Exergoeconomic optimization of thermodynamic systems using Particle Swarm Intelligence

Transcription

1 Budapest University of Technology and Economics Faculty of Mechanical Engineering Department of Energy Engineering Exergoeconomic optimization of thermodynamic systems using Particle Swarm Intelligence Theses Booklet Written by: AXEL GRONIEWSKY MSc in Mechanical Engineering Budapest 2015

2 The evaluation of the dissertation and the protocol of the defense can be inspected at the Dean s Office at the Faculty of Mechanical Engineering of the Budapest University of Technology and Economics

3 1. Introduction Over the past decade there have been significant changes in terms of technical and economic aspects of power generation. Considering the issues related to the structure and organization of the energy system, a paradigm shift took place: decentralized and embedded generation gradually begins to take over the former role of conventional highperformance power plants in electricity production. Prosumer 1 appears as a new element in the electricity market. In this altered and constantly changing environment conventional power plants, which still play an important role in electricity production, must fulfil their obligations. Challenges have to be addressed in different ways: 1. during the period of planning and preparation of the investment, using appropriate planning and decision-making methods, best-fit alternatives shall be chosen, as well as 2. operational strategy of already existing units must be designed to ensure the balanced performance. For analyzing such processes occurring with different likelihood methods and procedures (scenario and opportunity analysis) are required, which satisfy the following conditions: give an accurate picture of the thermodynamic characteristics of the power plant process, explore the thermodynamic losses of the cycle with possible locations for intervention, create both a qualitatively and quantitatively accurate link between technical and economical features, provide relatively accurate results under reasonable computation time even for incomplete input. 2. Objectives The main objective of this thesis was to design a method which assists in decision-making or finds optimal operational strategy, regardless of the quantity and type of products, under changing environmental and economic conditions, simultaneously taking thermodynamic and economic considerations into account. Such a decision-support tool might be appropriate for: 1 prosumer: expression to emphasize the blurring roles between producer and consumer in electricity production 1

4 estimating combined costs of entropy production and enthalpy losses during energy conversion; determining costs of products from poly-generation systems; comparing different feasible alternatives for investments; comparing different equipment for restoration or performance improvement; analyzing the impact of macroeconomic processes on power plant investments. Designing general methodology involving several academic disciplines cannot be appropriately accomplished without setting boundaries. Accordingly, in my thesis work: I only considered thermodynamic and economic analysis of steady state - steady flow systems, operating at base load; I did not consider part-load or overload operations neither did I examine control issues; I applied study estimate method for the economic calculations with the primary purpose of comparing alternatives in the same economic environment rather than presenting detailed analysis and provide accurate results. 3. Scientific background An exergy-based system which combines thermodynamic and economic approaches, applying swarm intelligence satisfies the requirements of the main objective. I performed a literature review in the areas of thermoeconomics, foundation of the complex system and swarm intelligence, the algorithm of the optima search. THERMOECONOMIC METHODOLOGIES Thermoeconomics is a discipline that combines economic and thermodynamic calculations [Kanoglu, 2009], [El-Sayed, 1989], [Gaggioli RA, 1989]. Second Law Costing Methods can be divided into three basic categories: calculus procedures, algebraic procedures and exergoeconomic diagnosis. Calculus Procedure With optimization of the design variables of the structure and system, methodology provides optimal balance between capital costs and operations and maintenance costs. I introduced 4 of the methods of which I presented 2 (EFA, STT) in detail. Development and performance of the methods were largely influenced by the lack of high-performance personal computers. Given the magnitude and complexity of the system, methodology does not examine the system globally but introduces thermoeconomic isolation and decomposes 2

5 it during optimization. Calculus methods are built on differential equations while optimization of marginal costs is based on Lagrange multipliers. Algebraic Procedure This is the most frequently applied method among thermoeconomic procedures. I introduced 19 of the methods of which I presented 4 (LIFO, AvCon / SPECT, mops and ECT) in detail. In contrast to calculation procedures, this method does not apply thermoeconomic isolation but examines the system globally. Interactions among system components are expressed by linear algebraic cost-balance equations derived from conventional economic analysis and auxiliary cost equations for each subcomponent of any system presented. They are related to the cost formation process of the system in order to investigate the average costs. Exergoeconomic Diagnosis Aim of the procedure is to detect possible anomalies affecting the energy consumption efficiency deviation of energy conversation processes, as well as identifying the components where these anomalies occur and their quantification. Since VALERO at al. were among the first introducing this method, at an early stage procedure showed resemblance with Exergetic Cost Theory (ECT). Thermoeconomic diagnosis has its foundations in the principle of non-equivalence of irreversibilities (exergy destruction or entropy production). In my work, I introduced 4 of the methods of which I presented all 4 (RMV, FI, FI FIE and CC) in detail, describing the basic concept, definitions, advantages and disadvantages. OPTIMUM SEARCH I briefly reviewed the optimum searching methods used for increasing the efficiency of power generation systems, describing genetic algorithms as the most commonly used search techniques of numerical problem-solving and particle swarm intelligence as the most important nature inspired meta-heuristic search engine in more detail. 4. Presentation of research After the historical overview, I carried out a comparative evaluation of the most common methods of thermoeconomic analysis. Since decision-making, as well as evaluation of operations strategy of existing systems as part of the thesis objectives relies largely on thermodynamic models of observed systems, I integrated an exergoeconomic method into the decision-making tool, which is capable of cooperating with any commercially available plant performance monitoring software. Based on the Tisza II. and Debrecen power plants I devised the thermodynamic model of a LANG-BBC 215 MW steam turbine and a 100 MW combined cycle gas turbine with double 3

6 pressure HRSG and 25.5 MW heat supply. By determining the maximum cycle efficiency of these models with swarm intelligence I demonstrated that PSO is suitable for optimum search in thermodynamic models. In order to carry out exergetic and exergoeconomic evaluations based on the results of thermal simulations, I determined the pressure and temperature dependent enthalpy, entropy, heat capacity and Gibbs functions of the working fluids using 9-term polynomial approximations of thermodynamic properties of ideal gas mixtures from NASA. I confirmed the consistency of state functions with the same coefficient. Through an ideal Joule-Brayton cycle I also demonstrated that differences due to numerical errors do not substantially affect the calculations. During the definition of physical and chemical exergy functions of streams I proved that standard Gibbs free energy of elements is zero only if reference frames of enthalpy and entropy functions are in line with each other otherwise it might undermine the definition of molar Gibbs free energy. I determined the exergetic efficiencies for both systems (Rankine cycle and CCGT) and components. I proved with calculations that swarm intelligence is suitable for exergy-based optimization of thermal systems. I devised a conventional economic model for power plant investments which estimates costs associated with capital investment and operation and maintenance using purchased equipment costs of the main equipment. The model is suitable for calculating the levelized costs of subsystems. In order to minimize the cost of electricity production, I defined the objective functions by minimizing the exergoeconomic costs of both power plants. For the evaluation of the objective functions I applied and solved the exergoeconomic cost balances and auxiliary relations formulated for all system components. Investment-related parameters of the main components were determined with the help of the cost functions taken from literature [Bejan, 1996]. I performed the exergoeconomic optimization for the steam turbine cycle and for the CCGT as well, both before (2006) and after (2012) the economic crisis. 5. New scientific results The scientific innovations presented in my dissertation can be summarized in the following theses: THESIS 1 ([1], [2], [3], [4]) By calculations performed with the application of swarm intelligence I have shown that this search engine is suitable for optimizing thermodynamic systems. Regardless of the characteristics of the thermal system, sensitivity of the swarm intelligence to 4

7 discontinuities depends on the velocity update algorithm, on the maximum size of velocity vector, and on the ratio of convergent and non-convergent solutions. Well-chosen parameter sets and constrains could increase the efficiency of the algorithm and reduce the average number of non-convergent solutions per iteration. THESIS 2 ([4]) By calculations performed with the application of conventional particle swarm intelligence on thermodynamic systems I have shown that the maximum size of the reduced velocity vector has an optimum. For a fixed search space, reduced velocity increases the search efficiency of conventional swarm intelligence, decreases standard deviation and the number of non-convergent solutions per iteration. Small velocity maximum however decreases global search ability and increases iteration. THESIS 3 ([4]) By calculations performed with the application of multiple elite dependent particle swarm intelligence on thermodynamic systems I have shown that high diversity in velocity updating is ineffective on thermodynamic optimization of a power plant due to increased computation time. Multiple elite dependent CLPSO requires significantly more iterations for a successful search than single elite dependent CPSO and has more non-convergent solutions per iteration as well. Due to high computational time CLPSO is less suitable for power plant optimization if the thermodynamic model is developed in a plant performance monitoring software. Computation time is affected only slightly by iteration threshold and swarm size but is significantly affected by the quality of the variables. This is primarily due to the fact that evaluation time of a particle depends on the number of internal iterations of the simulation software, which is always higher when particles do not converge. THESIS 4 ([5]) During the definition of physical and chemical exergy functions of streams I have shown that standard Gibbs free energy of elements is zero only if s ( p, T ) h% p T (, ) 0 0 % 0 0 =. T0 Old conventions which without putting reference frames of enthalpy and entropy functions in line with each other state that standard Gibbs free energy of elements is zero and corresponds chemical potential of product to Gibbs free energy of formation it undermines the definition of molar Gibbs free energy. THESIS 5 ([6]) 5

8 By calculations performed on thermodynamic systems I have shown that a natural gas-fired dual pressure combined cycle when heat is provided through back pressure turbine or from extraction steam produces heat on a higher exergy specific levelized cost than electricity. This is mainly the consequence of heat production preceding electricity production within the energy conversion chain. As heat production requires more steps in energy conversion than electricity generation, the magnitude of incremental exergy destruction and exergy loss is higher, therefore it has a greater impact on the fuel supplied to the overall system and has a higher economic importance. The longer the chain of energy conversion, the higher is the unit cost of the product ( cp, k 1 cp, k ) <. Also, assuming that the product of one component is the fuel of the next, it can be stated that ( ) ( ) C& + C& = c E& + E& = c E& + E& which shows that exergy destruction or loss of L, k D, k P, k 1 L, k D, k F, k L, k D, k the same magnitude is more expensive if it is farther from the overall fuel input. THESIS 6 ([3], [6], [7]) I formed a swarm intelligence based, modular, complex and integrated system to help decision-making process and evaluate operational strategy of power plants. The well-define interfaces between modules allow for the upgrade and substitution of each block. The system is capable of simultaneously considering thermodynamic and economic aspects of the observed power plant, regardless of the quantity or type of the products, under changing environmental and economic conditions. I demonstrated the viability of the system on a conventional regenerative Rankine cycle and on a typical dual pressure combined cycle. THESIS 7 ([6], [7]) By calculations performed on a regenerative Rankine cycle and on a dual pressure combined cycle I have shown that: regardless of the construction of the thermal system, base case specific fuel costs ( C& F C& F, Base ), and base case specific purchase equipment costs ( Base ) PEC PEC under the same thermodynamic conditions do not depend on financial environment, while other calculated exergoeconomic indicators do; regardless of the construction of the thermal system, condensate leaving the main condenser has the highest levelized cost per unit of exergy streams, as it has the lowest exergy level in the system; 6

9 the financial crisis altered the economic environment in a way which provides lower electricity prices in exchange for higher investment costs or in case of combined heat and power production electricity and heat prices. The conclusions are based on exergoeconomic indicators which were determined by cost functions and financial indicators introduced in subchapter Utilization of results The decision-making tool developed in this research is mainly suitable for comparing different aspects of technical solutions for a variety of power plant investments. Since continuous cost functions do not always provide reliable equipment costs they can be substituted with data provided by vendors. Based on available trends and experience, probability can be assigned to unknown values of technical data and economic indicators enabling not only developing and comparing various scenarios but also determining their probabilities. As part of the research I solved the problem of dynamic data exchange between Matlab and GateCycle, expanded the solver with entropy and exergy functions and made GateCycle suitable for optima search. 7

10 7. References [Kanoglu, 2009] [El-Sayed, 1989] [Gaggioli RA, 1989] [Bejan, 1996] Abusoglu A., Kanoglu M.: Exergoeconomic analysis and optimization of combined heat and power production: a review, Renewable and Sustainable Energy Reviews, 13/9/ , El-Sayed YM, Gaggioli RA.: A critical review of second law costing methods. 1. Background and algebraic procedures, Journal of Energy Resour-ASME, 111/1-7, El-Sayed YM, Gaggioli RA.: A critical review of second law costing methods. 2. Calculus procedures, Journal of Energy Resour-ASME, 111/8-15, Bejan A., Tsatsaronis G., Moran M.: Thermal Design and Optimization, Wiley, NewYork, Publications connected to the theses [1] Groniewsky A.: Bird flocking and power plants, 10 th International Conference on Heat Engines and Environmental Protection, May 23-25, Balatonfüred, Hungary, [2] Groniewsky A.: Optimization of power plants using nature inspired swarm intelligence, 8 th International Conference on Mechanical Engineering 2012, May 25, Budapest, Hungary, [3] Groniewsky Axel: Exergoeconomic optimization of a thermal power plant using particle swarm optimization, Thermal Science, 17/2/ , (IF=0,962) [4] Groniewsky Axel: Analysis of Particle Swarm-Aided Power Plant Optimization, Periodica Polytechnica Mechanical Engineering, 59/3/ , [5] Groniewsky Axel: Állapotfüggvények illesztése GateCycle hőséma-számító programhoz Energiagazdálkodás, 56/5-6, [6] Groniewsky Axel: Egy kombinált ciklusú erőmű exergoökonómiai optimálása, Energiagazdálkodás, 56/3-4/2-8, [7] Groniewsky Axel: Exergoeconomic analysis and optimization of a steam plant, 12 th International Conference on Heat Engines and Environmental Protection, Pécs, Hungary, May,

11 9. Other publications [8] Groniewsky A.: Termoökonómiai módszerek áttekintése, Energiagazdálkodás, 55/3/9-14, [9] Groniewsky A.: Jelentősebb termoökonómiai módszerek értékelő áttekintése: Algebrai eljárások, Energiagazdálkodás, 55/4/11-13, 2014 [10] Groniewsky A.: Jelentősebb termoökonómiai módszerek értékelő áttekintése: Diagnosztikai eljárások, Energiagazdálkodás, 55/4/14-16, [11] Groniewsky A.: Jelentősebb termoökonómiai módszerek értékelő áttekintése: Kalkulációs eljárások, Energiagazdálkodás, 55/5/16-18, [12] Groniewsky A., Czél B., Gróf Gy.: Lágy számítási módszerek alkalmazása az energetikában, Energiagazdálkodás, 52/2-5, [13] Gács I, Groniewsky A.: Klíma és energia IV., Magyar energetika, 17/4/25-33, [14] Groniewsky A., Gács I.: A hazai megújuló energiaforrások várható helyzete 2010-re, Magyar épületgépészet, 55/4/7-10, [15] Groniewsky A., Gács Iván: Megújuló energiaforrások támogatása, Magyar energetika, 14/6/16-19, [16] Groniewsky A.: Geotermális lehetőségek Magyarországon, Magyar energetika, 13/3/ 38-44, [17] Groniewsky A.: Geotermális energiahasznosítás környezetvédelmi kérdései, Környezetvédelem, 13/3/22-23, [18] Groniewsky A.: Exergoeconomic analysis of thermodynamic systems, 11 th International Conference on Heat Engines and Environmental Protection, June 3-5,Balatonfüred, Hungary, [19] Groniewsky A., Sándor Cs.: Tüzelés utáni széndioxid leválasztás hatásvizsgálata szénerőműben, 13. Energetika-Elektrotechnika Konferencia, October 11-14, Alba Iulia, Romania, [20] Groniewsky A., Gács I.: Introductory notes on the Reliability Assessment of Power Plants, 9 th International Conference on Heat Engines and Environmental Protection, May 25-27, Balatonfüred, Hungary, [21] Groniewsky A., Gács I.: Evaluation of biogas factories on the basis of their reliability, 8 th International Conference on Heat Engines and Environmental Protection, May 28-30, Balatonfüred, Hungary,

12 [22] Groniewsky A., Gács I.: Integration of renewable sources of energy into the existing electricity, 5th Conference on Mechanical Engineering 2006, May 25-26, Budapest, Hungary, [23] Groniewsky A., Gács I.: Megújuló energiaforrások támogatása, 7. Energetika- Elektrotechnika Konferencia, October 22-24, Cluj-Napoca, Romania, [24] Groniewsky A., Gács I.: Bevezető megjegyzések erőművek megbízhatósági vizsgálataihoz, V: Klímaváltozás Energiatudatosság Energiahatékonyság Nemzetközi konferencia, April 16-17, Szeged, Hungary, [25] Gács Iván et al.: Szén-dioxid leválasztás és eltárolás, Budapest: BME Energetika Tanszék, [26] Groniewsky A.: Gate Cycle hősémaszámító program alapjai, Elektronikus Jegyzet, [27] Groniewsky A.: Laboratory note for practical temperature measurements, Oktatási segédanyag,