Combined Heat and Power Dynamic Economic Dispatch with Emission Limitations Using Hybrid DE-SQP Method

Combined heat and power dynamic economic emission dispatch (CHPDEED) problem is a complicated nonlinear constrained multiobjective optimization problem with nonconvex characteristics. CHPDEED determines the optimal heat and power schedule of committed generating units by minimizing both fuel cost and emission simultaneously under ramp rate constraints and other constraints. This paper proposes hybrid differential evolution (DE) and sequential quadratic programming (SQP) to solve the CHPDEED problem with nonsmooth and nonconvex cost function due to valve point effects. DE is used as a global optimizer, and SQP is used as a fine tuning to determine the optimal solution at the final. The proposed hybrid DE-SQP method has been tested and compared to demonstrate its effectiveness

Opportunisme et traitement des contraintes dans MADS

RÉSUMÉ: Dans le domaine de l’optimisation de boites noires, l’utilisateur n’a pas d’expressions analytiques de la fonction objectif et des contraintes. De fait, il n’a pas accès aux gradients. Le gradient est une information importante en optimisation étant donné qu’il permet de fournir une direction de montée de la fonction. De plus, pour récupérer les différentes valeurs de la fonction objectif et des contraintes, des simulations informatiques ou des tests en laboratoires doivent être effectués. Ceci rajoute de nombreuses difficultés supplémentaires : temps de calculs importants pour récupérer les données, bruitage des données et certaines simulations peuvent échouer. Pour résoudre ce genre de problèmes, des algorithmes ont été développés. Parmi eux, MADS a été proposé par Audet et Dennis en 2006. C’est un algorithme itératif de recherche directe qui évalue des points de proche en proche sur un treillis. Il offrait à la base un traitement rudimentaire des contraintes, en associant une valeur infinie à tous points non -réalisables. Il a depuis été étoffé pour offrir un traitement plus souple à des contraintes de plus en plus hétéroclites. Cette thèse propose trois nouvelles fonctionnalités à l’algorithme MADS. Premièrement, MADS calcule des modèles des contraintes afin d’ordonner les points du plus prometteur au moins prometteur. Cependant, un traitement adéquat des contraintes binaires, qui ne retournent que deux valeurs, manque dans MADS. Pour pallier cette absence, des modèles des contraintes binaires seront proposés en utilisant des outils de régression, issus de la classification supervisée. Deuxièmement, ces mêmes outils permettent de proposer un ordonnancement nouveau des points à évaluer quand aucune fonction substitut n’est accessible dans MADS. Les points qui ont le plus de chance d’être réalisables seront évalués en premier pour favoriser la recherche de solutions réalisables de qualité. Cette stratégie sera comparée à une méthode favorisant les points les plus éloignés des points déjà évalués et à la méthode par défaut dans ce cas dans MADS, qui favorise les points qui sont le plus dans la direction du dernier succès par rapport au centre du treillis. Enfin, il peut être noté que la mise à l’échelle des contraintes choisie par l’utilisateur au moment de définir le problème a un impact sur le fonctionnement de MADS. MADS propose un traitement de mise à l’échelle des variables en entrée de la boite noire, mais rien pour les contraintes en sortie. Cette thèse propose une façon de les mettre à l’échelle, de sorte qu’elles prennent des valeurs de même ordre de grandeur. Cela permettra qu’elles aient globalement la même importance.----------ABSTRACT: In the field of blackbox optimization, the user does not have access to the analytical expressions of the objective function and of the constraints. Thus, there is no access to the gradient. But the gradient is an important piece of information since it gives an increasing direction of the function. Moreover, in order to obtain those values, computer simulations or experiments in laboratory have to be done. This adds further difficulties: heavy computational times to get the data, noisy data and some simulations may fail. To solve this kind of problems, algorithms have been developed. Among them, MADS has been proposed by Audet and Dennis in 2006. It is a direct search iterative algorithm that evaluates points on a mesh. At first, it offered a basic management of the constraints by associating an infinite value to all infeasible elements. Since then, more flexible ways have been proposed to handle various types of constraints. There are for example models for most of the constraints in order to sort points from the most to the least promising. However, in MADS, there is no specific management of binary constraints, which can return only two different values. Thus, models of binary constraints will be offered using tools of regression from supervised classification. Those tools also offer new ordering methods to sort the points that need to be evaluated when no models are available in MADS. The points which are the most likely to be feasible will be evaluated first in order to look most likely for feasible solutions. This strategy will be compared to one evaluating first the elements the furthest from the ones already evaluated and to the default, in that situation, in MADS which sorts the points according to the direction of last success. Finally, it should be pointed out that the scaling of the constraints provided by the user chosen while defining the problem has an impact on MADS’s behaviour. MADS deals with the scaling of the input variables of the blackbox, but nothing is done for the constraints in the output. This thesis offers to handle the scaling of the output so that they take values of about the same range so that they have more or less the same influence

Solution of combined heat and power economic dispatch problem using genetic algorithm.

Masters Degree. University of KwaZulu-Natal, Durban.The combination of heat and power constitutes a system that provides electricity and thermal energy concurrently. Its high efficiency and significant emission reduction makes it an outstanding prospect in the future of energy production and transmission. The broad application of combined heat and power units requires the joint dispatch of power and heating systems, in which the modelling of combined heat and power units plays a vital role. This research paper employed genetic algorithm, artificial bee colony, differential evolution, particle swarm optimization and direct solution algorithms to evaluate the cost function as well as output decision variables of heat and power in a system that has simple cycle cogeneration unit with quadratic cost function. The system was first modeled to determine the various parameters of combined heat and power units in order to solve the economic dispatch problem with direct solution algorithm. In order for modelling to be done, a general structure of combined heat and power must be defined. The system considered in this research consists of four test units, i.e. two conventional power units, one combined heat and power unit and one heat-only unit. These algorithms were applied to on the research data set to determine the required decision variables while taking into account the power and heat units, operation bound of power and heat-only units as well as feasible operation region of the cogeneration unit. Power and heat output decision variables plus cost functions from Genetic Algorithm, differential evolution, Particle Swarm Optimization and artificial bee colony were determined using codes. Also, the decision variables and cost function value were obtained by calculations using direct solution algorithm. The findings of the research paper show that there are different ways in which combined heat and power economic dispatch variables can be determined, which include genetic algorithm, differential evolution, artificial bee colony, particle swarm optimization and direct solution algorithms. However, each solution method allows for different combined heat and power output decision variables to be found, with some of the methods (particle swarm optimization and artificial bee colony) having setbacks such as: large objective function values, slower convergence and large number solution. The analysis revealed that the differential evolution algorithm is a viable alternative to solving combined heat and power problems. This is due in most part to its faster convergence, minimum cost function value, and high quality solution which are diverse and widespread, more as a result of its effective search capability than genetic algorithm, particle swarm optimization, direct solution and artificial bee colony algorithms. The methods investigated in this research paper can be used and expanded on to create useful and accurate technique of solving combined heat and power problems

Renovation and optimization of existing district heating networks: towards smart low carbon thermal grids

District heating and cooling (DHC) systems are attracting increased interest for their low carbon potential. However, most DHC systems are not operating at the expected performance level. Optimization and Enhancement of DHC networks to reduce (a) fossil fuel consumption, CO2 emission, and heat losses across the network, while (b) increasing return on investment, form key challenges faced by decision makers in the fast developing energy landscape. This thesis hypothesises that optimization of existing district heating networks can contribute to the development of smart thermal grids by integrating sustainable energy and intelligent management technology. This requires an accurate simulation capability at the district level factoring in building fabric and optimization of the system through energy generation, energy distribution, heat substation and terminal users. First, the thesis presents a novel concept to determine building envelope thermal transmittance (known as U-values) and air infiltration rate by a combination of energy modelling (DesignBuilder and EnergyPlus), regression models and genetic algorithm at quasi-steady state conditions. The calibrated U-values and air infiltration rate are employed as inputs in EnergyPlus to model one workday heat consumption. When compared with thermal demand from measured data, the accuracy of the calibrated model has improved significantly. Next, dynamic simulation of distribution network is demonstrated. A numerical simulation model is developed in Simulink to analyse dynamic heat losses in the pipe network at different periods of the week. Results show that heat losses vary between 1-2% during the weekday daytime, while the heat losses increase to 8-12% at other time periods. Supply and return temperatures of each building are presented and simulation results are in line with measured data. Meanwhile, Heat losses of the next generation DH are investigated based on the constructed model. Results show that lower distribution temperature and advanced insulation III technology greatly reduce network heat losses. Also, the network heat loss can be further minimized by a reduction of heat demand in buildings. Finally, a holistic district heating simulation capability is proposed. The simulation capability is carried out under the BCVTB (Building Controls Virtual Test Bed) environment. And the results display the operational schedule under the current operation scheme. Economic and environmental evaluation of the current operation scheme shows that biomass boiler is the cheapest option for heat generation due to renewable heat incentive. This district simulation capability is used to perform day-ahead optimization to determine the optimal schedules, targeting operation cost minimization. MILP is employed for optimization as it can be used to represent non-linear boiler efficiency without sacrificing the advantages brought by linear programming. Efficiency with respect to heat output level is introduced. The results indicate that smart control can be used for peak shaving, installation capacity reduction and operation cost saving. Future work involves investigating the optimization in a broader perspective sense toward smart thermal grid realization