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

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

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

Economic Model Predictive Control for Smart and Sustainable Farm Irrigation

The joint effects of rise of global population, climate change and water scarcity makes the shift towards an efficient and sustainable agriculture more and more urgent. Fortunately, recent developments in low-cost, IoT-based sensors and actuators can help us to incorporate advanced control techniques for efficient irrigation system. This paper proposes the use of an economic model predictive control at a farm scale. The controller makes use of soil moisture data sent by the sensors, price signals, operative restrictions, and accurate dynamical models of water dynamics in the soil. Its performance is demonstrated through simulations based on a real case-study, showing that it is possible to obtain significant reductions in water and energy consumption and operation costs

Economic model predictive control for interactions of water sources connected crop field

Interest in predicting and optimizing irrigation to minimize water usage in agriculture is growing. In this paper, we present how different water sources interconnected in a farm (surface and underground reservoirs) can provide the optimal amount of water to the crop, considering the water available in each water source and the energy cost associated with pumping, without compromising the crop yield. For this purpose, the formulated economic Model Predictive Control makes use of the dynamical non-linear agro-hydrological model, considering the Volumetric Water Content (VWC) at different depths of the soil and the mass balance of the surface reservoir to generate optimal interactions and flow control strategies from the water sources to the crop field to meet future irrigation demands and finally consider the use of these water sources to alleviate the effects of environmental changes and water scarcity

Stochastic packetized model predictive control for networked control systems subjects tot time-delays and dropouts

Networked Control Systems (NCS) are systems in which serial communication networks are used to exchange system information and control signals between various physical components of the systems that may be physically distributed. Major advantages of NCS include low cost, reduced weight and power requirements, simple installation and maintenance, and high reliability. Nonetheless, closing a control loop on a shared communication network introduces additional dynamics and constraints in the control problem. In addition to being bit-rate limited [1], [2], practical communitacion channels are commonly affected by packet dropouts and time delays, mainly due to transmission errors and waiting times to access the medium; see, e.g., [3]-[5] and the many references therein

Distributed consensus-based Kalman filtering considering subspace decomposition

The aim of this paper is to provide a new observer structure able to deal with the distributed estimation of a discrete-time linear system from a network of agents. The main result is an innovative consensus-based structure that decompose the state in the observable and unobservable subspace of the agent using the observability staircase form. The paper proposes a design in which Kalman-like gains are synthetized to minimize the variance of the error on both subspaces. Finally some simulations are shown to compare the proposed estimator with centralized Kalman filter and other distributed schemes found in literture

Distributed estimation design for LTI systems: a linear quadratic approach

This paper deals with the problem of distributedly estimate the state of a plant through a network of interconnected agents. Each of these agents must perform a real-time monitoring of the plant state, counting on the measurements of local plant outputs and on the exchange of information with neighbouring agents. The paper introduces a distributed LQ-based design that is applied to a distributed observer structure based on a multi-hop subspace decomposition. Stability and optimality conditions are derived and tested in simulation. Finally, the design method presented allows the user, through the tune of two scalar parameters, to modify the observer gains according to their experience about the plant

Stochastic packetized model predictive control for networked control systems subjects tot time-delays and dropouts

Networked Control Systems (NCS) are systems in which serial communication networks are used to exchange system information and control signals between various physical components of the systems that may be physically distributed. Major advantages of NCS include low cost, reduced weight and power requirements, simple installation and maintenance, and high reliability. Nonetheless, closing a control loop on a shared communication network introduces additional dynamics and constraints in the control problem. In addition to being bit-rate limited [1], [2], practical communitacion channels are commonly affected by packet dropouts and time delays, mainly due to transmission errors and waiting times to access the medium; see, e.g., [3]-[5] and the many references therein

Pulse-based, Periodic MPC for Irrigation in Smart and Sustainable Agriculture

The growing population, together with global warming and the difficulty of accessing water, makes the increase of efficient and sustainable agriculture a priority. Undoubtedly, the recent development of low-cost IoT-based sensors and actuators presents great opportunities in this direction, since these devices can be easily deployed to implement advanced monitoring and irrigation control techniques at a farm scale. This paper proposes a pulse-based, periodic, economic predictive controller. Its goal is to find the irrigation pulse trains that optimize water and energy consumption while ensuring adequate levels of soil moisture for the crops. For this purpose, the developed MPC makes use of soil moisture data at different depths, sent by a set of field sensors, and formulates a constrained optimization problem that takes into account water costs, electricity prices, and an accurate dynamical nonlinear agro-hydrological model. Its performance is tested by simulating real case studies, which show that water and energy consumption can be significantly reduced