting a policy position. This paper proposes a methodology to measure the energy efficiency of farms based on the Data Envelopment Analysis (DEA) approach. In a manner similar to the cost framework, we decompose energy efficiency measurements into technical and allocative efficiencies in energy contents of inputs. In this study, we replace input prices used in traditional economic efficiency measurements by their energy content. We use the energy efficiency model to explore the optimal input-mix that produces the current outputs at minimum energy-consumption. We demonstrate that this decomposition can help policy makers design accurate energy policies. However, the uncertainty of the data and, more particularly, the energy content of the inputs leads us to recommend using the methodologies to calculate the bounds of efficiency to obtain more plausible and robust results. Based on our analysis, energy efficiency is not a fixed value, and policy-makers should consider it with caution. We use a 2007 database of French farms specialised in field crops for empirical illustration.     

Machine learning approach to predict the thermal performance of closed-loop thermosyphon

This paper presents the machine learning (ML) algorithm to predict the thermal performance of closed-loop thermosyphon (CLT). The experimentation is carried out on the acetone-charged CLT at different test conditions such as heat inputs, filling ratios, and adiabatic lengths. The test data is used to calculate the performance parameters such as thermal resistance, heat transfer coefficient, and effectiveness of the system. Based on the experimental dataset, the ML algorithms are developed to predict the performance parameters of the CLT system. The ML algorithms such as linear regression, decision tree (DT), random forest (RF), and lasso regression are used for the development of the prediction model. The hyperparameters are well-tuned and optimized. The prediction measuring parameters (mean absolute error, R²) are analyzed carefully. It is noticed that the DT model outperformed the prediction of the other used models. The R² score of the DT model was 98.504; whereas, the R² scores of the RF model and linear regression model were about 94.76 and 92.17, respectively. This study will become a roadmap to the ML approach in the thermosyphon system.     

An almost ideal supply system estimate of US energy substitution

This paper introduces a Linear Almost Ideal Supply System (LAISS) model to examine aggregate US energy demand. Based on the Almost Ideal Demand System (AIDS) model, the LAISS model is a flexible functional form for imposing and testing properties of demand for the inputs of production. Own and cross price elasticities are derived for aggregate capital, labor, and energy, and compared to the translog cost function. Both models reduce to estimating a system of input share equations on input prices and output, with output normalized by the Stone's input price index in the LAISS model. Results indicate that all inputs are substitutes in both models, but elasticities differ. A log likelihood dominance criterion shows that the LAISS model dominates the translog cost function for this dataset. Results suggest that inputs are substitutes, and that an energy import tariff would increase demand for labor and capital.     

Robust fault tolerant control of DFIG wind energy systems with unknown inputs

This paper proposes a Fuzzy Dedicated Observers (FDOS) method using a Nonlinear Unknown Input Fuzzy Observer (UIFO) with a Fuzzy Scheduler Fault Tolerant Control (FSFTC) algorithm for fuzzy Takagi-Sugeno (TS) systems subject to sensor faults, parametric uncertainties, and time varying unknown inputs. FDOS provide residuals for detection and isolation of sensor faults which can affect a TS model. The TS fuzzy model is adopted for fuzzy modeling of the uncertain nonlinear system and establishing fuzzy state observers. The concept of Parallel Distributed Compensation (PDC) is employed to design FSFTC and fuzzy observers from the TS fuzzy models. TS fuzzy systems are classified into three families based on the input matrices and a FSFTC synthesis procedure is given for each family. In each family, sufficient conditions are derived for robust stabilization, in the sense of Taylor series stability and Lyapunov method, for the TS fuzzy system with parametric uncertainties, sensor faults, and unknown inputs. The sufficient conditions are formulated in the format of Linear Matrix Inequalities (LMIs). The effectiveness of the proposed controller design methodology is finally demonstrated through a wind energy system with Doubly Fed Induction Generators (DFIG) to illustrate the effectiveness of the proposed method.     

On-Line Diagnosis of Turbine-Generators using Artificial Intelligence

An Artificial Intelligence (AI) based, on-line Turbine Generator diagnostic expert system which is is presently under development is described. Utilizing the inputs from a number of sensors, the diagnostic system evaluates the condition of the equipment and communicates appropriate action. This is an improvement over present monitoring systems which simply apply alarm limits to the value of each sensed variable. In comparison to a monitoring system, a diagnostic system identifies equipment and instrument problems earlier with a higher degree of accuracy.     

集成的汽轮发电机组状态管理系统

介绍了一种与电厂DEH/DCS控制系统集成的汽轮发电机组状态管理系统(XTCM)。XTCM提供了对机组过程状态和设备健康状态进行监视、记录、分析、诊断和决策的统一平台。通过信息的共享和集成,XTCM为用户及时、准确地做出运行和维修决策提供了有力的保障。     

Regular Linear Systems Governed by a Boundary Controlled Heat Equation

In this paper we consider a class of distributed parameter systems governed by the heat equation on bounded domains in ⁿ . We consider two types of boundary inputs (actuators) and two types of boundary outputs (sensors). Allowing for any possible pairing of these, we consider a totality of four possible arrangements of our system. The first type of input (control) is through the Neumann boundary condition on a part of the boundary, together with a homogenous Neumann boundary condition on the remaining part of the boundary. For this type of input, the input space is infinite-dimensional. The second type of input (with a finite-dimensional input space) is obtained by imposing constant normal derivatives on each element of a finite partition of the boundary. The first type of output (observation) is given by evaluation (trace) of the state of the system on a part of the boundary, so that the output space is infinite-dimensional. For the second type of output (with a finite-dimensional output space), we again consider a partition of the boundary of the spatial domain (which can be different from the one considered for the inputs) and each output channel contains the average of the values of the state of the plant on one element of this partition. Our main result is that any possible combination of the aforementioned inputs and outputs provides a regular linear system.