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Über dieses Buch

The lecture notes presented in these pages were originally developed for use in the Energy Management Training Program (EMTP), sponsored by the Office of Energy, U. S. Agency for International Development. This program, held at Brookhaven National Laboratory (BNL) and the Institute for Energy Research, State University of New York at Stony Brook, is designed to train mid-career and senior government officials in developing countries in the techniques of energy policy analysis and planning, and covers, in addition to the material presented here, more detailed case studies in resource evaluation, pricing, conservation, financial analysis, and investment planning. Since its incep­ tion in 1978, some 220 individuals from 57 countries have attended the course. These notes have also been used in executive level seminars and in­ country training programs in the Sudan, the Dominican Republic, and the People's Republic of China. Attendance at the course is diverse, and typically includes planners, managers, engineers, and economists from energy planning agencies, ministries of finance and economic development, electric utilities, refineries and State Oil Companies, and specialized energy planning units for energy conservation and for regional cooperation. The monograph is designed not just as reading material to support lectures, but also as a general self-contained reference text for a very diverse audience: we have therefore included much introduc­ tory material. The presentation is focused on a discussion of the basic principles of systems analysis: and the case material has been specially designed to illustrate these principles.

Inhaltsverzeichnis

Frontmatter

1. Introduction

Abstract
Energy systems analysis is a young field, with no long history of academic scholarship. Indeed, it is not even clear that energy planning and policy analysis itself has much recognition as an academic discipline, and the body of such knowledge as might be termed energy planning remains ill defined. Moreover, the body of knowledge that is germane to the unique problems of the third world is extremely fragmented, much of it not generally available in the places where lessons learned in one country could be usefully applied in another.
Peter Meier

2. Mathematical Fundamentals

Abstract
Matrix algebra is one of the essential tools of energy systems analysis. A grasp of even the most fundamental concepts allows much that would otherwise be extremely complex to be reduced to a few simple matrix equations; and the definition of most statistical tools, such as least squares regression, becomes extremely simple when the tedium of scalar algebra is replaced by the elegance of matrix expressions.
Peter Meier

3. Network Models

Abstract
A Reference Energy System (RES) is a way of representing the activities and relationships of an energy system, depicting estimated energy demands, energy conversion technologies, fuel mixes, and the resources required to satisfy those demands.2 The pictorial format for the Reference Energy System is a network diagram which indicates energy flows and the associated conversion efficiencies of the technologies employed in various stages of the nergy system. A simplified RES is shown in Figure 3.1. For each energy resource, a complete reference Energy System specifies the technologies employed in the following activities.
Peter Meier

4. Econometric Models

Abstract
A major difficulty with the approach of Chapter 3 is the matter of energy prices. We have seen how in the process model approach we build up a projection for fuel demand by a very detailed examination of end-use devices and their efficiency. The impact of prices is considered only indirectly: obviously a major incentive for the installation of more efficient end use devices is an increase in fuel price to the user. For example, the extent to which air conditioners are installed in households (quantified in our previous analysis by the saturation coefficient) depends on both the price of the unit, and the cost of running it; and once installed, its use ought to be dependent on the cost of electricity. But we have left unsaid exactly how the many coefficients of the process model are to be determined, leaving it a matter of judgement to the analyst in the construction of the scenarios.
Peter Meier

5. Petroleum Sector Models

Abstract
This chapter introduces the first building block of a comprehensive energy system optimization model with a discussion of the petroleum sector, formulated as a linear program. In Chapter 9 we build upon this model by integrating the refinery and electric sector LPs into a simple energy model appropriate to the energy planning problems of a developing country.
Peter Meier

6. Input-Output Models

Abstract
Consider an economy that consists of three sectors: agriculture, machinery, and construction. Let the domestic output of each of these three sectors be denoted x1, X2 and X3, respectively. Let the final demand in each sector be denoted yi: final demand is typically broken down to private consumption, government consumption (say for defense), investment and foreign trade (exports less imports). For given final demand, say machines, we write
$$ {a_{21}}{\kern 1pt} {x_1}{\kern 1pt} + {\kern 1pt} {a_{22}}{\kern 1pt} {x_2}{\kern 1pt} + {\kern 1pt} {a_{23}}{\kern 1pt} {x_3}{\kern 1pt} + {\kern 1pt} [\frac{{\$ mach}}{{\$ Agr.}}]{\kern 1pt} [\$ Agr.][\frac{{\$ mach}}{{\$ mach}}][\$ mach][\frac{{\$ mach}}{{\$ Const}}][\$ Const]{y_2} = {x_2} \times {\kern 1pt} [Final{\kern 1pt} Demand{\kern 1pt} for{\kern 1pt} machines][Gross{\kern 1pt} output{\kern 1pt} of{\kern 1pt} machine{\kern 1pt} sector] $$
(6.1)
Peter Meier

7. Industrial Process Models

Abstract
While application of the econometric approach outlined in the previous sections can give a general indication of how energy demand will evolve in response to fuel prices, it should be noted that this response cannot be stated in terms of specific technological changes, even though it is recognized that for a change in energy demand to occur, some change or modification to production technology must take place. Indeed, proponents of the econometric approach would argue that this is one of the advantages of the approach, in that the effect of technologies yet to be developed can be taken into account (without actually defining them). Of course, for this to be valid, one must suppose that the type of technological response induced by the price changes in the historical period that is subject to the econometric estimation will continue into the future.
Peter Meier

8. Electric Sector Models

Abstract
Analysis of investment decisions in the electric sector is a subject with a long history of scholarship, with a rich literature, and extensive applications in practice. Many complex mathematical models are used by electric utilities for planning purposes, some of which, such as the WASP model, have found application in a number of developing countries.1 But given the existence of a number of excellent literature reviews, such as that of Anderson (1972), we shall not attempt to provide any detailed review here. Rather, the emphasis will be on an elaboration of the fundamental concepts involved, and an exposition of modelling approaches that lend themselves to integration with other energy sectors, or to integration with energy system-wide and economy-wide models. Thus, while many of the finer points of reliability analysis, and transmission line planning, are not taken up in any great detail here, we pay a great deal of attention to the interaction between investment requirements in the electric sector to investment flows throughout the economy — a subject taken up in some detail in Chapter 10.2 In this chapter, then, we develop the fundamental concepts; the linearization of load duration curves, formulation of the investment decision as a mathematical programming problem, the complications arising from the addition of a spatial dimension, and some environmental considerations.
Peter Meier

9. Energy System Optimization Models

Abstract
Consider the simple Reference Energy System depicted on Figure 9.1. The end use demands shown on the right, D1, D2, and D3, are assumed given (from a demand forecasting model); as in our previous discussion of Reference Energy Systems (RES) in Chapter 3, the system is demand driven. Recall that in the RES, intermediate fuel and supply variables are defined by the series of matrix transitions discussed in Section 3.2. However, the coefficients of those transition matrices, which represent the market shares of each fuel meeting end use demand categories, or the electric generation mix, require exogenous specification by the analyst — which translates, in practice, to a great deal of judgement on the part of the analyst in an attempt to bring supply and demand into balance. Indeed, in many early developing country energy assessments, such supply-demand balances were derived manually even for future years.
Peter Meier

10. Simulation Models

Abstract
Simulation models differ quite fundamentally to the type of optimization model discussed in the previous Chapters. An optimization model can be represented by
$$ Max (Or Min) f(x)$$
$$ subject to g(x)=0 $$
that is, some function of x is optimized subject to a set of constraints on x; if the objective function and the constraint set are linear equations in x, and x ≥ 0, we then have a socalled linear programming model, many applications of which we have already discussed. In contrast, a simulation model can be represented simply by
$$h(x) = 0.$$
Peter Meier

11. Energy Economic Linkages

Abstract
Some of the difficulties of using even the extended, energy denominated input-output framework for analyze energy policy issues were alluded to in the closing sections of Chapter 6. In our analysis of Republica, for example, we saw the need to use an iterative approach if one were interested in determining that combination of economic activities that would result in oil imports of a given level. One way around such situations is to combine the input-output model with some kind of optimization framework, that in particular allows the solution of constrained optimization problems (whose constraint set might include, for example, upper bounds on energy imports, or lower bounds on particular levels of renewables). Moreover, given that we introduce an element of choice in the energy system, there is no reason why one cannot relax some of the more confining assumptions of conventional input-output analysis (only one industry producing one product using a single technology) to allow choice also in terms of the technologies of the productive sector: and include in the analysis, for example, the choice between say, electric arc, open hearth, or sponge iron processes in steelmaking. Indeed, there is no reason why industrial process models cannot be directly integrated into the energy system LP.
Peter Meier

Backmatter

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