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Network temperatures in district heating systems are important operational factors for obtaining efficient performance. A low network return temperature allows for the recovery of low-grade heat from assets such as condensing boilers, waste incineration, geothermal sources and industrial waste heat. Fluctuations in heating and cooling demands affect the return temperatures of the building substations and in the network. This variability impacts the economic viability and environmental sustainability of the entire system. This paper presents a nonlinear optimization strategy to maintain sufficient energy flows in the network's primary and secondary circuits to achieve low return temperatures from all substations in the network. The defined optimization strategy incorporates the thermodynamic model of the substation and building heating system as opposed to traditional weather-based supply temperature adjustments. The estimated heat demands and tariffs, CO2 penalties are inputs used by the optimizer to find theoptimal solution. The total operational expenditure for electricity and gas consumption shows an 18% reduction with 8% reduction in emissions and 6% efficiency improvement when compared with the measured weather-based approach. The developed strategy will aid the network operators in the economic dispatch of heat generation while ensuring the user's thermal comfort.

Network temperatures in district heating systems are important operational factors for obtaining efficient performance. A low network return temperature allows for the recovery of low-grade heat from assets such as condensing boilers, waste incineration, geothermal sources and industrial waste heat. Fluctuations in heating and cooling demands affect the return temperatures of the building substations and in the network. This variability impacts the economic viability and environmental sustainability of the entire system. This paper presents a nonlinear optimization strategy to maintain sufficient energy flows in the network's primary and secondary circuits to achieve low return temperatures from all substations in the network. The defined optimization strategy incorporates the thermodynamic model of the substation and building heating system as opposed to traditional weather-based supply temperature adjustments. The estimated heat demands and tariffs, CO2 penalties are inputs used by the optimizer to find theoptimal solution. The total operational expenditure for electricity and gas consumption shows an 18% reduction with 8% reduction in emissions and 6% efficiency improvement when compared with the measured weather-based approach. The developed strategy will aid the network operators in the economic dispatch of heat generation while ensuring the user's thermal comfort.

The focus of this study is to analyse and compare the predictive capabilities of univariate and multivariate methods of forecasting the global horizontal irradiance (GHI) for an hour ahead. The forecasting problem is addressed using supervised machine learning methods. In order to simplify the model, a feature selection algorithm is used to identify the highly correlated features. The forecasting is performed by utilizing popular machine learning algorithms viz., random forest (RF), K-nearest neighbors regression (KNN), support vector machine (SVM) and artificial neural networks (ANN). The paper evaluates and contrasts the effectiveness of these models for this application. Additionally, the study examines how the forecasting models' performance varies throughout the year and across seasons.

The focus of this study is to analyse and compare the predictive capabilities of univariate and multivariate methods of forecasting the global horizontal irradiance (GHI) for an hour ahead. The forecasting problem is addressed using supervised machine learning methods. In order to simplify the model, a feature selection algorithm is used to identify the highly correlated features. The forecasting is performed by utilizing popular machine learning algorithms viz., random forest (RF), K-nearest neighbors regression (KNN), support vector machine (SVM) and artificial neural networks (ANN). The paper evaluates and contrasts the effectiveness of these models for this application. Additionally, the study examines how the forecasting models' performance varies throughout the year and across seasons.

Abstract: To maximize the sustainable and economic benefits of collective heating systems, proper sizing is fundamental. This paper presents the validation of a novel sizing approach for collective systems producing and distributing heat for both space heating and domestic hot water, utilizing residential heat meter data. A validation methodology is developed to overcome the limitations of this type of data to identify the peak heat demand and estimate the peak heat demand under design outdoor conditions. The latter is estimated utilizing multiple linear regression coupled with an analysis of the maximum deviations. The power-storage characteristic, which shows all combinations of thermal power and thermal storage to meet the peak heat demand is determined and used to validate the novel sizing approach for six case studies. Although the results are promising, undersizing problems may arise in cases with decentralized heat storage

Abstract: To maximize the sustainable and economic benefits of collective heating systems, proper sizing is fundamental. This paper presents the validation of a novel sizing approach for collective systems producing and distributing heat for both space heating and domestic hot water, utilizing residential heat meter data. A validation methodology is developed to overcome the limitations of this type of data to identify the peak heat demand and estimate the peak heat demand under design outdoor conditions. The latter is estimated utilizing multiple linear regression coupled with an analysis of the maximum deviations. The power-storage characteristic, which shows all combinations of thermal power and thermal storage to meet the peak heat demand is determined and used to validate the novel sizing approach for six case studies. Although the results are promising, undersizing problems may arise in cases with decentralized heat storage

Abstract: To achieve net-zero emissions by 2050, as outlined in the European Green Deal, nuclear power is expected to double between 2020 and 2050, mainly due to its low-carbon baseload capacity. Small modular reactors, new nuclear reactors designed to generate up to 300 MW of electricity, could help achieve this goal. Small modular reactors have unique advantages over existing large reactors, such as modularization, learning and co-location economics. However, these small modular reactors should also be economically viable. This review therefore focuses on the costs of small modular reactors. This review found an average capital cost of €7,031/kW and an average levelized cost of electricity of 85 €/MWh for small modular reactors, while capital costs were found to be on average 41% higher than for the large reactors. Carbon and gas prices are not included in this cost estimate, yet these volatile prices also affect small modular reactor costs. However, as the absolute cost is lower, the financial risk is lower for small modular reactors. The importance of regulations, discount rates, country and project specifications and public acceptance are also considered.

Abstract: To achieve net-zero emissions by 2050, as outlined in the European Green Deal, nuclear power is expected to double between 2020 and 2050, mainly due to its low-carbon baseload capacity. Small modular reactors, new nuclear reactors designed to generate up to 300 MW of electricity, could help achieve this goal. Small modular reactors have unique advantages over existing large reactors, such as modularization, learning and co-location economics. However, these small modular reactors should also be economically viable. This review therefore focuses on the costs of small modular reactors. This review found an average capital cost of €7,031/kW and an average levelized cost of electricity of 85 €/MWh for small modular reactors, while capital costs were found to be on average 41% higher than for the large reactors. Carbon and gas prices are not included in this cost estimate, yet these volatile prices also affect small modular reactor costs. However, as the absolute cost is lower, the financial risk is lower for small modular reactors. The importance of regulations, discount rates, country and project specifications and public acceptance are also considered.

Vacuum pressure swing adsorption (VPSA) has demonstrated promising features for the upgrading of biogas to biomethane. In this study, a biogas upgrading plant, comprised of a hybrid of an N-column VPSA unit (2 ≤N≤ 6) with a combined heat and power (CHP) engine, was developed and its techno-economic characteristics were assessed via a mathematical approach. Moreover, the techno economic analysis was used for the state-of-the-art VPSA configuration (a sophisticated configuration) and compared with the developed hybrid process. The prominent parameters including feedstock transport, biogas production, desulfurization, drying, upgrading, combustion, and grid injection were considered in the analyses of the plant for the upgrading capacity in the range of 100 - 6,500 Nm3/h. Sensitivity analysis of the most influencing parameters, i.e., electricity price, gas price, and feed processing revenues, was conducted for the developed models. Beside comparing upgrading cost of the sophisticated VPSA with other upgrading technologies, a detailed comparison with the best available membrane unit for biogas upgrading was conducted. The limitations of adsorption process and VPSA in reducing the upgrading cost were also investigated. The results showed that in the absence of subsidies and requirements for CO2 capture, the hybrid plant outperforms the sophisticated VPSA units. Also, higher market price of natural gas or feedstock processing revenues were necessary in order to render the plant profitable. The results showed that, at flowrates larger than 175 Nm3/h, the sophisticated VPSA unit required a lower investment cost than the membrane unit for identical outputs. The results also show that even at the most idealistic conditions in the adsorption process, the upgrading is not economically favorable without subsidies. The findings of this study shed light on the importance of process design for biogas upgrading.