Solar Photovoltaic System Economic Analysis for the Electric Energy Consumer

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The electric industry makes an appealing argument that utilities should charge net metering customers for all these energy deposits and withdrawals. On the other side of the argument, there is a compelling case that says utilities should let solar customers use the grid at no charge or even that utilities should provide incentives to solar customers.

After all, solar photovoltaic systems produce the most during long sunny summer days when the demand for electricity is high and it costs more money to generate power. Thus, according to this argument, the excess solar generation deposited onto the grid during the day is more valuable than the electricity withdrawn at nighttime. So which logic is more compelling? Is net metering a burdensome requirement or is it beneficial to everyone? Last year I heard the president of a midwestern utility speak about this burden solar created, and his logic seemed very good.

So I looked at the electricity production data that were automatically collected by my own rooftop solar panels and compared it to the peak and off-peak electric rates that I pay. I then decided to systematically dig into the numbers for a large region in my home state of Pennsylvania and do a more scientific analysis — and the results surprised me.

Pennsylvania has a deregulated market in which utilities distribute electricity, rather than produce it. Most customers choose and pay a separate supplier for the generation of electricity from a power plant. During times of high demand, the price for power on the wholesale market goes up due to supply and demand. Utilities and electric suppliers make daily predictions for the amount of energy needed by all customers so that electricity can be generated, purchased and placed onto the grid.

A very hot summer day, for instance, will require more energy to meet high air conditioning loads. Using short-term weather forecasting, one can also project the impact of solar energy on the overall demand for electricity on the day-ahead market. I looked at the hourly price data for the day-ahead market for wholesale energy in the Pittsburgh region.

And then I looked at solar production data for many photovoltaic arrays for this same region. In other words, solar energy allows suppliers and utilities to realize savings and increase profit margins, even though the solar customers are purchasing less electricity overall. But how would we quantify the full value of solar?

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The best way is to analyze the impact of solar that is not already priced into the market. First, we assume that the price for a given demand remains constant. Then create a scenario or model in which a large amount of solar energy is placed onto the grid. This would create a noticeable decrease in the current daytime demand for electricity and a corresponding decrease in the price of electricity during these daytime hours. Five percent is quite a large amount of solar for Pennsylvania, where solar energy provides about 0.

Amazingly, the savings are greater than the lost revenue. This is savings for every customer, not just solar customers. The few solar customers would be responsible for lowering the cost of electricity for all customers. These savings are based only on the generation of electricity. There are additional similar savings for the distribution of electricity that I have not yet addressed. Just as it costs more to generate electricity when the demand is high, it also costs more to distribute electricity when the demand is high.

In fact, sometimes it can cost so much more that utilities are willing to pay customers not to use electricity. I should stress that even with solar energy, we need utilities and power generators. There will always be an infrastructure that needs to be supported and thus utilities and generators need to maintain a dependable revenue.

It is used to determine the for this study. We have min, mode, and max values for the conditions under which one outcome may be preferred to another degradation rate from the literature, and we can calculate mean outcome. There are two kinds of stochastic dominance we con- values as shown in Table 1.

We also consider failure rate of the solar PV system. Since there is no real experiment for tic dominance. In system is 0. However, our analysis is the cost of solar PV systems and annualized electricity price in focused on cost. This means that our results are cases in which the each scenario case. With this, we can get the probability that the lower the outcomes are, the better the results are.

This means PV systems in houses with the distributions. The detailed combination of policies for each case is inequality at some a; that is, Eq. Economic analysis Table 4. Thus, this analysis Inc. Sensitivity analysis In addition, we also do two other economic analyses. The variables we no federal tax credits. However, many panels currently come with longer available in Indiana at present.

Thus, we do the sensitivity analysis over 25 breakeven cost of electricity from solar PV systems, and compare it year and 30 year lifetime. We also do the sensitivity analysis over 15 years. We have used 0: Scenarios considered As will be seen below, that results in a fairly wide distribution for annualized electricity price, so we do sensitivity using 0.

PV panel capacity larger size 7. Installation cost of PV panel 2. Annual electricity generated by PV panel 5. Annual electricity generated by PV panel 7. Failure rate of panel 0. Degradation rate of electricity generated from PV system mode, 1st through 18th year 0. Energy Information Administration Annualized electricity price 0.

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Energy Information Administration Table 5 to be presented below. We get the distribution of the difference Annualized electricity price without and with carbon tax. Annualized electricity price with 0.

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Without federal tax credits results may not change much since it is 20 years in the future, but The result for the difference is shown in Table 6, and it is more it is still important to do the analysis. The salvage value rates of expensive for consumers to adopt solar PV systems without any 3. We do sensitivity on the real discount rate using values of 1. As illustrated in Table 6, however, the probability is still change.

Results and discussion 3. Annualized electricity price The results for scenarios considered are illustrated in Table 7. Financial analysis the base case carbon tax. The result The annualized real electricity prices for both cases are shown shows that the cost of solar PV systems decreases with net-metering, in Table 5.

The case with carbon tax shows higher value. This annualized electricity price distribution will be compared There is a Table 7 Results for scenario analyses. Sensitivity analysis for the standard deviation of electricity price. Case System capacity Probability solar is less Case Multiplied 5.

Base 15 Years Base case 5. This is because our 7. The base case and the case without federal tax credits Case without FTC 5. The probabilities that the 7. Since the federal tax credits takes the largest part in Case with D and CT 5. The probabilities that solar is less expensive for both the larger system produces more electricity and there is more systems are very high; This means that the solar PV system can be a lot less less expensive for the larger system increases more than for the expensive with tax deduction from depreciation introduced.

The base case and the case with carbon tax 3. The base case and the case without net-metering If carbon tax is imposed on the grid generating electricity with The breakeven cost increases if net-metering is removed. The fossil fuels, the burden may be passed onto customers. Thus, the probability that solar PV system without net metering can be less annualized electricity price for the analysis increases. The solar expensive than the electricity price becomes less than the base cost is much lower for the 7. The probability is This may be associated with how much and the 7.

Clearly net metering plays more electricity consumers still need to purchase even after an important role in reducing the cost of solar PV systems. The smaller the solar system is and the less electricity it generates, the larger amount of electricity the 3. As a result, the seems to be reasonable. For the probability that solar can be less probability that solar is less expensive decreases from the base expensive, both system sizes show essentially the same high case.

Case System capacity Probability solar is less 3. This indicates that the solar electricity can be Base case 5. Financing period Case without FTC 5. For most cases, as indicated in Table 9, Case with D 5. This means that the change of 7. The mean solar costs are almost the same for all systems are The probability does not change much Table Salvage value rate We examine how much the probability solar is less expensive is 3.

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The base case and the case with depreciation and carbon tax changed with the changes in salvage value rate. The results are Although the results show a slight difference between the two shown in Table Changes are not large, so a change in salvage system capacities, the probability solar is less expensive is The base case and the case with depreciation, carbon tax, and The result shows that the probability solar is less expensive no federal tax credits decreases but it does not change much in most cases Table The probabilities that the solar systems can be less expensive than the electricity price are Discount rate the 5.

This and a carbon tax to correct the GHG externality. Table 14 Table 15 Results for stochastic dominance for 5. Results for stochastic dominance for 7.

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For solar, most of the 20 year cost is incurred at the beginning of the other cost for each case as described in Tables 14 and Conclusions Tables 14 and 15 represent the results of the stochastic In the economic analyses and the case without net-metering, dominance analysis. Without net-metering, excess second degree is not relevant. Thus, the larger system shows lower economic viability. That probability falls if any of these policies or depreciation of the solar PV systems.

Solar Photovoltaic System Economic Analysis for the Electric Energy Consumer

From an economic perspec- practices is not available. On the other hand, depreciation and carbon tive, that option may also be attractive because the effect is to level tax are not current policy. If both are added, solar has tricity. Allowing depreciation for solar simply gives home solar assumptions of this analysis. For the last case, we remove federal the same tax treatment. A review of solar photovoltaic investment in solar energy than the current federal tax credit.

Renewable Sustainable Energy Rev. However, there are differences in this change of probability Cai, D.

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Impact of residential PV adoption on retail electricity rates. Energy Policy 62, — This difference in the change of California Public Utilities Commission. Rate Charts and Tables—Electricity. Available probability is attributed to the system size. This may be because Darling, S. Assumptions and the levelized cost of energy for photovoltaics. Available tion of solar PV systems is smaller for 7.