Estimating the Economic Impacts of Net Metering Schemes for Residential PV Systems with Profiling of Power Demand, Generation, and Market Prices
Abstract
:1. Introduction
- Make a brief overview of the support schemes in use.
- Formulate the problem of maximising the profit of the prosumers and substantiate the algorithm for its resolution.
- Solve the problem of estimating the influence of a large number of generators on the operation of the power system as a whole.
- Based on the collected experimental data and the selected scenarios, demonstrate the possibility of increasing the profitability and number of prosumers.
2. Methodology and Models
2.1. The Methodology of Feasibility Studies
2.2. Evaluation of the Economic Benefit from Support Schemes
- The fixed volume (FV) component is based on the connection capacity (kVA);
- The variable progressive (VP) component: in this component, the tariff per kWh increases along with an increasing measured consumption level;
2.3. The Basic Tasks of the Study
- To evaluate the benefits received by the individual prosumer and the factors that influence them, as a result of equipping the household in question with PV.
- To evaluate the influence of a sharp increase in the number of prosumers and their capacity on the power system and the other energy consumers.
- A diminished dependence on gas imports;
- A diminished amount of atmospheric emissions of CO2.
2.3.1. Implementation of the First Task
2.3.2. Implementation of the Second Task
2.4. Input Information and Assumptions
- The base case, when the householder does not have an NS.
- The case, when the householder has an NBS.
- Power demand: information is required regarding the energy demand profiles of a large number of prosumers. We use the records of the smart meters of one hundred different randomly selected consumers over a period of one year [23];
- Power generation: the generation of the potential 100 prosumers is simulated by using the records of a newly installed generator for the year 2017. These records are modified by changing the equipment capacity for each of the 100 prosumers in such a way as to achieve a balance between the annual energy generation and consumption figures;
- Electricity market prices: the Nord Pool market prices are used. By using the Fourier transform [39], we single out the constant component, which we set as a variable depending on the number of the planning year. The average price changes are set by using the published results and the selected scenarios [39].
- The NS billing period is maintained according to the valid legislation, i.e., from April 1 till March 31. This period is suitable for the prosumers of Nordic countries [1] since in the winter period the prosumer has the possibility to use the electricity submitted to the grid to the maximum extent. This period increases the economic profitability for the prosumers.
- Retail prices are assumed to rise by 7.5% each year [50] (from 2018 to 2030), then by 2% (from 2030 till 2040) and fall by 1% (from 2040 till 2043) according to the EU Outlook 2050 energy price scenario, released by Energy Brainpool (June 2017).
- For the hourly load of end users, the time series collected by the smart meters were used, covering a whole year.
- We assume that, for the consumers connected to the grid, the consumption remains the same for future years as well. The loan interest rate was assumed in accordance with the interest rates laid down by the Bank of Latvia, i.e., 2.6% per annum [53]. The discount rate was assumed to be 2.0% per annum. The credit period is assumed to be equal to the equipment service life—25 years.
- The capacity of the PV technology and the corresponding amount of investment was determined on the basis of solar radiation in Latvia, to enable the prosumer with its individual consumption to use the energy produced by the PV technology for end consumption to a maximum extent.
- The NPV is calculated for two alternatives: Alternative 1 presumes taking a loan; Alternative 2 entails no loan; using the prosumer’s savings, Expstore.
- The NPV is calculated by taking into account the prosumer’s income, Expstore, which is obtained from the energy produced by the PV technology.
- No subsidy is applied. There are currently no grants or financing incentives available for PV generation in Latvia.
3. Results
4. Discussion
5. Conclusions
Author Contributions
Funding
Conflicts of Interest
References
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Name of the Parameter; Measuring Unit | Value |
---|---|
Number of phases | 3 |
Payment for the trading service, €/kWh | 0.00564 |
FV component, €/year | 115.11 |
VP component, €/kWh | 0.07104 |
Planning time, years | 25 |
Loan interest rate, % | 2.6 |
Discount rate, % | 2.0 |
Scenario | According to MC | Loan | Sum of Annual Mean Costs, € | NPV, € | PP, Years | LCOE, €/kWh | LCOE Changes in Respect to the Base Case, % |
---|---|---|---|---|---|---|---|
Base case | Accounting | - | 37420.72 | - | - | 0.16 | 0.00% |
NBS | Billing | present | 16446.07 | 7468.60 | 11 | 0.11 | −33.40% |
Billing | absent | 11852.97 | 8 | 0.10 | −38.13% |
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Sauhats, A.; Zemite, L.; Petrichenko, L.; Moshkin, I.; Jasevics, A. Estimating the Economic Impacts of Net Metering Schemes for Residential PV Systems with Profiling of Power Demand, Generation, and Market Prices. Energies 2018, 11, 3222. https://doi.org/10.3390/en11113222
Sauhats A, Zemite L, Petrichenko L, Moshkin I, Jasevics A. Estimating the Economic Impacts of Net Metering Schemes for Residential PV Systems with Profiling of Power Demand, Generation, and Market Prices. Energies. 2018; 11(11):3222. https://doi.org/10.3390/en11113222
Chicago/Turabian StyleSauhats, Antans, Laila Zemite, Lubov Petrichenko, Igor Moshkin, and Aivo Jasevics. 2018. "Estimating the Economic Impacts of Net Metering Schemes for Residential PV Systems with Profiling of Power Demand, Generation, and Market Prices" Energies 11, no. 11: 3222. https://doi.org/10.3390/en11113222