Economic Dispatch Thermal Generator Using Modified Improved Particle Swarm Optimization

Fuel cost of a thermal generator is its own load functions. In this research, Modified Improved Particle Swarm Optimization (MIPSO) is applied to calculate economic dispatch. Constriction Factor Approach (CFA) is used to modify IPSO algorithm because of the advantage to improve the ability of global searching and to avoid local minimum, so that the time needed to converge become faster. Simulation results achieved by using  MIPSO method at the time of peak load of of 9602 MW, obtained generation cost is Rp 7,366,912,798,34 per hour, while generation cost of real system is Rp. 7,724,012,070.30 per hour. From the simulation result can be concluded that MIPSO can reduce the generation cost of  500 kV Jawa Bali transmission system of Rp 357,099,271.96 per hour or equal to 4,64%

Intelligent power system operation in an uncertain environment

This dissertation presents some challenging problems in power system operations. The efficacy of a heuristic method, namely, modified discrete particle swarm optimization (MDPSO) algorithm is illustrated and compared with other methods by solving the reliability based generator maintenance scheduling (GMS) optimization problem of a practical hydrothermal power system. The concept of multiple swarms is incorporated into the MDPSO algorithm to form a robust multiple swarms-modified particle swarm optimization (MS-MDPSO) algorithm and applied to solving the GMS problem on two power systems. Heuristic methods are proposed to circumvent the problems of imposed non-smooth assumptions common with the classical approaches in solving the challenging dynamic economic dispatch problem. The multi-objective combined economic and emission dispatch (MO-CEED) optimization problem for a wind-hydrothermal power system is formulated and solved in this dissertation. This MO-CEED problem formulation becomes a challenging problem because of the presence of uncertainty in wind power. A family of distributed optimal Pareto fronts for the MO-CEED problem has been generated for different scenarios of capacity credit of wind power. A real-time (RT) network stability index is formulated for determining a power system\u27s ability to continue to provide service (electric energy) in a RT manner in case of an unforeseen catastrophic contingency. Cascading stages of fuzzy inference system is applied to combine non real-time (NRT) and RT power system assessments. NRT analysis involves eigenvalue and transient energy analysis. RT analysis involves angle, voltage and frequency stability indices. RT Network status index is implemented in real-time on a practical power system --Abstract, page iv

The optimal solution for unit commitment problem using binary hybrid grey wolf optimizer

The aim of this work is to solve the unit commitment (UC) problem in power systems by calculating minimum production cost for the power generation and finding the best distribution of the generation among the units (units scheduling) using binary grey wolf optimizer based on particle swarm optimization (BGWOPSO) algorithm. The minimum production cost calculating is based on using the quadratic programming method and represents the global solution that must be arriving by the BGWOPSO algorithm then appearing units status (on or off). The suggested method was applied on “39 bus IEEE test systems”, the simulation results show the effectiveness of the suggested method over other algorithms in terms of minimizing of production cost and suggesting excellent scheduling of units

Comparison of different redispatch optimization strategies

In den letzten Jahren hat die Häufigkeit des Auftretens von Engpässen in den elektrischen Übertragungsnetzen stark zugenommen, weil die Stromnetze ursprünglich für die aktu-elle Energiemenge und deren starke Schwankung nicht ausgelegt sind. Darüber hinaus bringen die weiter steigende Nutzung der erneuerbaren dezentralen Energiequellen, die zunehmende Netzkomplexität, die Abschaltung konventioneller Kraftwerke, Progno-sefehler und der starke Wettbewerb auf dem Strommarkt die elektrischen Netze immer öfter an ihre Übertragungsgrenzen. Daher ist die Gefahr von Engpässen permanent ge-stiegen, insbesondere in Mitteleuropa. Wenn ein Engpass im Stromnetz entstanden ist, sind die Übertragungsnetzbetreiber ver-pflichtet, eine geeignete Abhilfemaßnahme so schnell wie möglich anzuwenden, um ihn zu beseitigen, z. B. durch den deutschlandweit verbreiteten Redispatch. Allerdings kann diese Gegenmaßnahme hohe Kosten für die Übertragungsnetzbetreiber verursachen, die zum Schluss die Stromverbraucher zahlen müssen. Deswegen ist die Realisierung eines kosten- und technisch effizienten Redispatches ein sehr wichtiges Thema des Netzbe-triebs geworden. Daher ist das Hauptziel dieser Arbeit, unterschiedliche Möglichkeiten und Ansätze für eine kostengünstige Redispatchumsetzung bei Entstehung der Engpässe zu entwickeln. Dafür werden verschiedene numerische und metaheuristische Optimierungsmethoden hinsichtlich ihrer Komplexität, Effizienz, Verlässlichkeit, Detaillierung und Rechenzeit verglichen und durch ein kleines Netzmodell sowie durch ein vereinfachtes ENTSO-E-Netzmodell verifiziert. Schließlich werden die Übertragungsnetzbetreiber durch die Erkenntnisse in dieser Arbeit in die Lage versetzt, ihre Stromnetze effizienter zu betreiben, in dem der Redispatchpro-zess verbessert wird. Dabei werden die hohen Redispatchkosten, insbesondere in Deutschland, deutlich gesenkt.In the recent years, line congestions in the electric transmission networks occur quite fre-quently due to the power grids were not originally designed for the current amount of energy and its strong fluctuation. Furthermore, the increasing utilization of renewable distributed energy sources, growth of the network complexity, reduction of the conven-tional power plant utilization, forecast errors and strong electricity market competition frequently bring the power grids to their transmission limits as well. Therefore, the risk of congestions has permanently increased, especially in central Europe. If a line congestion occurs in the electric network, the transmission system operator has to apply a suitable remedial action to overcome the problem as fast as possible, e.g by utilization of redispatch, which is very common in Germany. However, this measure can cause high costs for the transmission network operators. For this reason, the realization of an economically efficient and optimal redispatching has become very important issue in the power system operation. The main goal of this work is a consideration and development of various possibilities and methods for realization of a technically sound and cost-efficient redispatch in case of network congestions. Therefore, different numerical and metaheuristic optimization tech-niques are implemented, compared with respect to their complexity, efficiency, reliabil-ity, simulation time etc. and verified through a small test grid and simplified ENTSO-E network model. Furthermore, it is shown which technical and economic aspects of redispatching have a major influence on its realization and should always be taken into account or can be ne-glected while solving the redispatch optimization problem. Here, different approaches of the network sensitivity analysis are evaluated and compared as well. Finally, the transmission network operators can use the knowledge and results of this work to improve the current redispatch realization in their power grids, and thus to reduce the redispatch costs, which are especially high in Germany

Economic Load Dispatch for IEEE 30-Bus System Using PSO

ELD or economic load dispatch is a crucial aspect in any practical power network. Economic load dispatch is the technique whereby the active power outputs are allocated to generator units in the most cost-effective way in compliance with all constraints of the network. The traditional methods for solving ELD include Lambda-Iterative Technique, Newton-Raphson Method, Gradient method, etc. All these traditional algorithms need the incremental fuel cost curves of the generators to be increasing monotonically or piece-wise linear. But in practice the input-output characteristics of a generator are highly non-linear leading to a challenging non-convex optimisation problem. Methods like artificial intelligence, DP (dynamic programming), GA (genetic algorithms), and PSO (particle swarm optimisation) solve non-convex optimisation problems in an efficient manner and obtain a fast and near global and optimum solution. In this project ELD problem has been solved using Lambda-Iterative technique, GA (Genetic Algorithms) and PSO (Particle Swarm Optimisation) and the results have been compared. All the analyses have been made in MATLAB environment

Solving optimal generation dispatch problem in power networks through pso and lambda iteration techniques

Efficient solution to the problem of economic dispatch of network generators has been a growing concern to power system utilities in recent times. This is aimed at determining the optimal allocation of the total network demand among the available generating units such that the total cost of generation is reduced while maintaining an acceptable generation output subject to specified system constraints. This paper, therefore, attempts to resolve this issue from two main perspectives; Lambda Iterative-based approach and Particle Swarm Optimization (PSO) technique. The theoretical backgrounds as well as the mathematical formulations for the two approaches are presented. The standard IEEE 14-Bus, IEEE 30-Bus and the Indian 62-Bus networks are used as case studies to present illustrative examples for the approach. The simulation results obtained using the two approaches are presented and compared. The comparisons of the results obtained show that the two approaches are suitable for providing efficient solutions to economic dispatch problems in large power networks.Keywords: economic dispatch; network demands; lamda iterative; particle swarm optimization;generation cos