Michael de Paly

Optimization of the energy extraction of a shallow geothermal system

Geothermal energy use from shallow groundwater systems is attractive for the supply of heat and hot water to buildings. It offers economic and environmental advantages over traditional fossil-fuel based technologies, in particular when large scale systems are well adapted to the always unique hydrogeological conditions. Computer based numerical simulations are used to examine the performance of multiple borehole heat exchangers installed in the ground. This paper demonstrates how evolutionary algorithms can be utilized to configure the elements of a geothermal system in an ideal way, and thus substantially enhance the energy extraction rate in comparison to standardized approaches. Differential evolution (DE), evolution strategies (ES) and particle swarm optimizers (PSO) are combined with a local search approach and compared with respect to their efficiency in the optimization of synthetic, real case oriented and static systems. First results are promising, especially for the PSO and the DE with the local search approach.

Optimal Irrigation Scheduling with Evolutionary Algorithms

Efficient irrigation is becoming a necessity in order to cope with the aggravating water shortage while simultaneously securing the increasing world populations food supply. In this paper, we compare five Evolutionary Algorithms (real valued Genetic Algorithm, Particle Swarm Optimization, Differential Evolution, and two Evolution Strategy-based Algorithms) on the problem of optimal deficit irrigation. We also introduce three different constraint handling strategies that deal with the constraints which arise from the limited amount of irrigation water. We show that Differential Evolution and Particle Swarm Optimization are able to optimize irrigation schedules achieving results which are extremely close to the theoretical optimum.

Evaluation of the performance of evolutionary algorithms for optimization of low-enthalpy geothermal heating plants

In this paper, we present the application of Evolutionary Algorithms (EAs) and linear programming for minimizing thermal impacts in the ground by operating a low-enthalpy geothermal plant with a field of multiple borehole heat exchangers (BHEs). The new methodology is demonstrated on two synthetic case studies with 36 BHEs that are grounded in reality and operated to produce given seasonal heating energy demand. We compare the performance of six different Evolutionary Algorithms (EAs) (two Differential Evolution variants, Particle Swarm Optimization, two Evolution Strategy based Algorithms, real valued Genetic Algorithm) and Monte-Carlo random search to find the optimal BHE positions. Additionally, linear programming is applied to adjust the energy extraction (loads) for the individual BHEs in the field. Both optimization steps are applied separately and in combination, and the achieved system improvements are compared to the conditions for the non-optimized case. The EAs were able to find constellations that cause less pronounced temperature changes in the subsurface (18% - 25%) than those associated with non-optimized BHE fields. Further, we could show that exclusive optimization of BHE energy extraction rates delivers slightly better results than the optimization of BHE positions. Combining both optimization approaches is the best choice and, ideally, adjusts the geothermal plant.

Determining crop-production functions using multi-objective evolutionary algorithms

The determination of crop production functions which describe the relationship between irrigation water and crop yield under the assumption of optimal irrigation scheduling is a major building block for a more efficient and sustainable water management. In this paper we introduce a methodology to determine the entire crop production function for a given scenario within a single run of a multi-objective evolutionary algorithm. Further we compare the performance of four major algorithms (NSGA-II, NSDE, DEMO, and MO-CMA-ES), and a single-objective approach based on differential evolution on three different scenarios and two different population initialization methods on this problem. We show that the combination of a problem specific initialization with MO-CMA-ES is able to determine crop production functions which are extremely close to actual ones.

Global optimization of deficit irrigation systems using evolutionary algorithms

Water is a limited resource and the dramatically increasing world population requires a significant increase in food production. For improving both crop yield and water use efficiency, the usual optimization strategy in furrow irrigation at the field level considers scheduling parameters, i.e. when and how much to irrigate, as well as control parameters, i.e. the intensity and the irrigation time, for each water application. Optimizing control and schedule parameters in irrigation is considered as a nested problem. The objective of the global optimization is to achieve maximum crop yield with a given, but limited water volume, which can be arbitrary distributed over the number of irrigations. It is difficult to solve the global optimization problem, because the target function has many locally optimal solutions and the number of optimization variables, i.e. the number of irrigations is unknown a-priori. For this reason, a made to measure evolutionary optimisation technique (EA) is employed to find a near-optimal solution of the global optimization problem within acceptable computational time. The results provided by the new optimization strategy are compared with the popular SCE-UA optimization algorithm and Mesh-Adaptive Direct Search (MADS). The comparison demonstrated a striking superiority of the new tool with respect to both the achieved irrigation efficiency and the required computational time.

Automatic Optimization of Multiple Borehole Heat Exchanger Fields

Borehole heat exchangers (BHE) are often applied in multiple BHE fields. In current planning practice, interaction between adjacent BHEs is rarely considered, and all BHEs are operated in the same mode. This means, potential adverse effects from superimposed cold or heat plumes, which simultaneously evolve around individual neighboring BHEs, are neglected. The long-term heat extraction over decades, however, may lead to a significant local cooling, especially in the interior of the field. As a consequence, the performance of the complete ground source heat pump (GSHP) system is attenuated, and ground temperatures below regulation thresholds may develop. In our work, we employ mathematical optimization techniques to strategically operate and arrange BHEs in such fields. Linear programming and an evolutionary algorithm are applied in combination with analytical equations to solve realistic problems. The presented methodology is flexible and robust, and it can be applied to various conditions. The two scenarios studied in this chapter represent conditions with negligible and significant groundwater flow. We inspect a field with 36 BHEs, which has a seasonally variable heating energy demand. It is demonstrated, by taking the maximum temperature decline in the ground as objective, that the BHE field performance can be improved by both case-specific ideal arrangement and time-dependently regulated individual BHE operation. It is found that instead of standard lattice arrangements, optimized geometries are favorable, with BHEs concentrated along the fringe of a field. Apparently, this enhances lateral conductive heat provision into the field. Groundwater flow means additional energy provision by advection towards the field.