A New Modeling and Application of Hierarchical Production Planning Approach

A New Modeling and Application of Hierarchical Production Planning Approach

Reza Tanha Aminlouei (University of Tehran, Iran)
DOI: 10.4018/978-1-4666-2098-8.ch007
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In real power systems, power plants are not in the equal space from the load center, and their fuel cost is different. With common utilization conditions, production capacity is more than total load demand and losses. Therefore, there are different criteria for active and inactive power planning in each power plant. The best selection is to choose a framework in which the utility cost is minimized. On the other hand, planning in power systems has different time horizons; thus, for effective planning in power systems, it is very important to find a suitable mathematical relationship between them. In this chapter, the authors propose a modeling by selecting a Fuzzy Hierarchical Production Planning (FHPP) technique with zone covering in the mid-term and long-term time horizons electricity supply modeling in the Iran global compact network.
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Describe the general perspective of the chapter. Toward the end, specifically state the objectives of the chapter.

Electricity production planning which is called generation planning in power systems is divided into: long-term, mid-term and short-term planning (Alonso, etc. 1992). Planning and operating modern electric power systems involve several interlinked and complex tasks. Optimizing a production plan, however, is difficult for thermal and hydro power plants, which could be solved with proper computer tools.

Long-term energy generation planning is of key importance to the operation of electricity generation. It is employed for strategic planning, budgeting, and fuel acquisitions and to provide a framework for short-term energy generation planning.

A long-term planning period (one year) is usually subdivided into shorter intervals of weeks or months, for which parameters like load–duration curve should be predicted, and variables like expected energy generations for each plant unit must be optimized. The Load–Duration Curves (LDC’s) predicted for each interval are used as input data, which are equivalent to load-survival functions. This is appropriate since load uncertainty can be suitably described using the LDC. It is assumed that the probability of failure for each thermal unit is known.

In power system management, the problem of planning production for the next 10–30 days is known as the mid-term planning problem. Production planning problems with up to one week time horizon is known as short-term planning.

The short-term and mid-term planning problems could be principally considered alike, except in some specific conditions, when the problems are more or less relevant to the variety of time horizons. Since uncertainty exists in prediction of electricity demand as well as electricity price, the prediction of the mid-term problem can become difficult. On the other hand, the short-term model can be detailed due to the relatively good predictions that can be derived for the next few days. This high level of detail implies that in practice a short-term model, can only implement one district heating system at a time. Another purpose of the mid-term model is the model restrictions that connect the different systems. For example in principal planning procedure, the outputs of solved mid-term problems are used as the inputs to the short-term problems.

Production planning in the electricity industry and PPGP problems are very complex with extensive features. Also, due to the specific condition of respective product, electricity generation planning is mainly different from the other production planning problems that have specific characteristics. Some of these characteristics are:

  • Not being able to suppose the backorder state.

  • Generating electricity in a specific time period for use in future time periods is not directly possible.

  • Flexible and specific electricity generation planning generate more electricity than predicted output to satisfy the expected demand.

An appropriate approach to alleviate this deficiency is to use FHPP by introducing imprecise/fuzzy data along with soft constraints, allowing some minor deviations from the outputs of the upper level while making a decision in the lower level.

A rigorous mathematical analysis of Hierarchical Production Planning (HPP) is found in the pioneering work of Hax and Meal (Hax & Meal, 1975). Theoretical work on the topic has followed (Golovin, 1975), (Bitran & Hax, 1981) and (Ozdamar, etc. 1996). Nowadays, HPP method is used as a structured method in various fields.

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