of the utility, but some knowledge about the company goals. ... generation and trading electric utility companies, subject to competitives markets regims.
A STRATEGIC DECISION SUPPORT SYSTEM FOR ELECTRIC UTILITY COMPANY IN A COMPETITIVE ENVIRONMENT R. C. G. Teive*
F. S. V. Silveira** M. Morozowski Filho** *Universidade do Vale do Itajaí – UNIVALI Grupo de Pesquisa em Tecnologia e Sistemas - UNITEC **Santa Catarina Federal Univesity – UFSC - Santa Catarina – Brazil
Abstract This paper describes an expert system (ES) based decision support system (DSS), which aids the user to select feasible projects among a strategic portfolio supplied by a dynamic simulator. The purpose of developing a DSS embbeded in na ES structure, is to search for the modelling of the decision-maker rather than the decision process. Thus, the ES stores not only the expertise from engineers of the utility, but some knowledge about the company goals. The dynamic simulator (DS) was developed by using the system dynamics paradigm, allowing the simulation of investment policies, taking into account the dynamic behavior of the involved variables and assuming that the decisions are taken continuously, leading to continuous actions. The proposed DSS is integrated with a strategic database (SDB), which contains information about competitors, regulatory system, special consumers and the energy wholesale market in general, forming an integrated computational system. The ES manages the integration between the DSS and the SDB, retrieving the information necessary to the simulation in progress. The ES tries to match this acquired knowledge with the performance indices calculated by the simulator, searching for infering conclusions in order to support the decision-making process, with regards the selection of coherent business opportunities. Thus the developed ES plays a strategic role in this system. 1. Introduction While the competition in the electrical energy sector becomes stronger, the processes traditionally used in the systems planning, although still important in the definition of technical-economic features of the electrical systems; present increasing limitations (such) as ways to structure and to analyse the company strategies. Hence, the both metodologies and models available currently should be complemented by analysis and synthesis tools that might focus on the relevants strategic and financial aspects to the generation and trading electric utility companies, subject to competitives markets regims. In this context, this paper describes the struture and aspects of implementation of an appropriate DSS to formulation and assessment of strategies and the economic-financial analysis of investment projects portfolios. Basically, the developed DSS is composed by an expert system and a DS, coupled to the SDB. The DS, which is based on system dynamics modelling technique, takes into account all the interactions existing between the parameters of a real world situation as well as the dynamic behavior of the variables. The developed ES, for your turn, manages the simulation process, defining feasible adjustment range of the simulation parameters and stablishing a coherent combination of these parameters in order to avoid that the user might simulate impracticable strategies or policies. This ES has three main roles : to manage the rules inferenciation process, to integrate the DSS with the SDB and to suport the user during the simulation process in the dynamic simulator.
The SDB contais strategic information about competitors, regulatory system, special consumers and the energy wholesale market in general and eventually information obtained through the competitive intelligence process. During the simulation these information can be combined with rules-based knowledge existing in the ES and the results of simulation supplied by the DS. The matching between information and knowledge forms new knowledge, which will support the user in the decision making process. This approach (SE + DS) is convenient to deal with the problem of analysis of investment projects in electric utility companies because it is an unstructured problem. A problem is said to be unstructured when some variables can not be precisely quantified (rates and prices for instance) and other variables can not be even qualified (social variables for example). Thus, in this situation the decision maker cannot precisely identify the significant parameters within the decision process. On the other hand, a decision is said to be well-structured [1] if the decision maker can identify all elements of the decision process and quantify them for determining an aanswer. Between these two extremes is the semistructured decision, which cointains well-structured and unstructured elements. Where it is unstructured from the perspective of the decision maker, computerized mathematical/statiscal models are generally inappropriate. In this case, usually, to reach a decision, there is a need for managerial experience, know-how, intuition, judgment, rules of thumb and past experiences. Therefore the use of computational simulation is appropriate in this problem, and in particular, the DS paradigm is convenient due to the possibility of implementing cause-effect patterns and the facility to model social systems, where there is a natural lack of well-defined relationships among variables. In the following sections the three main parts of this DSS is discussed. First of all, the developed DS is briefly presented in the next section. In the section 3, the ES is discussed in details . In the section 4, the structure and the roles of the SDB are considered. The computational integration between the DS, ES and SDB is tackled in the section 5. Lastly, in the section 6 it is shown a discussion of an investiment decision real situation, involving an electric utility company. 2. Dynamic Simulator Structure The DS consists of a framework for simulation of financial policies and strategies in an electric utility company, with regards to the evaluation of projects under economic, financial, social and technical aspects. This system dynamics based simulador will work for electric utilities like a ‘flight simulador’, allowing the user to foresee performance of a portfolio of projects under different scenarios constructed by modifying economic-financial parameters. Vis-á-vis the traditional methodologies, which usually calculate just net present value (NPV) and the studies are often performed by varying a single parameter at a time, this DS allows both the simultaneous variation of parameters as guaranteed energy, electricity price, amortization time and payback period and the calculation of performance indices such as: NPV, return on asset, return on equity, economic value added and market value added. System dynamics is a technique for analysis, modelling and simulation of systems, whose foundations were created by Jay Forrester in 1968 []. This methodology was result of a cross-fertilization of ideas from fields of systems general theory and control engineering. The adaptation of control engineering control methods aimed to allow its application to high complexity economic and social problems and it is charactezed by two aspects: a visual representation of the algebric and differential equations, that represent the systems structure, and the possibility of visualization of the systems behavior through simulation methods similar to one used in the tehcnical systems analysis. The system dynamics technique, different from traditional simulation techniques that emphasize the modelling of physical flows, aims to represent not only physical flows, which can be stored, but information flows that can be just observed. Hence, aspects like for instance, the influence of interest rate
on loans and vice versa, can be modelled through the causal relationship between the monetary flow (physical) and economic-financial performance indices of the company. The reasons for using this model are basically two: in this kind of problems is not feasible to use analytical models due to the lack of information for constructing the model (unstructured problem), leading to simplifications that might invalidate the obtained solution, and the other reason is that the knowldege involved in these models are composed in its most part by tacit knowledge [2]. The tacit knowldege different from the explicit knowledge, which refers to the articulated knowledge (the words we speak, the books we read, etc) is related to the unarticulated knowledge, including intuition, perspectives, beliefs and values that people form as a result of their experiences. Na expert in a company, for instance, usually, for doing his job needs to deal with this kind of knowledge and he makes his decisions based on a mental model [5], which he takes years to develop. The mental model is formed when the observer of a real system creates a mental image of the cause-effect pathways and draws inferences [3]. In the most of our social organization we use mental models, particularly in unstructured decison-making situations. Unfortunately, man is constrained by his limited rationality and mental models suffer from many deficiencies. Thus, the bigger the number of cause-effect relationships involved in the problem, the more unfeasible to threat the problem with a mental model. In other words, mental models are poorly defined and there is the needs to represent properly these mental models in a computer. A model is a simplified representation (an abstraction) of a real system. The broad purposes of building a model of a real system are [3]: - to understand how a real system works; - to determine which factors which exert biggest influences on the system behavior; - to assess the consequences of application the policies and controls; - to obtain feedback feasible functions that guarantee the satisfaction of the goals. In other words, as it is said in [5], models can be viewed as maps that capture and activate knowledge or framework that filter and organize knowledge. A model can be formulated through several ways, such as: mathematical model, when the model is represented by algebric or differentials equations; mental model, when it is represented by abstrations arise from personal expertise; or descritive model, when it is represented by a rules set or behavioral standarts. The system dynamics modelling technique combines the features of mathematical and descritives models, built from mental model, with simulation processes via computer. In some cases, the state of a system can also be obtained through an analytical solution. However, the most dynamic systems only can be represented by nonlinear models, whose analytical solutions , when possible, are complex. In these cases, just the simulation, by numerical methods, supplies a feasible way of analysis. The main characteristic of system dynamics modelling is the identification of feedback loops, which represents an advantage in relation to the analysis traditional methods that threat parts of a system apart (Cartesian paradigm), without taking into account the interaction between the components of the system. The use of system dynamics for building the DS has important further advantages : • it is possible to incorporate both long and short-term aspects in the same model; • it is allowable to represent complex and nonlinear causal relationships; • it is possible to represent the effects of social variables and behavioral standarts ( via rules);
• it facilitates the sensibilities analysis of empresarial performance indices regarding alternatives gestão policies The basic struture of a feedback loop is depicted in Figure 1, based on [4]. A feedback loop is a closed path that conects three basic elements: the system level (state or condition of the system), the information about the system level, the decision that controls the action, which for your turn affects the system level, closing the feedback cycle. Decision
Information about system level
Action
level (state or condition) of the system
Figure 1 – Feedback Loop
The system level (real level) is the information generator (visual level), which may be different from the real level due to delays and/or noises. However, it can be observed that the basis of decision making process is the information and not the real level. The structure shown in Figure 1 is the simplest form of a feedback system, which contais, in general, besides delays, distortions and feedback multiloop interconected. A feedback loop is formed by two types of elements : level variables and rate variables.. The level variables (or state) describe the system condition in any moment. The levels accumulate or integrate the difference between inflows and outflows (discrepancy) that for your turn suffer influence by the actions on the system. A level variable just can be changed by rate variables and, hence, it can not vary instantaneuosly. The rate variables (or action) informate how fast the levels are changing in a system. A rate variable do not determine the current value of a level variable, but the speed that a level changes its value. The rate variables are associate with actions, or else, the rate equation defines the action to be made in a decision point, taking into account the available information. The value of a rate variable is based only on level variables and constants. This kind of variable does not depend nor on the its past values neither on other rate variables. 3. Causal Relationships The rate variables permit to represent a large variety of logical and functional relationships that determine the systems behavior. The main relationships used in the system dynamics modelling technique are the following : causal relationships, delays, nonlinear responses and feedback loops. The meaning of some implications of each causal relationship in context of electric utilities modelling, are presented below, based on [6]: •
Cause-effect relationships, relating for example, price changes and eletricity demand. This example is depicted in Figure 2;
Variações na tarifa
Demanda de eletricidade
Figure 2 – Cause-effect relationship
•
The time delays between an action and its ultimate effect: for example, the delay between the implementation of energy conservation polices and changes in the consum habits of the consumers (Figure 3). Demanda
Tarifa
Tempo
Figure 3 – Delays in management responses
•
Nonlinear responses to actions, as it is depicted in Figure 4, small reductions in price do not increase significantly the energy consum, but, after a determined point, small reductions might lead to relevants consum increasings, up to achieve a saturation point, beyond the consum becomes insensitive to price
Consumo
changes.
Variação da Tarifa
Figure 4 – Nonlinear response
•
The decision-rules which produce actions, as for example, the definition of price level based on pruduction costs, competitive conditions and regulatory constraints.
•
Positive feedback loops (self-reinforcing feedback loops), as it is depicted in Figure 5 . In this example, price reductions increase energy consum that for its turn leads to reductions in the production
costs and consequently becoming feasible a new price reduction, considering the other factors that affect the demand growth as constants.
Tarifa Demanda
Custos Unitários
Figure 5 – Self-reinforcing feedback loop
•
Negative feedback loops, as it is depicted in Figure 6. In this case, the consum increasing esgota the available capacity of the system, which decreases the supply reliability level. The reliability reduction tends to diminishing the demand growth rate, or for reducing the consum of cativo consumers or for actions of big industrial and comercial consumers through the migration or self-production. Then, this feedback loop (-) tends to mantain the demand into level that the system can afford.
Tarifa Demanda
Nível de Confiabilidade
Expansão da Capacidade Custos Unitários
Figure 6 – Negative feedback loop
♦ Coorporative management tools :it might be represented by further feedback loops. For example, the capacity can be expanded as long as the demand increases, aiming to avoid the reduction of the reliability level (Figure 6). The capacity increasing, for its turn, increases the marginal costs, causing a reverse of the declining trend of the tarifs and consequently, a reduction in the demand growth.
4. Developed Expert System The main role of the developed expert system is to match the tacit knowledge, acquired from the company’s expert, with the performance indices supplied by the simulator, searching for infering conclusions in order to support the decision-making process, with regards the selection of coherent business opportunities. The developed ES manages the simulation process, defining feasible adjustment range of the simulation parameters and stablishing a coherent combination of these parameters in order to avoid that the user might simulate impracticable strategies or policies. This ES presents two main roles: management of the knowledge flow, considering knowledge in all levels, and support to the user, during the process of preparation and policies and strategies simulation analysis, actuating in this case in an integrated way with the DS. In the first case, the ES retrieves from SDB the necessary information to the inference and simulation process and stores these information into its knowledge base. The ES also stores into its objects results of procedural programs (like models of technical analysis) and knowledge inferred by the ES. In the latter case, the ES is responsible to communicate with the DS, leading the simulation process of the projects economic-financial dynamic, aiming to construct a investment portfolio. Since there are several combinatios of the initial parameters, some of these inconsistent, the ES might be used too to deal with the parameters adjustment, improving the simulation process. In this project, the following initial parameters are taken into account: ♦ cost of equity e cost of debt; ♦ weighted average capital cost (wacc); ♦
debt / equity;
♦ payout index; ♦ payment period. The ES might define consistents ranges of adjustment for these parameters, by using heuristics rules, which are based on relaships, such as:
♦ relação entre CCP e CCT; ♦ proporção entre CP e CT (risco financeiro); ♦ índices de Payout e tempo de amortização coerentes;
♦ % de CP dentro de faixa admissíveis pela empresa; ♦ valores viáveis de tarifa, etc. Além disto, o SE pode auxiliar na análise dos resultados da simulação, dando apoio à tomada de decisão com base em indicadores econômico-financeiros, tais como: VPL, ROA, ROE, EVA e MVA. Neste caso, as regras do SE incorporam não somente o objetivo tradicional de maximização do VPL, ou projeto de maior VPL, mas também objetivos financeiros, relacionados aos índices ROA, ROE, EVA e MVA. As distintas funções do SE podem ser realizadas por dois SE’s distintos (um dedicado ao BDE e outro ao SD) ou por um único SE, neste caso constituindo um único ambiente computacional. No caso de se optar por um único SE, considera-se que tanto o BDE quanto os programas procedimentais e o simulador dinâmico estarão embutidos em um SE, como esquematizado na Erro! A origem da referência não foi encontrada., que explicita as interfaces do SE com a BDE, o SD e programas procedimentais em uso na empresa. As características dessas interfaces são descritas a seguir.
5. DS-ES-SDB integration 1 GRAPHICAL INTERFACE
DYNAMIC SIMULATOR
Delphi
DDE
DDL
Powersim
2
EXPERT SYSTEM Kappa_PC
3 SDB OCR/FTR System
DataBase Oracle
Documents GUI Graphical Interface for SDB
6. SDB – Roles 7. Examples
Manually
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[3]
Mohapatra, P. K. J. ; Mandal, P.; Bora, M. C. Introduction to System Dynamics Modeling. Universities Press Limited (India), 1994.
[4]
Forrester, J. Policies, decisions and informations sources for modeling. European Journal of Operational Research, 59, 1992.
[5]
Morecroft, J. D. W. Executive Knowledge, Models and Learning. European Journal of Operational Research, 59, 1992.
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