This paper analyses the economics of pooling small UK based local electricity prosumers with back-up access to the National Grid and compares it to the current conventional UK electricity supply model—business as usual (BAU) approach. This is contextualized against the UK energy market framework, prosumer research and changing energy market dynamics. For the economic assessment a three-tiered production/supply and consumption model is developed based on site specific levelized cost of electricity (LCOE) and other cost parameter to operate the model. Modeling results indicated the economic feasibility and advantage of a prosumer approach in a significant number of modeling scenarios. Additionally, a break-even analysis for the two approaches was undertaken to understand the sensitivity of individual input parameters.
With the emergence of distributed energy resources (DER), the relevance of so-called “prosumers”―entities/households that are producer and consumer of energy in one―increases [
Energy markets tend to be heavily regulated and are subject to political decisions. Such regulation is not only motivated by consumer protection but is also desired by utilities and investors to have a stable commercial framework for investments [
The UK electricity market is divided along the following functional lines: 1) generation; 2) transmission; 3) distribution; and 4) supply. See
Economic mechanisms like quota [
demand and providing a benign investment environment. Tveten et al. [
Prosumers are a relatively new concept in energy markets but have been present in a wide range of other markets for a long time [
Most energy markets have not lent themselves to prosumer models so far. That is mainly due to the centralized nature and structure along the value chain and the economics of scale in conventional electricity generation. Nonetheless, increasing competitiveness of DER and the development in Information and
Communication Technology (ICT), often associated with smart grid technology, are the basis for new opportunities [
In the context of the German Energiewende (“Energy Transition”) the German government initiated in 2008, a 4-year long field research program called “E-Energy” with six subprojects focusing on DER, DSM and prosumer models. The main objective has been to show how optimization and use of ICT can help to achieve an economical solution, energy security and environmental compatibility [
The methodology is designed to analyze the economics of pooling small local electricity prosumers in an actual or virtual micro-grid environment with back-up access to the National Grid (“Prosumer-Model” or “Prosumer-Approach”). The Prosumer-Model results are compared to the established electricity supply model or business as usual approach (“BAU-Model” or “BAU-Approach”). The economic advantage/disadvantage between the two approaches is measured as difference between the present value of the annual and lifetime cost for a given electricity consumption in British Pounds (£) (“Model”).
The Prosumer-Approach is based on three-tiered hierarchical production/ consumption model to balance electricity demand at any point in time:
・ level-1, the prosumers consume their own production;
・ level-2, they share/provide excess production to or draw/consume additional demand from a member-energy-pool; and
・ in level-3, they feed into or take electricity from the National Grid.
See
Name | Postcode | Latitude | Longitude |
---|---|---|---|
Inverness | IV1 | 57'44' North | 4'19' West |
Carlisle | CA2 | 54'87' North | 2'99' West |
York | YO24 | 53'93' North | 1'16' West |
Aberystwyth | SY23 | 52'37'North | 4'09' West |
Kingston upon Thames | KT2 | 51'43' North | 0'28' West |
Camborne | TR14 | 50'19' North | 5'24' West |
The Prosumer-Model is based on the assumptions that, firstly, it assesses the economics of a single prosumer only and secondly, that each prosumer produces exactly as much electricity as they consume in one calculation period, being one year. This results in a zero-sum balance over the three hierarchy levels.
The BAU-Approach calculates the annual electricity cost by multiplying the consumed energy with a price per kWh as it is the standard approach in today’s market. In order to make it comparable with the assumed project life of the Prosumer-Approach an annual price inflation factor is introduced.
The economic indicators to compare the BAU-Model with the Prosumer-Model are the “Advantage/Disadvantage vs Grid” results, which are simply the difference between the cost of electricity in the BAU-Approach and the Prosumer-Approach. See “Equation (1)”. They can be extracted for each year of the project life and a cumulative sum can be made for the full project life. Additionally, they are calculated as nominal values and as net present values in order to reflect the time value of money.
P / L = BAU_Cost − Prosumer_Cost
where:
P/L = Prosumer-Approach Advantage/Disadvantage vs BAU-Approach in £
BAU_Cost = annual BAU-Approach electricity cost in £
Prosumer_Cost = annual Prosumer-Approach electricity cost in £
Equation (1): Prosumer Advantage/Disadvantage vs Grid
The BAU-Model cost is simply the annual consumption multiplied with the grid electricity cost. See “Equation (2)”.
BAU_Cost = Consumption ∗ Grid
where:
Consumption = Annual electricity consumption in kWh
Grid = grid electricity price in £/kWh
Equation (2): BAU Electricity Cost
For the Prosumer-Approach various intermediate steps are undertaken to achieve cost transparency. Compare with “Equation (3) to (5)”. The LCOE is applied to the electricity consumption at each hierarchy level to ensure that the implied economics in the LCOE calculation are achieved. For the network and administration charges the user has to decide if they are applicable at hierarchy 1 and 2 level or only at level 2. For PV it might be assumed that these charges are only applied at level 2 since such an installation is typically installed on site and never hits the local grid. Whilst a wind installation is more likely to be installed off site and electricity will need to be transported to the consumer. To consider FiT “income” effects the Model applies production credits at each hierarchy level.
Prosumer Cost = + H 1 _Cost + H 2 _Cost + H 3 _Cost
where:
Hn_Cost = hierarchy n electricity cost in £
n = hierarchy level
Equation (3): Prosumer Electricity Cost
H n _Cost = H n _Consumption ∗ ( LCOE + H n _Net + H n _Admin − PC )
where:
Hn_Consumption = hierarchy n electricity consumption in kWh
LCOE = applicable LCOE in £/kWh
Hn_Net_Charge = applicable hierarchy n network charge in £/kWh
Hn_Admin = applicable hierarchy n administration charge in £/kWh
PC = applicable production credit in £/kWh
n = hierarchy level
Equation (4): Hierarchy 1 and 2 electricity cost
At hierarchy-3 the prosumer has to pay an electricity provider for the provided back-up electricity to balance the system, which is on top of the LCOE. In addition to the production credit they are also credited with the electricity sellback price for the electricity which is sold to the grid. See “Equation (5)”.
H 3 _Cost = H 3 _Consumption ∗ ( LCOE + BackUp − Sellback − PC )
where:
H3_Consumption = hierarchy 3 electricity consumption in kWh
LCOE = applicable LCOE in £/kWh
BackUp = back-up electricity price in £/kWh
Sellback = grid sellback electricity price in £/kWh
PC = applicable production credit in £/kWh
Equation (5): Hierarchy 3 electricity cost
The Model allows for various input parameters, which are mainly load and cost related. Load related parameters were:
・ “Annual Production” in kWh, which equals the “Annual Consumption”;
・ “Hierarchy 1 and 2 Consumption %”, which also determines “Hierarchy 3 Consumption %” (equals: 100%―“Hierarchy 1 Consumption %”―“Hierarchy 2 Consumption %”);
・ “Hierarchy Consumption kWh” at each level as a function of “Annual Consumption” multiplied with “Hierarchy Consumption %” at the respective level; and
・ “Sales into Grid” representing excess supply, which cannot be consumed at prosumer pool level and is equal to “Hierarchy 3 Consumption kWh”.
Cost/income related inputs included:
・ An “Inflation Assumption”, which is used to adjust “Production Credits”, “Network Charges”, “Administration Charges” “Back-up Price Electricity” and “Grid Electricity Prices” over the modeled “Project Life”.
・ The “Cost-of-Capital” is set at the level of cost-of-capital used in the LCOE calculation and is the basis for the discount factors1 used to present value the annual economic advantage/disadvantage over the project life.
・ “LCOE” without FiT adjustments.
・ “Network and Administration Charges”, which reflect the potential cost to transport electricity from point of production to point of consumption, e.g. cost for distribution network operator (DNO) line utilization, and to manage and administer the scheme.
・ The “Back-up Price Grid Electricity” is the price for grid electricity, which is used at hierarchy-3.
・ The “Grid Price Electricity” represents the BAU-Approach energy price.
・ The “Production Credits” and “Sellback Price to Grid” can be set to reflect current UK FiT parameters.
The unit of all cost/income parameters is £/kWh apart from the inflation and the cost-of-capital parameters, which are denominated in %.
1Discount factor for year n = 1/(1 + cost-of-capital %)n.
For each site and technology, a set of simulation results was generated. The following Model input parameters were fixed:
・ Annual Production and Consumption = 3902 kWh;
・ Inflation = 2.5%,
・ Project Life = 20 years;
・ Hierarchy-1 Administration and Network charges = 0;
・ Hierarchy-2 Administration and Network charges = 3 p/kWh;
・ Back-up Price Grid Electricity = 15 p/kWh;
・ Sellback Price to Grid = 4.64 p/kWh;
・ Grid Price Electricity = 15 p/kWh;
・ Production Credit PV = 13.5 p/kWh (UK FiT high rate for 4 - 10 kWp PV as of July 2013 [
Cost-of-capital, Hierarchy-1 Consumption %, Hierarchy-2 Consumption % and Production Credits were varied. See
This chapter presents an overview of the Model assumptions, summary results, site-specific results and the sensitivity analysis based on LCOE break-even calculation.
The term P/L (profit/loss) will be used interchangeably for the “Advantage/Disadvantage vs. Grid” results, whilst a positive P/L indicates an advantage for the Prosumer-Approach (hence a disadvantage for the BAU-Approach). An overview of the summary results can be found in Tables 2-4.
The lowest PV P/Ls coincided with highest LCOE, which had the following parameter settings:
・ lowest modeled irradiation value,
・ maximum capital multiplier,
・ 20 years panel life,
・ West orientation,
・ cost-of-capital of 10%,
・ hierarchy-1 consumption of 30% and
・ hierarchy-2 consumption of 10%.
The highest PV P/Ls coincided with lowest LCOE, which had the following parameter settings:
・ highest modeled irradiation value,
・ minimum capital multiplier,
PV Identifier | LCOE £/kWh | Advantage/Disadvantage yr 1 £/1000 kWh/yr | Advantage/Disadvantage Project Life £/1000 kWh/yr | |
---|---|---|---|---|
PV generic IV1 | 0.580 | −£363.16 | −£2783.00 | Min |
PV generic IV1 | 0.132 | £130.64 | £4507.61 | Max |
PV generic CA2 | 0.569 | −£352.16 | −£2687.87 | Min |
PV generic CA2 | 0.134 | £128.64 | £4465.61 | Max |
PV generic YO24 | 0.530 | −£313.16 | −£2350.57 | Min |
PV generic YO24 | 0.115 | £147.64 | £4864.61 | Max |
PV generic KT2 | 0.491 | −£274.16 | −£2013.27 | Min |
PV generic KT2 | 0.118 | £144.64 | £4801.61 | Max |
PV generic SY23 | 0.448 | −£231.16 | −£1641.38 | Min |
PV generic SY23 | 0.106 | £156.64 | £5053.61 | Max |
PV generic TR14 | 0.432 | −£215.16 | −£1503.00 | Min |
PV generic TR14 | 0.105 | £157.64 | £5074.61 | Max |
Wind Identifier | LCOE £/kWh | Advantage/Disadvantage yr 1 £/1000 kWh/yr | Advantage/Disadvantage Project Life £/1000 kWh/yr | |
---|---|---|---|---|
Enercon 33 45 m KT2 | 0.215 | £47.24 | £852.38 | Min |
Enercon 33 45 m KT2 | 0.031 | £277.04 | £7892.44 | Max |
Enercon 33 45 m YO24 | 0.206 | £56.24 | £930.22 | Min |
Enercon 33 45 m YO24 | 0.030 | £278.04 | £7913.44 | Max |
Enercon 33 45 m CA2 | 0.188 | £74.24 | £1085.90 | Min |
Enercon 33 45 m CA2 | 0.028 | £280.04 | £7955.45 | Max |
Enercon 33 45 m IV1 | 0.178 | £84.24 | £1172.38 | Min |
Enercon 33 45 m IV1 | 0.027 | £281.04 | £7976.45 | Max |
Enercon 33 45 m SY23 | 0.154 | £108.24 | £1379.95 | Min |
Enercon 33 45 m SY23 | 0.024 | £284.04 | £8039.45 | Max |
Enercon 33 45 m TR14 | 0.130 | £132.24 | £1587.52 | Min |
Enercon 33 45 m TR14 | 0.021 | £287.04 | £8102.44 | Max |
LCOE | Production Credit | Inflation assumption | Cost of Capital | Hierachy 1 consumption % | Hierachy 2 consumption % | Hierachy 3 consumption % | |
---|---|---|---|---|---|---|---|
Parameter | up | up | up | up | up | up | up |
P/L | down | up | up | down | up | up | down |
Hierachy 1 Network Charge | Hierachy 2 Network Charge | Hierachy 1 Admin Charge | Hierachy 2 Admin Charge | Back up Price Grid Electricity | Sellback Price to Grid | Grid Price Electricity | |
Parameter | up | up | up | up | up | up | up |
P/L | down | down | down | down | down | up | up |
・ 25 years panel life,
・ South orientation,
・ cost-of-capital of 0%,
・ hierarchy-1 consumption of 70% and
・ hierarchy-2 consumption of 20%.
For wind the same P/L pattern results were observable and the same and parameter settings were used apart from lowest/highest wind speed and 15/25 yrs turbine life.
・ lowest LCOE Wind: TR14 £8102.44 vs KT2 £7892.44, difference £210.00.
・ lowest LCOE PV: TR14 £5074.61 vs CA2 £4465.61, difference £609.00.
・ highest LCOE Wind: TR14 £1587.52 vs KT2 £852.44, difference £735.14.
・ highest LCOE PV: TR14 £−1503.00 vs IV1 £−2783.00, difference £1280.00.
For all six sites, load class type and technology, a further analysis step based on anchor RE cover ratios was undertaken. These were derived from the self-consumption levels calculated in the Authors earlier paper [
Every scenario showed positive P/L for all sites. The sites with the best wind resource also had the tightest LCOE and P/L range. See
Wind | Load Class 1 & 3 | |||
---|---|---|---|---|
Site | LCOE range £/kWh | lowest avg P/L at 10% cost of capital | highest avg P/L at 0% cost of capital | highest minus lowest |
KT2 | 0.031 - 0.210 | £1088.00 | £7099.00 | £6011.00 |
YO24 | 0.030 - 0.206 | £1166.00 | £7120.00 | £5954.00 |
CA2 | 0.028 - 0.188 | £1322.00 | £7162.00 | £5840.00 |
IV1 | 0.027 - 0.178 | £1408.00 | £7183.00 | £5775.00 |
SY23 | 0.024 - 0.154 | £1616.00 | £7246.00 | £5630.00 |
TR14 | 0.021 - 0.130 | £1878.00 | £7462.00 | £5584.00 |
observed, although the average P/L values shifted upwards with higher hierarchy-1 and -2 consumption values.
The PV sites recorded a wide range of positive to negative P/Ls. A better solar resource also coincided with a tighter LCOE and P/L range. The P/L swing between wind and PV was similar, although the LCOE for PV were significantly higher. See
PV | Load Class 1 | |||
---|---|---|---|---|
Site | LCOE range £/kWh | lowest avg P/L at 10% cost of capital | highest avg P/L at 0% cost of capital | highest minus lowest |
IV1 | 0.132 - 0.580 | −£2783.00 | £3074.00 | £5857.00 |
CA2 | 0.134 - 0.569 | −£2688.00 | £3032.00 | £5720.00 |
YO24 | 0.115 - 0.530 | −£2351.00 | £3431.00 | £5782.00 |
KT2 | 0.118 - 0.491 | −£2013.00 | £3368.00 | £5381.00 |
SY23 | 0.106 - 0.448 | −£1641.00 | £3620.00 | £5261.00 |
TR14 | 0.105 - 0.432 | −£1503.00 | £3641.00 | £5144.00 |
PV | Load Class 3 | |||
IV1 | 0.132 - 0.580 | −£2665.00 | £3394.00 | £6059.00 |
CA2 | 0.134 - 0.569 | −£2570.00 | £3352.00 | £5922.00 |
YO24 | 0.115 - 0.530 | −£2233.00 | £3751.00 | £5984.00 |
KT2 | 0.118 - 0.491 | −£1895.00 | £3688.00 | £5583.00 |
SY23 | 0.106 - 0.448 | −£1523.00 | £3940.00 | £5463.00 |
TR14 | 0.105 - 0.432 | −£1385.00 | £3961.00 | £5346.00 |
40% and hierarchy-2 10% setting). Still, the P/L turns negative at a break-even point. These points are presented in
In order to assess the impact of single parameters on the economic viability of projects, LCOE break-even calculations have been performed and the results are shown below. The LCOE break-even represents the point where the Prosumer-Approach and BAU-Approach are considered economically neutral and where the discounted project-lifetime P/L is zero. The higher the LCOE break even the higher is the chance that the RE installation is economical and competitive against conventional electricity. The following default settings have been used to perform the analysis:
・ Annual Production and Annual Consumption = 1000 kWh
・ Hierarchy 1 Consumption: 40%
・ Hierarchy 2 Consumption: 10%
・ Inflation: 2.5%
・ Cost-of-capital: 5%
・ Project Life: 20 years
・ Production Credit: 0.135 £/kWh (UK FiT high rate for 4 - 10 kWp PV as of
PV − P/L break-even − Load Class-1 | ||||
---|---|---|---|---|
Site | Low LCOE break-even in £/kWh | at cost of capital of | High LCOE break-even in £/kWh | at cost of capital of |
IV1 | £0.262 | 10% | £0.279 | 0% |
CA2 | £0.261 | 10% | £0.279 | 0% |
YO24 | £0.259 | 10% | £0.277 | 1% |
KT2 | £0.259 | 10% | £0.274 | 2% |
SY23 | £0.259 | 10% | £0.272 | 3% |
TR14 | £0.261 | 10% | £0.270 | 3% |
PV − P/L break-even − Load Class-3 | ||||
IV1 | £0.276 | 8% | £0.293 | 1% |
CA2 | £0.276 | 8% | £0.289 | 2% |
YO24 | £0.272 | 10% | £0.289 | 2% |
KT2 | £0.272 | 10% | £0.287 | 3% |
SY23 | £0.272 | 10% | £0.285 | 4% |
TR14 | £0.272 | 10% | £0.291 | 4% |
July 2013 [
・ Sellback Price to Grid: 0.0464 £/kWh (UK export tariff as of July 2013 [
・ Hierarchy 1 Network and Administration Charge: 0.00 £/kWh
・ Hierarchy 2 Network and Administration Charge: 0.03 £/kWh
・ Back-up Price Electricity and Grid Electricity Price: 0.15 £/kWh
Based on these input parameters and a zero project-lifetime P/L a LCOE break-even of 0.282 £/kWh was calculated. This value will be used as benchmark to assess further simulation results.
Hierarchy-1 consumption % has been modified in 10% steps from 30% - 70% and hierarchy-2 consumption % has been modified in 5% steps from 0% - 20%. Summary results can be found in
The LCOE break-even difference between lowest and highest simulated consumption percentage was ca. 0.05 £/kWh and ca. 0.01 £/kWh for hierarchy-1 consumption % and hierarchy-2 consumption % respectively. The higher the hierarchy-1 and -2 consumption % inputs were the higher was the LCOE break-even. In comparison to the LCOE benchmark the best consumption scenario moved the LCOE break-even by around 18% up whilst in the worst scenario the LCOE break-even was only 7.5% lower.
Inflation has been varied in 1% steps from 0% - 4% and cost-of-capital has been varied in 1% steps from 0% - 10%. Summary results can be found in
Hierachy 1 consumption % | |||||||
---|---|---|---|---|---|---|---|
Hierachy 2 consumption % | 30% | 40% | 50% | 60% | 70.0% | ||
0% | 0.261 | 0.275 | 0.289 | 0.304 | 0.318 | ||
5% | 0.264 | 0.279 | 0.293 | 0.307 | 0.322 | ||
10% | 0.268 | 0.282 | 0.296 | 0.311 | 0.325 | ||
15% | 0.271 | 0.285 | 0.300 | 0.314 | 0.328 | ||
20% | 0.274 | 0.289 | 0.303 | 0.317 | 0.332 | ||
delta £/kWh | delta % | ||||||
Benchmark break-even | 0.282 | −0.021 | −7.5% | Min | |||
0.050 | 17.7% | Max |
Cost of Capital | |||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|
Inflation | 0.0% | 1.0% | 2.0% | 3.0% | 4.0% | 5.0% | 6.0% | 7.0% | 8.0% | 9.0% | 10.0% |
0% | 0.227 | 0.227 | 0.227 | 0.227 | 0.227 | 0.227 | 0.227 | 0.227 | 0.227 | 0.227 | 0.227 |
1% | 0.251 | 0.250 | 0.249 | 0.249 | 0.248 | 0.247 | 0.246 | 0.246 | 0.245 | 0.244 | 0.244 |
2% | 0.278 | 0.277 | 0.275 | 0.273 | 0.271 | 0.270 | 0.268 | 0.266 | 0.265 | 0.263 | 0.262 |
3% | 0.310 | 0.307 | 0.304 | 0.301 | 0.298 | 0.295 | 0.292 | 0.290 | 0.287 | 0.285 | 0.282 |
4% | 0.346 | 0.341 | 0.337 | 0.332 | 0.328 | 0.324 | 0.320 | 0.316 | 0.312 | 0.309 | 0.305 |
delta £/kWh | delta % | ||||||||||
Benchmark break-even | 0.282 | −0.055 | −19.4% | Min | |||||||
0.064 | 22.7% | Max |
The inflation impact on the LCOE break-even was up to 0.12 £/kWh at 0% cost-of-capital. At higher cost-of-capital that effect is less pronounced. The Cost-of-capital impact over the simulated range was a maximum of 0.04 £/kWh (assuming same inflation) representing a modest relative increase from lowest and highest recorded LCOE break-even of around 13%. Compared to the benchmark LCOE the simulated break-even value varied in a ca. +/- 20% corridor.
Network and administration charges at hierarchy-1 and -2 have been simulated as pairs in 0.02 £/kWh steps from 0.00 to 0.08£/kWh for each charge. Summary results can be found in
Any network and administration charge pair in isolation impacts on the LCOE break-even by 0.02 £/kWh and 0.08 £/kWh for hierarchy-2 and hierarchy-1 charges respectively. If both pairs are stressed to the maximum simulated value than the difference is 0.10 £/kWh, which then represents a percentage
H1 Network & Admin Charge £/kWh | |||||||
---|---|---|---|---|---|---|---|
H2 Network & Admin Charge | 0.00 | 0.02 | 0.04 | 0.06 | 0.08 | ||
£/kWh | 0.00 | 0.290 | 0.269 | 0.249 | 0.229 | 0.208 | |
0.02 | 0.284 | 0.264 | 0.244 | 0.224 | 0.203 | ||
0.04 | 0.279 | 0.259 | 0.239 | 0.219 | 0.198 | ||
0.06 | 0.274 | 0.254 | 0.234 | 0.213 | 0.193 | ||
0.08 | 0.269 | 0.249 | 0.229 | 0.208 | 0.188 | ||
delta £/kWh | delta % | ||||||
Benchmark break-even | 0.282 | −0.094 | −33.3% | Min | |||
0.008 | 2.7% | Max |
difference of over 50% compared to the lowest LCOE break-even. Versus the benchmark point the break-even values change by plus ca. 3% for zero charges and ca. −33% for very high charges of 8p for network and administration each at both hierarchy levels.
The back-up, grid sellback and grid electricity price have been modeled in 0.05 £/kWh steps in ranges from 0.15 to 0.35 £/kWh, 0.00 to 0.20 £/kWh and 0.15 to 0.35 £/kWh respectively. Summary results are presented in
Back-up electricity prices stressed in isolation had an impact on the LCOE break-even range of ca. 0.13 £/kWh with a higher back-up electricity price causing the LCOE break-even to go down. Sellback to grid price variations had the reverse impact on the LCOE break-even price level with a 0.20 £/kWh change impacting the LCOE break-even by around 0.10 £/kWh. Higher grid electricity prices caused up to 0.25 £/kWh higher LCOE break-even prices. However, calculated combinations where grid electricity price is higher than the back-up electricity price can be considered unlikely and are hence shaded in the table.
The production credits and grid electricity prices have been modeled in 0.05 £/kWh steps in ranges from 0.00 to 0.15 £/kWh and 0.15 to 0.35 £/kWh respectively. In the analysis the back-up electricity price was also always at the same level as the grid electricity price. Summary results are presented in
A production credit difference of 0.15 £/kWh has an impact on the LCOE break-even of ca. 0.19 £/kWh. The range around the benchmark value was −60% to 50%. The analysis shows that higher grid price have a strong impact on LCOE break-even and can be a catalyst for lower production credits.
The Model results suggest that a Prosumer-Approach can have its economic merits and offer benefits to single consumers and communities. All analyzed sites
Back up Price £/kWh | ||||||
---|---|---|---|---|---|---|
Sellback Price | 0.15 | 0.20 | 0.25 | 0.30 | 0.35 | |
£/kWh | 0.00 | 0.259 | 0.227 | 0.195 | 0.164 | 0.132 |
0.05 | 0.284 | 0.252 | 0.220 | 0.189 | 0.157 | |
0.10 | 0.309 | 0.277 | 0.245 | 0.214 | 0.182 | |
0.15 | 0.334 | 0.302 | 0.270 | 0.239 | 0.207 | |
0.20 | 0.359 | 0.327 | 0.295 | 0.264 | 0.232 | |
delta £/kWh | delta % | |||||
Benchmark break-even | 0.282 | −0.150 | −53.2% | Min | ||
0.077 | 27.2% | Max | ||||
Back up Price £/kWh | ||||||
Grid Price £/kWh | 0.15 | 0.20 | 0.25 | 0.30 | 0.35 | |
0.15 | 0.282 | 0.250 | 0.218 | 0.187 | 0.155 | |
0.20 | 0.345 | 0.314 | 0.282 | 0.250 | 0.218 | |
0.25 | 0.409 | 0.377 | 0.345 | 0.314 | 0.282 | |
0.30 | 0.472 | 0.440 | 0.409 | 0.377 | 0.345 | |
0.35 | 0.536 | 0.504 | 0.472 | 0.440 | 0.409 | |
delta £/kWh | delta % | |||||
Benchmark break-even | 0.282 | −0.127 | −45.0% | Min | ||
0.254 | 90.0% | Max |
and technologies indicated the economic attractiveness of the Prosumer-Model under specific assumptions. Yet, there is not a straight linear pattern since the final economics are dependent on a whole set of factors, which can either neutralize or amplify each other.
The LCOE is a major input factor into the Model and it is dependent on various assumptions. The LCOE of PV and wind is characterized by little expected volatility, which protects prosumers from rising energy prices but also excludes them from the benefits of falling prices. However, in the BAU-Approach the grid electricity price is the dominant factor when considering energy consumption as relatively stable.
The LCOE break-even analysis was designed to isolate Model inputs, vary them and quantify their impact on the LCOE break-even. In order to rank their impact the p/kWh difference of the simulated min/max values has been taken into account and can be seen in brackets. The least relevant parameters in the simulations were hierarchy-2 consumption (1 p/kWh), hierarchy-2 network &
Production Credit £/kWh | ||||||
---|---|---|---|---|---|---|
Grid Price £/kWh | 0.00 | 0.05 | 0.10 | 0.15 | ||
0.15 | 0.111 | 0.174 | 0.238 | 0.301 | ||
0.20 | 0.142 | 0.206 | 0.269 | 0.333 | ||
0.25 | 0.174 | 0.238 | 0.301 | 0.364 | ||
0.30 | 0.206 | 0.269 | 0.333 | 0.396 | ||
0.35 | 0.238 | 0.301 | 0.364 | 0.428 | ||
delta £/kWh | delta % | |||||
Benchmark break-even | 0.282 | −0.171 | −60.7% | Min | ||
0.146 | 51.7% | Max |
administration charges (2 p/kWh), cost-of-capital (4 p/kWh) and hierarchy-1 consumption (5 p/kWh). A moderate impact was observed for hierarchy-1 network & administration charges (8 p/kWh), sellback electricity prices (10 p/kWh), inflation (12 p/kWh) and back-up electricity prices (13 p/kWh). The highest impact could be observed in production credits (19 p/kWh) and the grid electricity price (25 p/kWh).
Grid electricity prices had a significant impact on the Model results. That can be explained by the fact that it is an essential part of the BAU-Approach calculation and that it is inflation-linked, i.e. these costs can only be stable or go up in the Model settings. A change of the grid electricity price is also likely to correlate positively with back-up electricity prices and sellback to grid prices.
Production credit has been the next biggest effect on the LCOE break-even. The modeled production credit level has been relatively substantial, almost the same as the simulated back-up electricity price, and both were inflation-linked. This means that over time an increasingly positive effect on the Prosumer-Approach calculation results is observable in high inflation scenarios. However, when considering modeled RE LCOE levels and assuming continuing trends for lower RE equipment prices and higher electricity prices then even scenarios with no production credits are economically plausible and attractive.
The back-up electricity prices impact is less pronounced since the used back-up electricity is a function of the hierarchy-3 demand to balance any remaining electricity need. The impact can actively be reduced with increasing hierarchy-1 & 2 consumption levels. Back-up electricity had been modeled inflation-linked, which amplifies this effect since the LCOE is fixed. This also ties in with results from Energiewende and DSM research, which showed that consumers do adjust consumption behavior to price signals [
The impact of inflation is based on its links to the various input parameters and the compounding effects. In the BAU-Approach increasing inflation always has an increasing effect on P/L. However, in the Prosumer-Approach inflation affects different variables that partially offset each other.
Cost-of-capital had very little impact in the Model simulation itself, because it was only used to discount the difference between BAU-Approach and Prosumer-Approach. Still, it should be noted that the cost-of-capital is one of the most important factors in the LCOE calculation feeding into the model [
Network and administration charges on their own have limited influence on Model results. Nonetheless, it is probably realistic to assume they could move in parallel for both hierarchy levels, hence they start becoming more meaningful, e.g. for wind. The hierarchy-1 & 2 consumption parameters seemed to have relatively little impact at current electricity price levels.
All Model assumptions are static, e.g. they assume constant energy supply and demand over time, or follow a trend, e.g. a constant inflation number. Although static assumptions were useful for the modeling process they are not realistic. In real life scenarios prosumer needs and behavior might change, inflation of various costs will not be uniform and natural resources will fluctuate. Moreover, technical problems or advances could influence the supply level of electricity. The hierarchal structure and mix of technologies and installations in the pool should be able to smooth some of these effects. However, on an individual level fluctuations of these factors would imply P/L swings and therefore compromise the simulation results.
Overall it can be concluded that a Prosumer-Approach could offer significant economic opportunities to the UK. Based on the current conditions such an energy model could bring economic advantages to consumers, yet by doing so it would necessitate/cause changes to market structures over time. The UK electricity market is shaped by a highly concentrated market structure. Its infrastructure is based on a centralized energy generation and a bias towards a top-down electricity distribution system. A prosumer system is by design decentralized and more democratic. Hence, the widespread adoption of such a system would impact almost all elements of the market over time. That could mean that society and communities might see more direct involvement with electricity markets and on different levels. Regulation and market rules would need to be adapted to ensure consumer protection, competition, energy security and grid access. Economically, it would open the market for new business models and technologies, with the aim to abolish the necessity for FiT as they are known today. The opportunity would be to reduce the dependency on volatile fossil fuel based energy markets. It could also be a catalyst to develop new funding sources for the modernization of the generation infrastructure and the implementation of ICT as means to increase efficiency in the electricity market. Technically a gradual adoption to a bi-directional grid and the co-ordination of changing generation and consumption profiles would be required. All the above would be a gradual or partial process with the need to balance conflicting interest.
The Model simulation demonstrated that a Prosumer-Model could bring economic advantages to prosumers across the UK. For wind scenarios an economic advantage of the prosumer approach vs the BAU approach of £47 to £287 per 1000 kWh/yr consumption was calculated. Whilst for PV scenarios the comparison was less favorable with showing a range of £−363 to £158 per 1000 kWh/yr consumption. However, the results are heavily dependent on assumptions and future cost developments. The Model worked with a total of 15 input parameters, which partly neutralize or amplify each other. Some could be considered correlated and that introduced a considerable uncertainty and complexity to the Model results. The LCOE break-even analysis provided some indication, to which parameters the Model results were particularly sensitive to. In this context specifically, the electricity price parameters used were of significant importance and the production credits available via the FiT as well. In a high grid price scenario of 0.35 £/kWh the break-even price was modeled at 0.43 £/kWh vs the benchmark break-even of 0.28 £/kWh whilst in a zero production credit scenario the break-even price was modeled at 0.11 £/kWh. Through its compounding effects and the effects of the starting value, inflation can amplify these effects even further. Although the cost-of-capital element has a limited direct effect on the Model results it was a very influential factor to the LCOE Prosumer-Model input as demonstrated in earlier paper of the Authors [
Correlations of individual parameters have not been considered separately and it would be feasible to extend future analysis to incorporate this. Specifically cost-of-capital and inflation; the relationship between the electricity price parameters, equipment cost (as factor of the LCOE calculation) and production credits; as well as hierarchy-1 consumption and grid electricity prices in the context of grid parity and DSM could provide further interesting results.
The presented P/L results focused on the project lifetime advantage/disadvantage of the simulated scenarios. Yet, it has been quite a common observation that projects were having a negative P/L in the early years, with positive P/L in the later years compensating for this. This delayed harvesting of benefits of an investment could be seen as a negative point in a decision making process, since it increases the dependency on the underlying assumptions and hence the uncertainty of the expected results.
The paper has been focusing on the quantitative Model results. However, there are also qualitative aspects of the Prosumer-Model, which are worth highlighting in the wider UK market context. Qualitative aspects of the Model results may be relating to sociological, economic, technological and regulatory issues and opportunities that need to be considered. From a social aspect the behavior changes towards energy generation and consumption are unpredictable. A prosumer system, as proposed by this research, might help balancing out some of these aspects by increasing transparency, influencing demand behavior and boosting community acceptance of electricity infrastructure investments. Economically it might be an opportunity to mobilize private investment into the energy infrastructure, stabilize energy prices and thus stimulate the economy across the UK over the years. Technologically the design and management of the supporting grid infrastructure is very important and the mix of RE technologies might even help to improve system stability and economics even if it seems disadvantageous for the individual. Regulatory decisions will influence all aspects of the electricity market and without their consideration a widespread implementation of a prosumer system is very unlikely.
Kästel, P. and Gilroy-Scott, B. (2018) Economics of Pooling Small Local Electricity Prosumers―Prosumer vs Business as Usual Approach. Energy and Power Engineering, 10, 226-252. https://doi.org/10.4236/epe.2018.105016