Photovoltaic System Design for a Contaminated Area in Falun – Comparison of South and East- West Layout

In this thesis the solar part of a large grid-connected photovoltaic system design has been done. The main purpose was to size and optimize the system and to present figures helping to evaluate the prospective project rationality, which can potentially be constructed on a contaminated area in Falun. The methodology consisted in PV market study and component selection, site analysis and defining suitable area for solar installation; and system configuration optimization based on PVsyst simulations and Levelized Cost of Energy calculations. The procedure was mainly divided on two parts, preliminary and detailed sizing. In the first part the objective was complex, which included the investigation of the most profitable component combination and system optimization due to tilt and row distance. It was done by simulating systems with different components and orientations, which were sized for the same 100kW inverter in order to make a fair comparison. For each simulated result a simplified LCOE calculation procedure was applied. The main results of this part show that with the price of 0.43 €/Wp thin-film modules were the most cost effective solution for the case with a great advantage over crystalline type in terms of financial attractiveness. From the results of the preliminary study it was possible to select the optimal system configuration, which was used in the detailed sizing as a starting point. In this part the PVsyst simulations were run, which included full scale system design considering near shadings created by factory buildings. Additionally, more complex procedure of LCOE calculation has been used here considered insurances, maintenance, time value of money and possible cost reduction due to the system size. Two system options were proposed in final results; both cover the same area of 66000 m2. The first one represents an ordinary South faced design with 1.1 MW nominal power, which was optimized for the highest performance. According to PVsyst simulations, this system should produce 1108 MWh/year with the initial investment of 835,000 € and 0.056 €/kWh LCOE. The second option has an alternative East-West orientation, which allows to cover 80% of occupied ground and consequently have 6.6 MW PV nominal power. The system produces 5388 MWh/year costs about 4500,000 € and delivers electricity with the same price of 0.056 €/kWh. Even though the EW solution has 20% lower specific energy production, it benefits mainly from lower relative costs for inverters, mounting and annual maintenance expenses. After analyzing the performance results, among the two alternatives none of the systems showed a clear superiority so there was no optimal system proposed. Both, South and East-West solutions have own advantages and disadvantages in terms of energy production profile, configuration, installation and maintenance. Furthermore, the uncertainty due to cost figures assumptions restricted the results veracity.

Solving the Vehicle Routing Problem with Genetic ALgorithm and Simulated Annealing

This Thesis Work will concentrate on a very interesting problem, the Vehicle Routing Problem (VRP). In this problem, customers or cities have to be visited and packages have to be transported to each of them, starting from a basis point on the map. The goal is to solve the transportation problem, to be able to deliver the packages-on time for the customers,-enough package for each Customer,-using the available resources- and – of course - to be so effective as it is possible.Although this problem seems to be very easy to solve with a small number of cities or customers, it is not. In this problem the algorithm have to face with several constraints, for example opening hours, package delivery times, truck capacities, etc. This makes this problem a so called Multi Constraint Optimization Problem (MCOP). What’s more, this problem is intractable with current amount of computational power which is available for most of us. As the number of customers grow, the calculations to be done grows exponential fast, because all constraints have to be solved for each customers and it should not be forgotten that the goal is to find a solution, what is best enough, before the time for the calculation is up. This problem is introduced in the first chapter: form its basics, the Traveling Salesman Problem, using some theoretical and mathematical background it is shown, why is it so hard to optimize this problem, and although it is so hard, and there is no best algorithm known for huge number of customers, why is it a worth to deal with it. Just think about a huge transportation company with ten thousands of trucks, millions of customers: how much money could be saved if we would know the optimal path for all our packages.Although there is no best algorithm is known for this kind of optimization problems, we are trying to give an acceptable solution for it in the second and third chapter, where two algorithms are described: the Genetic Algorithm and the Simulated Annealing. Both of them are based on obtaining the processes of nature and material science. These algorithms will hardly ever be able to find the best solution for the problem, but they are able to give a very good solution in special cases within acceptable calculation time.In these chapters (2nd and 3rd) the Genetic Algorithm and Simulated Annealing is described in details, from their basis in the “real world” through their terminology and finally the basic implementation of them. The work will put a stress on the limits of these algorithms, their advantages and disadvantages, and also the comparison of them to each other.Finally, after all of these theories are shown, a simulation will be executed on an artificial environment of the VRP, with both Simulated Annealing and Genetic Algorithm. They will both solve the same problem in the same environment and are going to be compared to each other. The environment and the implementation are also described here, so as the test results obtained.Finally the possible improvements of these algorithms are discussed, and the work will try to answer the “big” question, “Which algorithm is better?”, if this question even exists.