Scenarios for more efficient cost allocation

In this section, based on some ideas from literature explained briefly in section §2.2, and the results of simulations from previous sections, three hypothetical scenarios are presented. These scenarios aim to recombine the parameters already present in the model in order to provide solutions that are more efficient in allocating costs and resolving the conflicts of interest. It is assumed each of these scenarios is more relevant than the others under some circumstance; therefore, the analysis in each of the following sections is independent from the others. In other words, each scenario is added to the model presented in §2.3.2. After presenting all scenarios, the possibility of combining these scenarios are discussed in §2.4.1.

2.3.3.1 Cooperative smart metering tariff

The first scenario looks at the intermediary role of the retailers and the positive externalities brought by the implementation of new policies. As mentioned, the costs and benefits for this actor group are usually neglected in the CBA studies. Therefore, a

smart metering tariff is introduced as a cooperative strategy in order to include

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The general idea is that the retailer should pay for the availability of Smart Meters through tariffs. Retailers have advantages in the smoother production and reduced risk due to new pricing strategies. Therefore, they may contribute to the cost allocation of the DSOs by paying a share proportional to the potential benefits to the DSO as the smart metering tariff. In this situation, they pay a certain amount of money for each client who has a Smart Meter installed, as a fixed cost per client. It would represent a more plausible situation, where the retailer is motivated to exploit the benefits of this new technology and contribute to the costs. This fixed contribution in the revised model is calculated by multiplying a constant share of retailers’ benefits by the average potential benefit of retailer in the absence of smart metering tariff over the period of analysis. Therefore, monthly smart metering tariff for each consumer is calculated as:

. / (2-13) Where:

- : Smart Metering Tariff - : The duration of analysis

- : Smart metering tariff coefficient - : average potential profit for retailer

The parameter α is set based on the initial investment of the DSO in order to reduce the negative benefits in the early years of technology development. Since the benefits for both retailers and DSOs increase over time, this new tariff partially shifts the collective benefits of the late years to the early years along with contributing to the cost allocation. Figure 2-9 shows the results of simulation after introducing the smart metering tariff.

Figure 2-9. Benefits for actors after introducing Smart Metering Tariff

700 523.5 347 170.5 -6 0 24 48 72 96 120 144 168 Time (Month) Consumer benefit DSO profit Retailer profit

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Compared to the results of dynamic pricing policies, figure 2-9 shows the new tariff does not substantially change the consumer’s benefits since it has negligible impact on the variables influencing consumer’s bill. However, it can lead to the convergence of retailers and DSOs profits and recovery of costs for the DSO in the early years. The critical variable in this scenario is the smart metering tariff coefficient ( calculated based on the potential profit for the retailer over the period of analysis, and therefore the smart metering tariff.

Figure 2-10. Impact of smart metering tariff on DSO profit

Figure 2-10 shows simulation results of DSO profit based on three different values of smart metering tariff. The results show moderate values of smart metering tariff can partially compensate for initial investments, but a full compensation requires a very high level of contribution from the retailer side (a smart metering tariff equal to 0.75 is equivalent to 50% contribution to smart metering installation cost by the retailer). 2.3.3.2 Dynamic network tariff

The second hypothetical scenario investigates the dynamics of interaction between DSO and consumers. The network tariff in general is composed of power-based and energy-based components (Belonogova et al., 2011). By introducing new policies described in §2.3.2, the increase in the power-based component of the network tariff is used as a compensation mechanism. This is an increase based on technical characteristics of the network and initial expenditures; therefore, it is independent from consumption profile. Although increased network tariff aims to compensate for the initial investments done by the DSO, consumers are heterogeneous in terms of their consumption patterns. It means different peak-time consumptions have different

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contributions to the future costs of network development, a parameter currently missing in the model. Therefore, a dynamic network tariff is proposed to find a more efficient compromise between the costs and benefits of individual consumers. The new dynamic tariff is calculated by adding the extra cost of consuming at peak times to the energy-based component of the original network tariff. As a result, the share of peak consumption in consumer profile is multiplied by the extra cost of supplying one unit of electricity over the peak hours. Then, the new network tariff is increased in proportion to the additional costs incurred to the system. Equation 2-14 shows the new network tariff to be used in the model.

⁄ . (2-14) Where:

- : Revised variable network tariff

- : Unit Peak Cost

This dynamic network tariff shifts part of the benefit of the consumers to the DSO’s revenue. However, its impact is limited since it further increases consumer bills already impacted by new network tariff described in §2.3.2. On the other hand, this new policy does not influence the profit structure of the retailer; therefore, a more plausible way to use this scenario is in combination with other solutions.

Figure 2-11. Benefits for actors after introducing Dynamic Network Tariff

The results of simulating this scenario as depicted in figure 2-11 are very similar to figure 2-6. This figure shows a very small portion of consumer benefit is shifted to the

700 512.5 325 137.5 -50 0 24 48 72 96 120 144 168 Time (Month) Consumer benefit DSO profit Retailer profit

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DSO, while the retailer benefit is almost unchanged. In other words, network tariff constitutes a small portion of both DSO revenue and consumer bill; therefore, its impact on system dynamics is limited.

2.3.3.3 Outsourcing data exchange services

The third scenario analyzes the possibility of introducing an intermediary for outsourcing the new IT tasks. By deploying smart meters, a smart meter operator, which is the DSO, needs a secure and reliable communication infrastructure and capabilities to use the infrastructure in an effective way in order to exploit the potential benefits brought by smart meters. Such an infrastructure and its associated capabilities are not the general characteristics of the DSOs, incurring significant fixed costs for information technology (Strüker et al., 2011). However, an alternative approach is outsourcing IT tasks to a more competent actor with access to IT infrastructure, which can provide more innovative services for the consumers and lower the capital expenditure for the DSO.

Following Strüker et al. (2011), here the assumption is that such an intermediary can lead to resource savings and extra benefits for the DSO. Apart from lowering the initial investments for the DSO, such an intermediary can reduce contact costs between smart meters and market actors, and both contact and agreement costs within market actors by providing centralized data collection schemes. In addition, since the DSO is still the owner of smart meters and authorized actor to gather and the data, the gather data can be sold to the intermediary as an extra source of revenue for the DSO. Finally, the intermediary can provide more innovative solutions for the consumers to facilitate the introduction of demand management programs and improve control over consumption. Therefore, its role in the system is to provide the complementary ICT infrastructure, gather consumer data, distribute the data to the authorized actors, and distribute the messages back to the customers.

These benefits have a positive correlation with the number of smart meters added to the system. Such a network effect helps the ICT firm to benefit from economies of scale gained from increased capacity utilization and bulk data purchasing (bulk purchasing price can be reduced to 1/5 to 1/7 of the price offered to a medium-size data center (Rangen, 2008)). This creates a virtuous cycle; a lower price attracts more consumers to use smart metering services, which increases the network effect and leads

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to further decrease in contact and agreement costs; thus, more positive network externalities.

The impact is added to the model by deducting ICT infrastructure cost from smart metering installation costs and consequently from DSO costs. Therefore, equation 2-2 is changed to equation 2-15.

(2-15) The second impact is the price of data sold by the DSO to the ICT firm as the information cost . Total cost of the intermediary is the sum of information cost and operation cost of running the infrastructure. Information cost is also added to DSO’s revenue. The intermediary provides new services for the consumers and constitute the revenue for the ICT firm. Therefore, equation 2-16 depicts the new revenue structure for DSO.

∙ (2-16) It is assumed the new services have potential benefits for the consumers in the form of increasing potential conservation and peak-load shift effects. Therefore, the price of these services are added to consumer costs, through fixed increase in consumer bills or any other financial instrument (equation 2-17). In addition, the profit structure for the new ICT firm can be analyzed based on the revenue of ICT solutions as well as the information and operation costs affected by a factor representing the economies of scale provided by the network effect over the long-term (equation 2-18).

.

- : cost of new ICT solutions for consumer - : profit of the ICT firm

- : revenue of the ICT firm - : cost of the ICT firm

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- : economies of scale coefficient - : ICT operation cost

Figure 2-12. Benefits for actors after introducing the intermediary ICT firm

The results of simulating this scenario are depicted in figure 2-12. Based on this figure, adding the new intermediary firm has the potential to compensate for negative benefits of the DSO in the early years. The benefits of consumers and retailers slightly increase because of the value added of new innovative services to energy conservation and peak-load shift. The critical variable in this scenario is the impact of new ICT solutions on consumer benefits in the form of extending the range of both conservation and peak-load shifting effects. Increasing this factor improves profits and benefits for all the actor groups included in the analysis, but it depends on ICT firm-related characteristics.