Planning of community heating systems modernization and development

https://doi.org/10.1016/j.applthermaleng.2007.11.020Get rights and content

Abstract

New approach to community heating systems modernization and development planning process has been proposed. It is based on the general decision making aid algorithm. The proposed algorithm takes into account both demand and supply side of community heat market modernization and development.

The first step of algorithm – analytical step, refers to data base creation, which is needed for the description of community heating system energy, ecology and economic characteristics. Analysis of those characteristics allows for the identification of heating system market modernization and development potential scenarios.

The second algorithm step – decision step, allows for the identification of the most compromise scenarios of system modernization and development. To make the planning process more transparent and to increase the influence of decision makers on the planning process the ELECTRE III method was chosen as the tool of decision aid. The ELECTRE III method is based on the construction of outranking relation and definition of pseudo-criterion. The iteration mode of method application allows the decision maker and analyst for the investigation of the sensitivity of final solution to the changing preference model.

One of the methods of statistics – the creditability of mean range method was used for the determination of initial definition of pseudo-criterion.

Proposed algorithm and decision aid method were employed for the case study analysis referring to the choice of the heating system for new developing urban area.

Introduction

Sustainable development of local heat market requires application of planning procedures, which include optimization of both demand and supply side of local energy market. The methods of local energy planning can be classified in three separate categories [1]: (i) planning by models, (ii) planning by analogy and (iii) planning by inquiry.

The planning by models can be based either on econometric or optimization models. Econometric models utilize mathematical or statistical methods and relay on statistical data. Optimization model allows for the identification of best possible solution – minimization or maximization of objective function, with the predefined set of constrains which describe the space of acceptable solutions.

The planning by analogy utilize the simulation of energy market in less developed country by the behavior of energy markets in well developed countries. That kind of local energy planning is usually used for the verification of planning results achieved by other planning methods [2].

The planning by inquiry is used in the case when other mentioned above methods are not reliable. Good example of planning by inquiry is DELPHI method, which is based on the questioning of group of energy market experts and statistical evaluation of their answers [3].

In majority of energy planning cases dominating planning model is the linear programming, which refers to minimization or maximization of objective function, with the set of constrains in the form of linear functions. The example of practical utilization of linear programming methods in local energy planning are: BESOM [4], MENSA [5], EFOM [6]. Linear programming methods has been also used as the decision tool in IEA Annex 36 – Advanced Local Energy Planning (ALEP). In all the described case study calculations authors utilized MARKAL (Market Allocation) energy planning method [7], [8], which is based on modified SIMPLEX algorithm.

All the methods of local energy planning listed above have a limited transparency, especially for decision makers who have usually not good mathematical background. Those methods do not give opportunity to create decision makers preference model or define that model a priori.

The paper presents new approach to community heating systems modernization and development planning process, which reduces the obstacles of traditional linear programming methods. It is based on the general decision making aid algorithm. The proposed algorithm takes into account optimization of both demand and supply side of community heat market modernization and development.

Section snippets

General algorithm of local energy planning

Community heating systems modernization and development planning process requires active action of local authorities responsible for safe and economically reasonable operation of local heat market.

In the sense of decision making theory local authorities are decision maker. It is rather often during planning process, that there is a lack of precise information needed decision makers for specification of technically, economically and socially acceptable scenarios of heat market modernization and

The evaluation criteria

The proposed set of evaluation criteria has been described in detail in [12].

Criteria thresholds

The choice of criterion thresholds is required for the creation of so called pseudo-criterion. The evaluation of acceptable heat system modernization and development scenarios using pseudo-criterion allows for the determination of three basic relation among scenarios [14], [15]:

  • Indifference (appointed by operator I),

  • preference (appointed by operator P),

  • weak preference (appointed by operator Q).

In the case when none of the mentioned above relations is identified the state of incomparability of

Description of the selected multicriteria decision aid (MCDA) method

As the multicriteria decision aid tool the ELECTRE III method was chosen. This method: (i) accounts for the lack of precision and accuracy of the values describing the criteria, (ii) gives a decision maker a degree of freedom in submitting information on preferences regarding individual criteria in the form of indifference and preference thresholds, using the pseudo-criterion model and the veto threshold, as well as information regarding relative significance of individual criteria, (iii)

Case study description

The exemplary multicriteria aid of energy planning process has been conducted for ranking of possible scenarios of heating system development for 12 residential buildings, each consisting of 10 separate apartments, located in the northern part of Poznań, Poland – see Fig. 4. Total heat demand of each of residential buildings is Q˙co=55 kW for space heating and Q˙cwu=25 kW for domestic hot water.

The decision maker has been defined as energy company willing to sell heat to the energy end users –

Conclusions

New approach to community heating systems modernization and development planning process based on general decision making aid algorithm has been presented. The first step of algorithm – analytical step, refers to data base creation, which is needed for the description of community heating system energy, ecology and economic characteristics. Analysis of those characteristics allows for the identification of heating system market modernization and development potential scenarios.

The second

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