Feed In Premium (FIP)

1. Overview
2. Merits of FIP
3. Demerits of FIP
4. FIP in various countries
   1. Spain
   2. Czech
   3. Germany
   4. UK
5. References

Overview

In a feed-in premium (FIP) program, renewable energy producers sell their electricity at market rates and get extra money on top. FIPs can be fixed (a set amount) or sliding (changes with market prices). Fixed FIPs are simple but can lead to overpaying in high-priced markets and underpaying in low-priced ones. To avoid this, they often set minimum and maximum payment levels.

Sliding FIPs adjust continuously based on market prices compared to a reference price. If market prices are higher, no extra payment is made. Some use a minimum market price to make sure producers pay attention to market changes and to save money when prices are low.

FIPs can change depending on the type of renewable energy, project size, and location. They can also include bonuses for specific technologies and for handling the costs of selling electricity directly. There might be limits on how much can be paid to make sure the program stays sustainable over time.

Merits of FIP

FIPs encourage renewable energy operators to produce electricity when it’s most needed and when other energy sources aren’t producing much. They also motivate renewable energy investors to plan their projects based on when people use the most electricity (like picking the right location for wind turbines or solar panels). So, FIPs help renewable energy fit better into the electricity market, making it more efficient in matching electricity supply with demand. This is super important as we use more renewable energy.

For fixed FIPs or total payments (FIP + market price), having a minimum level (called “floor”) makes things less risky for renewable energy investors. It guarantees that they’ll make at least a certain amount of money. This also applies to sliding FIPs where a set reference price is guaranteed to renewable energy investors, kind of like a safety net. In some cases, FIPs can even bring in more money than traditional fixed payments when market prices are higher.

Demerits of FIP

Market-based support systems like FIP work well for renewable energy sources (RES) that can be controlled, like biomass and geothermal, or those that can be combined with energy storage, like hydropower and concentrated solar power (CSP). However, for variable RES such as wind and solar, which can’t easily adjust their output to market price changes, FIP schemes bring added expenses for balancing services.

Similar to Feed-in Tariffs (FIT), FIP schemes have the risk of either overcompensating or undercompensating RES producers. This happens because the government decides the FIP rate (for fixed FIP) or the reference tariff (for sliding FIP), and the same goes for setting minimum and maximum payment levels or degression mechanisms within the FIP scheme.

For investors in renewable energy, FIP schemes introduce extra uncertainty due to market price fluctuations and revenue changes, leading to higher financing costs. However, this risk can be reduced by setting price boundaries (for fixed FIP) or by adjusting the FIP according to market conditions (for sliding FIP). Selling electricity directly on the market adds complexity and expenses (for forecasting systems, balancing services, and electricity trading), which can make it challenging for small-scale renewable energy operators to participate in a FIP program.

FIP in various countries

Spain

Spain has been a pioneer in the use of FIP in Europe with the introduction of a sliding FIP scheme in 1998. RES operators generally had the choice between a guaranteed fixed FIT and a guaranteed FIP paid on top of the wholesale electricity price, except for photovoltaic projects where only the FIT was applicable. For RES projects with a capacity above 50 MW, the FIP scheme was compulsory. Maximum and minimum levels (cap and floor) for the overall remuneration level for each RES technology have been introduced with the Royal Decree 661/2007. Between these levels, the RES producer receives the reference FIP. Above and below these levels, the FIP is decreased or increased so that overall remuneration is always within the maximum and minimum levels. The calculation of the overall remuneration is done either on an hourly or on a monthly basis. In February 2013, all FIP have been reduced to zero, thus effectively abolishing this mechanism.

Czech

In 2006, the Czech Republic introduced an optional Feed-in Premium (FIP) program. Under this scheme, operators of Renewable Energy Source (RES) plants had the choice to receive a “Green Bonus” annually or hourly, in addition to the revenue they earned from selling their electricity production to an electricity trader or any other customer. The program also allowed for bonuses on self-consumed RES electricity.

Feed-in Tariffs (FIT) were applicable only to RES plants with a capacity up to 100 kW (30 kW for photovoltaic and 10 MW for hydro power). The Energy Regulatory Office determined the green bonus levels for various RES technologies so that the bonus slightly exceeded the difference between the FIT and the expected average hourly electricity price for the upcoming year. This arrangement encouraged RES producers to participate in the FIP program. However, it’s worth noting that both FIT and FIP schemes have been closed for new RES projects installed after the end of 2013.

Germany

In Germany, they introduced an optional sliding Feed-in Premium (FIP) in 2012, known as the “market integration model.” This FIP is an extra payment received on top of the money earned from selling Renewable Energy Source (RES) electricity directly on the spot market (EPEX). It’s calculated as the gap between specific reference values for each RES technology (like solar and wind) and the average monthly reference market value for that technology.

For dispatchable RES, the market value is based on the monthly average of hourly contract values on the electricity spot market (EPEX). In 2014, with changes to the Renewable Energy Law, the FIP scheme became mandatory for all new RES plants, and the management premium was eliminated. Exceptions were granted only for small RES plants with capacities below 500 kW starting in August 2014 and 100 kW from 2016.

In cases where electricity prices go negative, meaning operators receive less money from wholesalers than the FIP, the difference is covered by an EEG surcharge, which is then passed on to German consumers. However, for larger renewable energy installations, they don’t receive the premium if prices remain negative for six consecutive hours.

UK

The United Kingdom is in the process of implementing a sliding Feed-in Premium (FIP) system through “Contracts for Difference” (CfD). This new scheme will replace the existing Renewable Energy Source (RES) quota system until 2017. It aims to financially incentivize low-carbon technologies, including RES, carbon capture and storage, and nuclear energy.

Here’s how it works: The government sets a “strike price,” which is agreed upon in long-term (15-year) contracts between RES operators and the government-owned company known as the “CfD Counterparty Company Ltd.” If electricity prices fall below this strike price, the RES operator receives the difference as a FIP payment. The reference electricity price is determined based on either short-term (daily) or long-term (annual) electricity prices, depending on the specific RES technology. However, if the electricity price surpasses the strike price, RES operators are required to repay the excess to the government.

The strike price levels for most RES technologies were established at the end of 2013 for the period of 2014-2019, sometimes with predetermined reductions. The first round of CfD applications began in October 2014, with the first CfD contracts expected to be finalized by the end of January 2015. There are plans to introduce competitive bidding for mature RES technologies through the CfD program. For small and medium-scale RES projects with capacities below 5 MW, the existing Feed-in Tariff (FIT) scheme will continue to be applicable.

References

https://energypedia.info/wiki/Feed-in_Premiums_(FIP)

List of volcanoes in Japan

There are more than 200 volcanoes in Japan. The zip file contains the list of volcanoes in google kml file, csv file and a shape file format. The data on elevation and the year of its last eruption is provided. It could be helpful to study geology.

https://drive.google.com/file/d/1eJdNalQhocOkjgZUunRJHtym2uCVKdI4/view?usp=sharing

Water legislation in Japan in perspective of hydropower development

Japan is rich in water resources. The average annual precipitation is about 650 billion m3, out of which 420 billion m3 is the theoretical maximum that can be utilized for various purposes including hydropower generation. In 1951, the electricity business of Japan was handed to 10 regional power utilities. In 1995, the independent power producer (IPP) were allowed to sell electricity. According to NEF, by 2010 there were 1754 small and medium power plants (less than 30 MW). The feed-in tariffs for various types of hydro electricity, set after Fukoshima disaster, are as follows. (Ref: https://www.meti.go.jp/english/press/2016/0318_03.html)

• Under 200 kw->Yen 34/24 (new construction/using exiting canal)
• 200-1000 kw-> Yen 29/21
• 1000-30,000kw->Yen 24/14

The feed in tariffs are lucrative. However, the hydropower development in Japan is not straightforward due to fragmented government offices requiring permissions from multiple agencies.

River legislation and management

The new “River Law” started since 1964 to manage water resources in Japan. It has been revised multiple times.

The River Law classifies the rivers into two categories- A and B. Class A rivers are administered by the Minister of Construction (MLIT). There are 17798 rivers grouped into 109 river systems in this category totaling to about 87,150km. Class B rivers are administered by the prefectural government. There are 6,631 rivers grouped in 2,691 river systems in this category totaling to about 35,700 km. Some section of rivers (both A and B) may be administered by local cities or villages.

Once the water is extracted from the river, it is managed by other laws. A list of ministries and agencies handling the water resource are listed in table below:

MILIT	It is responsible for over all water resource development
MOE	Development of guidelines, policy and planning for water conservation and setting of water quality standards.
Ministry of health	Regulation of domestic water supply facilities
METI	Regulation of industrial water supply facilities
Ministry of agriculture	Regulation of agricutural water and conservation of forest water resorces
Japan water agency	It is responsible for the supply of safe, quality water at a reasonable price. It is engaged in the construction and refurbishment of major dams for water utilization (for domestic, industrial and agricultural use) and river management purposes (flood control, maintenance and environmental flow).
Inter-ministerial Liaison Council	This is responsible to study how procedures could be simplified fir water resource development

Base on:

• Small and Micro-Scale Hydropower in Japan by Yveline Lecler

Cobweb plot

While conducting multi variable analysis, it may be useful to see how the output changes due to change in inputs. For example humidity, temperature and water-level may change the deflection of dam. Cobweb plot combined with normal distribution will help to visualize the risks easily. An example of such plot is shown below.

An octave (or matlab) code to generate such plot is given below. Basically, it requires to plot each entity separatly. The data should be normalized to set equal scale.

%shaded plot
clear all;clf; % clear figure (just in case something was there before)

epdata=[-0.0008959	-0.00059726	-0.0013775	-0.00080342	-0.002345	-0.0012454	-0.0014801	-0.00097711	-0.00096319	-0.00030882	-0.0015608	-0.00099932	-0.00082923	-0.00105	-0.0013915	-0.00021562	-0.0011739	-0.0015718	-0.00087249	-0.0016794	-0.00036352	-0.00059309	-0.0011483	-0.0010326	-0.00034531	-0.0020304	-0.0014554	-0.00092798	-0.0012557	-0.0016694	-0.0010306	-0.001245	-0.0013191	-0.0016508	-0.0010848	-0.0018287	-0.0010912	-0.0014441	-0.0012839	-0.00089405	-0.0024034	-0.0015758	-0.00026981	-0.0014717	-0.0023293	-0.0020914	-0.00076314	-0.00056843	-0.0014572	-0.00083516	-0.0012168	-0.0013884	-0.0012994	-0.0010078	-0.00045422	-0.0015285	-0.00061293	-0.00091066	-0.0014499	-0.0010975	-0.001501	-0.0012706	-0.0019573	-0.0019029	-0.0013711	-0.0019286	-0.00099036	-0.0016533	-0.001049	-0.0015944	-0.0016858	-0.0014946	0.000314	-0.0016982	-0.0012176	-0.0016377	-0.0012109	-0.0015727	-0.001435	-0.0010661	-0.0012172	-0.0020125]
tcdata=[31.007	17.279	71.559	42.942	30.757	64.937	56.682	34.429	48.118	51.053	46.382	70.517	49.346	56.116	38.708	39.931	61.857	52.765	58.071	55.669	34.416	38.345	36.623	42.745	66.059	83.693	40.504	62.218	64.917	20.47	65.233	37.162	69.894	27.246	3.5394	65.416	65.68	43.018	95.189	59.176	59.944	56.573	53.296	38.949	41.054	39.729	28.056	43.599	59.708	46.291	49.449	48.566	57.104	51.747	68.345	48.032	63.941	41.263	26.673	48.541	31.849	56.63	39.846	49.557	80.801	37.334	37.459	36.248	57.355	39.958	32.182	24.017	75.422	61.597	43.402	11.902	58.988	42.176	17.418	44.48	56.916	41.854]
tldata=[61.235	77.001	86.213	75.22	43.764	67.211	76.459	52.866	77.028	54.373	34.241	90.087	80.787	60.298	75.159	107.12	84.327	39.385	104.76	66.38	62.929	42.629	88.112	83.559	59.862	85.192	50.859	55.306	66.787	56.848	82.743	95.426	112.39	44.012	70.265	54.671	76.631	76.642	88.841	39.25	56.877	60.893	104.51	59.836	63.706	71.222	63.624	32.153	70.074	79.584	72.205	72.182	73.233	67.998	80.533	40.931	63.258	42.223	79.662	81.851	52.266	80.383	83.354	57.363	93.174	64.228	87.165	93.264	79.508	67.241	98.564	86.5	37.035	59.61	64.813	68.867	72.937	81.84	61.348	92.968	57.552	64.609]
dpdata=[-0.016616	0.0012712	-0.018897	-0.01016	-0.092141	-0.024361	-0.023951	-0.026684	-0.013089	-0.0056231	-0.065296	-0.011672	-0.0096546	-0.023527	-0.018468	0.0022514	-0.015527	-0.056125	-0.0052227	-0.0348	-0.0046483	-0.020469	-0.0072833	-0.010084	-0.0052599	-0.030438	-0.044018	-0.022032	-0.024795	-0.031457	-0.014002	-0.0055563	-0.0096733	-0.063971	0.003394	-0.046408	-0.017231	-0.019991	-0.016868	-0.028567	-0.061399	-0.036514	0.00094998	-0.034091	-0.050397	-0.03461	-0.011083	-0.023391	-0.027504	-0.0096521	-0.020398	-0.023604	-0.022343	-0.018593	-0.0046534	-0.055475	-0.011241	-0.03279	-0.0084095	-0.013231	-0.043265	-0.018161	-0.020591	-0.04901	-0.016887	-0.038989	-0.0064535	-0.0089957	-0.014825	-0.029465	-0.0045653	-0.0031735	0.013363	-0.040035	-0.024299	-0.00078227	-0.020854	-0.018003	-0.015243	-0.0074379	-0.02939	-0.042169]
% normalize
ep=epdata/min(epdata);
tc=tcdata/max(tcdata);
tl=tldata/max(tldata);
dp=dpdata/min(dpdata);
x=[0 1 2 3.5]*7;
x1=ones(1,length(ep))*x(1);
x2=ones(1,length(ep))*x(2);
x3=ones(1,length(ep))*x(3);
x4=ones(1,length(ep))*x(4);

%calculate mean
m_ep=mean(ep);
m_tc=mean(tc);
m_tl=mean(tl);
m_dp=mean(dp);
%standard deviation
sd_ep=std(ep);
sd_tc=std(tc);
sd_tl=std(tl);
sd_dp=std(dp);

%coords to plot normal distribution
nd_x_ep=[m_ep-4*sd_ep:sd_ep/20:m_ep+4*sd_ep]
nd_y_ep=1/sd_ep/(2*pi())^0.5*exp((nd_x_ep-m_ep).^2/-2/sd_ep^2)

nd_x_tc=[m_tc-4*sd_tc:sd_tc/20:m_tc+4*sd_tc]
nd_y_tc=1/sd_tc/(2*pi())^0.5*exp((nd_x_tc-m_tc).^2/-2/sd_tc^2)

nd_x_tl=[m_tl-4*sd_tl:sd_tl/20:m_tl+4*sd_tl]
nd_y_tl=1/sd_tl/(2*pi())^0.5*exp((nd_x_tl-m_tl).^2/-2/sd_tl^2)

nd_x_dp=[m_dp-4*sd_dp:sd_dp/20:m_dp+4*sd_dp]
nd_y_dp=1/sd_dp/(2*pi())^0.5*exp((nd_x_dp-m_dp).^2/-2/sd_dp^2)

figure 1
% plot vertical lines
hold on;
plot (x1,ep,"o"); 
plot (x2,tc,"o"); 
plot (x3,tl,"o"); 
plot (x4,dp,"o"); 
%plot connecting lines
p=[ep; tc; tl; dp ];
plot (x, p, "color",[.8 .8 .8] );

%plot the normal disribution
patch(nd_y_ep*2+x(1),nd_x_ep,'facecolor',[1 1 0.8],'edgecolor',[1 0 0], 'facealpha',0.3)
patch(nd_y_tc*2+x(2),nd_x_tc,'facecolor',[1 1 0.8],'edgecolor',[1 0 0], 'facealpha',0.3)
patch(nd_y_tl*2+x(3),nd_x_tl,'facecolor',[1 1 0.8],'edgecolor',[1 0 0], 'facealpha',0.3)
patch(-nd_y_dp*2+x(4),nd_x_dp,'facecolor',[1 1 0.8],'edgecolor',[0 0 1], 'facealpha',0.3,"linewidth", 3)

%yaxis lines
ymin=-0.5;ymax=1.5;
plot ([x(1) x(1)],[ymin ymax],"color",[0 0 0] );
plot ([x(2) x(2)],[ymin ymax],"color",[0 0 0] );
plot ([x(3) x(3)],[ymin ymax],"color",[0 0 0] );
plot ([x(4) x(4)],[ymin ymax],"color",[0 0 0] );
%other properties
ylim([ymin ymax])
axis off;
set(gca, "linewidth", 2, "fontsize", 12);
%labels
%xlabels
text(x(1)+0.3,ymin,"Strain","color","black","fontsize", 18)
text(x(2)+0.3,ymin,"TC","color","black","fontsize", 18)
text(x(3)+0.3,ymin,"TL","color","black","fontsize", 18)
text(x(4)-3.5,ymin,"Max. Deflection","color","black","fontsize", 18)
%informations
text(x(1)+0.3,ymax-.2,["Mean=" num2str(-0.0012, "%5.2e") char(10) "SD=" num2str(0.005, "%5.2e")],"color","black","fontsize", 15)
text(x(2)+0.3,ymax-.2,["Mean=" num2str(50, "%5.0f") char(10) "SD=" num2str(15, "%5.0f") " years"],"color","black","fontsize", 15)
text(x(3)+0.3,ymax-.2,["Mean=" num2str(70, "%5.0f") char(10) "SD=" num2str(20, "%5.0f") " years"],"color","black","fontsize", 15)
text(x(4)-3.5,ymax-.2,["Mean=" num2str(mean(dpdata), "%5.2e") char(10) "SD=" num2str(std(dpdata), "%5.2e") " m"],"color","black","fontsize", 15)

hold off;
print -dpng figure1.png

Evolution and entropy

Some salt required.

Once born we slowly evolve physically by gaining tissues and bones. The food we eat is transformed into the machinaries of body. This indicates that the entropy or randomness decreases over time to create a useful thing. But once we cross certain age decay starts and the biological machinaries starts to disfunction. It seems decay and dissolution is the final destiny. And in long term degeneration is imenent.

If so how about the evolution theory? Why should a simple being evolve to a complex one? More natural way would be a complex being degenerate into simple ones. Because this is the way we observe, its easy to break things than to create one. May be we human too are degenerated form of more complex superhumans, not the complex form of monkeys.