Heating Degree Days Research Articles

Energy performance gap of the Italian residential building stock: Parametric energy simulations for theoretical deviation assessment from standard conditions

Energy Performance Gap (EPG) is a crucial issue in the building sector that can lead to an overestimation of the national energy policies. It is the difference between the calculated energy consumption and actual energy use, and it is relevant mainly for space heating. As EPG quantification or correction methods could lead to more realistic energy policies, EPG has become a focus of many studies and research. In this framework, this study aims to quantify the theoretical deviation of EPG, i.e., concerning to the standard conditions, for the Italian residential building stock by performing parametric energy simulations of thousands of representative reference buildings. After a comprehensive thermophysical characterization of the national building stock, parametric simulations were carried out by varying the main standard conditions set in the Energy Performance Certificate (EPC) calculation. This approach allowed the quantification of EPG according to the climatic zone, building type, usage profile, and thermal insulation level of buildings, while also analysing the influence of these parameters on the EPG and checking for prebound or rebound effects (i.e. when standard consumption is greater or smaller than actual one). The study identified a range of EPG variability for both prebound (0% to +80%) and rebound (−30% to 0%) effects and quantified an average EPG between −3 and +16 kWh per heating degree day of the selected location as a function of the usage profile. This work represents the first attempt to calculate the EPG of the Italian residential building stock and it could lead to the correction of the national energy policies implemented in the building sector.

On the energy performance of the Mediterranean school building stock: The case of the Apulia Region

The refurbishment opportunities provided by climate policies require an adequate knowledge of the school building stock, characterised by an urgent need of maintenance. Nevertheless, empirical evidence on energy performance of school samples appears limited due to the difficulty in retrieving data, although field data analysis is crucial in the built environment management. This study aims to explore existing energy conditions of an educational building sample hosting pre-schools, primary and lower secondary schools, located in southern Italy (Apulia Region). Firstly, an overview of the schools based on data retrieved from the regional dataset was performed. Then, more than 1000 buildings were clustered based on two predictors (construction year and surface-to-volume ratio), identifying five clusters representing the majority Apulian schools. In addition, billed gas and electricity data collected for 47 schools over a five-year period (2017–2021) were analysed, identifying annual and monthly trends, benchmarks, and mean values, which account for 46.5 (gas consumption), 15.59 kWh/m2 (electricity consumption). On average, source total consumption in 2020 experienced a reduction of 20%, partly due to Covid-19 restrictive measures. Finally, factors affecting heating consumptions were explored, and a regression analysis was performed, identifying heating degree days, construction year and boiler power to be the most significant.

Skillful prediction of UK seasonal energy consumption based on surface climate information

Climate conditions affect winter heating demand in areas that experience harsh winters. Skillful energy demand prediction provides useful information that may be a helpful component in ensuring a reliable energy supply, protecting vulnerable populations from cold weather, and reducing excess energy waste. Here, we develop a statistical model that predicts winter seasonal energy consumption over the United Kingdom using a multiple linear regression technique based on multiple sources of climate information from the previous fall season. We take the autumn conditions of Arctic sea-ice concentration, stratospheric circulation, and sea-surface temperature as predictors, which all influence North Atlantic oscillation (NAO) variability as reported in a previous study. The model predicts winter seasonal gas and electricity consumption two months in advance with a statistically significant correlation between the predicted and observed time series. To extend the analysis beyond the relatively short time scale of gas and electricity data availability, we also analyze predictability of an energy demand proxy, heating degree days (HDDs), for which the model also demonstrates skill. The predictability of energy consumption can be attributed to the predictability of the NAO and the significant correlation of energy consumption with surface air temperature, dew point depression, and wind speed. We further found skillful prediction of these surface climate variables and HDDs over many areas where the NAO is influential, implying the predictability of energy demand in these regions. The simple statistical model demonstrates the usefulness of fall climate observations for predicting winter season energy demand prediction with a wide range of potential applications across energy-related sectors.

Impact of green retrofits on heating energy consumption of apartment buildings based on nationwide energy database in South Korea

In South Korea, apartment buildings account for 63% of residential buildings and approximately 40% of apartment buildings were built before 2000, which leads to the growing demand for green retrofits of old apartment buildings. In this study, heating energy consumption of apartment complexes in Seoul and Busan was analyzed using a nationwide database, in order to evaluate the current energy status of apartment complexes. The energy consumption trend with building age were analyzed by classifying the heating bill data into four groups by construction year of apartment complexes. Based on these data, a simulation model of an old apartment building was developed to predict energy performance improvement through green retrofits. The results clearly showed that heating energy consumption increased with building age regardless of heating degree days in both regions. In particular, a significant difference in energy consumption was found in apartment complexes constructed before and after 2000, which implied the necessity of green retrofits for old apartments. The results of green retrofit simulation showed that heating energy consumption was reduced by up to 31.6% and 32% in Seoul and Busan, respectively. The method and results of this study can be applied to establish a green retrofit strategy based on a nationwide database of residential energy.

The drivers and barriers of energy efficiency

Energy efficiency is “the first fuel” due to its abundant availability and low cost in terms of supply, based on the IEA. The primary energy intensity was often employed to measure energy efficiency. Among 46 countries, the improvement of energy efficiency from 2000 to 2020 ranged between 61.54% and −3.16%, suggesting the situation and influence factors of energy efficiency may be different across countries. To systematically find the drivers and barriers of energy efficiency, panel regression models were built. The variation of energy efficiency can be explained by not only the traditional GDP and structure effects in the decomposition methods, but also the weather, trade, and infrastructure effects. For the heterogeneous countries, the average performer, instead of the best one in the data envelopment analysis, was compared. Estimated results indicated that key drivers of energy efficiency were the growth of GDP, increases of renewable energy, and electrification. The global warming decreased heating degree days and increased cooling degree days, causing the climate factors partially offset each other. Other factors of the energy efficiency improvement included the industrial transformation, trade openness, and increasing population. These influences were different across countries; thus, the country-specific routes of energy efficiency improvement were found.

Weather rebate contracts for different risk attitudes of supply chain members

Firms that deal with weather-sensitive products are often exposed to weather risk. How a weather rebate contract can be implemented to improve the performance of a supplier-retailer supply chain is investigated. The supplier offers a weather rebate to the retailer to compensate for the loss of sales due to weather risk. Depending on certain weather conditions, the retailer receives the rebate if it orders beyond a predefined ordering quantity. Depending on its risk attitude, the supplier uses weather derivatives to minimize the downside risk. The performance of weather rebate contracts is analyzed for three cases: risk-neutral supplier and risk-averse retailer, risk-averse supplier and risk-neutral retailer, and risk-averse supplier and risk-averse retailer. Conditional Value at Risk (CVaR) is used as the risk measure. The supply chain coordination under weather risk is investigated, and the specific conditions of a weather rebate contract leading to a Pareto-improving solution for both parties are obtained. To the best of the authors’ knowledge, this is the first study that investigates the weather rebate contract incorporating the risk attitude of the firms using a cooling degree days (CDD)/heating degree days (HDD)-based rebate structure. A comparative analysis between the weather rebate and wholesale price contracts is carried out based on the actual temperature and demand data. The results show that the weather rebate contract outperforms the regular wholesale price contract in all three cases. The study also demonstrates how to use weather derivatives to improve the performance of a supply chain dealing with weather-sensitive products.

How climate change affects electricity consumption in Chinese cities-a differential perspective based on municipal monthly panel data.

Addressing the impacts of climate change has become a global public crisis and challenge. China is characterized by a complex and diverse topography and vast territory, which makes it worthwhile to explore the differential impacts of climate change on urban electricity consumption in different zones and economic development conditions. This study examines the differential impact of climate factors on urban electricity consumption in China based on monthly panel data for 282 prefectures from 2011 to 2019 and projects the potential demand for future urban electricity consumption under different climate change scenarios. The results show that (1) temperature changes significantly alter urban electricity consumption, with cooling degree days (CDD) and heating degree days (HDD) contributing positively to urban electricity consumption in areas with different regional and economic development statuses, with elasticity coefficients of 0.1015-0.1525 and 0.0029-0.0077, respectively. (2) The temperature-electricity relationship curve shows an irregular U-shape. Each additional day of extreme weather above 30 °C and below -12 °C increases urban electricity consumption by 0.52% and 1.52% in the north and by 2.67% and 1.32% in the south. Poor cities are significantly more sensitive to extremely low temperatures than rich cities. (3) Suppose the impacts of climate degradation on urban electricity consumption are not halted. In that case, the possible Shared Socioeconomic Pathways 1-1.9 (SSP1-1.9), SSP1-2.6, and SSP2-4.5 will increase China's urban electricity consumption by 1621.96 billion kWh, 2960.87 billion kWh, and 6145.65 billion kWh, respectively, by 2090. Finally, this study makes some policy recommendations and expectations for follow-up studies.

Long‐term cyanobacterial dynamics from lake sediment DNA in relation to experimental eutrophication, acidification and climate change

Abstract Cyanobacterial blooms in aquatic environments have impacted ecosystem health, altered food webs and contributed to substantial regional economic losses. The relative impacts of climate change, eutrophication and other environmental stressors on the formation of cyanobacterial blooms remain unclear as a consequence of the lack of long‐term data. The analysis of lake sediment archives can help address such questions. The abundance of target cyanobacterial genes was quantified using droplet digital PCR from two experimental and two reference lakes at the IISD ‐ Experimental Lakes Area, Northwestern Ontario, Canada where various anthropogenic drivers have been manipulated and phytoplankton biomass and taxonomy (via microscopy) tracked over the past 50 years. Based on sediment DNA (sedDNA) records, Lake 227 has seen more than two full orders of magnitude rise in cyanobacterial abundance in response to experimental nutrient (nitrogen and phosphorus) loading since 1969. Lake 223 also experienced a small increase in cyanobacterial abundance during a period of experimental acidification. The sedDNA archives also identified an increase in abundance of mcyE, a gene involved in microcystin synthesis, in Lake 227 after 1990 when experimental nitrogen loading ceased and only phosphorus loading continued. Sequencing of the cyanobacterial 16S rRNA gene from sedDNA revealed a shift towards Nostocales dominance in response to eutrophication in Lake 227; this was also seen in the more recent sediments of the other lakes but to a much lesser extent. When comparing the sedDNA data with historical phytoplankton records of the past ~50 years, moderate‐to‐strong correlations were found between the two. This research validated the use of sedDNA for the analysis of long‐term trends in lakes and the results revealed additional changes that were not recorded from the surface‐water records. Experimental nutrient loading had the greatest influence on the cyanobacterial community in Lake 227. However, climate change, via declines in heating degree days, was also significant in explaining variation in the cyanobacterial communities in the experimental lakes as well as the reference lakes.

Forecasting energy demand of PCM integrated residential buildings: A machine learning approach

Forecasting energy demand has become an essential element for energy stakeholders in planning and reducing the energy consumption of buildings. Machine learning techniques have become popular for forecasting building energy demand owing to their reliability and cost efficiency. This research aims to propose a model for predicting the energy consumption of PCM-integrated residential buildings in the Mediterranean climate region. For the model development, Multiple Regression (MR), Support Vector Machines (SVM), and Artificial Neural Networks (ANN) were used. For the first time, the PCM melting point, building, and environment parameters were considered simultaneously as input parameters to predict the energy consumption of PCM-integrated buildings. The energy simulation of nine different building types located in seven different cities of the Mediterranean climate region were performed to generate the database. After the model development, the most influential design parameters were established by performing sensitivity and parametric analysis. The results showed that the optimum PCM for annual energy savings varied from PCM-25 to PCM-27. The shape factor significantly influenced the specific heating and cooling demand of buildings. Moreover, the statistically evaluated prediction models showed that SVM and ANN methods are more reliable, with R2 value of over 0.99. The externally validated prediction models demonstrated that the ANN model can estimate the energy consumption of PCM-integrated buildings with more accuracy. From sensitivity analysis, it was found that cooling degree days, heating degree days, volume, shape factor, and PCM melting point are the key influencing parameters affecting the energy demand of buildings.

Perspectives on peak demand: How is ERCOT peak electric load evolving in the context of changing weather and heating electrification?

This analysis characterizes the past and future evolution of peak electricity demand in the Electric Reliability Council of Texas (ERCOT) service area and assesses how these trends may be driven by changing weather and heating electrification. To accomplish this, historical ERCOT demand data from 1997 to 2021 were utilized along with weather data for the days in which peak demand occurred and from a future climate scenario. The results inform resource planners on the relationship between peak heating and cooling load and ambient temperature and present peak demand growth scenarios for 2025 through 2050. We found that historically, summer peak demand growth has been generally stable and approximately linear with time. Conversely, the winter peak demand growth has been less consistent, varying much more around the broader trend. These phenomena are likely consequences of temperatures that were fairly constant on summer peak demand days, but varied widely on winter peak demand days. The erratic nature of winter peak demand is also likely caused by the fact that electrical heating equipment becomes increasingly inefficient at lower temperatures, as is demonstrated by the polynomial relationship identified between per capita winter peak demand and heating degree days. Additionally, historical winter peak demand was shown to be growing more quickly than summer peak demand. This phenomenon is likely the result of increases in electrical efficiency of cooling and increases in electricity consumption that result from the rising penetration of electrical heating equipment that replace gas furnaces. Future peak demand scenarios indicate that winter peak demand will remain more erratic and will sporadically surpass summer peak demand between 2025 and 2050. Thus, resource planners in ERCOT should place less certainty on winter peak demand projections and an increased level of winter preparedness on both the supply and demand sectors appears warranted for resource planners. These findings might foreshadow future resiliency challenges that other regions will face as electric heating equipment is deployed in place of boilers or furnaces for decarbonization purposes.

Temperature, storage, and natural gas futures prices

AbstractPrevious literature suggests that daily changes in US natural gas (NG) futures prices and their variance can be explained by changes in oil futures prices and volatilities, storage announcements, and temperature shocks. These studies measure temperature shocks using the National Oceanic and Atmospheric Administration definitions of heating degree day (HDD) and cooling degree day (CDD). We show how hourly temperatures can be used to better measure how long as well as how much temperatures depart above and/or below the “comfort level” within a day, the effects of temperature variability within a day, and the effects of thermal inertia. Hourly temperatures also allow us to construct measures of extreme temperatures that better match the timing of release of other market information. Another innovation is that we use more economically relevant weights to convert regional temperature shocks to national averages, and measure expected values of variables via econometric models instead of historical averages. Our results show that the proposed measures of HDD and CDD and temperature changes are important for explaining weekly changes in NG in storage and daily changes in NG futures prices and their variance.

Spillover Effects among Electricity Prices, Traditional Energy Prices and Carbon Market under Climate Risk

With the increase in global geopolitical risks and the frequent occurrence of extreme climate in recent years, the electricity prices in Europe have shown large fluctuations. Electricity price has an important impact on the cost of production and living, while electricity demand will also affect other energy markets. A double-layer system based on the spillover effects from a systematic perspective is constructed in this paper to explore the connectedness between different electricity markets and other related energy markets in Europe, considering the impact of climate risks. The results show that there are certain spillover effects among electricity markets in different countries, with a temporary upward trend in the beginning of the Russia-Ukraine conflict, and the electricity markets in the UK and Germany have a more important role in Europe. There are two-way spillover effects between the electricity market and fossil fuel markets, carbon market and carbon emission. Since 2022, the electricity market is affected by gas prices, while it has a certain impact on carbon emissions. The heating degree day (HDD) has significant spillover effects on the electricity market and other energy markets, while the spillover effects of the cooling degree day (CDD) are relatively small.

The Effects of Climate and Oil Prices on Residential Natural Gas Prices: With an Application to 11 OECD Countries

This study examines the effects of climate and oil prices on residential natural gas prices in selected 11 OECD countries by using panel data for the period 1992-2016. After applying the panel unit root tests, the parameters are estimated using Common Correlated Effects Pooled (CCEP) method. Moreover, Emirmahmutoglu-Kose (2011) test is used to test the panel causality between the variables. The results revealed that in the long run, the heating degree days have a statistically significant and negative effect on natural gas prices used in the residential sector in selected OECD countries, while there is an insignificant relationship between oil prices and natural gas prices used in the residential sector in these countries. It is also found to be a causality of heating degree days to natural gas prices.

Hourly air temperature projection in future urban area by coupling climate change and urban heat island effect

Global climate change and urban heat island (UHI) effect are changing the urban microclimate and ecosystem of building and outdoor environment. In this study, CMIP5 family, which was found to have the most consistent projection with the current worldwide observation till now, was chosen to evaluate climate change’s impact on urban climate. This research uses Principal Component Analysis and Gaussian Mixture Model based building type clustering method to obtain urban morphological data and input parameters of the UHI model, and the validated UHI model is then coupled to the projections from various global climate models (GCMs).It is found that future urban microclimate prediction after considering the UHI effect can significantly improve the prediction accuracy of air temperature when downscaling future weather data from various selected GCMs, indicating the UHI shall be considered in predicting future weather data in urban tissue. After using the best-fit climate change prediction model MRI-CGCM3 to couple with the UHI model, the projections of the heating degree days (HDD) and cooling degree days (CDD) of Shenzhen from 2020 to 2099 has shown that comparing with the typical meteorological year, the average HDD in Shenzhen will be reduced by 57.5% in 2020–2099 under the RCP8.5 scenario, and the CDD will increase by 25.1%, which will exert considerable impacts on building energy use in urban tissue.

Optimum insulation thickness design of exterior walls and overhauling cost to enhance the energy efficiency of Albanian's buildings stock

In the present climate, thermal insulation is crucial for reducing energy consumption for heating or cooling in buildings, increasing the lifespan of structures, and building elements, and improving human comfort. The wall's composition layer has advanced regularly since the building regulations and established norms. However, calculation methods have remained quite the same, governed by Fourier's law. Therefore, to address this gap, a linearization process, and a correction factor of the optimized insulation thickness for four different insulation materials (i.e., EPS Graphite, EPS, GW, and RW) and five energy carriers (i.e., electricity, diesel, natural gas, liquefied petroleum gas, and biomass) by removing the boundaries of heating degree days by using Mathcad software is proposed. Other financial parameters (i.e., inflation rate, interest rate, lifespan, and present worth factor) and heating system features that lead to the optimized insulation thickness are considered. The RETScreen Expert model was coupled with a Mathcad script to compute a standardized corrected optimized insulation thickness (COIT) value by applying sensitivity analyses at ±35% of the most influencing parameters to calculate the total overhauling cost for different building types in Albania. A correction of the existing window's heat transfer coefficient for a different glass-to-wall ratio is proposed. Our findings suggest that the overall heat transfer value (U) should be equal to or less than 0.30 (W/m2•K), leading to a range of corrected optimized insulation thickness (COIT) from 0.03 m up to 0.11 m. Moreover, results exhibited encouraging economic and environmental values concerning the unrenovated buildings leading to annual net savings of 23.50–84.50 €/m2, CO2 reduction up to 39.10 kgCO2/m2, and a simple payback period (SPP) of 0.30 maximum up to 1.20 years. The outputs of this study can bring benefits in terms of reduced energy consumption and CO2 footprint, and the proposed COIT value can be applied not only in Albania but in other regions that exhibit similar climate conditions.

An Integrated Approach to Long-Term Fuel Supply Planning in Combined Heat and Power Systems

This paper examines the issue of strategic planning of fuel supplies in combined heat and power systems. This is a major challenge in energy modeling because heating-degree day calculation methods only address short-term horizons and are not suitable for the long-term planning of fuel supplies. In this work, a comprehensive method is proposed for strategic fuel supply planning of independent heat producers. The method considers changes in the market dynamics of residential and commercial properties, the annual rate of customer acquisition by the network operator, customer disconnections, as well as the thermal modernization of buildings for estimating the long-term thermal energy demand of an urban area. Moreover, the method develops a mathematical model to minimize production costs, taking into account the technical constraints of the system. The proposed strategic planning tool, in addition to information on the quantities of fuel consumed for heat and electricity production, also provides valuable management information on the operational costs of the CHP system and its environmental impact. The application of the method is illustrated with the analysis of a large-scale combined heat and power plant supplying heat and electricity to a city with over 500,000 inhabitants. The results indicate that depending on the changes in the primary and secondary heat markets, the demand for energy carriers may range from 107.37 TWh to 119.87 TWh.

Assessment of Occupant Adaptive Heating Behaviour in Office Buildings - A Pilot Field Study

Buildings are responsible for 39% of total global CO2 emissions worldwide, with 33% of energy consumption attributed to space heating in commercial buildings. Adaptive Heating Behaviour (AHB) of office users has a direct impact on energy use and depends on building characteristics which determine the heating load. This pilot field study focuses on the assessment of the AHB of office users of a recently retrofitted university building in Christchurch, New Zealand. AHB was reported by users during three consecutive days with a total heating degree days (HDD at 18°C) of 26.8. Indoor temperature and relative humidity were monitored during the study, and a qualitative analysis was performed to understand occupants’ behavioural patterns to maintain or recover thermal comfort in office settings. Participants were categorized according to (i) their reported thermal comfort issues at work, (ii) the adaptive responses they took to recover thermal comfort, and (iii) the effectiveness of such actions. The methods used in this pilot study to assess occupant AHB, were informative to future research on how to link environmental conditions to user behaviour in office settings, through the analysis of qualitative and quantitative data. Identifying patterns and triggers for AHB on a per-case basis is necessary, especially as AHB can significantly influence the building energy consumption.

The role of renewable energy sources in residential electricity prices: A club convergence analysis across selected European countries

ABSTRACT This research analyses the convergence of residential electricity prices in 22 European countries between 1995 and 2019. The findings reveal the existence of two sub-convergent clubs of residential electricity prices. Half of the countries studied belong to the club with higher residential electricity prices, while the other half belong to the lower-price club. Household income increases the probability of belonging to the club of lower residential electricity prices. An increase in heating degree days decreases the probability of belonging to the club of lower residential electricity prices. A similar effect is observed for the price of non-renewable energy sources used in electricity production. Further, although a country’s likelihood of belonging to the club of lower residential electricity prices diminishes with a rise in the share of renewable electricity production, the converse pattern is obtained at the residential level, such that residential renewable electricity share has a beneficial effect. Our findings suggest that households have benefitted in paying lower prices for renewable sourced electricity. Therefore, this research indicates that ongoing clean energy initiatives and the availability of affordable substitutes for conventionally generated electricity are being realized to a certain extent, improving electricity markets for the countries we analysed.

Potential energy savings benefits and limitations of radiative cooling coatings for U.S. residential buildings

Radiative coatings are a promising strategy to implement passive cooling that provides an eco-friendly pathway to achieve energy efficiency in buildings. This study investigates the benefits and limitations of implementing radiative coatings on the building envelope, particularly the roof, of residential buildings in the United States. The analysis compares the performance of an ideal radiative coating with broadband surface properties to a realistic radiative coating with diffuse and semitransparent properties. To enhance the net energy efficiency of buildings, we carried out a comprehensive parametric analysis comprising key radiation surface properties to maximize cooling energy savings while minimizing heating energy penalties. A countrywide assessment for 68 locations across the United States showed that net energy savings are a strong function of the climatic and atmospheric conditions. Broadly, southern locations showed high overall energy savings whereas northern locations exhibited high overall energy penalties. Locations in International Energy Conservation Code (IECC) climate zones 1A, 2A, and 2B showed a net annual energy savings above 5%, whereas locations in IECC climate zones 5B, 6B, and 7 showed a net annual energy penalty greater than 3%. The hot and dry climate of Phoenix, Arizona, showed the highest overall energy savings of 426 kWh (6.2%). Additionally, the mathematical correlation reveals that the net energy savings of a location is positive only when its average cooling degree days is greater than 5.5 or its average heating degree days is less than 10.

The progress, impact analysis, challenges and new perceptions for electric power and energy sectors in the light of the COVID-19 pandemic

Understanding the COVID-19 crisis that arose in 2019 is a significant important case study to prepare scenarios and meet electrical energy consumption and high renewable energy production (REP), especially in the context of the power systems. Although many researchers have investigated the medical field and electric power sectors associated with the COVID-19 pandemic, critical factors affecting the development of the REP like electric demand, power system, electric markets, economy, and environment have are still not studied in great detail. In this perspective, this paper analyses the impact of the COVID-19 outbreak on the development of the REP, electrical energy consumption, power system maintenance, electric markets, energy demand, ongoing investment projects/investment plans, economy and renewable energy sectors. Estimation of energy demand based on cooling degree days (CDDs) and heating degree days (HDDs) methods has been examined and contrasted with the same period in prior years to observe electricity consumption Besides, climate change and energy efficiency or energy intensity related to the energy demand have been investigated and analysed before and following the pandemic. Some important energy statistical data is addressed and examined in detail. In addition to these, various critical factors such as driver and barrier forces affecting the REP has been discussed during lockdown restrictions of the COVID-19. These findings will help researchers and academicians to analyse the far-reaching implications of the COVID-19 outbreak on the energy demand and contribute to figuring out and plan higher renewables share scenarios and power system management issues.