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Good intentions

…apparently motivated creators of Law of Ukraine "On the market of electric energy" (ukr. Закон України "Про ринок електричної енергії" від 13.04.2017, № 2019 - VIII)  with regard to fining of electric power producers on "green" tariff for 24-hours generation imbalances into the grid in relation to (attention!) prognosis (!), accepted and concerted (!) with the "assured customer" from a producer. Responsibility for forecast precision is attached to... a producer (!). And it is done, on a larger scale… (quote) With the aim to limit the influence of electric energy producers’ support on "green" tariff on electric energy prices – according to Chapter XVII ( - 19/page7).

That’s quite an honest statement. The essence of innovations is that in two years the market of electric power starts working on "twenty-four hours advance" basis with fines either for prognoses non-presentation or for non-fulfillment of such provided prognoses. Fines will be imposed by “guaranteed buyer” (for which read: natural monopolist, at least at regional scale, because buyer is oblenergo), which, in addition to all other, has “… other rights…”.

The Law does not contain a word about mechanisms of data collection and analysis for short-term prognosis of generation according to electric power needs. There is not a word also about mandated informational database, algorithms, methodology and other grounds for prognostication on "twenty-four hours advance" basis, nothing about communication means and reliable dedicaded channels of such information transfers. What is more, in Ukraine there is no infrastructural base for such weather prognosis (which, actually, is the one and only basis for solar and wind power generation), responsibility of such third parties that provide data for these prognoses is not mentioned. There are no provisions about acceptable accuracy of such meteorological data for prognostication. There are no mentions about the structure of supervisory control, monitoring and short-term prognostication for making and consumption of electric power, there are no rules of priority on distribution of stakes among market participants, no regulations about levers of management for different types of generation traffic from different owners, as it organized, for example, in Germany (see pic. 1 in Economy of Energy Independence Good intentions to make "better" without corresponding prepared infrastructure conduct us on the former way of "resulted as usual".


Stick without any carrots

In addition, the Law enters, on larger account, only stick without any carrots. And this stick is placed into hands of "assured buyer", but the buyer is not liable for guaranted terms of assured delivery of "green" electricity into grid. Practical example: an accident within grid section where the a "green" energy producer is connected, is removed by organization, that does not have any liability for "assured" acquisition of this "green" energy. This is another legal entity, as a rule, local  Power Distribution Zone (ukr. - район електричних мереж, РЕМ, rus. - район электрических сетей, РЭС), which has a contract with oblenergo on electrical network maintenance. During accident removal process this section of network, naturally, becomes disconnected, and the "green" solar station also becomes disconnected completely, despite of the all beforementioned "prognoses". How many days will such "force-majeure" last and whether there such disconnection will be considered as an "act of God", and who determines this circumstance – these are matters nobody can define: neither producer of "green" electricity nor "assured" buyer, nor maintenance service provider company (the latter, as famous Gaidai’s character would say, commonly doesn’t give a hoot about someone’s losses).  

And this situation of someone’s irresponsibility with other’s fining happens pretty often. A week-long outage of a local solar station because of, for example, wire breakage in a local network after high wind or from a thunderstorm is quite real and frequent situation of practical generation on small solar stations (SSSs).

Thus Law enters fines for actual hourly deviation from concerted prognosis charts on hours advance basis. Hourly deviation! (see p. 5, article 71 of the Law, - 19/paran1472#n1472). And obligates a producer "…to carry financial liability for imbalances of electric power"… towards a customer.  The Law suggests other participants of electro-generation market to set 10 % from "their monthly amount of electric power allotment during corresponding period of  prior year" as the base for minimum allotment of electric power (see Fines are envisaged for "green energy" producers, if there are any deviation from ratified prognosis, 10 % fine for solar power stations (20% for wind power stations). For large producers with 5%+ share in general energetic balance of the country, accordingly, possible deviation will be 5%  (solar power stations) and 10 %  (wind power stations). The Law claims (see p. 11, chapter on transitional provisions - 19/page7), that:

«Compensation share… (from) "green" tariff producers to guaranred buyer for the costs of imbalance settlement is calculated as:

  • to 31 December, 2020 - 0 percents;
  • from 1 January, 2021 - 10 percents;
  • from 1 January, 2022 - 20 percents;
  • from 1 January, 2023 - 30 percents;
  • from 1 January, 2024 - 40 percents;
  • from 1 January, 2025 - 50 percents;
  • from 1 January, 2026 - 60 percents;
  • from 1 January, 2027 - 70 percents;
  • from 1 January, 2028 - 80 percents;
  • from 1 January, 2029 - 90 percents;
  • from 1 January, 2030 - 100 percents»


But while, according to data of State energy efficiency agency (ukr. - Держенергоефективність) for the first half-year of 2017, already 1635 private houses in Ukraine are equipped by solar panels and it is almost 4 times more than 2016 installations amount. How all this fining will affect on motivation of small producers of electricity (home stations with installed power no more than 30 kW) to join more actively into the process of "public" production of ecologically clean energy on "green" tariff? There is no chance to say that. How this matter will complicate lives, what additional costs it will charge and what problems will it create even to professional producers of "green" electric power? That quite obvious. How "…limitation of influence of support for producers of electric energy on "green" tariff…" corresponds with "Power strategy of Ukraine to 2035: Safety, energy efficiency, competitiveness" (issued by ukrainian Government in spring of this year), that prescribes to increase shares of renewable energy in energy balance of the country to 25% till 2035 -  the answer is also too clear. A stick only never promotes any realized motivation without carrots.


Problems of short-term prognostication of meteorological conditions

So, the problem of ajustments of generation from RES and from traditional fuel sources consists of three parts: it is fair concordance of RES producer’s prognoses, taking into account interests of the third parties and reliable predictions of energy consumption, plus actual problems of short-term prognostication of meteorological (weather) conditions "on twenty-four hours advance basis", that actually means generation prognostications by the hour. And it is the most "difficult" part of the problem.

It is necessary to make a reservation at once, that this problem is not solved reliably and exactly nowhere in the world. Whole mass of algorithms and software complexes is offered, international conferences are held on algorithms, debates are opened, new software products are promoted, but universally recognized "industry standards" for prognostication of VRE are still not present for today.

Except solving of mathematical design problems on weather processes and informational-calculating difficulties for weather prognoses drafting of such kind (the more with deviation within 5-10% limits, creation of very wide automatic stations-based network infrastructure is required for collection of meteorological data, that would automatically pass local atmosphere conditions indormation in on-line mode. And then these data need to be processed and distributed among local consumers of such information with exactness and regularity, as they need it.

To illustrate an example of the complexity of the task in generation prediction, let’s take a look on a fig. 1, where a screenshot of prognostication system is presented on American Xcel Energy RES-station within the limits of 36-hours planning horizon. Such depth of planning is considered optimal for "twenty-four hours advance" operational mode. On the basis of data collected from the weater data monitiring system, the system of company forecasts generation amounts automatically. Calculations in "twenty-four hours advance” mode include data on actual electric power production (green points), including the initial point of generation prognosis (continuous vertical line) and typical prognosis error for previous and on next seven days (shaded area). 10% error range (±5%) for prognostication itself, not even for actual deviation of generation from a prognosis (!), includes, as the best option, forecasting not further than 1,5 hours advance.

Is it necessary to plan and co-ordinate generation from different sources? Is it necessary to manage generation from RES? Undoubtedly, it is.

Prognostication of VRE influences on several operations, related to grid management, including planning, supervisory control, real-time balancing, reserve requirements to the grid and  commands issuing for preliminary start (muffling) of compensative powers. Integrating prognoses from local producers of VRE, grid operators can foresee rapid changes of VRE generation, to economically balance energy consumption and pre-arranged generation for a day and within day period. It results in cost cutout on unrenewable fuel, in increase of grid reliability on the whole and in expenses minimization on energy acquisition from RES. It will help not only to balance the grid on power and voltage, but also will improve energy quality in electricity networks, make its frequency and phase changes better.


Pic. 1. Persistent prognostication of RES generation with 36 hours horizon and prognosis laying out using 15 minutes and 1 hour timeline. Data provided by USAID Office of Global Climate Change, 2017.

Methods of prognostication

Methods of prognostication are generally divided into two categories. Physical methods transform data about weather conditions (for example, temperature, pressure, speed and direction of wind, taking into account surface topography and obstacles) into numeric data (numerical weather prognostication, NWP) for the prognosis of specific local weather terms that can be further converted into prognoses on energy production. Statistical methods use statistical data in real time mode to get statistically reliable results on the basis of NWP models.

Prognostication of stability (persistence) is a simple statistical method, supposing, that present levels of generation will remain unchanging in short-term scope and they conform to mediated previous actual statistics upon this date and time. Persistent prognoses are often used as standard or base model for estimation of more advanced methods.

Prognosis error (also prediction error, forecasting error) is a difference between forecasted data and actual generation. Errors are used to calculate forecast precision, that allows system operators to forecast vagueness in planning and to compare different forecasting methods. Best prognostication "on a day advance"-systems provide errors within ±13 - ±15% limits. Three most widely used methods of precision measuring are:

  • Middle bias error specifies, how systematic the system is forecasted with over- or under-estimation.
  • Middle absolute error measures middle prognoses precision regardless of errors gradient.
  • Root-mean-square minimum criterion error measures middle prognoses precision regardless of errors direction and appropriates relatively larger weight to large errors.

Factors, influencing on efficiency of hour-scale forecasting, include forecasting horizon, local weather terms (influencing on changeability of VRE resources), geographical scope, data availability (for example, amount, location, methods and reliability of provided information) and quality of data (for example, timing co-ordination, accuracy, laying out and correction on territory scope) et al.

Forecasting accuracy of VRE usually increases at shorter periods of getting current meteorological data. For example, for hour-scaled forecasting within ±5 % error limits you need to have data for prognostication with 15-minutes laying out, that in turn must use minimum 9 series of current data for calculations. So, actually, fresh data from weather stations must be provided and processed every single minute.

In a table. 1 (See pic. 2) typical terms and used methods of prognostication for VRE generation are shown, recommended by USAID Office of Global Climate Change:



Pic. 2. Typical terms and used methods of prognostication for VRE generation.


"Excellent result", in IEA opinion, for 0-5-6 hours advance forecasting horizons was received during a year on seven different sites (see pic. 3). When forecasting "on a day advance", as shown on the picture, it means mean-root-square error from 70 to 130 W on every square meter of solar panel surface. For a typical 250 W panel (1,6 sq.м) deviation of generation forecasting can make from approximately 40%  to over 80% from nominal value. It’s no way to say about forecasting accuracy within ±[5%; 10%]. Red line on pic. 3 shows reference "forecasting" from satellite during satellite image capturing. Prognoses of clouds motion up to 5 hours advance, received from satellite (yellow and white lines), happened to be better, than numeral weather prognostication from data of National Digital Forecast Database (NDFD) in USA. Numeral weather prognostication (NWP) has similar accuracy for forecast horizons from 1 hour to 3 days.


 Pic. 3. Relative mean square error (RMSE) of solar generation forecasting in twenty-four hours advance mode by SURFRAD surface measuring  system, using NWP and stochastic self-training methods, Watt/m2. Source: International Energy Agency (IEA), Report IEA-PVPS T14-01: 2013.


Surface systems of permanent monitoring of cloudiness and solar irradiation (so-called Sky Watchers) can make an alternative to satellite prognoses. But surface optical stations can not  calculate local vector of clouds motion as well as satellite stations do, and forecast local cloud thickness well, because optical surface systems have much less sky review zone, than satellite observers do.

According to the researches results (IEA, Photovoltaic and Solar Forecasting: State of the Art, 2013), PV-generation forecasting accuracy also very depends on volatility of local weather conditions, that is especially noticeable in mountain areas. For example, research of prognostication during one year for 26 surface measuring stations in Spain showed, that local features of climate and relief strongly influence on prognoses accuracy. In this research indexes of prognoses accuracy (both RMSE and bias error) measured by identical system, appeared far more worse for stations in the north region of Spain compared to central and south regions of the country, where weather is less changeable. And relative mean square error (RMSE) for central European stations fluctuates from 40% to 60% as compared to results from 20% to 35% for Spanish stations.

RMSE is reduced by simultaneous accounting of monitoring data from maximum possible number of local "sky watchers". So in the Japanese region of Kanto 64 local "Sky watchers" were unified into single research and information network, and prognostication accuracy succeeded to be 60% higher (coefficient of error reduction for the measuring devices network attained maximal value of 0,4, compared with relative error coefficient of 1 for point measuring). Also is turned out, that unification of 24 local systems in a district with an area of 100 kilometers х 60 kilometers made mean absolute error (MAE) for regional prognoses 22% below than MAE for prognoses from point monitoring stations. Total errors reduction of incorporated prognostication network made about 70% (coefficient of error reduction about 0,3).

According to IEA data, deviation of RMSE of best systems of "clever" prognostication from persistent model (taking into account long-term monitoring data) makes from 18-35% (Spain) to 68-75% (Switzerland, Belgium, Netherlands, Canada, USA).  

Quite massive report named "Adapting Market Design to High Shares of Variable Renewable Energy" on the problems of forecasting and about other factors, impedimental to wide coexistence of RES and traditional generation in grids of different level, was published in June 2017 by International Renewable Energy Agency (IRENA), it is publicly available to download here (click to open pdf-file):



"Where is this street, where is this house"?

In Law of Ukraine "On the market of electric energy" there is such an enigmatic phrase, that, they say, anything, that is not clearly stated here, regarding prognostication of solar electro-generation and fined imposing order, is regulated, let’s say, "according to market rules". There is no sighs, how is that "market" defined, and who is in charge there, and who actually formalizes these "market rules".

It is necessary to acknowledge that in Ukraine now there is NOT any system of instant automatic monitoring of local meteorological data in online mode, suitable for hour-based and daily prognostication of PV-generation. Averaging of registered data of intrahour generation upon every date for few years can become the only method, taking into account data of current generation ("persistence"). More or less this method is useful at estimation of annual generation. However taking into account current weather volatility, accuracy of persistent method for "twenty-four hours advance" mode, especially in a local scope, doesn't stand up to any critical judgement.

According to data from IEA report within "PV - power Systems Program", use of mesoscale meteorological models "probably does not improve quality of prognostication". Post-processing of data (mainly spatial averaging and error correction) can relatively decrease invalidity of local estimation on 15- 25%, but however for short- and medium-term prognostication it still will be no less than 25-30%.

In Ukraine there is no system of automated processing and distribution of data among concerned parties (neither on generation prospects of market participants nor about weather conditions). There is also nothing about acknowledged and non-conflicting rules of concordance for generation amounts. There is no clear forecasting mechanism of energy consumption in local networks on the same hour-scope base. There is not even reliable statistics on RES in Ukraine. There is no solid infrastructure of stations for surface cloudiness monitoring, unified into coordinated informational network.

There is no permanent simultaneous satellite watching system to track motion and thickness of clouds above all territory of Ukraine. There is no publicly accessible National database for short-term prognostication of weather and local solar luminosity. There are no certificated software data complexes, that would be acknowledged by all market participants. The Law doesn’t give any clues about financial sources for creation and development of mandated systems of prognostication and accounting of VRE. There is no long-term experience of research and optimization for such systems. And all these "no" are quite possible to continue and to expand.

But fines are already here, stipulated in the Law. It discourages, for sure. Will "harshness of laws" be compensated again by their non-fulfillment, or fines will be simply appointed to every producer of solar electricity by monopolistic "Guaranteed Buyer"?


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