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Original article / Оригинальная статья УДК 621.311.1
DOI: http://dx.doi.org/10.21285/1814-3520-2018-5-166-182
FLEXIBILITY OPTIONS FOR SMART GRIDS. BASIC CONCEPTS
© Przemyslaw Komarnicki', Michael Kranhold2, Tatiana Sokolnikova3 and Zbigniew Styczynski4
Fraunhofer Institute IFF Magdeburg, University of Applied Sciences Magdeburg, Germany 250Hertz Transmission GmbH Berlin, Germany 3I rkutsk National Research Technical University, 83, Lermontov St., Irkutsk, 664074, Russian Federation 4Otto-von-Guericke University Magdeburg, Germany
ABSTRACT. PURPOSE. Increased renewable generation requires new operation strategies of power systems especially at distribution levels. New mechanisms - so-called flexibility options - are necessary for smoothing the weather dependence of renewable generation and stabilizing the Smart Grid operation in this way. The paper presents different flexibility options of a Smart Grid. Moreover, some practical examples of modern Smart Grid operation have been given and the methodology for flexibility option planning has been presented. A wide range of modern references and summary make this review paper useful as an introduction to this future-oriented scientific topic. METHODS. Operation of power system with renewable sources often causes bottlenecks in the power grid. The used method is redispatshing the power generation or application of local flexibility options e.g. energy storages or demand management. RESULTS AND THEIR DISCUSSION. The electric power system of the future, which will use renewable generation, needs a flexibility option for stabile and economically profitable operation. The list of such flexibility options and the selection methodology of optimal flexibilities mix is presented in this paper. The optimal selection depends on the power system structure and current operation condition as well as can change rapidly. CONCLUSIONS. The strategies of power system operation are to be checked in some countries due to the wide presence of the large proportion of renewable and other decentralized generation. In the future the percentage of renewable generation will increase even faster therefore today there is the need to develop a wide range of flexibility options to stabilize and optimize the power system operation. Having overviewed the possible measures, this paper shows how to transfer them in general into the planning and operational practice. The paper provides both general remarks and real examples. The future works will focus on the development of algorithms for optimal flexibility and practical operation of Smart Grids.
Keywords: electric power system, power system control, renewable generation, flexibility options
Information about the article. Received March 12, 2018; accepted for publication April 09, 2018; available online May 31, 2018.
Przemyslaw Komarnicki, Candidate of technical sciences, Professor for the Power System at the University of Applied Sciences in Magdeburg, Head of the Department in the Fraunehofer Institute IFF Magdeburg, Germany Пшемыслав Комарницки, кандидат технических наук, профессор по энергетической системе в Университете прикладных наук Магдебур и руководитель отдела в Институте Фраунгофера IFF Магдебург, Германия.
2Michael Kranhold, Director of Customer Management and Network Calculation at the Company 50 Hertz Transmission GmbH Berlin, Germany and the Doctor Honores Causa at the St. Petersburg Economic University. Михаель Кранхольд, директор по управлению клиентами и расчету сети в электроэнергетической компании 50Hertz Transmission GmbH Берлин, Германия и почетный доктор Санкт-Петербургского экономического университета.
Tatiana V. Sokolnikova, Postgraduate, e-mail: [email protected] Татьяна Сокольникова, аспирант, e-mail: [email protected]
4Zbigniew Styczynski, Professor of the Department of Electric Power Systems and Renewable Sources of Energy of Otto-von-Guericke University, Doctor of technical sciences, Doctor Honores Causa of Donetzk Technical University and Honory Professor of the Wroclaw University of Technology, e-mail: [email protected]
Збигнев Стычински, профессор кафедры электроэнергетических систем и возобновляемых источников энергии Университета Отто-фон-Герике, доктор технических наук, почетный доктор Донецкого технического университета и почетный профессор Технологического университета Вроцлава, e-mail: [email protected]
For citation. Przemyslaw Komarnicki, Michael Kranhold, Tatiana Sokolnikova, Zbigniew Styczynski. Flexibility options for smart grids. Basic concepts. Vestnik Irkutskogo gosudarstvennogo tehnicheskogo universiteta = Proceedings of Irkutsk State Technical University, 2018, vol. 22, no. 5, pp. 166-182. DOI: 10.21285/1814-3520-2018-5-166-182. (In Russian).
ВАРИАНТЫ ГИБКОСТИ ДЛЯ ИНТЕЛЛЕКТУАЛЬНЫХ СЕТЕЙ ЭЛЕКТРОСНАБЖЕНИЯ. ОСНОВНЫЕ ПОНЯТИЯ
12 3
Пшемыслав Комарницки , Михаель Кранхольд , Татьяна Сокольникова , Збигнев Стычински4
1Институт Фраунгофера ИФФ Магдебург, Университет прикладных наук Магдебург, Германия.
250Hertz Transmission GmbH Берлин, Германия.
3Иркутский национальный исследовательский технический университет,
664074, Российская Федерация, г. Иркутск, ул. Лермонтова, 83.
4Университет Отто-фон-Герике в Магдебурге, Германия.
РЕЗЮМЕ. ЦЕЛЬ. Для увеличения производства электроэнергии от возобновляемыми источниками требуются новые стратегии эксплуатации энергосистемы, особенно на уровнях ее распределения. Новые механизмы, называемые вариантами гибкости, необходимы для сглаживания зависимости возобновляемой генерации от погодных условий, и таким образом стабилизации работы Smart Grid (интеллектуальной энергосистемы). В данной работе представлены различные варианты гибкости для интеллектуальной энергосистемы. Кроме того, представлены некоторые практические примеры работы современных интеллектуальных сетей, а также методология планирования вариантов гибкости. Широкий список современных ссылок и краткое изложение делают эту обзорную статью полезной как введение в эту ориентированную на будущее научную тему. МЕТОДЫ. Эксплуатация энергосистемы с возобновляемыми источниками зачастую формирует так называемые узкие места в электроэнергетической системе. Используемый метод - это перепланирование производства электроэнергии или применение локальных вариантов гибкости, например, накопителей энергии или управления спросом. РЕЗУЛЬТАТЫ И ИХ ОБСУЖДЕНИЕ. В электроэнергетической системе будущего, которая будет основана на использовании возобновляемых источников энергии, требуется так называемый вариант гибкости для стабильной и экономически выгодной эксплуатации системы. В этой статье представлен список таких вариантов гибкости и методологии выбора их оптимального сочетания. Оптимальный выбор зависит от структуры энергосистемы и текущего рабочего состояния и может быстро изменяться. ВЫВОДЫ. В некоторых странах стратегии эксплуатации энергосистемы необходимо проверять в связи с большой долей возобновляемой и другой децентрализованной генерации. В будущем доля возобновляемых источников энергии будет увеличиваться еще быстрее, поэтому в настоящее время необходимо разработать широкий спектр вариантов гибкости для стабилизации и оптимизации работы энергосистемы. В данной работе авторы приводят обзор возможных мер и показывают, как можно в целом перенести эти меры в планирование и эксплуатацию. Также приводятся не только общие рекомендации, но и реальные примеры. В будущих работах основное внимание будет сосредоточено на разработке алгоритмов для оптимальной гибкости в практической работе Smart Grids.
Ключевые слова: электроэнергетическая система, управление энергосистемой, возобновляемая генерация, варианты гибкости.
Информация о статье. Дата поступления 12 марта 2018 г.; дата принятия к печати 09 апреля 2018; дата онлайн-размещения 31 мая 2018 г.
Introduction
Fossil or nuclear primary energy sources (PES) have been widely used in energy systems worldwide, but this PES are finite and are forecasted to last only for the next 60 (natural gas) or 200 (hard coal) years at today's level of consumption [1, 2]. But the energy consumption rises continually, e.g. about 28% between 2015 and 2040 [2]. Furthermore, an increase of CO2 emissions has been observed as a negative result of the increase in consumption, which has become
evident from the global warming effect. It has become necessary to define global counter-measures, some of them will be mentioned below, to stabilize the increase in the Earth's temperature.
The shares of primary energy consumption changes globally every year for two main reasons, how mentioned above: the shortage of PES and the necessary reduction of CO2 emissions (see Fig. 1). Independent of the current low price for oil, this type of PES
has been systematically experiencing a decrease in its dominating role in the energy mix (Fig. 1, a), and likewise, coal and nuclear energy consumption have also decreased (Fig. 1, b). At the same time, there has been an especially dynamic increase in the use of renewable energy, and this trend is forecast to continue over the next twenty years (see Fig. 1, c). Natural gas will also be used in the future in more flexible power stations (Fig. 1, b), resulting in lower CO2 emissions than that produced by combusting other fossil fuels.
Considering these trends in the changing energy mix, one can consider that this is a global effect. Not only the nations of Europe, and especially Germany with the national energy strategy called "Energiewende", but all other countries worldwide are working intensively (e.g. China) to develop new, levelled-cost renewable technologies (Fig. 2, a). Wind and especially solar photovoltaic (PV) energy have become two to four times cheaper, considering energy-production costs, over the last 20 years. The installed power using those technologies has been growing consistently and exponentially. Wind power equipment, with energy production costs at 50 $/MWh, is currently a strong competitor to the traditional
technologies (Fig. 2, b for North American costs), and also a driver for a wider use of renewable energy. A global market for wind and PV solar power is already established. The prices for energy production using these technologies are comparable per MWh worldwide [3].
Two positive global effects, from the environmental point of view, have been observed more generally over the recent few years:
- Uncoupling of primary energy use from GDP beginning in the 1990s-as already discussed previously in this chapter [3, 2];
- Uncoupling of CO2 emission in the power sector from demand, for the recent few years [4].
To summarize, not only the growth of renewable generation but also, and maybe even more importantly, the clear trend in the decrease of energy intensity is very promising and could result from fulfilling the goals formulated in the Kyoto Protocol. Some countries have reduced (Fig. 3) their energy intensity by more than a factor of two (e.g., China, Russia), but Europe is still leading with the lowest value.
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Fig. 1. Percentage of primary energy (a) and annual growth of fuel demand in short (b) and long time range (c). Source: BP 2018 Energy Outlook Рис. 1 Доля первичной энергии (а) и ежегодный рост спроса на топливо в краткосрочный (b) и долгосрочный период (с). Источник: BP 2018 Energy Outlook
Fig. 2. Percentage of renewable power generation (a) and levelled cost of electricity in North America (b). Source: BP 2016 Energy Outlook Рис. 2. Доля производства возобновляемой энергии (а) и выровненная стоимость электроэнергии в Северной Америке (b). Источник: BP 2016 Energy Outlook
О - -
О 100 200 300 100 500 600 700
Energy intensity (TPES/CDP PPP), [toe/M$ 2005]
Fig. 3. Regional decrease of energy intensity. Source: IEA [5] Рис. 3. Региональное снижение энергоемкости. Источник: IEA [5]
Towards a Smart Grid
Renewable energy and modern, efficient economic productions have significantly increasing the CO2 emission, but the current power system was planned 30-50 years ago for other conditions and his operation in this new environment must be optimized. Furthermore, the mix of energy predicted by the Energy Information Administration (EIA) for 2030 (given in Fig. 4) [6] designates a share of 14.6% for renewables worldwide, which will result primarily from the reduction of coal use and will need new operation strategies.
The EU in toto and some European countries individually have more concrete plans for the power system in the future [7].
Consequently, the EU has set ambitious objectives for the year 2020 in order to:
- lower energy consumption by 20% by enhanced efficiency of energy use;
- reduce CO2 emissions by 20% and;
- ensure that 20% of the primary energy is generated by renewable energy resources (RES).
Fig. 4. Word Energy Mix 2030. Source: EIA Рис. 4. Соотношение используемых видов энергии в мире 2030. Источник: EIA
These aspects are market-related, but they may be supported the network operation strategies. In the Smart-Grid context, the market and grid operations will influence each other mutually. In the environment of large-scale volatile power production, it will become mandatory to coordinate the network and market operations in a smart way.
The operation of the current power system consists of large, centralized power plants, a hierarchical network and a huge number of dispersed consumers, all of which have to be controlled by central control centers. The future system will be characterized by a large number of small Distributed and Renewable Energy Sources (D&RESs), many of them with intermittent power output. All these D&RESs have to be operated in parallel with conventional power plants, which is the reality in Germany already. Furthermore, on the consumer side, there will be possibilities to influence the consumption by means of flexible tariffs and other mechanisms [8]. Demand-side management will play a growing role for power balancing in the future. Coordinated energy generation, load management and an integrated planning process for the power system will be necessary [9, 15].
One possible solution for this problem is to transfer a part of the control intelligence close to the D&RES units and controllable
loads by using "agents." Such an agent receives instructions from the higher-level control structure and has a certain range within which it can control its unit or group of units [9].
The clustering of many such units (e.g. small controllable loads, generation and storage) into pools with a manageable power import/export from/to the outer grid provides the function of a VPP that can contribute to the system services. This principle is shown in Fig. 5. Such a system can only be based on a powerful and reliable communication structure [9].
The communication tasks of the future distribution networks include:
- The contribution to the active power balancing with dispatch of power generation, storage and controllable loads creating a VPP. The VPP of the future will be able to deal with islanded operation by means of generation and demand-side management.
- The transfer of metered values as a support for widespread energy management and for billing.
- The provision of further system services, such as congestion management, reactive power and voltage control, fault location, supply restoration after faults, islanded operation and black-start capability.
Fig. 5. Power system operation today (left) and in future (right) [10] Рис. 5. Работа энергетической системы сегодня (слева) и в будущем (справа) [10]
System services are currently provided mainly by TSOs [20]. In the future, the TSOs will also be responsible for the load balancing, but more and more aspects will be provided on the distribution level. Fig. 6 shows the system services and the changes of their provision.
The delivery of the system services will be partly shifted (Fig. 6) from the TSO to the distribution system operator during the next 15 years. It is planned that, in 2020, all system services, for example, primary control power or reactive power control, will also be provided on the distribution level [3].
Fig. 6. System services provision today and in the future [9] Рис.6 Услуги системы сегодня и в будущем
The performance of the smart rid mentioned previously requires some new measures to fulfil the requirements for safe
and secure energy delivery. These measures are called flexibility options for the Smart Grid [3, 16].
Flexibility options
The systematic chart of flexibility options is shown in Fig. 7. The flexibility option are divided there into three groups:
- technological measures;
- Operational measures;
- Others.
Each group includes some specific measures which are discussed below in detail.
Power-to-Heat. Power-to-Heat (P2H) is an alternative technology which is having a renaissance in areas where highly-renewable generation is present in the power system. Conversion of electrical energy from heat is not normally economic, but, if there is a surplus of electric power from renewable energy, e.g., wind or PV generators, this cheap electric energy can be converted into heat. Furthermore, if the P2H technology is in use, it is not necessary to deregulate the renewable gener-
ation and waste the potential energy. The second smart-grid-specific application of P2H involves the control of the negative power range. In this case, it is possible to transfer this system service functionality from the power station to the P2H facility. Taking this into account, some power stations in the power system, which are used for must-run power, can be shut down at times of high renewable generation. The advantages of P2H technologies are additionally [12]:
- low investment costs, for example, 100 US$/kW;
- simple, reliable technology, and very short turn-on, turn-off time.
Currently, one observes a great deal of interest in this technology, which is illustrated by the many new projects.
Fig. 7. Flexibility options for the smart grid [3] Рис. 7. Параметры гибкости для «умной электросети» [3]
Electric Energy Storage. Electric energy storage is also a very well-known technology which offers a great deal of flexibility options depending on the power and capacity of the storage [18, 19]. Nowadays also the battery storage of electric vehicles can be used for the system services when the electric cars are connected with the grid [17]. The storage planning is also important issue for economic use of storage [25]. Further information about this can be found in the book [3].
Power-to-Gas (P2G). This technology requires a technically complicated conversion process involving gas production. Power-to-Hydrogen is one of the P2G technologies, jointly with Power-to-Synthetic Methane, Power-to-Liquid, Power-to-Chemicals and Power-to-Materials. Electrolysis is normally used, and hydrogen is obtained as the primary gas (see Fig. 8). The hydrogen can be stored in compressed form under various pressures [13] (at 350 or 700 bar) and is used not only used in reverse for power production, but also as a fuel for vehicles. The hydrogen vehicles can use pure hydrogen if they are equipped with fuel cells, which can produce electric energy using the reverse process. The fuel-cell car uses electric motors.
The hydrogen does not necessarily have high energy density [14], so the CO in a
chemical process can be fed into a so-called methanizing process. Finally, methane gas is produced, and this synthetic gas can be used as a fuel in the gas turbine. Green hydrogen can commonly replace fossil fuels in the future. Currently, neither the amount of renewable generation, nor the state of the technology (very high investment costs of more about 5,000 US$/kW) allows wide utilization of this method.
Flexible Power Plant. The efficiency of power-plant operation depends strongly on the output power. Consequently, the power plants are operated close to the nominal power. If the power plant is operating on partial load, the efficiency is worse. The partial load is generally also limited to, for example, 40% of nominal load. When very high renewable generation occurs, many power plants must run on a partial-load mode and maintain a must-run operation. Such an operation is very uneconomic and increases the energy costs to the customers drastically. The power plant manufacturers are already working on new power plant designs where the smallest units, for example, 100 or 200 MW, will not have the disadvantages mentioned above and will operate with a full-scale load with almost the same efficiency.
Fig. 8. Diagram of a renewable hydrogen-storage system Рис. 8. Схема системы хранения возобновляемого водорода
Power-Operated CHP. Normally, CHP are operated in a head-output-controlled mode. The electric power was, basically, useful waste from the heat production. The balancing of power fluctuation in the smart grid is one of the most important issues, and the CHP can help with fluctuation compensation by using the output power-controlled operation mode. This only requires a small change in the control panel of the CHP and can contribute many advantages to the smart-grid operation.
DSM-Industry. Depending on the country, industry constitutes a high, or even the highest, demand on the total energy consumption. The power required by technological processes is delivered on time by the traditional power plants. However, the output of weather-dependent RES cannot be controlled in the same manner as the traditional power plant. Therefore, a paradigm shift is necessary. Instead of demand-dependent energy delivery to industry, in the future, the control of industrial processes will be dependent on the level of renewable generation [24]. The driver for this paradigm shift could be tariffs, which can be varied depending on available renewable generation. If the renewable generation is very high, the price for the energy will be low, which will be preferential for the high demand. If the paradigm shift mentioned above occurs, industry will have, thanks to the new flexibility, the possibility to deliver additional system services (see also Table 1.3).
Demand-Controlled Biogas CHP.
The general operation mode of a biogas CHP delivers constant maximal power. In the smart grid, with large amounts of volatile renewable generation, this operation mode must be modified. The biogas CHP will play the role of an almost-peak-load generation unit, which should sensitively, reacts to maintain the power balance in the system. The economic use of the biogas will also be necessary, because the biogas CHP must establish the base load in a time of renewable-energy deficit [28].
DSM Household and E-mobility. The control of household demand can effectively help the energy balancing in the smart grid. Some studies [15] have shown that the demand-side potential in households is high,
e.g., about 5-10 GW in Germany, but, unfortunately, this potential is normally available immediately for less than one hour, but sometimes repeatedly, in the course of one day. Nevertheless, the DSM in households, because of small investment costs, can be activated quite simply. The driver for this flexibility option could be the adaptation of tariffs in the same way as for the DSM in industry mentioned previously.
In the future, when millions of electrical cars, mostly driven by the energy stored in the car batteries, exist on the market, each home power connection will be equipped with an electric car charging station [8]. This charging station will allow bi-directional electric energy flow and control. In this case, the potential for providing system services by DMS control in households will be very high.
Power Network Expansion. The natural method for improving power system features is the expansion of the power network. New, stronger overhead lines and cables and more suitable equipment-such as short-circuit breakers or power transformers-make it possible to increase the transport capacities. Furthermore, network expansion results in a higher short-circuit current and the implementation of dynamic features. In the smart grid, these measures must be better coordinated between the distribution and transmission systems for an economic balance of costs.
ICT. The realization of the Smart-Grid concept is not possible without the rapid development of ICT. Digital protection and measuring systems trigger a high flow of data between various players (traders, producers, consumers). The data must be transported, evaluated, interpreted and saved. All these processes must be very fast, safe and secure.
Generally the flexibility options listed in Fig. 7 are necessary to perform the transition from the power system today to the smart grid of the future. They should support, either separately or in combination, or even sometimes replace, the current providers of system services cited in Fig. 6. Table 1 gives a systematic overview of which flexibility options are applicable for specific system services.
Table 1
Applicability of various flexibility options for system services [3]
Таблица 1
Применимость различных вариантов гибкости для услуг системы [3]
PD VT VQ
FP Primary control FS Secondary control FM Minute reserve Schedu- Tap React- RB RI SQ SO Asset manag.
ling & redis-patch chang er control ive power Control Black start cap. Island operation Power qual. ass.
Power-to-Heat - X (negative) X (negative) X - - X - - X
EES X X X X - X X X X X
Power-to-Gas X X X X - X X X X X
Flexible power plant X X X X - X X X X X
Power-operated CHP X X X X - X X X X X
DSM-Industry X X X - - - X - X
Demand-
controlled X X X X - X X X X X
Biogas CHP
DSM
Household - - X - X X X X X X
& E-Mobility
Power
Network - - - X X X - - X X
expansion
ICT* X X X X X X X X X
The ICT cannot contribute directly to system services, but is necessary for optimal operation and coordination of all services in a smart grid [18].
How large the requirement is for various flexibility options in a future smart grid has also been investigated within the scope of the ESYS study by the Academies of Science in Germany, mentioned previously.
The total power of these measures was computed at 84 GW for Germany. Power-to-heat, biogas, H2 Storages and DSM were selected for the secure operation of the future power system investigated in this scenario [16].
Fig. 9. Flexibility options necessary for a no-emission system in Germany in 2050. Results of the Acatech investigation for scenario P3S4. Normalized diagram Рис. 9. Варианты гибкости энергетической системы без выбросов в Германии в 2050 г. Результаты исследования Acatech для сценария P3S4. Нормализованная диаграмма
First experiences. An example
Generally, unbalance in power system and resulted local overloading can lead to critical situation [22, 23]. Therefore, in flexible systems [27] or also in insulated power system [21] it is necessary to exact planning of flexibilities usage.
Over the past few years in Germany, as a result of the "Energiewende" and the related Renewable Energy Law (EEG), the re-
newable-energy supply has grown rapidly. At the end of 2015, installed power in renewable energy had increased up to 89 GW and is now higher than the peak power1. Figs. 10 and 11 show the growth of renewable generation in Germany, especially in the TSO 50Hertz Transmission2 operation area which is characterized by very high renewable generation in conjunction with relatively small demand.
Fig. 10. Development of renewable energy generation in Germany and by the TSO 50Hertz Рис. 10. Развитие производства возобновляемой энергии в Германии и в компании TSO 50Hertz Transmission GmbH. Источник: Almanac 2015 компании 50Hertz Transmission GmbH
Transmission GmbH. Source: Almanac 2015 of 50Hertz Transmission GmbH
Taking into account various renewable technologies, the energy supplied depends on the total generation time of nominal power per year3. The growth of this important factor is shown in Fig. 1.21. The 151 TWh energy means that about 23% of the total electric energy in 2015 was produced in Germany by renewable sources. The TSO 50hertz grid area used 2017 about 53% of renewable energy in the supply of the demand in its own area and delivered more than 30% of all the renew-
able energy delivered to the German power system.
This realistically high amount of renewable generation causes some difficulties for the secure operation of the power system (mentioned earlier), which will increase in the future and require use of different flexibility options (also previously mentioned). Some emerging problems in the secure operation of power systems with high renewable generation have already been identified.
1
The peak power in Germany in 2015 was 82 GW.
250hertz Transmission GmbH is one of four transmission operators in Germany (see also Fig. 1.11a). This regulation area is characterized by very high renewable generation and relatively small demand.
3This value depends on renewable technology and the geographical location of the generation devices. This value is an average for Germany: 1100 h for PV; 2100 h for wind onshore; and 3500 h for wind offshore.
Fig. 11. Development of supply from renewable energy in Germany (right column in each year) and in the grid area of the TSO 50Hertz (left column in each year). Source: Almanac 2015 of 50Hertz Transmission GmbH Рис. 11. Развитие поставок из возобновляемых источников энергии в Германии (в каждом году - правая колонка) и в области сети компании TSO 50Hertz (в каждом году - левая колонка). Источник: Альманах 2015 компании 50Hertz Transmission GmbH
The first is the uncertainty of renewable generation forecasts. The TSO use a few forecast companies and own computer forecasting tools for renewable generation which use historical data and current measurements to predict the renewable generation, and then use this predicted value for the unit commitment planning for the next few days. The medium forecast error is small (about 3-5%), but it depends on the weather conditions; the maximal error can be much higher. One ex-
ample of forecast error, for July 5, 2015, is shown in Fig. 12. A very strong wind front was forecast with a maximal wind-generation power in the TSO 50Hertz grid area of about 10 GW; but in reality, a maximal power of about 8 GW was measured. The error was on average about 20% during an 8 h period. This error must be compensated for by the unplanned run-up of power stations, which requires expensive rescheduling and additional costs.
Fig. 12. Wind-energy generation in the TSO 50Hertz grid area from 5 to 12 July, 2015. Source: Almanac 2015 of 50Hertz Transmission GmbH Рис. 12. Генерация ветровой энергии в сети компании 50Hertz TSO с 5 по 12 июля 2015 года. Источник: Almanac 2015 компании 50Hertz Transmission GmbH
This cost related to the forecast error increased drastically in Germany in 2015 and was more than a hundred million Euro in the TSO 50Hertz grid area in 2015 (see Fig. 13).
The extreme forecasting error could be a driver for more flexibility in the smart grid and leads to a decrease of pre-classified facilities (e.g., industrial loads or electric energy storage) for primary, secondary and minute
reserves in the power system. This process can already be observed in Germany. The growth of pre-classified facilities in Germany is shown in Fig. 14.
The power of pre-classified power providers has grown in the TSO 50Hertz grid area and amounts to 2.2 GW for primary, 4.5 GW for secondary and about 10 GW for minute reserves4.
Fig. 13. Redispatch caused by renewable generation. Energy and costs. Source: Almanac 2015 of 50Hertz Transmission GmbH Рис. 13. Перераспределение, вызванное генерацией возобновляемой энергии. Энергия и затраты. Источник: Almanac 2015 компании 50Hertz Transmission GmbH
Fig. 14. Number of primary control power providers in Germany. Source: Almanac 2015 of 50Hertz Transmission GmbH Рис. 14. Количество поставщиков мощности первичного регулирования в Германии. Источник: Almanac 2015 компании 50Hertz Transmission GmbH
4The peak power in the TSO 50Hertz Transmission GmbH zone is about 13 GW.
Optimization methodology for flexibility option
The range of flexibility options required use of optimization method for searching of optimal flexibility options mix. From the one hand a optimal mix depends of the flexibility options from the system service (see also Fig. 25) which should be controlled or improved
(e.g. voltage or reactive power) and from other hand it depends from the configuration and selected working point of the power system [5, 22].
In the Table 2 corrective boundaries of some flexibility options are listed.
Table 2 Таблица 2
Corrective boundaries of chosen flexibility options Корректирующие границы выбранных вариантов гибкости
No
Flexibility option
Corrective boundaries
Control of wind generation
Control of PV generation
Control of energy storage
Industrial load control
DSM
1
2
3
4
5
In the Tab. 2 one can see that different flexibility options (see Tab. 2) can deliver specific P/Q for the Smart Grid operation. For example the energy storage (Tab. 2, pos. 3) can deliver P&Q in negative and positive parameters area but generally the optimal distributed energy storage (batteries) has quite small power (e.g. 1 MW) and capacity (e.g. 2 MWh) and its limited the flexibility. Other way industrial load can be flexible controlled (Tab.2, pos. 4), generally in the positive part of the active power, by decreasing of the load. The range of control area, depends on technological processes, can make up to 20-30% of the nominal power, what can reach more tens of MW [24]. Both this mentioned measures are time limited, first by the battery capacity and second by particularly technological process, what must be take into account by planning of the flexibility option.
The control of local generation (e.g. wind or PV generation scheduling can help effectively by voltage control in the distribution) is especially promising because this measurer is more or less time restricted, but of course, the decreasing of renewable generation leads to energy wasting which should be consider in the economic calculations.
The flexibility options can be fixed in the planning and as well in the operation
phase of the power system. In the planning phase an optimal location and size of the equipment which can improve power system flexibility must be set. Optimization of this setting process is generally based on the cost of calculation. There are different methods to obtain the optimal size of such units like electric energy storage [26], power-to-gas unit [13, 14], or PV and wind turbine inverters [8] in coordination to the local power network issues.
Taking this into account, complex analyses (technical and economical) are necessary to optimize the flexibility options mix. This optimum depends strongly on the location of the unit, which will deliver the needed flexibility to "trouble area" in the networks (e.g weak places). The Fig. 15 addresses this problem exemplary for voltage control problem in the distribution.
If the problem occurs in the area of voltage control (AC - see Fig. 15) it is possible to support the voltage by measure from controllable renewable (A1) or storage (A2). It depends on "electrical" distance effects those measures with different effectivity, here e1 and e2. The voltage condition in the voltage control area after supported measures from i controlled area will change generally as given in the Eq. 1.
Fig. 15. Flexibility option impact on the smart grid operation. Example: voltage control - schematic statement.
Si and s2 - effectivity factors of flexibility options Рис. 15. Влияние опции гибкости на работу «умной электросети». Пример: регулирование напряжения - принципиальная схема. s1 и s2 - коэффициенты эффективности вариантов гибкости
F2=r+ + ........ziVAi=V1 +
+ If=i £iVAi (1)
The expected result in area AC can be reached using different combination of available measures. How the optimal measure mix can be found? Possibilities are: e.g. minimizing goal function (costs) or choosing of optimal technical measures under time limit.
Eq. 2 addresses the general optimization problem in the case 1.
{opt(M)|mm(lQ}^ ^ (Ml +M2 + - + Mi) ()
when M is the optimal mix of flexibility measures M1, M2,..Mi that minimize costs of optimized measures Ci.
The calculation of an optimal flexibility options mix based on minimal costs required advance power system analyses which can be done using load flow calculations [29]. In the case of the measures planning or use of a state estimation method [30] by the optimizations of the power system operation can be used.
References
1. BP Global (2018) Energy Outlook. https://www.bp.com/en/global/corporate/media/press-releases/energy-outlook-2018.html. Accessed March 20, 2018.
2. International Energy Agency (2017) World Energy Outlook. www.iea.org. Accessed March 20, 2018.
3. Komarnicki P, Lombardi P, Styczynski Z (2017) Electric Energy Storage System. Springer, Heidelberg New York.
4. Asafu-Adjaye J (2000) The relationship between energy consumption, energy prices and economic growth; time series evidence from Asian developing countries. Energy Economics 22, 615-625.
5. IEA (2008) Energy Consumption. https://www.iea.org/publications/freepublications/publica tion/Indicators_2008.pdf..Accessed March 20, 2018.
6. Energy Information Administration (2017) International Energy Outlook 2017. www.eia.gov/outlook/ieo/. Accessed March 20, 2018.
7. Commission of the European Communities. SEC (2007) A European Strategic Energy Technology Plan.Technology Map. 1510, Brussels 22.11.2007 https://ec.europa.eu/energy/en/topics/technology-and-innovation/strategic-energy-technology-plan. Accessed November 5, 2016
8. Buchholz B and Styczynski Z (2006) Integration of Renewable and Dispersed Resources: Lessons Learned from German Projects. General Meeting IEEE/PES, Montreal.
9. Buchholz B and Styczynski Z (2014) Smart Grid-Fundamentals and Technologies in Electricity Networks. Springer, Heidelberg
10. Buchholz B, Styczynski Z (2006) Communication requirements and solutions for secure power system operation. General Meeting IEEE/PES, Montreal.
11. Naumann A, Bielchev I, Voropai N, and Styczynski Z (2014) Smart grid automation using IEC 61850 and CIM standards. Control Engineering Practice, 25 (1), pp. 102-111. DOI: 10.1016/j.conengprac.2013.12.001
12. Bloess A, Schill W-P, Zerrahn A (2018) Power-to-heat for renewable energy integration: A review of tech-
nologies, modelling approaches, and flexibility potentials. Applied Energy, 210, pp. 1611-1626
13. De Santol L, Lo Basso G, Bruschi D, (2014) A small scale H2NG production plant in Italy: Techno-economic feasibility analysis and costs associated with carbon avoidance. Int. J. Hyd. Energy, 39 (12), pp. 6497-6517
14. Nastasi B, Lo Basso G (2015) Hydrogen to link heat and electricity in the transmission towards future Smart Grid Systems. Energy (in press).
15. Stötzer M, Hauer I, Richter M, Styczynski Z (2015) Potential of demand side integration to maximize use of renewable energy sources in Germany. Applied Energy, 146, pp. 344-352. DOI: 10.1016/j.apenergy.2015.02.015
16. Acatech (2018) Flexibilitätskonzepte für die Stromversorgung 2050: Technologien - Szenarien -Systemzusammenhänge
http://www.forschungsradar.de/studiendatenbank/studie
/detail/flexibilitaetskonzepte-fuer-die-
stromver-
sorgung2050.html?utm_source=Newsletter&utm_mediu m=newsletter&utm_campaign=renews_feb2016. Accessed March 20, 2018
17. Winkler T, Komarnicki P, Mueller G, Heideck G, Heuer M, Styczynski Z (2009) Electric vehicle charging stations in Magdeburg. 5th IEEE Vehicle Power and Propulsion Conference, VPPC '09, pp. 60-65. DOI: 10.1109/VPPC.2009.5289871
18. Price A, Wojszczyk B, Styczynski Z, Hatziargyriou N, Seethapathy R (2012) The practical application of advanced energy storage technologies within existing and planned market structures. CIGRE Session, Paris
19. Styczynski Z, Adamiak F, Abby C, do Vale Z, Cheng S, Favre-Perrod P, Ferret R, Itvani R, Iwasaki H, Joss G, Kieny C, Kleimaier M, Lazarawicz M, Lombardi P, Mecado PE, Soo Moon M, Ohler C, Pecas Lopes J, Pikutowski M, Price A, Roberts R, Seerhapathy R, Verma S.C, Vikelgaad H, Voropai N, Wojszczyk B (2011) Electric Energy Storage System. Report GIGRE WG C6.15. No. 458. GIGRE Paris ISBN: 978-2-85873147-3
20. Verband der Netzbetreiber - VDN - e.V. beim VDEW (2007) DistributionCode 2007. Regeln für den Zugang zu Verteilnetzen (idF v. Version 1.1) (08.2007). URL. https://www.bdew.de/internet.nsf/id/A2A0475F2FAE8F4 4C12578300047C92F/$file/DistributionCode2007.pdf -Accessed November 5, 2016
21. Lombardi P, Sokolnikova T, Suslov K, Voropai N., Styczynski Z A (2016) Isolated power system in Russia: A chance for renewable energies? Renewable Energy, 90, pp. 532-541
22. Hauer I., Styczynski Z A, Komarnicki P, Stötzer M, Stein J (2012) Smart grid in critical situation. Do we need some standards for this? A German perspective. IEEE Power and Energy General Meeting
23. Hauer I, Wolter M, Stötzer M, Richter M, Styczynski Z A (2015) A probabilistic load shedding concept considering highly volatile local generation. Electric Poaer and Energy Systems, 67, pp. 478-487
24. Gils H C (2016) Economic potential for future demand response in Germany - Modelling approach and case study. Applied Energy, 161, pp. 401-415
25. Hass J, Cebulla F, Cao K, Nowak W, Palma-
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Behnke R, Rahmann C, Mancarella P (2017) Challenges and trends of energy storage expansion planning for flexibility provision in low-carbon power systems a review. Renewable and Sustainable Energy Review, 80, pp. 603-619
26. Cebulla F, Naegler T, Pohl M (2017) Electric energy storage in highly renewable European energy systems: Capacity requirements, spatial distribution, and storage dispatch. Journal of Energy Storage, 14, pp. 211-223
27. Hojevac N, Capuder T, Zhang N, Kuzle I, Kang C (2017) Corrective receding horizon scheduling of flexible distributed multi-energy microgrids. Applied Energy, 207, pp. 176-194
28. Hartmann N, Thomsen J, Wanapinit N (2018) Using demand side management and CHP in renewable dominated decentral energy systems: a case study. Computer Science-Research and Development, Springer, 33, pp. 193-198
29. Zhu J (2015) Optimization of Power SystemOperation. John Wiley & Sons, Inc.
30. Conejo A J, Baringo L. (2018) Power System Operations. Springer Berlin, New York
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