Wind-solar-hydrothermal dispatch using convex optimization

In this research a convex optimization methodology is proposed for the Shortterm hydrothermal scheduling (STHS). In addition, wind and solar generation are also considered under a robust approach by modeling the equilibrium of power flow constraint as chance box constraints, which allows determining the amount of renewable source available with a specific probability value. The proposed methodology guarantees global optimum of the convexified model andfast convergences..

MILP-Based Short-Term Thermal Unit Commitment and Hydrothermal Scheduling Including Cascaded Reservoirs and Fuel Constraints

Reservoirs are often built in cascade on the same river system, introducing inexorable constraints. It is therefore strategically important to scheme out an efficient commitment of thermal generation units along with the scheduling of hydro generation units for better operational efficiency, considering practical system conditions. This paper develops a comprehensive, unit-wise hydraulic model with reservoir and river system constraints, as well as gas constraints, with head effects, to commit thermal generation units and schedule hydro ones in the short-term. A mixed integer linear programming (MILP) methodology, using the branch and bound & cut (BB&C) algorithm, is employed to solve the resultant problem. Due to the detailed modelling of individual hydro units and cascaded dependent reservoirs, the problem size is substantially swollen. Multithread computing is invoked to accelerate the solution process. Simulation results, conducted on various test systems, reiterate that the developed MILP-based hydrothermal scheduling approach outperforms other techniques in terms of cost efficiency

New Hybrid Non-Dominated Sorting Differential Evolutionary Algorithm

This paper presents a new multi objective optimization algorithm with the aim of complete coverage, faster global convergence and higher solution quality. In this technique, the high-speed characteristic of particle swarm optimization (PSO) is combined with non-dominated differential evolutionary (NSDE) and an efficient multi objective optimization algorithm is created. This method posses high convergence characteristic in quite less execution times. Generating fewer populations to find the Pareto front also makes the proposed algorithm use less memory. For the purpose of performance evaluation, the algorithm is verified with four benchmarking functions on its global optimal search ability and compared with two recognized algorithm to assess its diversity. The capability of the suggested algorithm in solving practical engineering problems such as power system protection is also studied and the results are discussed in detail

New Hybrid Non-Dominated Sorting Differential Evolutionary Algorithm

This paper presents a new multi objective optimization algorithm with the aim of complete coverage, faster global convergence and higher solution quality. In this technique, the high-speed characteristic of particle swarm optimization (PSO) is combined with non-dominated differential evolutionary (NSDE) and an efficient multi objective optimization algorithm is created. This method posses high convergence characteristic in quite less execution times. Generating fewer populations to find the Pareto front also makes the proposed algorithm use less memory. For the purpose of performance evaluation, the algorithm is verified with four benchmarking functions on its global optimal search ability and compared with two recognized algorithm to assess its diversity. The capability of the suggested algorithm in solving practical engineering problems such as power system protection is also studied and the results are discussed in detail

Improved particle swarm optimization algorithms for economic load dispatch considering electric market

Economic load dispatch problem under the competitive electric market (ELDCEM) is becoming a hot problem that receives a big interest from researchers. A lot of measures are proposed to deal with the problem. In this paper, three versions of PSO method such as conventional particle swarm optimization (PSO), PSO with inertia weight (IWPSO) and PSO with constriction factor (CFPSO) are applied for handling ELDCEM problem. The core duty of the PSO methods is to determine the most optimal power output of generators to obtain total profit as much as possible for generation companies without violation of constraints. These methods are tested on three and ten-unit systems considering payment model for power delivered and different constraints. Results obtained from the PSO methods are compared with each other to evaluate the effectiveness and robustness. As results, IWPSO method is superior to other methods. Besides, comparing the PSO methods with other reported methods also gives a conclusion that IWPSO method is a very strong tool for solving ELDCEM problem because it can obtain the highest profit, fast converge speed and simulation time

Evaluación mediante indicadores clave de rendimiento del despacho económico hidrotérmico resuelto por medio de técnicas heurísticas

El presente artículo resuelve el problema de coordinación hidrotérmica mediante la utilización de técnicas heurísticas como alternativa a los métodos de optimización exactos. Las técnicas utilizadas son el método de enjambre de partículas, algoritmos genéticos con sus variantes de selección por ruleta y torneo y el novedoso y reciente algoritmo lobo gris. El objetivo del modelo de optimización planteado radica en la minimización de la función objetivo referente al costo de combustibles de las centrales térmicas considerando, además, el efecto de punto de válvula que le da un toque más realista al problema. La metodología de solución propuesta incluye penalizaciones en la función objetivo relacionadas a las violaciones de las restricciones de balances de potencia y el balance dinámico de los reservorios. El despacho económico se ejecuta para un sistema de prueba compuesto por múltiples centrales térmicas y varias centrales hidroeléctricas con reservorios en cascada. Los algoritmos desarrollados se implementaron en el software Matlab. Adicionalmente, se evalúa innovadoramente los resultados logrados por cada técnica heurística mediante la utilización de diferentes indicadores clave de rendimiento.This paper solves the hydrothermal scheduling problem by using heuristic techniques as an alternative to exact optimization methods. The techniques used are the particle swarm method, genetic algorithms with its variants of selection by roulette and tournament and the novel and recent grey wolf algorithm. The objective of the proposed optimization model lies in the minimization of the objective function regarding fuel costs of thermal power plants, also considering the valve point effect that gives a more realistic touch to the problem. The proposed solution methodology includes penalties in the objective function related to the violations of constraints of the power balance and dynamic balance of reservoirs. The economic dispatch is executed for a test system composed of multiple thermal power plants and several hydroelectric power plants with cascaded reservoirs. The algorithms developed were implemented in the Matlab software. Additionally, the results achieved by each heuristic technique are innovatively evaluated using different key performance indicators

Dynamic Economic Load Dispatch of Hydrothermal System

A Quasi Oppositional Gray Wolf Optimization (QOGWO) algorithm has been used in this work to decipher the economic load dispatch of hydrothermal system. Dynamic economic load dispatch problem involves scheduling of committed generators to meet the load demand with minimum fuel cost and several constraints which are dynamic in nature. It is basically short-term hydrothermal scheduling (STHS) problems through cascaded reservoirs. Instead of pseudo-random numbers quasi-opposite numbers are used to initialize population in the proposed QOGWO method so that the convergence rate of GWO increases. The viability of the projected approach is verified in three standard multi-chain cascaded hydrothermal systems with four interconnected hydro systems. The load and number of thermal units differ from one system to another. Water transportation delay between interconnected reservoirs, Valve Point Loading (VPL) have been considered in different combination in three cases. The technique put forth with established superior to many recent findings for the STHS problems with increased complexities

Selected Papers from the ICEUBI2019 - International Congress on Engineering - Engineering for Evolution

Energies SI Book "Selected Papers from the ICEUBI2019 – International Congress on Engineering – Engineering for Evolution", groups six papers into fundamental engineering areas: Aeronautics and Astronautics, and Electrotechnical and Mechanical Engineering. ICEUBI—International Congress on Engineering is organized every two years by the Engineering Faculty of Beira Interior University, Portugal, promoting engineering in society through contact among researchers and practitioners from different fields of engineering, and thus encouraging the dissemination of engineering research, innovation, and development. All selected papers are interrelated with energy topics (fundamentals, sources, exploration, conversion, and policies), and provide relevant data for academics, research-focused practitioners, and policy makers

Particle Swarm Optimization

Particle swarm optimization (PSO) is a population based stochastic optimization technique influenced by the social behavior of bird flocking or fish schooling.PSO shares many similarities with evolutionary computation techniques such as Genetic Algorithms (GA). The system is initialized with a population of random solutions and searches for optima by updating generations. However, unlike GA, PSO has no evolution operators such as crossover and mutation. In PSO, the potential solutions, called particles, fly through the problem space by following the current optimum particles. This book represents the contributions of the top researchers in this field and will serve as a valuable tool for professionals in this interdisciplinary field