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Decomposition models of solar irradiance estimate the magnitude of diffuse horizontal irradiance from global horizontal irradiance. These two radiation components are well-known to be essential for the prediction of solar photovoltaic systems performance. In open-field agrivoltaic systems, that is the dual use of land for both agricultural activities and solar power conversion, cultivated crops receive an unequal amount of direct, diffuse and reflected photosynthetically active radiation (PAR) depending on the area they are growing due to the non-homogenously shadings caused by the solar panels installed (above the crops or vertically mounted). It is known that PAR is more efficient for canopy photosynthesis under conditions of diffuse PAR than direct PAR per unit of total PAR. For this reason, it is fundamental to estimate the diffuse PAR component in agrivoltaic systems studies to properly predict the crop yield. Since PAR is the part of electromagnetic radiation in the waveband from 400 to 700 nm that can be used for photosynthesis by the crops, several stand-alone decomposition models of solar irradiance are selected in this study to partition PAR into direct and diffuse. These models are applied and validated in three locations in Sweden: Lanna, Hyltemossa and Norunda, using the coefficients stated on the original publications of the models and locally fitted coefficients. Results showed weaker performances in all stand-alone models for non-locally fitted coefficients (nRMSE ranging from 29% to 95%). However, performances improve with re-parameterization, reaching highest nRMSE of 37.94% in Lanna. YANG2 decomposition model is the best-performing one, reaching lowest nRMSE of 24.31% in Norunda applying re-estimated coefficients. Country level sets of coefficients for the best-performing models, YANG2 and STARKE, are given after parameterization using joined data of the three locations in Sweden. These Sweden-fitted models are tested and showing nRMSE of 25.56% (YANG2) and 28.36% (STARKE). These results can be used to perform estimations of PAR diffuse component in Sweden where measurements are not available, and the overall methodology can be similarly applied to other countries.

Photosynthetically active radiation (PAR) is a key parameter for modelling the photosynthetic behaviour of plants in response to sunlight and, subsequently, for determining crop yield. Separating PAR into direct and diffuse components is of significance to agrivoltaic systems, which combine solar energy conversion and agricultural farming on the same portion of land. Placing photovoltaic on agricultural land results in varying shading conditions throughout the day and seasons, producing a higher contribution of incident diffuse PAR to the crops beneath the system in these shaded regions. Additionally, photosynthesis is more efficient under conditions of diffuse PAR than direct PAR per unit of total PAR. This work introduces a new separation model for PAR, which is able to accurately estimate diffuse PAR from the global one. The model modifies the YANG2 model, by adding four new predictors: the optical thickness of PAR, vapour pressure deficit, aerosol optical depth, and albedo of PAR. The proposed model has been calibrated, tested, and validated at three sites in Sweden with latitudes above 58° N, obtaining R2 exceeding 0.91 and nRMSE less than 17%. Compared to YANG2, which was previously found to be a high-performance model, the new model is superior by up to 1% both in R2 and nRMSE. Additionally, an analysis of the seasonal trends and variation of the different PAR components is provided to alleviate the dearth of PAR studies in high-latitude regions.

Agrivoltaic systems are becoming more popular as a critical technology for attaining several sustainable development goals such as affordable and clean energy, zero hunger, clean water and sanitation, and climate action. However, understanding the shading effects on crops is fundamental to choosing an optimal agrivoltaic system as a wrong choice could lead to severe crop reductions. In this study, fixed vertical, one-axis tracking, and two-axis tracking photovoltaic arrays for agrivoltaic applications are developed to analyse the shading conditions on the ground used for crop production. The models have shown remarkably similar accuracy compared to commercial software such as PVsyst and SketchUp. The developed models will help reduce the crop yield uncertainty under agrivoltaic systems by providing accurate photosynthetically active radiation distribution at the crop level. The distribution was further analysed using a light homogeneity index and calculating the yearly photosynthetically active radiation reduction. The homogeneity and photosynthetically active radiation reduction varied significantly depending on the agrivoltaic system design, from 91% to 95% and 11% to 34%, respectively. To identify the most suitable agrivoltaic system layout dependent on crop and geographical location, it is of fundamental importance to study the effect of shadings with distribution analysis.

Providing the energy needs of the cumulatively increasing population has become a challenge for the regional energy systems in the world. The most critical challenge is to supply enough energy in the forms of heat and power during the cold and warm periods of the year with the lowest production cost and minimum environmental impacts. A solution is to increase the green energy supply from renewable energy resources such as solar, wind power, and hydropower. In order to apply this solution in the real energy system, potentials for clean energy supply in an optimized manner should be evaluated. In this study, an optimization model is developed for a regional energy system in the central part of Sweden. The studied system consists of Combined Heat and Power (CHP) plants and heat water boilers together with renewable energy supply from rooftop Photo Voltaic (PV) - solar collectors and regional hydropower plants. The General Algebraic Modeling System (GAMS) is used to create the model based on the Mixed Integer Linear Programming (MILP) method. The goal is to evaluate the influence of local renewable energy systems on the production planning of CHP plants in a region. Two different scenarios are investigated based on the extremes in energy supply and demand concerning the increased use of Electrical Vehicles (EVs) and more application of Heat Pumps (HPs) in the system. The results show that installation of rooftop PV systems has the potential to reduce the electricity import to the region; however, it will at the same time reduce the operation time of the CHP plans during the summer period. With increased use of HPs for heating, the shut off time for CHP plants is further increased. Increase in electric passenger cars penetration in the system has no impacts on the production profiles of the plants. The regional electricity demand grows significantly by more utilization of EVs and increased application of heat pumps in the studied system. The high electricity demand will mainly be satisfied by importing electricity from outside the region together with low production from CHP plants and the power generated by the rooftop PV systems and regional hydropower. The developed optimization model with studied scenarios can be applied to other energy systems to increase the knowledge of production planning and feasibility of a fossil fuel free energy system.

Under the pressure of global environmental climate change, all countries in the world are developing renewable energy such as hydropower, wind energy, and solar energy As a result, the electricity price varies in different patterns depending on the penetration of renewable energy. In this paper, a non-parametric model is employed to analyze the historical data of electricity spot price from Danish price areas of the Nord Pool (with high percentage of wind power), Swedish price areas of the Nord Pool (with high percentage of hydropower) and PJM market (with little renewable energy penetrated). The objective is to deeply understand the influence of renewable energies on electricity price volatility. It is found that electricity prices are more stable in Swedish price areas as hydropower is a more stable energy source. The electricity price in PJM market is also comparatively stable, only more volatile than Swedish market, as fossil fuels are dominant energy resources. For Danish price areas, the volatility of electricity prices is clearly affected by wind power, which is a highly intermittent energy resource.

In this study, possible alternations in a regional transport sector are assessed to increase the use of renewable resources. Three scenarios are developed aimed to investigate different alternatives including potential straw-based bioethanol supply to fuel regional cars with combustion engines, more use of Electrical Vehicles (EVs) with use of potential power from solar energy, and the feasibility of application of hybrid cars fueled with electricity and bioethanol. The evaluation considers the reduction in CO2 emissions and increased balance in energy demand and supply. Results of the study indicate that application of hybrid vehicles with bioethanol-fueled engines and electrical motors could potentially reduce the CO2 emissions compared with other proposed approaches in the studied scenarios. At the same time, there would be a balance in the system, so that, the potential bioethanol supply from the available cereal straw in the region can meet the energy demand of suggested hybrid cars in wintertime. While, the energy supply from the grid-connected solar cells installed on the rooftop of the buildings can cover the electricity need of the motor during summer. This approach will also result in increased use of renewables in the transportation system.

Air pollution and increased CO2 concentration in atmosphere and other energy related issues caused a transformative shift in energy system which contributes to increased utilization of renewables as alternative to generate green energy carriers. The potential of renewable resources in different region and potential energy conversion have been largely considered by many researcher in many countries. The energy conversion technologies to produce heat, electricity, and transportation fuels have made impressive technical advances. Sweden has also been challenging with mitigation of CO2 emission and trying to shift into a fossil fuel free system in all energy sectors. This paper deals with the current status of fuel demand and supply in the transport sector in a County in Sweden. A scenario for a fossil fuel free transport sector at a regional level is developed to investigate the potential biofuel production from regionally produced straw. The results and analysis indicate that the potential for cereal based bioethanol production in the region is sufficient to meet the biofuel demand of the County. Using the fallow land for cereal cultivation, it is feasible to shift into a fossil fuel free transportation system where all passenger cars are fueled by bioethanol. The results and finding from the current paper will be used to develop further study on optimization of local biofuel production integrated with CHP plants considering application of other feedstock such as municipal wastes.

Electric Vehicles (EVs) have achieved a significant development because of the continuous technology revolution and policy supports in recent years, which leads to a larger demand of charging stations. Strategies about how to find the optimal location for charging facilities are urgently needed in order to further assist the development of EVs. This paper focus on the return of investments on EV charging stations and proposes a Mixed Integer Linear Programming (MILP) model based on Geographic Information System (GIS) to identify the optimal location of charging stations in cities. Traffic flow data and land-use classifications are used as important inputs, and six important constraints are included in the MILP model with the objective function of maximizing the total profits of new charging stations. The effectiveness of the proposed method is then demonstrated by implementing a case study in Vasteras, Sweden.

Being at the heart of modern pulp mills, continuous pulp digesters have attracted much attention from the research community. In this article, a comprehensive review in the area of modeling, control and diagnostics of continuous pulp digesters is conducted. The evolution of research focus within these areas is followed and discussed. Particular effort has been devoted to identifying the state-of-the-art and the research gap in a summarized way. Finally, the current and future research directions in the areas have been analyzed and discussed. To date, digester modeling following the Purdue approach, Kappa number control using model predictive controllers and health index-based diagnostic approaches by utilizing different statistical methods have dominated the field. While the rising research interest within the field is evident, we anticipate further developments in advanced sensors and integration of these sensors for improving model prediction and controller performance; and the exploration of different AI-based approaches will be at the core of future research.

Population growth and urbanization have led to increases in energy demand and consequently, greenhouse gas emissions. Therefore, the availability of the fossil fuel as the main source of energy supply has been changed. Utilization of renewable resources including solar, wind, and hydropower together with distributed energy systems could eliminate the dependency on fossil fuel energy sources. In this paper, energy use and supply trends have been studied for the Counties of Vastmanland and Sodermanland in Sweden in order to develop a scenario for the regional energy system in 2030. The aim is to use the scenario for evaluation of the impacts of regional renewable energy resources on the production planning of CHP plants. The scenario shows that there is not enough potential for electricity production from renewable resources such as solar, wind, and hydropower to fulfill the estimated demand in 2030. Around 75% of electricity needs in Vastmanland and 89% of power demands in Sodermanland need to be met by imported electricity to these regions. Efficiency improvements and a more complex energy system integrating also with other energy resources like biomass, waste and industrial waste heat are necessary to develop a sustainable energy system.

65% of the buildings in Vasteras, situated in the region of Malardalen, Sweden were built before 1970. It is thus time for renovation. The situation is the same in most cities in Sweden and Northern Europe. The depth of renovation can be quite different. In this paper we evaluate some examples where cost is compared to energy saving effect. How to plan renovation to make use of the available capital in the cities is discussed. As a complement to direct renovation actions also behavior change with respect to energy is discussed and exemplified. The cost for energy actions in relation to other renovation aspects is discussed especially for the passive house case in Allingsas, Sweden. The passive house center calculate an extra cost for passive house standard to be 10 000 €/apartment while an external consultant has the figure 40 000 € of the total cost of 120 000 €. With this space heating can be 18 kWh/m2.year, or a reduction by 84 % with respect to space heating and 62% for overall heat and hot water demand. If you use the latter cost figure passive house standard is not motivated from an energy savings perspective while if using the lower figure it is very interesting. For the other less deep renovations we see that adding more insulation and three glass windows is motivated if the degradation has been strong, while a simpler renovation may be ok if the outer surface coating is not too bad. For these less deep renovations we see cost figures of 65 €/m2 respectively 28 €/m2 with reduction of heating and hot water demand of 56 % respectively 34 %.

The share of renewable energy sources in the primary energy use is increasing worldwide. Given the intermittency of the energy supply from renewables, it is important to increase flexibility in the system to respond to the imbalances between energy demand and supply. Several flexibility options such as power storage and energy integration are currently in use, mostly at small scales. The increased energy supply from renewables and the flexibility solutions can influence the production planning of existing thermal energy conversion plants. In this study, integration of energy technologies including a hydrotreated pyrolysis oil production integrated with existing CHP plants is investigated as a flexibility solution. The system interacts with potential power generation from rooftop PV systems integrated with power-to-hydrogen storage. A cost-optimization model is developed using MILP method. The study focuses on the system flexibility and operational strategy of the existing CHP plants considering market trends, climate changes, and future energy developments with increased penetration of heat pumps and electric vehicles but less fossil fuels use. The results indicate that the suggested integrated system can increase the local energy supply by 33 GWh. Moreover, the power-to-hydrogen storage and onsite hydrogen use can increase the share of renewables in energy supply by 6%. Optimization of the developed scenarios for future energy-related changes indicates that the market trends could significantly reduce the performance of the system by 21% but increase the penetration of renewables in the system by 8%. Overall, scenario analysis shows the potential of using such a polygeneration system for flexible energy supply including existing CHP plants.

This paper focuses on the characterization of highly variable biofuel properties such as moisture content, ash content and higher heating value by near-infrared (NIR) spectroscopy. Experiments were ...

Kappa number variability at the continuous digester outlet is a major concern for pulp and paper mills. It is evident that the aforementioned variability is strongly linked to the feedstock wood properties, particularly lignin content. Online measurement of lignin content utilizing near-infrared spectroscopy at the inlet of the digester is paving the way for tighter control of the blow-line Kappa number. In this paper, an innovative approach of feedforwarding the lignin content to a model predictive controller was investigated with the help of modeling and simulation studies. For this purpose, a physics-based modeling library for continuous pulp digesters was developed and validated. Finally, model predictive control approaches with and without feedforwarding the lignin measurement were evaluated against current industrial control and proportional-integral-derivative (PID) schemes.

International audience; District heating networks are commonly addressed in the literature as one of the most effective solutions for decreasing the greenhouse gas emissions from the building sector. These systems require high investments which are returned through the heat sales. Due to the changed climate conditions and building renovation policies, heat demand in the future could decrease, prolonging the investment return period. The main scope of this paper is to assess the feasibility of using the heat demand-outdoor temperature function for heat demand forecast. The district of Alvalade, located in Lisbon (Portugal), was used as a case study. The district is consisted of 665 buildings that vary in both construction period and typology. Three weather scenarios (low, medium, high) and three district renovation scenarios were developed (shallow, intermediate, deep). To estimate the error, obtained heat demand values were compared with results from a dynamic heat demand model, previously developed and validated by the authors. The results showed that when only weather change is considered, the margin of error could be acceptable for some applications (the error in annual demand was lower than 20% for all weather scenarios considered). However, after introducing renovation scenarios, the error value increased up to 59.5% (depending on the weather and renovation scenarios combination considered). The value of slope coefficient increased on average within the range of 3.8% up to 8% per decade, that corresponds to the decrease in the number of heating hours of 22-139h during the heating season (depending on the combination of weather and renovation scenarios considered). On the other hand, function intercept increased for 7.8-12.7% per decade (depending on the coupled scenarios). The values suggested could be used to modify the function parameters for the scenarios considered, and improve the accuracy of heat demand estimations.

In waste water treatment using biological treatment processes normally phosphorous, nitrous compounds as well as organic matterare removed.It is also important to remove or kill pathogens that otherwisecould cause diseases. The surplus of bio-sludge is used to produce biogas. In thepaper four different alternatives for system design and operations of systems was discussed. The alternatives integrates thewaste water treatment and irrigation offarmland using the water taken out from different positions in the waste water treatment plant.

This paper presents the results of experimental measurements and multivariate statistical modeling concerning detection of soda-lime glass using near infrared (NIR) spectrometry technique. The purpose is to test if the glass is quantitatively detectable in a waste-based material and to assess what method of spectral data pretreatment is the most suitable in order to develop prediction models. The experiments were performed on six test samples containing a specific amount of glass distributed in background material. Pretreatment methods such as normalization and first and second derivatives were applied on the acquired absorbance spectral data. Principal component analysis (PCA) was employed in order to describe the relationship between pretreated data and the amount of glass in the test samples. Subsequently, principal component regression (PCR) was utilized for the development of prediction models. The results from the models show strong correlation between the pretreated data and the glass content. The most promising results were obtained from the model based on 1st derivative pretreatment when only absorbance spectral data from selected wavelengths are included.

Forest biomass is a renewable and sustainable source of energy that can be used for producing electricity, heat, and biofuels. The production of biomass for energy is considered to be an important step in developing sustainable communities and managing greenhouse gas emissions effectively. Biomass properties vary and are commonly associated with plant species. Hence, efficient methods to predict biofuel characteristics will greatly affect the utilization and management of feedstock production. In this paper attempt was made to correlate various chemical characteristics with NIR spectra. Wood chips from various plant species was analyzed for lignin content, heating value, ash content and NIR and the results were evaluated with correlation, PCA and PCR. Initial evaluation showed promising results where chemical components in the wood correlate to NIR spectra. A selection of results will be presented in this paper. Further analysis as well as results from PCA and PCR models will be presented in the full paper version.

The increasing atmospheric CO2 concentration has caused a transformative shift in global energy systems, which is contributing to an increased use of renewables. Sweden is among the countries trying to shift to a fossil-fuel-free system in all energy sectors. This paper addresses the fuel demand and supply in the transportation sector in the county of Vastmanland in Sweden. A Mixed Integer Linear Programming optimization model is developed to minimize cost in the studied system. The model is further used to investigate the influence of three different scenarios on production planning of regional Combined Heat and Power (CHP) plants: (1) straw-based biofuel production integrated with existing CHP plants to fuel combustion engine vehicles, (2) use of electric vehicles, and (3) use of hybrid vehicles fueled by both electricity and bioethanol. Potential solar power generation from rooftop solar cells is also included in the model. The energy system in scenario 2 is found to have the highest overall system efficiency; however, a large amount of power needs to be imported to the system. Hybrid vehicles can potentially reduce the electricity import and CO2 emissions compared to the current situation. Electricity production from rooftop solar collectors could provide the energy needs of the vehicles during summer, while regionally produced straw-based bioethanol integrated with CHP plants can satisfy the fuel needs of the vehicles in winter. This approach could affect the production planning of CHP plants, result in less fuel use and increase the share of renewable resources in the regional transportation system.

In Northern Europe, the production of many biobased CHP plants is getting affected due to the enormous expansion of wind and solar power. In addition, heat demand varies throughout the year, and existing CHP plants show less technical performance and suffer economically. By integrating the existing CHP plants with other processes for the production of chemicals, they can be operated more hours, provide operational and production flexibility and thus increase efficiency and profitability. In this paper, we look at a possible solution by converting an existing CHP plant into integrated biorefinery by retrofitting pyrolysis and gasification process. Pyrolysis is retrofitted in an existed CHP plant. Bio-oil obtained from pyrolysis is upgraded to vehicle grade biofuels. Gasification process located upfront of CHP plant provides the hydrogen required for upgradation of biofuel. The results show that a pyrolysis plant with 18 ton/h feed handling capacity (90 MWth), when integrated with gasification for hydrogen requirement and CHP plant for heat can produce 5.2 ton/h of gasoline/diesel grade biofuels. The system integration gives positive economic benefits too but the annual operating hours can impact economic performance.

A dynamic model is presented for a biomass-fired circulating fluidized bed boiler. The model is based on energy and mass balances for the components in the boiler and on a combustion model for the ...

This paper discusses the accuracy of different types of models. Statistical models are based on process data and/or observations from lab measurements. This class of models are called black box mod ...

This paper covers a method for operator decision support, where physical simulation models are used to connect different physical variables to each other. By comparing energy and material balances ...

A lot of efforts are normally done to design plants in an efficient way. Still, most plants are having varying production qualities and quantities and both the diagnostics of sensors and process performance and advanced control for optimal production are essential. Useful methods and applications of these for both power plant and process industry applications are presented. The benefits of using, for example, simulators are described in connection with start-up of new plants and the importance of diagnostics for maintenance of demand. Examples are shown where statistical methods are used to predict pulp paper quality properties and physical models to perform data reconciliation and decision support. The data reconciliation predicts true values for sensors that are drifting while decision support is giving advice when any kind of possible problem has been detected. In addition, process optimization and model-based control are discussed. Keywords: energy; process industry; simulation; diagnostics; optimization

To perform advanced model based control it is important to know what is fed into a system such as a waste or biomass fired boiler or a pulp digester. In this paper, we present correlations between ...

The implementation of model-based control and diagnostics suffer strongly from the fact that models deteriorate as a function of process and sensor deterioration. Also, changes in the raw material (i.e. wood) may occur and often the process control is not addressing these variations in reality. It is thus vital for the model system to be robust in the sense that it is transparent and easy for the operator to maintain. Robustness is essential in many parts of the system, including measurement, process model validation, the ability of the model to adapt to changes in the process, optimization algorithms, and of course the model itself. In this paper, we first show three real-life applications of the utilization of models for diagnostics and control. Thereafter conditions for on-line adaptation of the models are discussed. The challenges when designing such a system are in achieving operator confidence, filtering of misleading measured data, adaptation of process parameters when the process parameters change, and combining validation of measurements and process models. These challenges are met by using a combination of physical and statistical models and methods based on them such as model predictive control (MPC) and parameter estimation. The model should be maintained by a qualified engineer who should be able to explain the system to the operator so that it is understood and confidence can be maintained.