Evaluation of a high temperature solar thermal seasonal borehole storage

A solar thermal system with seasonal borehole storage for heating of a residential area in Anneberg, Sweden, approximately 10 km north of Stockholm, has been in operation since late 2002. Originally, the project was part of the EU THERMIE project “Large-scale Solar Heating Systems for Housing Developments” (REB/0061/97) and was the first solar heating plant in Europe with borehole storage in rock not utilizing a heat pump. Earlier evaluations of the system show lower performance than the preliminary simulation study, with residents complaining of a high use of electricity for domestic hot water (DHW) preparation and auxiliary heating. One explanation mentioned in the earlier evaluations is that the borehole storage had not yet reached “steady state” temperatures at the time of evaluation. Many years have passed since then and this paper presents results from a new evaluation. The main aim of this work is to evaluate the current performance of the system based on several key figures, as well as on system function based on available measurement data. The analysis show that though the borehole storage now has reached a quasi-steady state and operates as intended, the auxiliary electricity consumption is much higher than the original design values largely due to high losses in the distribution network, higher heat loads as well as lower solar gains.

GRASP and statistical bounds for heuristic solutions to combinatorial problems

The quality of a heuristic solution to a NP-hard combinatorial problem is hard to assess. A few studies have advocated and tested statistical bounds as a method for assessment. These studies indicate that statistical bounds are superior to the more widely known and used deterministic bounds. However, the previous studies have been limited to a few metaheuristics and combinatorial problems and, hence, the general performance of statistical bounds in combinatorial optimization remains an open question. This work complements the existing literature on statistical bounds by testing them on the metaheuristic Greedy Randomized Adaptive Search Procedures (GRASP) and four combinatorial problems. Our findings confirm previous results that statistical bounds are reliable for the p-median problem, while we note that they also seem reliable for the set covering problem. For the quadratic assignment problem, the statistical bounds has previously been found reliable when obtained from the Genetic algorithm whereas in this work they found less reliable. Finally, we provide statistical bounds to four 2-path network design problem instances for which the optimum is currently unknown.

Heuristic optimization of the p-median problem and population re-distribution

This thesis contributes to the heuristic optimization of the p-median problem and Swedish population redistribution.   The p-median model is the most representative model in the location analysis. When facilities are located to a population geographically distributed in Q demand points, the p-median model systematically considers all the demand points such that each demand point will have an effect on the decision of the location. However, a series of questions arise. How do we measure the distances? Does the number of facilities to be located have a strong impact on the result? What scale of the network is suitable? How good is our solution? We have scrutinized a lot of issues like those. The reason why we are interested in those questions is that there are a lot of uncertainties in the solutions. We cannot guarantee our solution is good enough for making decisions. The technique of heuristic optimization is formulated in the thesis.   Swedish population redistribution is examined by a spatio-temporal covariance model. A descriptive analysis is not always enough to describe the moving effects from the neighbouring population. A correlation or a covariance analysis is more explicit to show the tendencies. Similarly, the optimization technique of the parameter estimation is required and is executed in the frame of statistical modeling.