Search results for: linear combinatorial optimization

Authors: Aaron Aboshio, Jianqioa Ye

Abstract:

In this study, static and dynamic responses of a typical reinforced concrete solid slab, designed to British Standard (BS 8110: 1997) and under self and live loadings for dance halls are reported. Linear perturbation analysis using finite element method was employed for modal, impulse loading and frequency response analyses of the slab under the aforementioned loading condition. Results from the static and dynamic analyses, comprising of the slab fundamental frequencies and mode shapes, dynamic amplification factor, maximum deflection, stress distributions among other valuable outcomes are presented and discussed. These were gauged with the limiting provisions in the design code with a view of justifying valid optimization objective function for the structure that can ensure both adequate strength and economical section for large clear span slabs. This is necessary owing to the continued increase in cost of erecting building structures and the squeeze on public finance globally.

Keywords: Economical design, Finite element method, Modal dynamics, Reinforced concrete, Slab.

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Authors: C. Chitra, P. Subbaraj

Abstract:

The shortest path routing problem is a multiobjective nonlinear optimization problem with constraints. This problem has been addressed by considering Quality of service parameters, delay and cost objectives separately or as a weighted sum of both objectives. Multiobjective evolutionary algorithms can find multiple pareto-optimal solutions in one single run and this ability makes them attractive for solving problems with multiple and conflicting objectives. This paper uses an elitist multiobjective evolutionary algorithm based on the Non-dominated Sorting Genetic Algorithm (NSGA), for solving the dynamic shortest path routing problem in computer networks. A priority-based encoding scheme is proposed for population initialization. Elitism ensures that the best solution does not deteriorate in the next generations. Results for a sample test network have been presented to demonstrate the capabilities of the proposed approach to generate well-distributed pareto-optimal solutions of dynamic routing problem in one single run. The results obtained by NSGA are compared with single objective weighting factor method for which Genetic Algorithm (GA) was applied.

Keywords: Multiobjective optimization, Non-dominated SortingGenetic Algorithm, Routing, Weighted sum.

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Authors: Nahid Ardalani, Ahmadreza Khoogar, H. Roohi

Abstract:

In this paper we compare the response of linear and nonlinear neural network-based prediction schemes in prediction of received Signal-to-Interference Power Ratio (SIR) in Direct Sequence Code Division Multiple Access (DS/CDMA) systems. The nonlinear predictor is Multilayer Perceptron MLP and the linear predictor is an Adaptive Linear (Adaline) predictor. We solve the problem of complexity by using the Minimum Mean Squared Error (MMSE) principle to select the optimal predictors. The optimized Adaline predictor is compared to optimized MLP by employing noisy Rayleigh fading signals with 1.8 GHZ carrier frequency in an urban environment. The results show that the Adaline predictor can estimates SIR with the same error as MLP when the user has the velocity of 5 km/h and 60 km/h but by increasing the velocity up-to 120 km/h the mean squared error of MLP is two times more than Adaline predictor. This makes the Adaline predictor (with lower complexity) more suitable than MLP for closed-loop power control where efficient and accurate identification of the time-varying inverse dynamics of the multi path fading channel is required.

Keywords: Power control, neural networks, DS/CDMA mobilecommunication systems.

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Authors: Surinder Deswal, Mahesh Pal

Abstract:

An Artificial Neural Network based modeling technique has been used to study the influence of different combinations of meteorological parameters on evaporation from a reservoir. The data set used is taken from an earlier reported study. Several input combination were tried so as to find out the importance of different input parameters in predicting the evaporation. The prediction accuracy of Artificial Neural Network has also been compared with the accuracy of linear regression for predicting evaporation. The comparison demonstrated superior performance of Artificial Neural Network over linear regression approach. The findings of the study also revealed the requirement of all input parameters considered together, instead of individual parameters taken one at a time as reported in earlier studies, in predicting the evaporation. The highest correlation coefficient (0.960) along with lowest root mean square error (0.865) was obtained with the input combination of air temperature, wind speed, sunshine hours and mean relative humidity. A graph between the actual and predicted values of evaporation suggests that most of the values lie within a scatter of ±15% with all input parameters. The findings of this study suggest the usefulness of ANN technique in predicting the evaporation losses from reservoirs.

Keywords: Artificial neural network, evaporation losses, multiple linear regression, modeling.

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Authors: Azadeh Maroufmashat, Sourena Sattari Khavas, Halle Bakhteeyar

Abstract:

Multi-energy systems will enhance the system reliability and power quality. This paper presents an integrated approach for the design and operation of distributed energy resources (DER) systems, based on energy hub modeling. A multi-objective optimization model is developed by considering an integrated view of electricity and natural gas network to analyze the optimal design and operating condition of DER systems, by considering two conflicting objectives, namely, minimization of total cost and the minimization of environmental impact which is assessed in terms of CO2 emissions. The mathematical model considers energy demands of the site, local climate data, and utility tariff structure, as well as technical and financial characteristics of the candidate DER technologies. To provide energy demands, energy systems including photovoltaic, and co-generation systems, boiler, central power grid are considered. As an illustrative example, a hotel in Iran demonstrates potential applications of the proposed method. The results prove that increasing the satisfaction degree of environmental objective leads to increased total cost.

Keywords: Multi objective optimization, DER systems, Energy hub, Cost, CO2 emission.

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Authors: S. Beyou, B. Grondin-Perez, M. Benne, C. Damour, J.-P. Chabriat

Abstract:

The industrial process of the sugar cane crystallization produces a residual that still contains a lot of soluble sucrose and the objective of the factory is to improve its extraction. Therefore, there are substantial losses justifying the search for the optimization of the process. Crystallization process studied on the industrial site is based on the “three massecuites process". The third step of this process constitutes the final stage of exhaustion of the sucrose dissolved in the mother liquor. During the process of the third step of crystallization (Ccrystallization), the phase that is studied and whose control is to be improved, is the growing phase (crystal growth phase). The study of this process on the industrial site is a problem in its own. A control scheme is proposed to improve the standard PID control law used in the factory. An auto-tuning PID controller based on instantaneous linearization of a neural network is then proposed.

Keywords: Auto-tuning, PID, Instantaneous linearization, Neural network, Non linear process, C-crystallisation.

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Authors: B.Bahmani Firouzi, T. Niknam, M. Nayeripour

Abstract:

Clustering is a very well known technique in data mining. One of the most widely used clustering techniques is the kmeans algorithm. Solutions obtained from this technique depend on the initialization of cluster centers and the final solution converges to local minima. In order to overcome K-means algorithm shortcomings, this paper proposes a hybrid evolutionary algorithm based on the combination of PSO, SA and K-means algorithms, called PSO-SA-K, which can find better cluster partition. The performance is evaluated through several benchmark data sets. The simulation results show that the proposed algorithm outperforms previous approaches, such as PSO, SA and K-means for partitional clustering problem.

Keywords: Data clustering, Hybrid evolutionary optimization algorithm, K-means algorithm, Simulated Annealing (SA), Particle Swarm Optimization (PSO).

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Authors: Qingqing Wang, Shouming Zhong

Abstract:

This paper is concerned with the stability problem with two additive time-varying delay components. By choosing one augmented Lyapunov-Krasovskii functional, using some new zero equalities, and combining linear matrix inequalities (LMI) techniques, two new sufficient criteria ensuring the global stability asymptotic stability of DNNs is obtained. These stability criteria are present in terms of linear matrix inequalities and can be easily checked. Finally, some examples are showed to demonstrate the effectiveness and less conservatism of the proposed method.

Keywords: Neural networks, Globally asymptotic stability, LMI approach, Additive time-varying delays.

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Authors: Li Shoutao, Gordon Lee

Abstract:

Evolutionary robotics is concerned with the design of intelligent systems with life-like properties by means of simulated evolution. Approaches in evolutionary robotics can be categorized according to the control structures that represent the behavior and the parameters of the controller that undergo adaptation. The basic idea is to automatically synthesize behaviors that enable the robot to perform useful tasks in complex environments. The evolutionary algorithm searches through the space of parameterized controllers that map sensory perceptions to control actions, thus realizing a specific robotic behavior. Further, the evolutionary algorithm maintains and improves a population of candidate behaviors by means of selection, recombination and mutation. A fitness function evaluates the performance of the resulting behavior according to the robot-s task or mission. In this paper, the focus is in the use of genetic algorithms to solve a multi-objective optimization problem representing robot behaviors; in particular, the A-Compander Law is employed in selecting the weight of each objective during the optimization process. Results using an adaptive fitness function show that this approach can efficiently react to complex tasks under variable environments.

Keywords: adaptive fuzzy neural inference, evolutionary tuning

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Authors: Davod Khojasteh Salkuyeh, Sayyed Hasan Azizi

Abstract:

We study the semiconvergence of Gauss-Seidel iterative methods for the least squares solution of minimal norm of rank deficient linear systems of equations. Necessary and sufficient conditions for the semiconvergence of the Gauss-Seidel iterative method are given. We also show that if the linear system of equations is consistent, then the proposed methods with a zero vector as an initial guess converge in one iteration. Some numerical results are given to illustrate the theoretical results.

Keywords: rank deficient least squares problems, AOR iterativemethod, Gauss-Seidel iterative method, semiconvergence.

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Authors: Mahdi Pirmoradian, Olayinka Adigun, Christos Politis

Abstract:

Cooperative spectrum sensing is a crucial challenge in cognitive radio networks. Cooperative sensing can increase the reliability of spectrum hole detection, optimize sensing time and reduce delay in cooperative networks. In this paper, an efficient central capacity optimization algorithm is proposed to minimize cooperative sensing time in a homogenous sensor network using OR decision rule subject to the detection and false alarm probabilities constraints. The evaluation results reveal significant improvement in the sensing time and normalized capacity of the cognitive sensors.

Keywords: Cooperative networks, normalized capacity, sensing time.

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Authors: M. Siosemarde, M. Byzedi

Abstract:

Saturated hydraulic conductivity of Soil is an important property in processes involving water and solute flow in soils. Saturated hydraulic conductivity of soil is difficult to measure and can be highly variable, requiring a large number of replicate samples. In this study, 60 sets of soil samples were collected at Saqhez region of Kurdistan province-IRAN. The statistics such as Correlation Coefficient (R), Root Mean Square Error (RMSE), Mean Bias Error (MBE) and Mean Absolute Error (MAE) were used to evaluation the multiple linear regression models varied with number of dataset. In this study the multiple linear regression models were evaluated when only percentage of sand, silt, and clay content (SSC) were used as inputs, and when SSC and bulk density, Bd, (SSC+Bd) were used as inputs. The R, RMSE, MBE and MAE values of the 50 dataset for method (SSC), were calculated 0.925, 15.29, -1.03 and 12.51 and for method (SSC+Bd), were calculated 0.927, 15.28,-1.11 and 12.92, respectively, for relationship obtained from multiple linear regressions on data. Also the R, RMSE, MBE and MAE values of the 10 dataset for method (SSC), were calculated 0.725, 19.62, - 9.87 and 18.91 and for method (SSC+Bd), were calculated 0.618, 24.69, -17.37 and 22.16, respectively, which shows when number of dataset increase, precision of estimated saturated hydraulic conductivity, increases.

Keywords: dataset, precision, saturated hydraulic conductivity, soil and statistics.

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Authors: Konstantinos Metaxiotis, Konstantinos Liagkouras

Abstract:

The objective of this study is to examine the performance of three well-known multiobjective evolutionary algorithms for solving optimization problems. The first algorithm is the Non-dominated Sorting Genetic Algorithm-II (NSGA-II), the second one is the Strength Pareto Evolutionary Algorithm 2 (SPEA-2), and the third one is the Multiobjective Evolutionary Algorithms based on decomposition (MOEA/D). The examined multiobjective algorithms are analyzed and tested on the ZDT set of test functions by three performance metrics. The results indicate that the NSGA-II performs better than the other two algorithms based on three performance metrics.

Keywords: MOEAs, Multiobjective optimization, ZDT test functions, performance metrics.

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Authors: Zeshan Ahmad, Matteo Zoppi, Rezia Molfino

Abstract:

The stiffness of the workpiece is very important to reduce the errors in manufacturing process. The high stiffness of the workpiece can be achieved by optimal positioning of fixture elements in the fixture. The minimization of the sum of the nodal deflection normal to the surface is used as objective function in previous research. The deflection in other direction has been neglected. The 3-2-1 fixturing principle is not valid for metal sheets due to its flexible nature. We propose a new fixture layout optimization method N-3-2-1 for metal sheets that uses the strain energy of the finite elements. This method combines the genetic algorithm and finite element analysis. The objective function in this method is to minimize the sum of all the element strain energy. By using the concept of element strain energy, the deformations in all the directions have been considered. Strain energy and stiffness are inversely proportional to each other. So, lower the value of strain energy, higher will be the stiffness. Two different kinds of case studies are presented. The case studies are solved for both objective functions; element strain energy and nodal deflection. The result are compared to verify the propose method.

Keywords: Fixture layout, optimization, fixturing element, genetic algorithm.

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Authors: M. R. Aghaebrahimi, S. H. Zahiri, M. Amiri

Abstract:

In this paper a data miner based on the learning automata is proposed and is called LA-miner. The LA-miner extracts classification rules from data sets automatically. The proposed algorithm is established based on the function optimization using learning automata. The experimental results on three benchmarks indicate that the performance of the proposed LA-miner is comparable with (sometimes better than) the Ant-miner (a data miner algorithm based on the Ant Colony optimization algorithm) and CNZ (a well-known data mining algorithm for classification).

Keywords: Data mining, Learning automata, Classification rules, Knowledge discovery.

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Authors: Miaomiao Yang, Shouming Zhong

Abstract:

This paper studies the problem of stability criteria for neural networks with two additive time-varying delays.A new Lyapunov-Krasovskii function is constructed and some new delay dependent stability criterias are derived in the terms of linear matrix inequalities(LMI), zero equalities and reciprocally convex approach.The several stability criterion proposed in this paper is simpler and effective. Finally,numerical examples are provided to demonstrate the feasibility and effectiveness of our results.

Keywords: Stability, Neural networks, Linear Matrix Inequalities (LMI) , Lyapunov function, Time-varying delays

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Authors: Lekbir Adel, Alloui-Lombarkia Ourida, Mekentichi Sihem, Noui Yassine, Baississe Salima

Abstract:

The objective of this study was to optimize the extraction conditions for phenolic compounds, total flavonoids, and antioxidant activity from Deglet-Nour variety. The extraction of active components from natural sources depends on different factors. The knowledge of the effects of different extraction parameters is useful for the optimization of the process, as well for the ability to predict the extraction yield. The effects of extraction variables, namely types of solvent (methanol, ethanol and acetone) and extraction time (1h, 6h, 12h and 24h) on phenolics extraction yield were evaluated. It has been shown that the time of extraction and types of solvent have a statistically significant influence on the extraction of phenolic compounds from Deglet-Nour variety. The optimised conditions yielded values of 80.19 ± 6.37 mg GAE/100 g FW for TPC, 2.34 ± 0.27 mg QE/100 g FW for TFC and 90.20 ± 1.29% for antioxidant activity were methanol solvent and 6 hours of time. According to the results obtained in this study, Deglet-Nour variety can be considered as a natural source of phenolic compounds with good antioxidant capacity.

Keywords: Deglet-Nour variety, Date palm Fruit, Phenolic compounds, Total flavonoids, Antioxidant activity, Extraction, Optimization.

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Authors: Wayne Goodridge, Alexander Nikov, Ashok Sahai

Abstract:

Multi criteria decision making (MCDM) methods like analytic hierarchy process, ELECTRE and multi-attribute utility theory are critically studied. They have irregularities in terms of the reliability of ranking of the best alternatives. The Routing Decision Support (RDS) algorithm is trying to improve some of their deficiencies. This paper gives a mathematical verification that the RDS algorithm conforms to the test criteria for an effective MCDM method when a linear preference function is considered.

Keywords: Decision support systems, linear preference function, multi-criteria decision-making algorithm, analytic hierarchy process.

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Authors: Tooraj Hakim Elahi, Abdolamir Nekoubin

Abstract:

In many applications, magnetic suspension systems are required to operate over large variations in air gap. As a result, the nonlinearities inherent in most types of suspensions have a significant impact on performance. Specifically, it may be difficult to design a linear controller which gives satisfactory performance, stability, and disturbance rejection over a wide range of operating points. in this paper an optimal controller based on discontinuous mathematical model of the system for an electromagnetic suspension system which is applied in magnetic trains has been designed . Simulations show that the new controller can adapt well to the variance of suspension mass and gap, and keep its dynamic performance, thus it is superior to the classic controller.

Keywords: Magnetic Levitation, optimal controller, mass and gap

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Authors: Qingqing Wang, Shouming Zhong

Abstract:

Utilizing the Lyapunov functional method and combining linear matrix inequality (LMI) techniques and integral inequality approach (IIA) to analyze the global asymptotic stability for delayed neural networks (DNNs),a new sufficient criterion ensuring the global stability of DNNs is obtained.The criteria are formulated in terms of a set of linear matrix inequalities,which can be checked efficiently by use of some standard numercial packages.In order to show the stability condition in this paper gives much less conservative results than those in the literature,numerical examples are considered.

Keywords: Neural networks, Globally asymptotic stability , LMI approach , IIA approach , Time-varying delay.

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Authors: Fariba Azizi, Firoozeh Haghighi, Viliam Makis

Abstract:

In this paper, we propose a method to model the relationship between failure time and degradation for a simple step stress test where underlying degradation path is linear and different causes of failure are possible. It is assumed that the intensity function depends only on the degradation value. No assumptions are made about the distribution of the failure times. A simple step-stress test is used to shorten failure time of products and a tampered failure rate (TFR) model is proposed to describe the effect of the changing stress on the intensities. We assume that some of the products that fail during the test have a cause of failure that is only known to belong to a certain subset of all possible failures. This case is known as masking. In the presence of masking, the maximum likelihood estimates (MLEs) of the model parameters are obtained through an expectation-maximization (EM) algorithm by treating the causes of failure as missing values. The effect of incomplete information on the estimation of parameters is studied through a Monte-Carlo simulation. Finally, a real example is analyzed to illustrate the application of the proposed methods.

Keywords: Expectation-maximization (EM) algorithm, cause of failure, intensity, linear degradation path, masked data, reliability function.

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Authors: Farhad Kolahan, Mehdi Heidari

Abstract:

Gas Metal Arc Welding (GMAW) processes is an important joining process widely used in metal fabrication industries. This paper addresses modeling and optimization of this technique using a set of experimental data and regression analysis. The set of experimental data has been used to assess the influence of GMAW process parameters in weld bead geometry. The process variables considered here include voltage (V); wire feed rate (F); torch Angle (A); welding speed (S) and nozzle-to-plate distance (D). The process output characteristics include weld bead height, width and penetration. The Taguchi method and regression modeling are used in order to establish the relationships between input and output parameters. The adequacy of the model is evaluated using analysis of variance (ANOVA) technique. In the next stage, the proposed model is embedded into a Simulated Annealing (SA) algorithm to optimize the GMAW process parameters. The objective is to determine a suitable set of process parameters that can produce desired bead geometry, considering the ranges of the process parameters. Computational results prove the effectiveness of the proposed model and optimization procedure.

Keywords: Weld Bead Geometry, GMAW welding, Processparameters Optimization, Modeling, SA algorithm

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Authors: Miloš Šeda

Abstract:

Finding the minimal logical functions has important applications in the design of logical circuits. This task is solved by many different methods but, frequently, they are not suitable for a computer implementation. We briefly summarise the well-known Quine-McCluskey method, which gives a unique procedure of computing and thus can be simply implemented, but, even for simple examples, does not guarantee an optimal solution. Since the Petrick extension of the Quine-McCluskey method does not give a generally usable method for finding an optimum for logical functions with a high number of values, we focus on interpretation of the result of the Quine-McCluskey method and show that it represents a set covering problem that, unfortunately, is an NP-hard combinatorial problem. Therefore it must be solved by heuristic or approximation methods. We propose an approach based on genetic algorithms and show suitable parameter settings.

Keywords: Boolean algebra, Karnaugh map, Quine-McCluskey method, set covering problem, genetic algorithm.

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Authors: Suhas K, Sripadaraja K

Abstract:

This paper details few mechanical modeling and design issues of RF MEMS switches. We concentrate on an electrostatically actuated broad side series switch; surface micromachined with a crab leg membrane. The same results are extended to any complex structure. With available experimental data and fabrication results, we present the variation in dynamic performance and compliance of the switch with reference to few design issues, which we find are critical in deciding the dynamic behavior of the switch, without compromise on the RF characteristics. The optimization of pull in voltage, transient time and resonant frequency with regard to these critical design parameters are also presented.

Keywords: Microelectromechanical Systems (MEMS), RadioFrequency MEMS, Modeling, Actuators

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Authors: Henrik Prinzhorn, Peter Nyhuis, Johannes Wagner, Peter Burggräf, Torben Schmitz, Christina Reuter

Abstract:

Assembling large-scale products, such as airplanes, locomotives, or wind turbines, involves frequent process interruptions induced by e.g. delayed material deliveries or missing availability of resources. This leads to a negative impact on the logistical performance of a producer of xxl-products. In industrial practice, in case of interruptions, the identification, evaluation and eventually the selection of an alternative order of assembly activities (‘assembly alternative’) leads to an enormous challenge, especially if an optimized logistical decision should be reached. Therefore, in this paper, an innovative, optimization model for the identification of assembly alternatives that addresses the given problem is presented. It describes make-to-order, large-scale product assembly processes as a resource constrained project scheduling (RCPS) problem which follows given restrictions in practice. For the evaluation of the assembly alternative, a cost-based definition of the logistical objectives (delivery reliability, inventory, make-span and workload) is presented.

Keywords: Assembly scheduling, large-scale products, make-to-order, rescheduling, optimization.

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Authors: Lianglin Xiong, Shouming Zhong, Mao Ye

Abstract:

This paper addresses the stabilization issues for a class of uncertain switched neutral systems with nonlinear perturbations. Based on new classes of piecewise Lyapunov functionals, the stability assumption on all the main operators or the convex combination of coefficient matrices is avoid, and a new switching rule is introduced to stabilize the neutral systems. The switching rule is designed from the solution of the so-called Lyapunov-Metzler linear matrix inequalities. Finally, three simulation examples are given to demonstrate the significant improvements over the existing results.

Keywords: Switched neutral system, piecewise Lyapunov functional, nonlinear perturbation, Lyapunov-Metzler linear matrix inequality.

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Authors: Ephraim Gower, Thato Tsalaile, Monageng Kgwadi, Malcolm Hawksford.

Abstract:

In this paper, the statistical properties of filtered or convolved signals are considered by deriving the resulting density functions as well as the exact mean and variance expressions given a prior knowledge about the statistics of the individual signals in the filtering or convolution process. It is shown that the density function after linear convolution is a mixture density, where the number of density components is equal to the number of observations of the shortest signal. For circular convolution, the observed samples are characterized by a single density function, which is a sum of products.

Keywords: Circular Convolution, linear Convolution, mixture density function.

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Authors: N. Arulanand, P. Dhara

Abstract:

Optimizing the parameters in the controller plays a vital role in the control theory and its applications. Optimizing the PID parameters is finding out the best value from the feasible solutions. Finding the optimal value is an optimization problem. Inverted Pendulum is a very good platform for control engineers to verify and apply different logics in the field of control theory. It is necessary to find an optimization technique for the controller to tune the values automatically in order to minimize the error within the given bounds. In this paper, the algorithmic concepts of Harmony search (HS) and Genetic Algorithm (GA) have been analyzed for the given range of values. The experimental results show that HS performs well than GA.

Keywords: Genetic Algorithm, Harmony Search Algorithm, Inverted Pendulum, PID Controller.

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Authors: Jaedo Kim, Youngjoon Han, Hernsoo Hahn

Abstract:

The purpose of this paper is to detect human in images. This paper proposes a method for extracting human body feature descriptors consisting of projected edge component series. The feature descriptor can express appearances and shapes of human with local and global distribution of edges. Our method evaluated with a linear SVM classifier on Daimler-Chrysler pedestrian dataset, and test with various sub-region size. The result shows that the accuracy level of proposed method similar to Histogram of Oriented Gradients(HOG) feature descriptor and feature extraction process is simple and faster than existing methods.

Keywords: Human detection, Projected edge descriptor, Linear SVM, Local appearance feature

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Authors: Mohamed Ghorab

Abstract:

Local generated and distributed system for thermal and electrical energy is sighted in the near future to reduce transmission losses instead of the centralized system. Distributed Energy Resources (DER) is designed at different sizes (small and medium) and it is incorporated in energy distribution between the hubs. The energy generated from each technology at each hub should meet the local energy demands. Economic and environmental enhancement can be achieved when there are interaction and energy exchange between the hubs. Network energy system and CO₂ optimization between different six hubs presented Canadian community level are investigated in this study. Three different scenarios of technology systems are studied to meet both thermal and electrical demand loads for the six hubs. The conventional system is used as the first technology system and a reference case study. The conventional system includes boiler to provide the thermal energy, but the electrical energy is imported from the utility grid. The second technology system includes combined heat and power (CHP) system to meet the thermal demand loads and part of the electrical demand load. The third scenario has integration systems of CHP and Organic Rankine Cycle (ORC) where the thermal waste energy from the CHP system is used by ORC to generate electricity. General Algebraic Modeling System (GAMS) is used to model DER system optimization based on energy economics and CO₂ emission analyses. The results are compared with the conventional energy system. The results show that scenarios 2 and 3 provide an annual total cost saving of 21.3% and 32.3 %, respectively compared to the conventional system (scenario 1). Additionally, Scenario 3 (CHP & ORC systems) provides 32.5% saving in CO₂ emission compared to conventional system subsequent case 2 (CHP system) with a value of 9.3%.  

Keywords: Distributed energy resources, network energy system, optimization, microgeneration system.

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