As was indicated before, the usual approach in solar process economics is to use a life cycle cost method, which takes into consideration all future expenses and compares the future costs with today’s costs. Such a comparison is done by discounting all costs expected in the future to the common basis of present value or present worth, i. e., it is required to find the amount of money that needs to be invested today in order to have funds available to cover the future expenses.
It must be noted that a sum of money at hand today is worth more than the same sum in the future. Therefore, a sum of money or cash flow in the future must be discounted and worth less than its present-day value. A cash flow (F) occurring (n) years from now can be reduced to its present value (P) by
P = —F (12...Read More
Life cycle analysis, in fact, reflects the benefits accumulated by the use of solar energy against the fuel savings incurred. Compared to conventional fossil fuel systems, solar energy systems have relatively high initial cost and low operating cost, whereas the opposite is true for conventional systems. Therefore, in a naive selection, based on the initial cost alone, the solar energy system would have no chance to be selected. As will be proved in this chapter, this is not the case when a life cycle analysis is employed, because it considers all costs incurred during the life of the solar energy system...Read More
The right proportion of solar to auxiliary energy is determined by economic analysis. There are various types of such analysis, some simple and others more complicated, based on thermoeconomics.
The economic analysis of solar energy systems is carried out to determine the least cost of meeting the energy needs, considering both solar and non-solar alternatives. The method employed in this book for the economic analysis is called life cycle analysis. This method takes into account the time value of money and allows detailed consideration of the complete range of costs. It also includes inflation when estimating future expenses. In the examples given in this chapter, both dollars ($) and euros (€) are used...Read More
Although the resource of a solar energy system, that is, the solar irradiation, is free, the equipment required to collect it and convert it to useful form (heat or electricity) has a cost. Therefore, solar energy systems are generally characterized by high initial cost and low operating costs. To decide to employ a solar energy system, the cost of collectors, other required equipment, and conventional fuel required as backup must be lower than the cost of other conventional energy sources to perform the same task. Thus, the economic problem is to compare an initial known investment with estimated future operating costs, including both the cost to run and maintain the solar energy system and auxiliary energy used as backup...Read More
Simulations are powerful tools for solar energy systems design, offering a number of advantages, as outlined in the previous sections. However, there are limits to their use. For example, it is easy to make mistakes, such as assuming wrong constants and neglect important factors. As with other engineering calculations, a high level of skill and scientific judgment is required to produce correct, useful results (Kalogirou, 2004b).
It is possible to model a system to a high degree of accuracy to extract the required information. In practice, however, it may be difficult to represent in detail some of the phenomena taking place in real systems...Read More
Hybrid systems are systems that combine two or more artificial intelligence techniques to perform a task. The classical hybrid system is the neuro-fuzzy control, whereas other types combine genetic algorithms and fuzzy control or artificial neural networks and genetic algorithms as part of an integrated problem solution or to perform specific, separate tasks of the same problem. Since most of these techniques are problem specific, more details are given here for the first category.
A fuzzy system possesses great power in representing linguistic and structured knowledge using fuzzy sets and performing fuzzy reasoning and fuzzy logic in a qualitative manner. Also, it usually relies on domain experts to provide the necessary knowledge for a specific problem...Read More
Fuzzy inference is a method that interprets the values in the input vector and, based on some sets of rules, assigns values to the output vector. In fuzzy logic, the truth of any statement becomes a matter of a degree.
Fuzzy inference is the process of formulating the mapping from a given input to an output using fuzzy logic. The mapping then provides a basis from which decisions can be made or patterns discerned. The process of fuzzy inference involves all of the pieces described so far, i. e., membership functions, fuzzy logic operators, and if-then rules. Two main types of fuzzy inference systems can be implemented: Mamdani-type (1977) and Sugeno-type (1985). These two types of inference systems vary somewhat in the way outputs are determined.
Mamdani-type inference expects the output...Read More
Fuzzy sets and fuzzy operators are the subjects and verbs of fuzzy logic. While the differential equations are the language of conventional control, if-then rules, which determine the way a process is controlled, are the language of fuzzy control. Fuzzy rules serve to describe the quantitative relationship between variables in linguistic terms. These if-then rule statements are used to formulate the conditional statements that comprise fuzzy logic. Several rule bases of different complexity can be developed, such as
IF Sensor 1 is Very Low AND Sensor 2 is Very Low THEN Motor is Fast Reverse
IF Sensor 1 is High AND Sensor 2 is Low THEN Motor is Slow Reverse
IF Sensor 1 is Okay AND Sensor 2 is Okay THEN Motor Off
IF Sensor 1 is Low AND Sensor 2 is High THEN Motor is Slow Forward
IF Sensor 1 ...Read More