Optimización de plantas hidroeléctricas para abastecer poblaciones rurales en Honduras

El acceso a la electricidad sigue siendo un desafío para una gran parte de la población de Honduras. En un país con una población rural fuertemente diseminada en pequeñas comunidades, la naturaleza montañosa de la Cordillera Centroamericana se traduce en limitaciones para extender el sistema eléctrico. Desde 2003, la Fundación Hondureña de Investigación Agrícola (FHIA) combate estas dificultades instalando sistemas hidráulicos en pequeñas comunidades rurales. Sin embargo, la limitación provoca un amplio margen de mejora de estos sistemas rudimentarios. Este trabajo presenta una estrategia de mejora y optimización matemática de la metodología tradicional de diseño, permitiendo aprovechar al máximo los recursos naturales de estas comunidades. La nueva metodología se ha aplicado con éxito en una comunidad piloto. Las ventajas de esta metodología han sido tales que ha sido implementada por los técnicos de FHIA para ser replicada en las intervenciones sucesivas

Optimal design of actuators and sensors for linear systems using genetic algorithms

[Resumen] En este trabajo se revisita un problema clásico: el diseño óptimo de actuadores y/o sensores para sistemas lineales e invariantes en el tiempo. Considerando el sistema descrito en el espacio de estados, el objetivo sería el de escoger, bajo ciertas restricciones, la matriz de entrada B y/o la matriz de salida C para optimizar algún criterio asociado, habitualmente, con el grado de controlabilidad y/u observabilidad del sistema. Este trabajo propone resolver este problema empleando algoritmos genéticos. La principal ventaja de esta herramienta es que permite introducir las restricciones de forma muy sencilla, permitiendo resolver el problema de una forma mucho más general de las que existen en la literatura. Además, esta metodología permite considerar optimización multi-objetivo de forma directa, pudiendo analizar la situación en la que más de uno de estos criterios sean interesantes para la aplicación.[Abstract] This manuscript revisits a classic problem: optimal design of actuators/sensors for linear time-invariant systems. Considering a state-space description of the system, the goal is to design, subject to some constraints, the input matrix B and/or the output matrix C to optimize some criteria associated with the degree of controlability and/or observability of the system. This work proposes the use of evolutionary algorithms for this problem. The first advantage is that this tool allows to introduce constraints in a simple way, so the problem can be solved in a much general way than other approaches found in the literature. Furthermore, multi-objective optimization can be considered in a direct way with this methodology, making it possible to analyze the case in which more than one criterion is insteresting for the control/estimation application.Ministerio de Ciencia e Innovación; PID2020-117800GBI00Ministerio de Ciencia e Innovación; RYC2021-032919-

Optimizing the Layout of Run-of-River Powerplants Using Cubic Hermite Splines and Genetic Algorithms

Despite the clear advantages of mini hydropower technology to provide energy access in remote areas of developing countries, the lack of resources and technical training in these contexts usually lead to suboptimal installations that do not exploit the full potential of the environment. To address this drawback, the present work proposes a novel method to optimize the design of mini-hydropower plants with a robust and efficient formulation. The approach does not involve typical 2D simplifications of the terrain penstock layout. On the contrary, the problem is formulated considering arbitrary three-dimensional terrain profiles and realistic penstock layouts taking into account the bending effect. To this end, the plant layout is modeled on a continuous basis through the cubic Hermite interpolation of a set of key points, and the optimization problem is addressed using a genetic algorithm with tailored generation, mutation and crossover operators, especially designed to improve both the exploration and intensification. The approach is successfully applied to a real-case scenario with real topographic data, demonstrating its capability of providing optimal solutions while dealing with arbitrary terrain topography. Finally, a comparison with a previous discrete approach demonstrated that this algorithm can lead to a noticeable cost reduction for the problem studied

Optimized Micro-Hydro Power Plants Layout Design Using Messy Genetic Algorithms

Micro Hydro-Power Plants (MHPP) represent a powerful and effective solution to address the problem of energy poverty in rural remote areas, with the ad vantage of preserving the natural resources and minimizing the impact on the environment. Nevertheless, the lack of resources and qualified manpower usu ally constitutes a big obstacle to its adequate application, generally translating into sub-optimal generation systems with poor levels of efficiency. Therefore, the study and development of expert, simple and efficient strategies to assist the design of these installations is of especial relevance. This work proposes a design methodology based on a tailored messy evolutionary computational approach, with the objective of finding the most suitable layout of MHPP, considering several constraints derived from a minimal power supply requirement, the max imum flow usage, and the physical feasibility of the plant in accordance with the real terrain profile. This profile is built on the basis of a discrete topographic survey, by means of a shape preserving interpolation, which permits the appli cation of a continuous variable length Messy Genetic Algorithm (MGA). The optimization problem is then formulated in both single-objective (cost minimiza tion) and multi-objective (cost minimization and power supply maximization) modes, including the study of the Pareto dominance. The algorithm is applied to a real scenario in a remote community in Honduras, obtaining a 56.96% of cost reduction with respect to previous work

An Evolutionary Computational Approach for Designing Micro Hydro Power Plants

Micro Hydro Power Plants (MHPP) constitute an effective, environmentally-friendly solution to deal with energy poverty in rural isolated areas, being the most extended renewable technology in this field. Nevertheless, the context of poverty and lack of qualified manpower usually lead to a poor usage of the resources, due to the use of thumb rules and user experience to design the layout of the plants, which conditions the performance. For this reason, the development of robust and efficient optimization strategies are particularly relevant in this field. This paper proposes a Genetic Algorithm (GA) to address the problem of finding the optimal layout for an MHPP based on real scenario data, obtained by means of a set of experimental topographic measurements. With this end in view, a model of the plant is first developed, in terms of which the optimization problem is formulated with the constraints of minimal generated power and maximum use of flow, together with the practical feasibility of the layout to the measured terrain. The problem is formulated in both single-objective (minimization of the cost) and multi-objective (minimization of the cost and maximization of the generated power) modes, the Pareto dominance being studied in this last case. The algorithm is first applied to an example scenario to illustrate its performance and compared with a reference Branch and Bound Algorithm (BBA) linear approach, reaching reductions of more than 70% in the cost of the MHPP. Finally, it is also applied to a real set of geographical data to validate its robustness against irregular, poorly sampled domains.Agencia Española de Cooperación Internacional para el Desarrollo 2014 / ACDE / 00601

Optimal design of irrigation networks

[Resumen] En este estudio se presenta un enfoque basado en algoritmos genéticos (AG) para optimizar el diseño de redes de riego por goteo. El objetivo es reducir las inhomogeneidades en los caudales de salida en cada toma de riego, de manera que toda la parcela se riegue lo más parecido posible. Se asume que las tomas de riego y la entrada de agua son conocidas, centrándose en el diseño de la red de tuberías. El AG se utiliza para determinar la configuración óptima de las tuberías, es decir, qué tomas están conectadas entre sí. Para lograr esto, se emplea una estructura de cromosoma en la que cada par de genes representa la conexión entre dos tomas. El algoritmo evolutivo busca maximizar la uniformidad en los caudales de salida y minimizar la longitud total de la tubería. Los resultados demuestran que el enfoque propuesto logra reducir de manera significativa las inhomogeneidades en los caudales de salida, mejorando así la eficiencia del sistema de riego por goteo. Además, se logra una reducción en el coste total del sistema al minimizar la longitud de la tubería utilizada.[Abstract] In this study, we present an approach based on genetic algorithms (GAs) to optimize the design of drip irrigation networks. The objective is to reduce inhomogeneities in the discharge flow rates at each irrigation outlet, so the whole crop is watered similar amounts. We assume that the irrigation outlets and water supply are known, focusing on the design of the pipeline network. The GA is used to determine the optimal configuration of the pipelines, i.e., which outlets are connected to each other. To achieve this, a chromosome-like structure is employed, where each pair of genes represents the connection between two outlets. The evolutionary algorithm seeks to maximize uniformity in the discharge flow rates and minimize the total pipeline length. The results demonstrate that the proposed approach significantly reduces inhomogeneities in the discharge flow rates, thereby improving the efficiency of the drip irrigation system. Additionally, a reduction in the total cost of the system is achieved by minimizing the length of the utilized pipeline.Ministerio de Ciencia e Innovación; MPID2020-117800GBI0

An Evolutionary Computational Approach for the Problem of Unit Commitment and Economic Dispatch in Microgrids under Several Operation Modes

In the last decades, new types of generation technologies have emerged and have been gradually integrated into the existing power systems, moving their classical architectures to distributed systems. Despite the positive features associated to this paradigm, new problems arise such as coordination and uncertainty. In this framework, microgrids constitute an effective solution to deal with the coordination and operation of these distributed energy resources. This paper proposes a Genetic Algorithm (GA) to address the combined problem of Unit Commitment (UC) and Economic Dispatch (ED). With this end, a model of a microgrid is introduced together with all the control variables and physical constraints. To optimally operate the microgrid, three operation modes are introduced. The first two attend to optimize economical and environmental factors, while the last operation mode considers the errors induced by the uncertainties in the demand forecasting. Therefore, it achieves a robust design that guarantees the power supply for different confidence levels. Finally, the algorithm was applied to an example scenario to illustrate its performance. The achieved simulation results demonstrate the validity of the proposed approach.Ministerio de Ciencia, Innovación y Universidades TEC2016-80242-PMinisterio de Economía y Competitividad PCIN-2015-043Universidad de Sevilla Programa propio de I+D+

A MPC Strategy for the Optimal Management of Microgrids Based on Evolutionary Optimization

In this paper, a novel model predictive control strategy, with a 24-h prediction horizon, is proposed to reduce the operational cost of microgrids. To overcome the complexity of the optimization problems arising from the operation of the microgrid at each step, an adaptive evolutionary strategy with a satisfactory trade-off between exploration and exploitation capabilities was added to the model predictive control. The proposed strategy was evaluated using a representative microgrid that includes a wind turbine, a photovoltaic plant, a microturbine, a diesel engine, and an energy storage system. The achieved results demonstrate the validity of the proposed approach, outperforming a global scheduling planner-based on a genetic algorithm by 14.2% in terms of operational cost. In addition, the proposed approach also better manages the use of the energy storage system.Ministerio de Economía y Competitividad DPI2016-75294-C2-2-RUnión Europea (Programa Horizonte 2020) 76409

Application of Genetic Algorithms for Designing Micro-Hydro Power Plants in Rural Isolated Areas—A Case Study in San Miguelito, Honduras

The use of Micro-Hydro Power Plants (MHPP) has established itself as a fundamental tool to address the problem of energy poverty in rural isolated areas, having become the most used renewable energy source not just in this field but also in big scale power generation. Although the technology used has made important advances in the last few decades, it has been generally applied to big scale hydro-power systems. This fact has relegated the use of isolated MHPPs to the background. In this context, there is still a vast area of improvement in the development of optimization strategies for these projects, which in practice remains limited to the use of thumb rules. It results in a sub-optimal use of the available resources. This work proposes the use of a Genetic Algorithm (GA) to assist the design of MHPP, finding the most suitable location of the different elements of a MHPP to achieve the most efficient use of the resources. For this, a detailed model of the plant is first developed, followed by an optimization problem for the optimal design, which is formulated by considering the real terrain topographic data. The problem is presented in both single (to minimize the cost) and multi-objective (to minimize cost while maximizing the generated power) mode, providing a deep analysis of the potentiality of using GAs for designing MHPP in rural isolated areas. To validate the proposed approach, it is applied to a set of topographic data from a real scenario in Honduras. The achieved results are compared with a baseline integer-variable algorithm and other meta-heuristic algorithms, demonstrating a noticeable improvement in the solution in terms of cost.This research has been partially funded by the University of Seville under the contract “Contratos de acceso al Sistema Español de Ciencia, Tecnología e Innovación para el desarrollo del programa propio de I+D+i de la Universidad de Sevilla” of D. G. Reina

Three-dimensional optimization of penstock layouts for micro-hydropower plants using genetic algorithms

A Micro Hydro-Power Plant is a suitable and effective mean to provide electric power to rural remote communities without harming the environment. However, the lack of resources and technical training in these communities frequently leads to designs based of rules of thumb, compromising both the generation capacity and efficiency. This work makes an attempt to address this problem by developing a new tool to design the layout of the plants. The proposed mechanism relies on a discrete topographic survey of the terrain and utilizes a Genetic Algorithm to optimize the installation layout, making it possible to explicitly incorporate requirements and constraints, such as power supply, cost of the installation, available water flow, and layout feasibility in accordance with the real terrain profile. The algorithm can operate in both single-objective mode (cost minimization) and multi-objective mode (cost minimization and power supply maximization), including in the latter Pareto dominance analyses. The algorithm is applied to a real scenario in a remote community in Honduras, obtaining good results in terms of generation capacity and cost reduction