Optimal congestion management in an electricity market using particle swarm optimization with time-varying acceleration coefficients

AbstractThis paper proposes an optimal congestion management approach in a deregulated electricity market using particle swarm optimization with time-varying acceleration coefficients (PSO-TVAC). Initially, the values of generator sensitivity are used to select redispatched generators. PSO-TVAC is used to determine the minimum redispatch cost. Test results on IEEE 30-bus and 118-bus systems indicate that the PSO-TVAC approach could provide a lower rescheduling cost solution compared to classical particle swarm optimization and particle swarm optimization with time-varying inertia weight

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

Optimal power flow based congestion management using enhanced genetic algorithms

Congestion management (CM) in the deregulated power systems is germane and of central importance to the power industry. In this paper, an optimal power flow (OPF) based CM approach is proposed whose objective is to minimize the absolute MW of rescheduling. The proposed optimization problem is solved with the objectives of total generation cost minimization and the total congestion cost minimization. In the centralized market clearing model, the sellers (i.e., the competitive generators) submit their incremental and decremental bid prices in a real-time balancing market. These can then be incorporated in the OPF problem to yield the incremental/ decremental change in the generator outputs. In the bilateral market model, every transaction contract will include a compensation price that the buyer-seller pair is willing to accept for its transaction to be curtailed. The modeling of bilateral transactions are equivalent to the modifying the power injections at seller and buyer buses. The proposed CM approach is solved by using the evolutionary based Enhanced Genetic Algorithms (EGA). IEEE 30 bus system is considered to show the effectiveness of proposed CM approach

A Comprehensive Review of Congestion Management in Power System

In recent decades, restructuring has cut across all probable domains, involving the power supply industry. The restructuring has brought about considerable changes whereby electricity is now a commodity and has become a deregulated one. These competitive markets have paved the way for countless entrants. This has caused overload and congestion on transmission lines. In addition, the open access transmission network has created a more intensified congestion issue. Therefore, congestion management on power systems is relevant and central significance to the power industry. This manuscript review few congestion management techniques, consists of Reprogramming Generation (GR), Load Shedding, Optimal Distributed Generation (DG) Location, Nodal Pricing, Free Methods, Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Fuzzy Logic System Method, as well as Additional Renewable Energy Sources. In this manuscript a review work is performed to unite the entire publications on congestion management

A Comprehensive Review of Congestion Management in Power System

In recent decades, restructuring has cut across all probable domains, involving the power supply industry. The restructuring has brought about considerable changes whereby electricity is now a commodity and has become a deregulated one. These competitive markets have paved the way for countless entrants. This has caused overload and congestion on transmission lines. In addition, the open access transmission network has created a more intensified congestion issue. Therefore, congestion management on power systems is relevant and central significance to the power industry. This manuscript review few congestion management techniques, consists of Reprogramming Generation (GR), Load Shedding, Optimal Distributed Generation (DG) Location, Nodal Pricing, Free Methods, Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Fuzzy Logic System Method, as well as Additional Renewable Energy Sources. In this manuscript a review work is performed to unite the entire publications on congestion management

A Comprehensive Survey on Particle Swarm Optimization Algorithm and Its Applications

Particle swarm optimization (PSO) is a heuristic global optimization method, proposed originally by Kennedy and Eberhart in 1995. It is now one of the most commonly used optimization techniques. This survey presented a comprehensive investigation of PSO. On one hand, we provided advances with PSO, including its modifications (including quantum-behaved PSO, bare-bones PSO, chaotic PSO, and fuzzy PSO), population topology (as fully connected, von Neumann, ring, star, random, etc.), hybridization (with genetic algorithm, simulated annealing, Tabu search, artificial immune system, ant colony algorithm, artificial bee colony, differential evolution, harmonic search, and biogeography-based optimization), extensions (to multiobjective, constrained, discrete, and binary optimization), theoretical analysis (parameter selection and tuning, and convergence analysis), and parallel implementation (in multicore, multiprocessor, GPU, and cloud computing forms). On the other hand, we offered a survey on applications of PSO to the following eight fields: electrical and electronic engineering, automation control systems, communication theory, operations research, mechanical engineering, fuel and energy, medicine, chemistry, and biology. It is hoped that this survey would be beneficial for the researchers studying PSO algorithms