Threshold maximum day and night temperatures defined by NSWSS [19].
\\n\\n
More than half of the publishers listed alongside IntechOpen (18 out of 30) are Social Science and Humanities publishers. IntechOpen is an exception to this as a leader in not only Open Access content but Open Access content across all scientific disciplines, including Physical Sciences, Engineering and Technology, Health Sciences, Life Science, and Social Sciences and Humanities.
\\n\\nOur breakdown of titles published demonstrates this with 47% PET, 31% HS, 18% LS, and 4% SSH books published.
\\n\\n“Even though ItechOpen has shown the potential of sci-tech books using an OA approach,” other publishers “have shown little interest in OA books.”
\\n\\nAdditionally, each book published by IntechOpen contains original content and research findings.
\\n\\nWe are honored to be among such prestigious publishers and we hope to continue to spearhead that growth in our quest to promote Open Access as a true pioneer in OA book publishing.
\\n\\n\\n\\n
\\n"}]',published:!0,mainMedia:null},components:[{type:"htmlEditorComponent",content:'
Simba Information has released its Open Access Book Publishing 2020 - 2024 report and has again identified IntechOpen as the world’s largest Open Access book publisher by title count.
\n\nSimba Information is a leading provider for market intelligence and forecasts in the media and publishing industry. The report, published every year, provides an overview and financial outlook for the global professional e-book publishing market.
\n\nIntechOpen, De Gruyter, and Frontiers are the largest OA book publishers by title count, with IntechOpen coming in at first place with 5,101 OA books published, a good 1,782 titles ahead of the nearest competitor.
\n\nSince the first Open Access Book Publishing report published in 2016, IntechOpen has held the top stop each year.
\n\n\n\nMore than half of the publishers listed alongside IntechOpen (18 out of 30) are Social Science and Humanities publishers. IntechOpen is an exception to this as a leader in not only Open Access content but Open Access content across all scientific disciplines, including Physical Sciences, Engineering and Technology, Health Sciences, Life Science, and Social Sciences and Humanities.
\n\nOur breakdown of titles published demonstrates this with 47% PET, 31% HS, 18% LS, and 4% SSH books published.
\n\n“Even though ItechOpen has shown the potential of sci-tech books using an OA approach,” other publishers “have shown little interest in OA books.”
\n\nAdditionally, each book published by IntechOpen contains original content and research findings.
\n\nWe are honored to be among such prestigious publishers and we hope to continue to spearhead that growth in our quest to promote Open Access as a true pioneer in OA book publishing.
\n\n\n\n
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The number and cost of natural disasters related to climate have increased significantly in recent decades. Goklany [1] elaborated a study of the annual average number of climate-related extreme events, recorded per decade, during 1900–2008. He found a highly remarkable growth since the middle of the twentieth century; while 23 weather extreme disasters were observed in 1950–1959, and 354 were recorded only for the first decade of twenty-first century, until 2008.
According to Hoeppe [2], the number of events that caused human or material losses triplicated from 300 in 1980 to 900 in 2014. In fact, in the 2018 Global Risks Report of the World Economic Forum, the risk of extreme weather events shows the most unfavorable combination of impact and probability of occurrence, which poses the greatest global risk to humanity nowadays [3].
Heat wave events have become a major concern, mainly due to their effects on human health. According to the World Meteorological Organization (WMO), the number of casualties due to extreme temperature grew 2300% between 1991 and 2000 and 2001–2010 [4] being, by far, the greatest growing rate among climate-related disasters. This pervasive trend of heat wave events keeps going. For instance, the Centre for Research on Epidemiology of Disasters (CRED) have reported that 4 of the 10 deadliest disasters in 2015 were heat wave events [5] in agreement with one of the key findings of the Intergovernmental Panel for Climate Change (IPCC) Fifth Assessment Report saying that “changes in many extreme weather and climate events have been observed since about 1950. Some of these changes have been linked to human influences, including a decrease in cold temperature extremes, an increase in warm temperature extremes …” [6].
In the last few decades, records of particularly dangerous heat wave events causing several deaths, even in developed countries with good public health services, have been reported. For example, the heat wave at Chicago in July, 1995 causing 514 heat-related deaths [7], the extreme heat of 2003 in Europe, affecting mainly France, with almost 15,000 deaths [8] and the one at the Russian Federation in 2010, which caused 55,736 deaths [9].
Climate change is expected to increase the average temperature as well as the probability of extreme climate, including heat wave events [10, 11, 12]. Moreover, the best climate data estimates coming from the general circulation models are for average temperatures, making uncertainty in extreme temperatures and heat wave events much greater [13]. Predictions of temperature increases are based on results from the general circulation models. In this sense, the IPCC has estimated the performance of these models by comparing their results with climate parameters observed during 1980–1999. For temperature, once the multimodel results (the average of 23 general circulation models) have been analyzed, the estimated error (the difference between observed and modeled data) is rarely higher than 2°C, even though the individual models may show errors of up to 3°C [14]. Nevertheless, the IPCC has pointed out that large-scale trends are simulated with higher accuracy than regional ones. Therefore, the analysis of the vulnerability to and effects of climate change, at local or regional levels, must be based on observed evidence. Hence, local trend analysis is crucial for the analysis of heat wave events. Another reason, probably more important, for local analysis of heat wave events and extreme temperatures is that its effects on human health depend to a large extent on the level of acclimatization of the local population, which includes not only physiological aspects, but others like practices of prevention, transportation patterns, age and/or type of constructed environment (i.e. [8, 15]).
Heat wave events have not been sufficiently considered by most governments, while more attention and resources paid to other phenomena with greater public visibility, such as floods or severe storms, for example. Usually, the population is also not fully aware of the risk posed by extreme heat events. Therefore, any effort that spread heat wave events relevance, helps to mobilize governments, social organizations and individuals towards better prevention and a greater aware is of great importance; before, during and after a heat wave event.
A heat wave is a period, usually lasting several days, with temperatures significantly higher than average or maximum values observed in the past during the same dates. The importance of extreme temperatures, and hence of heat wave events, becomes more significant when it affects human health. However, because the health-related effects of temperature depend on local factors—such as population acclimatization, age or health precondition—and other meteorological variables—such as humidity and wind speed—dozens of indices to determine the risk threshold of a heat wave have been reported. For instance, Smith et al. [16] listed 16 indices used in the United States.
A group of indices suggests the use of apparent temperature, which is calculated as a function of the observed temperature and relative humidity. The best known of them has been recommended by the US National Oceanographic Administration (NOAA), where the apparent heat index is calculated with the following equation:
where Hi is the apparent temperature, R is the relative humidity and T is the ambient temperature (°F). A Hi of 124°F or higher is considered very dangerous.
The main drawback of the heat index is related with the lack of access to relative humidity information. Recently, an extensive analysis to compare different heat indices with the registered health effects was carried out [17]. These authors found that simple indices based solely on temperature may be the most applicable for use in alert systems, but that all the corresponding temperature thresholds should be considered for regional analysis. Another group of threshold criteria establishes an absolute boundary to declare a heat wave, which is a specific value of the maximum temperature. So, for example, in the United Kingdom, the Met Office National Severe Weather Service (NSWSS) establishes a regional system with a threshold of maximum day and night temperatures, as shown in Table 1. In Mexico, a threshold for heat wave events in Mexico City has been proposed [18] as a maximum observed temperature greater than or equal to 30°C for three or more consecutive days, with an observed average temperature of 24°C. These authors suggested that, to determine the absolute temperature limits used in these criteria, it is necessary to identify the temperature where the mortality increases over average values and can be attributed to heat waves.
NSWWS Region | Temperature, °C | |
---|---|---|
Day | Night | |
London | 32 | 18 |
South East | 31 | 16 |
South West | 30 | 15 |
Eastern | 30 | 15 |
West Midlands | 30 | 15 |
East Midlands | 30 | 15 |
North West | 30 | 15 |
Yorkshire and Humber | 29 | 15 |
North East | 28 | 15 |
Threshold maximum day and night temperatures defined by NSWSS [19].
A second group of thresholds is defined by statistical analysis, usually selecting some temperature value percentile as the upper boundary. Some indices use the average daily temperature as reference temperature, while others—most of them—use maximum temperature values. As for extreme temperature events duration, some indices consider a heat wave when temperature exceeds the threshold limit during two or more days, while others estimate a heat wave occurs when the limit is exceeded in a single day.
The WMO Task Team on Definition of Extreme Weather and Climate Events encourages the report of the number of days in a month above percentile 90th [20]. In France, after the heat wave in 2003, a warning system was defined with an index based on the maximum and minimum temperatures, on average of 3 days, applicable to 96 cities of that country, based on studies in 14 pilot cities. Limits were determined by studying excess mortality caused by extreme heat, and the 98th percentile temperature value was used in the 2004 warning system. Subsequently, in 2005, the limits were updated for some cities, based on a more detailed analysis, and adopting the 99.5th temperature value percentile as the threshold. The resulting limits rank from 18 to 36°C for Lille to 21 to 36°C for Toulouse [21, 22, 23].
The methodology used in the warning systems of the United Kingdom and France, based on local studies—by city or region—for the relationship between temperature and excess mortality is interesting. Nevertheless, mortality information may not be available or the costs of these studies may not be suitable to many communities, and protecting the population through warning systems remains necessary. In these cases, it is advisable to use a boundary based only on maximum temperatures, with a conservative threshold used in national or international experience, for example, the 90th percentile, while the detailed studies are conducted.
It is worth to note that differences between definitions of heat wave events used in different countries represent a limitation to elaborate comparative analysis on the phenomenon between different regions of the planet.
Extreme heat and effects on the population’s health (higher morbidity and mortality) have been consistently reported in the last few decades [24, 25, 26]. As climate change effects become more significant, understanding and taking actions that may help to prevent heat-related health effects in the exposed population is an imperative topic that worth effort [27] in order to identify major trends, and vulnerable subpopulations since the frequency of these type of events is expected to increase in the near future [28, 29]. Some regions are expected to experience more intense, frequent and long-lasting extreme heat events by the second half of the twenty-first century with the corresponding health consequences.
While the scientific community remains discussing the best definition of a heat wave, such extreme weather-related events have continuously produced notable impact on human mortality, regional economies and ecosystems [29]. When the consequences of climate change are considered, warmer climate is expected to generate more intense, longer lasting and frequent heat waves contributing not only to human mortality but causing economic impacts, inconvenience and discomfort. The threat posed by extreme heat in a society not properly prepared to respond to weather-related events is significant considering that heat waves are 3 of the 10 worst natural disaster-related to the number of deaths recorded in 2013 [30].
Climate change sensitive areas are of particular concern mainly related with the effect of heat wave episodes on most vulnerable population subgroups such as children, the elderly, outdoor workers or inhabitants of urban areas [31]. Extreme heat events already account for the highest number of annual fatalities in the United States, and the annual number of heat waves has been found increasing for the average US city with increased length and intensity. Just to mention an example, Southwestern United States is a region of particular interest because it is predicted with increased average temperature and probability of extreme climate events, particularly in the second half of this century, based on results from general circulation models [32]. Among the different consequences, effects on population resulting of exposure to extreme heat range from dehydration, heat exhaustion, deadly heatstroke, kidney problems, lethargy and death; along with other side effects such as poor work attendance and/or performance among the exposed population.
In general, according to IPCC, regions in the middle and high latitudes will experience the highest temperature increases in the course of this century with a mean global temperature expected up to 4°C [6], based on the results of general circulation models. They have noticed, however, that large-scale features of climate are simulated with more accuracy that regional- and small-scale features, identifying the need of improved models capable of considering not only that changes in extreme events are not evenly distributed in space but characterized by particular patterns related to larger scale climate changes. Several of these places are projected also having the largest increase in population over the next decade and a significant increase in the frequency, last and intensity of extreme heat events [31] putting people and businesses at risk to the impacts of climate-related hazards. Figure 1 shows the rising heat wave trends in the large US cities as an example of the overall tendency related with heat waves all around the world [32].
Decadal average trend for different heat wave characteristics across 50 US cities [32].
As shown, intensity on heat waves trends to increase as the frequency and heat wave season from the 1960s to 2000s, and only a slight decrease was identified for the duration of every heat wave event in the same period. The authors found the magnitude of the chance of the first three characteristics being rising by over 5% with the frequency of the heat wave increasing 20% per decade, generating undesirable effects on the population.
High body temperature is usually related with increase in heart and respiratory rate and, when reaching extreme values, also with brain, heart, lungs, kidney and liver damage. All these body responses to extreme heat can hardly be ignored, generating people to visit emergency rooms at hospitals and a significant amount of deaths every year. For example, only in the United States, more than 7400 people died from heat-related causes during 1999–2010 and over 65,000 visited emergency rooms each year [33, 34].
Table 2 shows the count of heat-health events accounted in California, USA between 2000 and 2009, including the excess hospitalization count and quantile. As shown, the count of excess hospitalization occurring statewide ranks as high as the frequency of heat wave event increases probably due to the exhaustion generated for the extreme heat events in the population organism [28].
Year | Event span | Peak date | Duration, days | Excess hospitalizations |
---|---|---|---|---|
2000 | May 18–24 | May 21 | 7 | 217 |
Jun 12–16 | June 14 | 5 | 299 | |
Sept 5–9 | Sept 7 | 5 | 700 | |
2001 | May 2–11 | May 8 | 10 | 959 |
May 29–Jun 1 | May 31 | 4 | 460 | |
2002 | Jun 7–13 | Jul 9 | 7 | 848 |
2003 | May 19–22 | May 20 | 4 | 845 |
May 27–29 | May 28 | 3 | 454 | |
Jun 24–30 | Jun 27 | 7 | 717 | |
Jul 8–22 | Jul 14 | 15 | 1063 | |
Sept 10–15 | Sept 13 | 6 | 629 | |
Sept 17–23 | Sept 22 | 7 | 839 | |
2004 | Sept 1–17 | Sept 7 | 17 | 1657 |
2006 | Jul 7–9 | Jul 8 | 3 | 367 |
Jul 13–26 | Jul 23 | 14 | 1254 | |
2007 | May 6–9 | May 7 | 4 | 327 |
2008 | May 13–18 | May 16 | 6 | 903 |
2009 | May 15–18 | May 17 | 4 | 160 |
Aug 7–17 | Aug 10 | 11 | 228 |
Heat-health events in California, US in 2000–2009 (taken from [28]).
Nevertheless, the trend in increasing the number of people suffering of heat-related effects is not privative of a specific location as suggested recently in a work where the temporal variation in heat-mortality associations in multi-country data set was assessed [35]. These authors found a significant prevalence of heat-related death in the studied period for the seven countries included in their study as shown in Table 3. Nevertheless, they found a decrease in the heat-related mortality risk in most of the countries analyzed and, in some cases, completely abated.
Country | Total deaths | Period | Summer temperature, °C | ||
---|---|---|---|---|---|
Minimum | Median | Maximum | |||
Australia | 361,135 | 1988–1998 | 14.5 | 21.6 | 32.2 |
1999–2009 | 14.8 | 22.1 | 32.7 | ||
Canada | 679,693 | 1986–1998 | 3.9 | 17.4 | 28.0 |
1999–2011 | 4.4 | 17.7 | 28.5 | ||
Japan | 8,117,084 | 1985–1998 | 14.9 | 24.3 | 31.7 |
199–2012 | 15.3 | 25.1 | 31.6 | ||
South Korea | 530,618 | 1992–2001 | 14.9 | 23.7 | 31.8 |
2002–2010 | 14.5 | 23.6 | 30.5 | ||
Spain | 1,050,433 | 1990–2000 | 12.4 | 22.2 | 31.1 |
2001–2010 | 13.3 | 22.6 | 31.5 | ||
UK | 2,285,519 | 1993–1999 | 8.5 | 15.3 | 24.3 |
2000–2006 | 11.1 | 23.6 | 31.8 | ||
USA | 6,994,609 | 1985–1995 | 11.1 | 23.6 | 31.8 |
1996–2006 | 11.7 | 23.6 | 31.9 |
Heat-related deaths in seven countries during the last two decades (taken from [35]).
Despite the controversial results, it is clear that exposure to extreme heat poses a significant threat to human health, mainly when pre-existing health conditions are present. The lack of information on the source of the risk or the physiologic response generated by extreme heat in the different individuals has been identified as a major gap in knowledge that worth to be filled [36] as well as the considerations for the proper diagnose of the heat-related effects on health.
The maximum temperature has shown a growing trend in recent decades. WMO has reported a steady increase in the number of countries reporting new maximum temperature records. As shown in Figure 2, while less than 10% of the countries reported new temperature records in 1961–1970, 44% (56 countries) reported new maximum temperature in 2001–2010 [4].
Absolute country records of the daily maximum temperature [4].
Most climate change publications have focused on average temperature values. However, a slight increase in the average temperature means, in a changing climate, a greater increase in maximum temperature values. Moreover, it can happen that average temperature does not change or even decrease while the maximum temperatures increase significantly. The explanation of this phenomenon is found in the possible changes in temperature distribution of probabilities, as indicated by the IPCC (see Figure 3). Changes in the average temperature can be produced, with a shift in the probability distribution toward areas of higher temperature (Figure 3a). An increase in variability may also occur, with more extreme temperatures—both, cold and heat (Figure 3b)—or changes in the distribution symmetry with decrease in average temperature and increase in temperature at both extremes (Figure 3c).
Effect of changes in temperature distribution extremes [37].
To illustrate this phenomenon, Figure 4 shows the observed variation of minimum, medium and maximum temperatures in Mexicali (Northwestern Mexico) where an analysis on average temperature would indicate that the climatic change direction is to decrease, although in fact, it warms up and is increasingly higher for both diurnal and nocturnal values. Because minimum temperatures increase faster than maximum temperatures, average temperature value seems to decrease. In this case, changes in the symmetry of distribution probabilities are assumed. Therefore, the study of heat wave trends should be based mainly on maximum and minimum temperature analysis rather than average temperature values.
Maximum, average and minimum temperature trends observed in Mexicali, Mexico.
The IPCC special report on the risk of extreme events clearly states that “is very likely that there has been an overall decrease in the number of cold days and nights, and an overall increase in the number of warm days and nights” [37]. On the other hand, the magnitude of the change in the extreme temperatures depends on the region. There is sufficient evidence of significant increases in maximum temperatures, day and night, in North America, Europe and Australia. For the rest of the regions, it is necessary to consider the lack of data and detailed studies before a conclusion can be stated.
In the United States, a detailed study showed that in most of the continental territory, there is a positive trend in temperature for 1979–2011 with a level of significance greater than 95% in the Mann Kendall test [16]. These authors indicate that the regions of the United Sates with the greatest positive tendency are found in the Southeast and the Great Plains. These results are complemented by a study in the Southeastern United States and Northwest Mexico—the Sonoran and Mojave Desert—where a positive trend, using the Spearman’s Rho test, during 1960–2010 in maximum temperatures of Tucson and Phoenix in the United States and Obregon City and Mexicali in Mexico was found [31]. As shown in Figure 5, there is a clear trend of increase in maximum temperatures throughout the region, clearly seen by comparing against average temperature over the analyzed time interval. During 2005–2010, the maximum temperature in Ciudad Obregon was 2.96°C higher than 1961–1965, 2.76°C higher in Phoenix, 2.52°C higher in Tucson, 2.3°C higher in Morelos Dam, 2.06°C higher in Mexicali and 1.34°C higher in Las Vegas. The number of days that exceeds the 90th maximum temperature percentile were found also growing fast, as shown in Figure 6.
Maximum monthly temperatures and linear trends for the Sonoran Mojave Desert region for the month August [31].
Number of days that exceeded the 90th percentile of average maximum temperatures in August, in the Sonoran Mojave Desert region [31].
Europe has been subject to exceptional heat wave events. The 2003 heat wave in Europe, widely documented, caused more than 33,000 deaths, and extended with record temperatures in the range of 35 to >40°C, from Portugal and England to Turkey [38]. France, the most affected country, reported more than 14,000 deaths [8]. During the 2010 heat wave in the Russian Federation, which caused more than 55,000 deaths, the temperature anomaly in Moscow was as high of +18°C in July [39]. In the United Kingdom, a definite trend in the increase of moderate heat wave events, both in number and duration, while the trends for extreme heat wave events are still unclear [40].
Australia is one of the countries that suffers most due to heat wave events. A report from the Australian Climate Council has found a notable increase in the magnitude, duration and frequency of heat waves. It has been found, for example, that in the last 50 years, the number of hot days in Australia has doubled and that “in the last hundred years, heat waves have caused more deaths than any other natural risk” [41]. Trend analyzes conducted so far show an increase in heat wave events’ intensity, duration and frequency [42].
There are reports from many other regions of the world, which report the same growing trends in the number and duration of heat waves [43, 44, 45]. However, it is essential to emphasize the remaining lack of information and analysis in large areas, for example, at Central America, South America and Africa.
The trends observed in extreme heat and heat wave events, unfortunately, are in the direction predicted by climate change scenarios. Indeed, the Fifth Report of the IPCC indicates that during the twenty-first century, “it is very likely that heat waves will occur more often and last longer” [6]. The special report on extreme events of the IPCC is even more conclusive, and states that “it is virtually certain that increases in the frequency and magnitude of warm daily temperature extremes and decreases in cold extremes will occur in the twenty-first century at global scale” [37]. The magnitude of these events at the regional level, however, will depend on local variables such as elevation, synoptic effects, the occurrence of anticyclones, and so on, which is why more precise modeling at smaller scales is essential as well as their calibration according to the changes will develop over time.
For Europe, the results of a simulation model for the twenty-first century, with a mesh resolution of 50 km were recently published [46]. The report shows a highly significant increase in the number of days with heat wave events per year, the maximum average temperature and the frequency. From the trends in their report, the number of heat wave events per year will increase approximately, during 2000–2100, from 1 to 2.5 in Western Russia, from 10 to 27 in Eastern Europe and from 1 to almost 2.5 in Western Europe. The mean maximum temperature, in the same period, will grow from 19 to >24°C in Western Russia, from 20 to almost 26°C in Eastern Europe and from around 18 to >24°C in Western Europe [46].
Forzieri et al. [47] analyzed the impacts on the European population due to climate change, in a period of 30 years. With respect to heat waves, Table 4 presents their results of the changes in exposed population and expected deaths during 1981–2000 and 2071–2100.
Northern Europe | Eastern Europe | Central Europe | Western Europe | Southern Europe | ||||||
---|---|---|---|---|---|---|---|---|---|---|
1981–2000 | 2071–2100 | 1981–2000 | 2071–2100 | 1981–2000 | 2071–2100 | 1981–2000 | 2071–2100 | 1981–2000 | 2071–2100 | |
Deaths /107 inhabitants | 0.98 | 27.24 | 4.81 | 177.6 | 61.51 | 2305.77 | 34.33 | 1022.67 | 105.77 | 7019.99 |
Exposed population /106 inhabitants | 9618 | 269,167 | 9989 | 341,025 | 9932 | 322,309 | 9993 | 298,558 | 9828 | 634,474 |
Prognosis of people affected and deaths in Europe by heat waves (with data from [47]).
For the United States, Kunkel et al. [48] estimated, for heat wave events in summer, that the annual 3-day heat wave temperature increased by 3–8°C and the number of heat wave days from 30 to 60 days per year. The analysis for other regions and countries also coincides in forecasting a growth of heat waves during the twenty-first century. For example, the study by Meehl and Tebaldi [29] can be cited who consider North America and Europe; the report by Steffen et al. [41] for Australia; another by Dutta and Chrsiya [49], focused on the effects on health due to climate change in India; or the projections of extreme heat performed by Salinas-Prieto et al. [50] for Mexico, forecasting increases between 4.6 and 5.8°C in maximum temperatures for northern Mexico during 2075–2099, using the RCP8.5 Scenario. Even since there is a consensus on the increase in intensity, frequency and duration of heat wave events due to climate change, there is still a great uncertainty about its magnitude at regional scale, so the study of the observed trends should be continued, and carried out where the information or analysis capabilities have not allowed it until now.
It seems evident that extreme heat, resulting from climate change, poses a significant treat not only for people’s health, but also in general for achieving the Millennium Sustainable Development Goals proposed by the UN by being within the top dangerous climate-related disasters with the greatest gap in knowledge.
The lack of consensus on the definition and threshold values for heat wave events among the scientific community is beyond the academic work as it may become a life or death issue for vulnerable population segments or regions. As a result, the urge for generating more reliable information and used for decision making becomes with a higher significance as the time goes by.
The relationship among extreme heat during heat wave events and the amount of deaths or the increase of people visiting emergency room in hospitals is undeniable, but the scientific work reported nowadays does not suffice to figure out what consequences may occur in other regions where climate variability is barely known or completely unknown. The need for more studies is clear, and additional resources to be invested in such sensitive knowledge areas seem mandatory in order to avoid potential catastrophic scenarios in the forthcoming years.
Recently, the installation of renewable energy systems (RES), such as photovoltaic (PV) generators, has increased due to dedicated policies and even lower investment costs [1]. The increasing share of RES has introduced new challenges, which in future can affect the proper operation of the system: for example, the decrease of the system inertia faced by introducing new inverter controls [2]. At distribution system level, the large share of RES led researcher to consider new way to manage the system, by means of optimal reconfiguration procedure based on different methodology [3] and time periods [4]. The main drawback of RES is the intermittency of power production, which often results in a not well match between electric consumption and generation profiles [5, 6], with consequent voltage deviations and reverse power flow issues [7]. In order to reduce voltage deviations, it is possible to upgrade grid lines and transformers, but it is generally expensive. Otherwise, it is possible to reduce injection from renewable sources by increasing the supply of local loads. It can be done by load shifting, which consists of the switching on of home appliances, when PV generators are working. This procedure can be manual by using simple timed switches; for example, the user has to switch on the washing machine or the dishwasher at midday, when the production is maximum. To perform load shifting, in [8], it is developed an algorithm to predict the consumption based on hourly historical data using artificial neural networks (ANNs).
\nUsing electrochemical storage with PV generators is a good alternative to mitigate or eliminate power injection issues. Storage is easy to install and manage in any site; in the last years, the cost of storage decreases, but it is still expensive and it cannot solve the seasonal correlation between low loads and high RES production, and vice versa. For this reason, in case of domestic users, the best technical-economic solution is the use of a small battery system (BS) and the adoption of load shifting. This solution permits the reduction of absorption or injection peaks and the increase of self-sufficiency level, that is, the ratio between the local RES production used to supply loads and the total loads.
\nA battery management system (BMS) is a hardware/software solution which checks the correct operation of batteries: in its basic version, it simply charges the batteries, when they are empty, and discharges them when necessary. It limits battery operation only to protect them: the exceeds of minimum and maximum state-of-charge (SOC) limits and fast charge/discharge cycles are not permitted to avoid fast degradation [9]. An improvement in the BMS management consists of the forecast of load and PV generation profiles [10, 11]. In this case, it is necessary to have accurate information about production profiles, which are generally missing. In addition, the BMS has to continuously obtain accurate weather forecasts, which are not easily available. In [12], a modified control strategy for batteries based on peak shaving is proposed to reduce power fluctuations of production in a PV-storage system and obtain benefits in terms of electricity price. In [13], a more accurate BMS for a PV-storage system is developed: the proposed management strategy aims to shave consumptions peaks, taking into account degradation of batteries and aging limits of the storage. A real-time battery management algorithm is proposed in [14] to reduce the peak demand power and the daily energy cost in grid-connected PV-storage systems. In particular, the charge/discharge of the storage is controlled using instantaneous load data. Each day, 1-day ahead prediction of PV generation and load profiles is performed to decide the power limit beyond which the peak shaving strategy works. Finally, in [15], several control strategies of batteries are compared for a residential battery energy storage system (BESS) coupled with a PV generator. In particular, a base control strategy charges the battery when PV production exceeds local loads and starts to discharge the storage in the evening, when PV generation is negligible. It is compared to three optimized BMSs: the first one aims to maximize the economic benefits for the users or the self-sufficiency, while the second one includes utility constraints to lower overvoltage risks on distribution grid and the third is a distributed control.
\nIn the present chapter, positive aspects regarding the grid stability, i.e., frequency and voltage control [16, 17, 18], are not taken into account, and only the benefit for the users, consisting of the reduction of absorption peaks with a possible consequent reduction of contracted power, is investigated. In addition, load shifting is not considered, due to difficulties in convincing domestic users to change their habits. Indeed, a smart battery management system (SBMS), which works with raw forecasts of production and historical consumption data, is proposed: the goal of the control is to reduce the absorption peaks from the grid with minimum reduction in self-sufficiency and no load shifting. In particular, in case of high consumption and low production, a traditional BMS completely discharges the batteries and all the renewable energy is locally consumed. In the proposed SBMS, the storage will not be totally discharged and will not completely supply the loads. In fact, the storage discharge is limited to satisfy possible absorption peaks in a period up to few days. Nevertheless, if the storage is not discharged waiting for possible consumption peaks, it means that the baseload could not be satisfied with a consequent reduction of self-sufficiency. The self-sufficiency is calculated to check the effectiveness of the proposed SBMS: the domestic user has to keep high its self-sufficiency level, because it corresponds to an economic return. The benefit for the grid is not taken into account, but it exists: it consists of a reduction in peak absorption from the grid resulting in higher power quality, lower voltage dips, and reverse power flow issues [19, 20].
\nThe next sections of the chapter will be organized in the following way. In Section 2, the description of the system setup, the inputs for the simulation, and the models of the PV generator and the battery will be presented. In Section 3, the provisional energy balance and the storage management are described in detail. In Sections 4 and 5, the results of the simulations and the conclusions are discussed, respectively.
\nA scheme of a PV-storage residential system is presented in Figure 1. The main components of the power system are a PV generator, an electrochemical BMS, DC/DC and DC/AC power converters, AC loads, and the distribution grid. The PV modules are connected to a maximum power point tracker (MPPT) in order to work in the maximum power point in every irradiance and temperature condition [21]. The BMS measures DC current and voltage and temperature of batteries. The SOC is continuously calculated in order to estimate the residual charge of the storage; in this way, the BMS avoids an abnormal degradation of the batteries due to not optimal charging patterns, overcharging, undercharging, and abnormal temperatures. The DC/AC converter connects the PV system and the BESS to the AC side, i.e., local loads and the grid. Moreover, the device is Internet-connected and downloads raw weather forecast of 1-day ahead, compares provisional load and production profile, and adopts the best strategy to reduce consumption peaks.
\nThe PV-storage system under study.
The production of PV generators depends on installation conditions (location and tilt and azimuth of the PV modules) and on weather conditions (solar irradiance and temperature). The Photovoltaic Geographical Information System (PVGIS) [22] database is a free online tool; it permits to know the average daily irradiance and temperature profiles corresponding to each month of the year. The monthly profiles can be obtained for every location in Europe, Africa, and Asia starting from the definition of the location of the generator and the tilt and the azimuth of the PV modules. Additional parameters can be selected, such as the typology of solar radiation database and the calculation of irradiation profiles, also for tracking systems. In Figure 2, the home screen of PVGIS database is shown.
\nPVGIS website.
The PVGIS database provides a temperature profile and three irradiance profiles for each month. In particular, the irradiance profiles correspond to a clear sky day, an average day, and an overcast day. During the clear sky day, the global irradiation is maximum; in fact, it is mainly composed of the beam contribution, because no clouds are present. During the overcast day, the solar irradiation is minimum; in fact, in case of cloudy and rainy days, only the diffuse component of the solar irradiance is present. The average day is an intermediate situation: it is based on the average irradiance condition occurring in the month under consideration. In Figure 3, an example of the output profiles of the software is presented for January; the selected location is in Italy (Turin, 45.05° Nord, 7° 40\' Est) and the PV modules are installed with an inclination of 15° and West oriented (azimuth = 90°, where South = 0°). Data are provided with a time step of 15 min.
\nIrradiance and temperature profiles for January in Turin (Italy) from PVGIS database; PV modules have inclination of 15° and West orientation.
In the present chapter, it is supposed to install a single device including both the PV converter and the BMS; the BMS will be equipped with additional hardware and software capable of accessing Internet and download data from the PVGIS database. After the installation of the PV generator, during the setting up of the converter, the input parameters requested by PVGIS to estimate the irradiance and temperature profiles are inserted in the software of the device.
\nThe device accesses the PVGIS database and downloads and elaborates the three above-described irradiation profiles for each month. Starting from these data and the rated power of the PV generator, the power converter calculates a total of 36 PV production profiles by an appropriate photovoltaic model, which will be described in detail in Section 2.3. Finally, the power generation profiles are integrated over the entire day: the result is a list of daily energy productions for each month in three different weather conditions. Table 1 shows the daily energy production of a PV generator with rated power of 1 kWp installed as defined in Figure 3.
\nDaily energy production for each month in three different weather conditions.
Regarding the PV power simulation, the AC power production PAC is calculated according to the model described in [23]. The inputs of the model are solar irradiance G, ambient temperature Ta, and rated power of the PV generator PPV,r. The thermal losses and consequently the DC input power change, while the other sources of losses are considered constant:
\nwith
\nLosses due to temperature \n
NOCT is the normal operating cell temperature, generally provided by the manufacturer of the PV modules; in this work, it corresponds to a typical value NOCT = 45°C. GNOCT is the solar irradiance occurring at NOCT condition and it is 800 W/m2; the overtemperature losses ηtherm (with respect to TSTC = 25°C) are calculated by the formula:
\nLosses due to dirt ηdirt provide an average 2% of reduction in energy production for the deposit of dust and other materials on the glass of the modules. Thus, a typical value ηdirt = 0.98 is used in the present work [27, 28]. Note that in case of horizontal modules, the cleaning made by rain is reduced, and in case of emission of pollution close to the plants from special industrial processes [29], losses can be more than 7%. Losses due to reflection from the glass on the front of the PV module are inevitable losses due to a not ideal transparency of the glass; according to [30], they can be considered equal to ≈3% (ηrefl = 0.97). Mismatch losses are due to nonuniformity in I-V characteristics of modules connected in series or in parallel. Thus, the conversion unit imposes to the whole PV generator a working point not perfectly corresponding to the optimum. According to [31], a typical value of ηmis = 0.97 is used. Joule losses take into account dissipation of electrical energy into heat by Joule effect in the cables. During design phase, cables should be sized in order to keep Joule losses within 3% in nominal conditions [30]. Since the PV system operates at nominal conditions (maximum power) for a short period during the year, and in other conditions (partial load) losses are lower, Joule losses are estimated equal to an average value of 1% (ηcab l = 0.99) [30]. Losses for shadings are due to external causes, as a wrong design; thus, in the performed simulations, these losses are neglected (ηshad = 1). Regarding the accuracy of the maximum power point tracking (MPPT) system, it causes losses, because the optimum value is generally not perfectly tracked, especially at low power: on average, this loss can be estimated ≈1%. (ηMPPT = 0.99) [32]. Finally, the DC/AC conversion introduces losses, which are quadratically dependent on the power output. For the sake of simplicity, an average value of ηDC/AC = 0.97 is considered in the present work [33].
\nA correct model of the storage is fundamental to evaluate energy flows. Many electric models are present in literature and they permit to simulate operation of batteries with different pros and cons [34, 35, 36, 37]. The simplest model describes a battery by an equivalent voltage source in series with an internal resistance. The equivalent voltage can easily be determined by measuring the open circuit voltage of the battery, while the measurement of the internal resistance requires a further test performed during battery charge. Obviously, this model has a limited use, because the parameters are constant: the accumulator results in having an infinite capacity and there is no way to determine the SOC. An upgrade with respect to the basic model is obtained using an equivalent resistive-capacitive model [36]. The values of resistances and capacitances can be determined through impulsive test of the battery. The advantage of this model is that it permits to evaluate the charge and discharge transients with variable loads in time. However, the SOC dependence on the voltage, which has to be determined, requires careful preliminary measurements on the battery. Another possible model consists of the impedance model, where a voltage source is in series with a resistance and an inductance. An additional series impedance is used to represent the electrochemical characteristics of the battery. Nevertheless, the definition of this impedance is complicated; in fact, it can be obtained starting from an electrochemical impedance spectroscopy to obtain an equivalent impedance in the frequency domain. In addition, the impedance has to be characterized varying the state of charge and the temperature [35].
\nThe most sophisticated models [38, 39] are developed to calculate also the state of health (SOH), which is a parameter useful to evaluate how the charge-discharge profiles affect the storage life and when the batteries have to be replaced. In fact, PV production is intermittent; thus, PV generators cannot guarantee the optimal charge-discharge cycles to have the longest possible life of storage and the highest efficiency. For example, the real-time model described in [39] is a blend of the previous battery models whose particular combination of components and dependencies eases the estimation of the equivalent parameters. In conclusion, this model permits to calculate the SOC, the SOH, and then the residual life. This information permits the evaluation of the economic investment of electrochemical storage system [40], taking in consideration the battery management. Nevertheless, the models that permit to estimate the SOH require a continuous measurement of battery parameters (i.e., voltage, current, and temperature of the batteries) [37]. For this reason, the calculation of the SOH cannot be performed with only simulations, but a real system with continuous measurements is required.
\nThe energy model is used in the simulations presented in this chapter, because it permits to simulate the SOC with a good approximation (a few percent points) without measurements and with a low computation effort (only the formulas (5) and (6) are used). The energy model permits to estimate the state of charge of batteries; i.e., how much energy is stored or can be stored in a battery with rated energy capacity Cbat, by the comparison with the limits imposed to preserve life of batteries. The calculation of the SOC(t) at the instant t is a function of the state of charge SOC(t − 1) at the previous time step, of the power exchanged Pbat during the time step Δt (in this chapter, Δt = 1 min) and of the charge efficiency ηbat. During the charge phase, the batteries behave as a generator (Pbat > 0) and it is considered a charge efficiency ηbat = 0.88; during discharge (Pbat < 0), efficiency is considered unitary.
\nThe proposed BMS periodically defines the strategy to minimize the power absorption from the grid. The strategy selection is performed two times per day to better match the consumption peaks of domestic users, which occur early in the morning and during the evening. Thus, the day is divided in three time slots. The first time slot starts at midnight and ends at 6:00 a.m. Between 6:00 a.m. and 6:00 p.m., there is the second time slot: the production is dominant and in case of people at home, part of generation is self-consumed. In this period, the consumption peak in the morning due to preparation to work and school activity (such as hairdryers, electric boiler, etc.) is included. Obviously, this peak cannot be totally satisfied by PV production, especially in winter. The third time slot starts at 6:00 p.m. and finishes at midnight, when the second consumption peak occurs, and PV production is low or negligible.
\nThe time 6:00 p.m. is selected for the download of raw weather forecasts for the next 24 h, for the calculation of provisional energy balance and the update of management strategy for batteries. In fact, at 6:00 p.m., the PV production is almost over: the BESS can accurately calculate the quantity of stored energy, which will be available for the next hours. In fact, during evening and night, the batteries will not be charged: supply from the grid is not considered.
\nThe provisional energy balance for 1-day ahead is performed comparing estimated energy production and consumption. Regarding the energy consumption, this value is calculated on the basis of measurement of local consumption profiles. Loads are monitored, and average values of energy consumption are calculated for each of the three time slots composing the day, as described in the previous paragraph. In addition, a distinction of average energy consumption between working days and holidays is considered.
\nRegarding the provisional production, every day at 6:00 p.m., the converter downloads raw weather forecasts for the next 24 h. Data are collected from commercial web services: they generally identify weather forecast with simplified symbols, i.e., showing a sun symbol for a clear sky day and lightning for rain. For the sake of simplicity, in the present work, it is considered a three-level forecast: a clear sky day, an average day with few clouds, and a cloudy/rainy day. These levels correspond to the three irradiance conditions provided by the database PVGIS. In this way, it is defined a raw correlation between the weather forecast and the expected production from the PV generator. The advantage consists of a free and easily accessible daily forecast of production, which can be used for free by the Internet-connected BESS to select the best battery management.
\nThe first step in the smart management of batteries consists of the definition of the total discharge time (TDT): Figure 4 shows the flowchart of the procedure. First, at 6:00 p.m., after weather forecast download, the provisional balance between expected production EPV_1day-ahead and loads Eloads_1day-ahead, 6 a.m.-6 p.m. occurring in the time slot 6:00 a.m.–6:00 p.m. of the day ahead is performed.
\nDefinition of the total discharge time (TDT).
In case of PV energy production higher than loads EPV_1day-ahead > Eloads_1day-ahead, 6 a.m.–6 p.m., a management of the storage to satisfy loads until 1-day ahead at 6:00 a.m. is performed. In this case, the TDT will be equal to 12 h. In fact, the day after, during light hours, energy will be self-consumed, and the surplus of PV production will charge the storage or will be injected into the grid. Vice versa, in case of low production and high loads EPV_1day-ahead < Eloads_1day-ahead, 6 a.m.–6 p.m., an SBMS is necessary not only for 1-day head but also for the day after. In this case, the PV production cannot satisfy local loads and storage has to be able to reduce loads for two nights, and the TDT will be equal to 36 h.
\nBatteries are expensive [40], and considering a storage with a too high capacity is not cost-effective for a grid-connected plant. For this reason, in the present work, the BESS can distribute the stored energy in a maximum TDT = 36 h. It means that storage must be able to supply the load when a single cloudy day occurs (2 nights and 1 day).
\nFigure 5 shows an example of PV and load profiles for 2 days: in the first day, the PV production is low, while the second one is a clear sky day. At 6:00 p.m. of day #1, the procedure starts with the converter downloading forecast for day #2: supposing a correct forecast, the result is a provisional high PV production. Thus, the BESS will manage the discharge of the storage from the evening of day #1 at 6:00 p.m. to the morning of day #2 at 6:00 a.m. (12 h). After 6:00 a.m. of day #2, storage and loads will again be mainly supplied by the PV production.
\nExample of PV and load profiles for 2 days.
The second case is shown in Figure 6. It presents an example of PV and load profiles for 3 days: in the first and second days, the real PV production is low, while the third one is a clear sky day. At 6:00 p.m. of day #1, the converter downloads forecast for day #2: supposing a correct forecast, the result is a provisional low PV production. Thus, the BESS will manage the discharge of the storage until the morning of day #3 (a total of 36 h, from 18 to 54 h in Figure 6).
\nExample of PV and load profiles for 3 days.
After the definition of the total discharge time (TDT), the procedure continues with the second part; i.e., the definition of the storage management strategy. The SOC is calculated at 6:00 p.m. by the BESS, which uses appropriate models starting from the real-time measurement of voltage and ambient temperature of batteries, as described in Section 2.1. The rated capacity of the storage and the SOC permit to calculate the energy that can be provided to the loads Ebatt,disch. The estimated energy production EPV_1day-ahead is the same quantity used in the previous step, while the consumption Eload,TDT corresponds to the estimated loads during the TDT (Figure 7). These raw energy quantities are compared and it is defined if there is an energy deficit EPV_1day-ahead + Ebatt,disch ≥ Eload,TDT or surplus EPV_1day-ahead + Ebatt,disch < Eload,TDT.
\nDefinition of storage management strategy.
If the PV production and the storage can satisfy the load EPV_1day-ahead + Ebatt,disch ≥ Eload,TDT in the selected TDT, no advanced management of the batteries is required (BMS Strategy #1).
\nOn the contrary, if loads are too high EPV_1day-ahead + Ebatt,disch < Eload,TDT, peak shaving strategy (BMS Strategy #2) or appropriate discharge profiles (BMS Strategy #3) are adopted. To select the most appropriate method between BMS Strategy #2 and BMS Strategy #3, a provisional self-sufficiency Rsuff parameter, that is, the ratio between the provisional PV production plus the available energy from the battery, and the provisional local loads, is calculated:
\nWhen the ratio Rsuff is lower than a user-defined threshold Rthres, the BMS Strategy #2 is adopted: the local generators and the storage will provide a low quantity of energy to the loads, which will be mainly supplied by the grid. It can result in high absorption peaks. In this case, the low energy quantity stored in the batteries will be used only when loads exceed a maximum limit Pload,max, such as the contracted power absorption limit or another user-defined threshold. The BMS Strategy #3 is adopted when the ratio Rsuff is higher than the user-defined threshold Rthres and lower than unit value. This case is better than the previous one, because great part of loads will be supplied by PV and storage and the quote from the grid is low.
\nThe storage management strategies consist of peak shaving and of a time-dependent discharge profile. According to the procedure described in the previous subsection, when the storage energy is much lower than loads, only the peak shaving technique is adopted (BMS Strategy #2). Thus, batteries are discharged only when strictly necessary, i.e., when a load peak occurs. In particular, storage will be discharged only by the quota exceeding an user-defined limit Pload,max.
\nIn the other case, if the energy stored in the batteries is slightly lower than loads, the charge is used both for baseload supply and peak shaving (BMS Strategy #3). Nevertheless, the exact time schedule of loads is not predictable and it is not possible to know when the load peaks will occur. In the worst case, storage will be discharged soon in the evening, while the peak will be in the next early morning, when batteries are already empty. For this reason, the SBMS limits the discharge of batteries during time with the definition of different levels of minimum SOCmin,x for an user-defined number of time slots x, in which the TDT is divided. According to the procedure proposed in Section 3.2, in case of TDT = 12 h, the number of time slots x = 2, otherwise with TDT = 36 h, the time slots are 5 (x = 5). The SOCmin,x limits are defined in order to distribute the stored energy proportionally to the provisional energy consumption. Thus, SOCmin,x limits are calculated starting from the SOC of the storage, measured in real time by the BMS, and the provisional energy consumptions:
\nwhere Eload, slot x is the provisional energy that will be required by loads in the time slot x. For example, let us suppose that the TDT is 12 h and the overall required load will be 10 kWh. In particular, during the evening (from 6:00 p.m. to midnight), the required load will be 4 kWh, and during the next night (from midnight to 6:00 a.m.), the load will be 6 kWh. The stored energy will be discharged as follows: 40% during the evening and 60% during the night. In this example, the storage is considered initially full and with a minimum SOCmin,safety = 0.2.
\nFigure 8 shows an example of load and SOC profiles in case of a basic battery management. In this case, the storage is charged when PV production is higher than loads and batteries are empty; on the contrary, storage is discharged if PV production is lower than loads [23]. The only limitation in charge/discharge is performed to avoid fast degradation of batteries, by limiting the SOC in a safety range SOCmin,safety < SOC < SOCmax,safety. For sake of simplicity, it is considered a rainy day and the production from the PV generator is negligible. In case of lithium batteries (Figure 8), the minimum level SOCmin,safety generally corresponds to SOCsafety ≈ 20%, while in case of lead-acid batteries, it can reach 50% [41, 42]. In the example of Figure 8, the storage supplies the loads until 10:50 a.m., when the SOCmin,safety is reached. After that, only the grid supplies the load and the highest absorption peak is not limited ≈2.9 kW.
\nExample of load and SOC profiles in case of basic BMS.
Figure 9 shows an example of load and SOC profiles in case of the SBMS, which reduces the absorption peaks from the grid. In this case, the SOC cannot drop down under a temporary minimum SOCmin,a = 75% before midnight; then, the discharge is limited by SOCmin,b = 70% between midnight and 06:00 a.m. Between 06:00 a.m. and 06:00 p.m., the minimum admitted SOCmin,c is 37%. Then, between 06:00 p.m. and midnight, the limit SOCmin,d = 22%. Finally, after 06:00 p.m., the last limit corresponds to the same level of the basic management SOCsafety ≈ 20%, which is a typical value to preserve life of lithium batteries. The main difference from the basic management consists of a small reserve in storage, which is always present, and the absorption peaks are always reduced. On the other hand, preserving the storage partially charged could reduce the self-sufficiency. The best solution consists of the abovementioned SOC levels selected to reduce absorption peak and keep as high as possible the self-sufficiency level.
\nExample of load and SOC profiles in case of the proposed SBMS.
Simulations of the PV-storage system are performed for the entire month of December with a 1-min time step for both basic and proposed BMS to compare their performance. During winter, the PV production is low, batteries are often empty, and the development of an efficient BMS is necessary to reduce the absorption peaks from the grid. On the contrary, in summer, PV generation generally charges storage and directly supplies part of the loads.
\nThe optimal management of the storage is investigated in case of different sizes of the PV system PPV,r and different capacities of the battery Cbat. Regarding PPV,r, it ranges between 2 and 6 kWp with a step of 1 kW, while the storage capacity Cbat is in the range 1–5 kWh (step of 1 kWh). The management parameters are the power value Pload,max beyond which the peak shaving strategy works and the threshold Rthres. The power limitation Pload,max ranges between 0.5 and 2 kW with a step of 0.5 kW, while the user-defined threshold Rthres varies between 50 and 80% (step of 10%). Regarding the loads, the measured consumption profile of a domestic user (a family composed of two persons) located in Northern Italy (45.05° Nord, 7° 40\' Est) is used. The annual consumption of the domestic user analyzed in the case study is ≈2800 kWh/year and its loads correspond to typical home appliances (e.g., hairdryer, oven, personal computer, lighting, and electric water heater).
\nThe results of the simulation show that the proposed BMS decreases the peaks of absorption from the grid with respect to a traditional management. The results are interesting especially in case of a small storage, while in case of higher storage capacity, there are negligible differences between the two managements. Figure 10 shows case #1: it corresponds to the analysis of 2 days of simulation for a PV system with PPV,r = 4 kW and a storage system with Cbat = 2 kWh. In the graphs, in case of battery discharge, the sign of the power supplied by the storage to the loads is negative. The 2 days are characterized by cloudy and rainy conditions, and the PV production is low. The proposed BMS calculates the provisional energy balance and a huge lack in storage is predicted; thus, the peak shaving method is used (BMS Strategy #2). Before 6:00 p.m., all the loads are supplied by PV and storage; then, peak shaving is applied and only the quota exceeding Pload,max = 2 kW is satisfied by batteries. The saved energy is then preserved and used to shave loads during the second day, with the result of keeping the absorption from the grid always ≤2 kW.
\nPower profiles for case #1 with the proposed SBMS.
On the contrary, if a standard BMS is used (Figure 11), all the stored energy is consumed before the end of the evening of the first day; furthermore, there is no energy from storage to supply the load peaks during the second day. The result is a maximum absorption peak of ≈4.2 kW: during these days, the proposed SBMS reduces the absorption peak of ≈50%.
\nPower profiles for case #1 with standard BMS.
Table 2 shows the energy balance of the case #1 related to Figures 10 and 11. With the proposed SBMS, the maximum power absorbed from the grid is half, while the deviations in terms of self-sufficiency and injected energy into the grid are negligible. Nevertheless, there is an increase in grid absorption: to guarantee power for peak shaving, a residual energy is kept in the storage, and at 6:00 p.m. of the second day SOC≈0.7.
\n\n | Proposed BMS | \nStandard BMS | \n
---|---|---|
Load (kWh) | \n11.45 | \n11.45 | \n
Self-consumption (kWh) | \n3.2 | \n3.2 | \n
Grid absorption (kWh) | \n5.5 | \n4.58 | \n
Grid injection (kWh) | \n0.52 | \n0.52 | \n
Self-sufficiency/load (%) | \n28 | \n28 | \n
Self-consumption/PV production (%) | \n43 | \n43 | \n
Grid injection/load (%) | \n4.5 | \n4.5 | \n
Pload,max (kW) | \n2 | \n4.22 | \n
Injection peak (kW) | \n−0.81 | \n−0.81 | \n
Energy results for case #1.
A second simulation is shown in Figure 12. The case #2 is characterized by two different days with respect to case #1: a negligible PV production occurs in both days, while the sizes of PV and storage systems and loads are the same of case #1. The provisional energy balance predicts that the energy in the storage will supply great part of the loads, but it will be not sufficient to supply them totally. The provisional self-sufficiency parameter is Rsuff > Rthres (with Rthres = 50%); thus, the converter selects the BMS Strategy #3. The most interesting part corresponds to the time window 6:00 a.m.–6:00 p.m. of the second day. Batteries start discharging at 6:00 a.m. and when peaks occur (10:00–12:00 a.m.), only the quota exceeding Pload,max = 2 kW is satisfied by batteries. In the same way, the other absorption peak occurring at 9:00 p.m. is shaved, thanks to the preserved energy in the storage. The maximum absorption peak is 2.4 kW.
\nPower profiles for case #2 with proposed SBMS.
On the contrary, in the same conditions, a traditional BMS would discharge the battery before 10:00 a.m. and the absorption peak would be 2.8 kW (higher than the proposed SBMS of ≈14%).
\nFinally, in Table 3, the above-described combination #A and other three combinations of PV and storage sizes, which permit to obtain significant improvements, are presented. The power and energy results of the proposed SBMS are compared to the standard BMS. In all the other cases, the improvement in terms of maximum absorption from the grid is confirmed, ranging from ~9 to ~10%. Regarding the maximum injection into the grid and the energy quantities, their deviations are negligible. The combination #A shows much better results, confirming that the performance of the proposed SBMS increases when the PV system size is high and when the storage is undersized. In addition, a low value of Rsuff permits to increase the use of peak shaving, without affecting the energy balance.
\nComb-inations | \nPPV,r (kW) | \nCbat (kWh) | \nPload,max (kW) | \nRthres (%) | \nStandard BMS Pmax,absorbed (kW) | \nProposed BMS Pmax,absorbed (kW) | \nImprove-ment (%) | \n
---|---|---|---|---|---|---|---|
#A | \n4 | \n2 | \n2 | \n50 | \n4.22 | \n2.56 | \n39 | \n
#B | \n2 | \n1 | \n2 | \n70 | \n4.42 | \n4 | \n9.5 | \n
#C | \n3 | \n2 | \n2 | \n80 | \n4.33 | \n3.91 | \n9.7 | \n
#D | \n5 | \n5 | \n1 | \n60 | \n4.10 | \n3.74 | \n8.8 | \n
Results of the alternative configurations.
In the present work, smart BMS for residential users with a grid-connected PV-storage system is proposed. The BMS is Internet-connected and it downloads 1-day ahead weather forecasts, which are used to obtain a provisional energy production for the PV generator. These data are compared with load estimations, based on historical data. The result is a provisional energy balance, which is used by the BMS to select the best strategy to discharge batteries. In particular, the BMS preserves battery charge, when high load and low production is expected, and performs peak shaving, when loads exceed a user-defined limit. The combination of these methods results in a reduction in absorption peaks from the grid, with negligible variations in terms of self-sufficiency. The proposed BMS is efficient in case of undersized batteries, where the energy available in the storage is often not sufficient to supply all the loads. For example, in case of a family composed of two persons with a PV plant with rated power 4 kW and a storage of 2 kWh, the reduction in absorption peak from the grid during winter days varies from 39 to 50%. Other combinations of PV and storage sizes are investigated and improvements in terms of peaks reduction are generally around 10%.
\nAcronyms | |
ANN | artificial neural networks |
BS | battery system |
BMS | battery management system |
BESS | battery energy storage system |
MPPT | maximum power point tracker |
PV | photovoltaic |
PVGIS | Photovoltaic Geographical Information System |
RES | renewable energy sources |
SBMS | smart battery management system |
STC | standard test conditions |
Symbols | |
γth | temperature factor of power of PV generator (%/°C) |
ηcabl | Joule losses |
ηcharge | charge efficiency of the battery |
ηDC/AC | DC/AC conversion losses |
ηdirt | losses due to dirt |
ηmis | losses due to mismatch |
ηmix | global losses of PV generator |
ηMPPT | DC/DC conversion losses |
ηrefl | losses due to reflection |
ηshad | losses due to shadings |
ηtherm | thermal losses of PV generator |
Cbat | rated capacity of the battery (kWh) |
Ebatt,disch | battery energy provided to the loads (kWh) |
Eload | energy consumptions (kWh) |
Eloads_1day-ahead, 6 a.m.–6 p.m. | 1-day ahead expected loads in the time slot 6 a.m.–6 p.m. (kWh) |
Eload, slot_x | provisional loads in the time slot x (kWh) |
Eload,TDT | estimated loads during the TDT (kWh) |
EPV | PV production (kWh) |
EPV_1day-ahead | 1-day ahead expected PV production (kWh) |
G | solar irradiance (W/m2) |
GNOCT | solar Irradiance at NOCT conditions (W/m2) |
NOCT | nominal operating cell temperature (°C) |
PAC | AC power production of PV generator (kW) |
Pbat | power exchanged by the battery (kW) |
Pload,max | maximum load power satisfied by the grid in case of peak shaving strategy (kW) |
Pmax,absorbed | maximum power absorbed from the grid (kW) |
PPV,r | rated power of PV generator (kW) |
Rsuff | provisional self-sufficiency parameter |
Rthre | threshold for the parameter Rsuff |
SOC | state of charge of the battery |
SOCmax,safety | maximum safety limit of SOC |
SOCmin,a | minimum SOC in the time slot a |
SOCmin,b | minimum SOC in the time slot b |
SOCmin,c | minimum SOC in the time slot c |
SOCmin,safety | minimum safety value of SOC |
SOCmin,slot_x | minimum SOC in the time slot x |
SOH | state of health of the battery |
Δt | simulation time step (min) |
t | simulation time (min) |
Ta | ambient temperature (°C) |
Tc | temperature of PV cells (°C) |
TSTC | temperature at standard test conditions (°C) |
TDT | total discharge time of the battery (h) |
x | user-defined number of time slots in which the TDT is divided |
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',metaTitle:"Horizon 2020 Compliance",metaDescription:"General requirements for Open Access to Horizon 2020 research project outputs are found within Guidelines on Open Access to Scientific Publication and Research Data in Horizon 2020. The guidelines, in their simplest form, state that if you are a Horizon 2020 recipient, you must ensure open access to your scientific publications by enabling them to be downloaded, printed and read online. Additionally, said publications must be peer reviewed. ",metaKeywords:null,canonicalURL:null,contentRaw:'[{"type":"htmlEditorComponent","content":"Publishing with IntechOpen means that your scientific publications already meet these basic requirements. It also means that through our utilization of open licensing, our publications are also able to be copied, shared, searched, linked, crawled, and mined for text and data, optimizing our authors' compliance as suggested by the European Commission.
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