Balance de materiales hasta febrero del 2015:

In configuring the BALMOREL model for the optimized representation of electricity generation and transportation in response to demand, all aspects of the electricity market apart from demand have to be abstracted into a number of ‘activities’ as part of the linear programming process. To see how it is done, one is asked to:

Suppose that the system under study (which may be one actually in existence or one which we wish to design) is a complex of machines, people, facilities, and supplies. It has certain overall reasons for its existence. For the military it may be to provide a striking force or for industry it may be to produce certain types of products. The linear programming approach is to consider the entire system as decomposable into a number of elementary functions called "activities"; each type of activity is abstracted to be a kind of "black box" into which flow tangible things such as supply, money, and out of which may flow the products of manufacture or trained crews for the military. What goes on inside the "box" is the concern of the engineer or the educator, but to the programmer, only the rates of flow in and out are of interest

(Dantzig, 1957, p. 132) At the most general level, the activities set up in BALMOREL pertain to the generation and transportation of electricity. Or as pointed out by one interviewee, if one was to take away all the additional features of BALMOREL such as the capacity for representing investments and demand-side reactions to various changes, a basic merit order optimization for electricity system operation is what will be left (Bregnbæk, 2012b). As a result of being based on linear programming in this way, the supply function or “generation cost function” (Ravn, 2001a, p. 6) is central to the functioning of BALMOREL. And using linear programming to control the operation of arrangements for generating and transporting electricity under an assumption of perfect competition “…implies that for any total output (e, h) [of electricity and heat] the generation is constituted such that it is done the cheapest possible way, and in the same way as if it had been centrally planned” [Italics in original] (Ibid.). What count as activities in the context of linear programming according to the definition provided by Dantzig and as realized in BALMOREL are exclusively found in the representation of the generation and

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transportation of heat and electricity. Demand does not qualify as an activity in the sense that it is not included as an element eligible for optimization. While the model still “…represents [the] generation, transportation and consumption of energy” (Ravn, 2001a, p. 28), it does so by optimizing the generation and transportation of electricity in accordance with demand. Demand as represented when running the model will change, for example in accordance with fluctuations in the price of electricity. Demand is thus a parameter that the ongoing optimization of the generation and transportation of electricity has to take into account, due to how the generation and transportation of electricity are set to influence demand and demand is set to be influenced by the production and transportation systems. But demand is not optimized using linear programming. Demand as defined within specific geographical areas and for specific periods throughout the year is represented in BALMOREL by introducing nominal consumption figures from historical data. Building on the data about past consumption patterns, the dynamics of demand are modelled by representing changes facilitated by the maximization of utility within a budget constraint in accordance with “…price elasticity, the cross price elasticity, and the income elasticity” (Ravn, 2001a, p. 12).

Having encompassed the electricity market in generation and transportation activities responding to an elastic demand-side, another significant step in the process of getting to represent Nord Pool in BALMOREL using linear programming involves quantifying the activities in question:

For a production type activity it is natural to measure the quantity of the activity by the amount of some product produced by it. This quantity is called the activity level. To increase the activity level it will be necessary of course to increase the flows into and out of the activity. In the linear programming model the quantities of flow of various items into and out of the activity are always proportional to the activity level. Thus it is only necessary to know the flows for the unit activity level. If we wish to double the activity level we simply double all the corresponding flows for the unit activity level

[Italics in original] (Dantzig, 1957, p. 132) In BALMOREL, the measure for the activity level is the MW, which in the model can be specified by means of the often more familiar MWh (e.g. Ravn, 2001a). As

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a quantitative measure, MW is used to denote the activity level of both the generation and transportation of electricity. That is, the measure of MWh is applied in referring both to the amount of electricity produced and the amount of electricity moved. The activity level involved in electricity production is specified for a time and place while factoring in the technologies applied. These technologies will initially include the established generation capacity as constituted by, for example, wind turbines. Power plants are in turn grouped according to type (a thermal power plant is defined as a condensing plant, for example) with each type of generator associated with “…a number of physical and economic parameter values, describing the fuel types, efficiency, environmental characteristics, [and] economic parameters (operation and maintenance cost, and investment costs for new units)” (Ravn, 2001b, p. 32). Again, in public energy planning projects the Technology Catalog is often an obligatory reference when it comes to ascribing these qualities and costs to the various units involved in the generation and transportation of electricity. And in accordance with the prescriptions of the linear programming method “All the characteristics of a unit are represented by a set of linear relations” (Ravn, 2001a, p. 5). The activity of electricity generation, for example, has an inflow of items such as fuel and maintenance costs and outputs of items such as electricity and CO2, SO2, and NOx

emissions. The representation of electricity transportation works in a similar manner. The main item flowing both in and out of the activity is electricity, with both transmission and distribution implying a loss of electricity and a cost proportional to the amount of electricity entering the transportation activity from the generation side (Ravn, 2001b).

Another step involved in reducing the operation of the supply side of the electricity market to a linear programming model involves finding the item or product flowing in the system around which the optimization should revolve. That is, there has to be an object within the representation guiding the optimization:

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One of the items in our system is regarded as precious in the sense that total quantity of it produced by the system measures the payoff. The contribution of each activity to the total payoff is the amount of the precious item that flows into or out of each activity. Thus if the objective is to maximize profits, activities that require money contribute negatively and those that produce money contribute positively to total profits

[Italics in original] (Dantzig, 1957, p. 133) As is the case in the application of linear programming for economic electricity system operation under a central planning regime and in an electricity market arrangement, the precious item flowing in the system representation when making scenarios using BALMOREL is set to be money. In this way, electricity system operation becomes a matter of meeting demand by relying on the patterns of activities for generating and transporting electricity which come at the lowest possible costs:

As minimizing total generation costs become a main aim in an integrated and liberalised power market generation technologies having low generation costs will be preferred to technologies having high generation costs. In a short term perspective facing existing generation capacities priority in generation will be based on short run marginal costs. In a long term perspective facing the need for investments in new generation capacity priority will be based on long run marginal costs, which take into consideration investment costs

(Ravn, 2001b, p. 20) The last step needed to reduce the generation and transportation of electricity to a linear programming model format as found in BALMOREL is the introduction of the requirement of a material balance equation. Put briefly, this requirement is the idea that “…for each item it is required that the total amount on hand equals the amount flowing into the various activities minus the amount flowing out” (Dantzig, 1957, p. 133). The idea of having a material balance equation characterize the way the items can be accounted for when flowing in the abstract system implies that the arrangement of activities has to be complete. In other words, getting a complete overview of the flows of various items in the model has to be possible by means of following the activities included. Characterizing the

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items in the system by a material balance equation in this way ensures that there can be no ‘gaps’ in the model.

To recount, configuring BALMOREL for the making of electricity market scenarios involved a series of steps reducing the electricity market to a programming problem in the form of a linear programming model. Initially, an important reconceptualization of the electricity market implied treating supply and demand in different ways. Supply was considered as equivalent to centrally planned electricity generation and transportation, while demand was conceptualized as price-, income-, and cross-price elastic electricity demand by building on historical consumption data. Making this fundamental distinction implied that Nord Pool could be modelled utilizing the higher order control of linear programming by having a marginal cost-based optimization of the generation and transportation of electricity meet the representation of a dynamic demand at all times.

At the highest or most general level, the generation and transportation of electricity came to constitute the activities of the production system to be optimized and controlled in accordance with variations in demand. These activities were in turn quantified in providing a measure of the activity level in the form of MW or MW/h, and the item flowing in the system regarded as precious was configured as money. Finally, the idea that the items in the abstracted system would have to be made completely accountable by following the activities inscribed in the system was introduced. As the supply system has been reduced to this form, the equation solver built into GAMS can be put to work. Having transformed the supply of electricity into this virtual system, GAMS can solve the programming problem “…which consists in determining values for the activities levels which are positive or zero such that flows of each item (for these activity levels) satisfy the material balance equations and such that the value of the payoff is maximum” (Dantzig, 1957, p. 133).

In outlining the making of a market device for the production of electricity market scenarios used in actualized policy recommendations for Danish electricity market development, the role of linear programming in wind power integration through electricity market construction was established. In doing so, it was illustrated how expertise in the form of a higher order control for production system operation and

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optimization was mobilized in the process of market construction. Having established the significance of control systems engineering for the making of BALMOREL, focus here shifts to a description of one of the central ways in which economics was mobilized in the making of the model. Through the metrics named producer and consumer surplus, economics was used to describe the objective guiding the making of scenarios made by means of BALMOREL in conceptualizing how the optimized marginal cost-based operation of the electricity system intersects with demand side marginal utility. In other words, arranging for equilibrium between the marginal costs of production and the marginal utility of consumers corresponds to a maximization of socio-economic value understood as the total producer and consumer surplus (e.g. Marshall, 1920; Ravn, 2013). It is thus possible to see how economics was mobilized in wind power integration through electricity market construction by making a market device set to produce scenarios conceived of as implying the maximization of producer and consumer surplus.