Finland has recently adopted a high profile in climate change mitigation. Finland has declared a national target of achieving carbon neutrality by 2035. As a part of this, the use of coal for energy ...purposes has been banned from year 2029 onwards. The Finnish electricity system is already very low-carbon, and more wind and nuclear power is being constructed. However, District heating (DH) is a backbone of the Finnish energy system, and it is still quite reliant on fossil fuels and domestic high-emission fuel peat, their share being 51% of DH fuels in 2018. This paper models the impacts of this transition on the electricity markets and DH systems and develops scenarios with a large-scale transition to wind and nuclear power and heat pumps in DH systems. The study finds that large-scale introduction of heat pumps would be profitable in cities Helsinki, Espoo, Turku and Vantaa, especially with the planned decrease of electricity tax. The study indicates that the impacts on Winter time capacity adequacy could be managed, but this requires considerable increases in nuclear and wind capacity.
•Finland abandons hard coal use by 2029 as part of achieving carbon neutrality.•Increasing wind and nuclear power mitigates electricity price increase and capacity deficit.•Heat pumps can provide alternative to coal-CHP.•Electricity distribution fee and tax limit economic potential of heat pumps.
We simulate Finnish future energy system with large amounts of CHP (combined heat and power) and wind power. The Nordic countries have ambitious wind power targets, which means a substantial need for ...balancing power. One third of electricity in Finland is produced by CHP, and a large amount of nuclear power is running constantly as base load. There is significant correlation in wind power patterns across a large geographical area in Northern Europe, so the interconnected networks don't solve the balancing problem completely. A precautionary principle is to have ability to balance the electricity production and consumption on a national level. CHP with thermal storages could be economical and technically easy option for balancing. This alternative has been largely neglected in European studies. We simulate future wind power by upscaling existing hourly data. The economically optimal storage size was here found to be from the current 0.3% up to 30% of the total annual heat demand, depending on the wind power share and carbon trade price. We find that the use of economically optimal thermal storage can increase CHP production by 15% in the case of wind energy providing 24% of the total electricity production in Finland.
•Large wind power share necessitates heat storages in CHP (combined heat and power) systems.•CHP with storages replaces condensing power and increases the system efficiency.•Without storages the system efficiency decreases.•High wind power share and emission trade price favour large storages.•Optimal storage size is from current 0.3% of the annual district heat use to even 30%.
The city of Espoo, Finland is planning to develop Kera as a green suburb with high level of energy efficiency and low CO2 emissions, using a high share of renewable energy and recycled or reused ...energy. For reaching this target, in this study, renewable energy resources such as solar, wind and waste heat are investigated for the study region. Two different technologies comprising heat pump (HP) and heat-only boiler (HOB) are investigated to retrieve waste heat from a data centre and LuxTurrim5G smart poles to use in a low-temperature district heating network. We investigate various scenarios to supply the required energy for the HP (which receives electricity from the electricity market, photovoltaic (PV) system, wind turbine (WT) and hybrid PV/WT; 4 scenarios) and HOB (which works with electricity, forest fuel wood, biogas, ammonia, wood pellets and industry wood residue; 6 scenarios). We found that the heat pump scenario is an efficient and cost-effective way to retrieve waste heat from the data centre and 5G smart poles with an LCOE of 3.192 ¢/kWh if electricity is produced by the PV system, and 3.516 ¢/kWh when the heat pump receives its electricity only from the electricity market.
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•An ejector heat pump was designed to retrieve waste heat from data center and 5G Smart Poles.•The possibility of using 5G Smart Poles as a heat source for heat pumps was investigated.•Heat pump system was compared with heat only boiler for waste heat recovery.•PV and wind turbine were investigated to supply the electricity demand of heat pump.•Heat pump/PV scenario was the cost-effective way to retrieve waste heat with LCOE of 3.192 ¢/kWh.
Assumptions related to energy generation often play a decisive role in abatement studies for estimating the effects of demand-side interventions. However, there can be significant geographical and ...temporal variation in the emission intensities of electricity generation in different regions. The aim of this article is to describe how spatial and temporal variations related to a particular energy system may affect an electricity generation unit operating on the margins. The approach takes the generation mix within interconnected electricity markets and the exchange of electricity between these markets into account. The short-term (2009–2010) and long-term (until 2030) hour-by-hour marginal electricity generation unit and marginal emission intensities are identified for electricity use, using the Finnish, Nordic and European energy systems as actual cases. The estimated marginal electricity generation technology and marginal emission intensities for electricity use differed significantly within the studied time horizon and between the studied countries and energy systems. Furthermore, due to the projected structural changes in the energy systems, i.e. changes in the fuel mix and electricity generation technologies, the carbon dioxide (CO2) intensity of marginal electricity generation will decrease in the Nordic countries and in the EU in the long-term. However, the approach used for calculating the effect of change in electricity exchange on the margin increased the variability of the results considerably for some Nordic countries, such as Sweden and Norway, whose export of electricity was high and whose marginal generation mix differed significantly from the European system. Furthermore, the spatial and temporal variations of marginal electricity generation will increase in the future. This variation, combined with the interconnections between market areas for exchanging electricity in the EU will require improved understanding of the impacts of exchanged marginal electricity generation on the possible emission leakage between EU countries to better inform policy decisions.
•Marginal generation technology varies in the Nordic countries and in the European energy systems.•Marginal generation technology can operate beyond the boundaries of particular energy system.•Marginal generation technology varies, especially from a seasonal perspective.•Spatial and temporal variations in marginal CO2 emissions for electricity use are large in Europe.
In this study, an energy and economic analysis of a novel hybrid system based on a biomass/solar system equipped with a multi-effect desalination unit is carried out. Part of the steam was directed ...into a heat exchanger to supply the required energy for the multi-effect desalination unit. The capacity of the system was 100 MWe. The proposed system is compared with a photovoltaic power plant, a solar dish/Stirling power plant, a biomass power plant, and a linear Fresnel Reflector solar power plant. The results demonstrate that a solar thermal collector can be considered a promising solution to prevail the problem of the increasing boiler temperature. It reduces the amount of fuel consumption throughout the year, resulting in reducing the levelized cost of energy. Increasing the solar irradiance augments the energy efficiency gains of a hybrid system. The comparison of the power plants showed that the proposed hybrid system had the minimum levelized cost of energy by 7.865 cents of dollar/kWh but has only slightly higher capital investment cost. Additionally, of the solar power plants studied, a photovoltaic system is more efficient for the study region, Natal-RN Brazil, by a levelized cost of energy of 10.45 cents of dollar/kWh.
•Energy and economic analysis of a novel hybrid system is carried out.•The hybrid system was designed based on biomass/solar system equipped with multi effect desalination unit.•The proposed system was compared with solar power plants.•The hybrid system possesses the minimum levelized cost of energy by 7.865 cents of dollar/kWh.
Heat pumps have rapidly gained popularity in the Nordic area, as they are marketed to provide considerable monetary savings and CO2 emission reductions. Heat pumps are installed even in buildings ...heated by CHP (combined heat and power production). In this paper we calculate CO2 emission factors of DH (district heating) from CHP and GSHP (ground source heat pumps) in Finland, based on hourly data at present and in various future scenarios. In LCA (life cycle assessment) analyses, usually only annual averages are used. We show that including seasonal variation can result in very different emission factors. Since during warm seasons, electricity production is significantly less carbon-intensive than in cold seasons. We find that the current emission factor of CHP DH consumption change is only 70–100 g/kWh. In the future it is 0…300 g/kWh, depending on the CO2 intensity of electricity production. The similar GSHP emission factor would develop from the present 200 g/kWh to 50…200 g/kWh. As long as electricity consumption has seasonal variation or coal condensing power is significant in the interconnected network, CHP has lower emissions than GSHP. We recommend using CLCA (consequential LCA) methodology and the inclusion of seasonal variation in heating option comparisons.
•In life cycle analyses, too high CO2 emissions are usually used for CHP heat.•Seasonal variation should be considered in life cycle assessment of heating options.•CHP is more efficient in terms of CO2 reduction than heat pumps in most future scenarios.•Only with very ambitious climate policy, heat pumps may be a better solution.
•Developing an intelligent approach for modeling of geothermal organic Rankine cycle.•The intelligent methods are ANFIS optimized with PSO (ANFIS-PSO) and MLP-PSO.•Intelligent methods are employed ...for thermodynamic and economic modeling of the system.•Intelligent methods have shown an excellent modeling ability.
Geothermal energy is a renewable resource that is constantly available. The low geothermal well operating lifetime is the main challenge in using this type of renewable energy. This problem can be covered by the aid of solar system (hybrid system). For complicated renewable energy systems, finding the optimum design parameters and operating conditions require to develop experimental apparatus or sophisticated thermodynamic models. Hence, in this study, artificial intelligence (AI) approach is proposed for modeling the geothermal organic Rankin cycle (GORC) equipped with solar thermal unit. Indeed, the current study depicts how AI methods can meticulously simulate the operation of a complicated renewable energy system. The developed intelligent methods are adaptive neuro-fuzzy inference system (ANFIS) optimized with particle swarm optimization (PSO) algorithm (ANFIS-PSO) and multilayer perceptron (MLP) neural network optimized with PSO algorithm (MLP-PSO). The models are composed based on the main design parameters of the geothermal system that are solar radiation, well temperature, working fluid mass flow rate, turbine output pressure, surface area of the solar collector and preheater inlet pressure. The intelligent models use the mentioned input variables to predict the net power output, energy efficiency, exergy efficiency and levelized cost of energy (LCOE) of the GORC. Energy, exergy and economic analyses are carried out for the low global warming potential (GWP) refrigerants. It was found out that although the intelligent models can meticulously predict the targets, ANFIS-PSO performs better than MLP-PSO for modeling the GORC with solar system. Root mean square error of this model for prediction of power generation, energy efficiency, exergy efficiency and LCOE was 12.023 (kW), 3.587 ×10-4, 3.278 ×10-4 and 1.332 ×10-4, respectively.
Data from two thermo-mechanical pulp mills are collected to simulate the refining process using deep learning. A multilayer perceptron neural network is utilized for pattern recognition of the ...refining variables. Results show the impressive capability of artificial intelligence methods in refining energy simulation so that the correlation coefficient of 98% is accessible. A comprehensive parametric study has been made to investigate the effect of refining disturbance variables, plate gap and dilution water on refining energy simulation. The generated model reveals the non-linear hidden pattern between refining variables, which can be used for optimal refining control strategy. Considering the disturbance variables' effect in refining energy simulation, model accuracy could increase by 15%. Removing the plate gape from predictive variables reduces the simulation determination coefficient by up to 25% in both mills, while the mentioned value for removing dilution water is 9-17% in mill 1 and about 35% in mill 2.
For geothermal energy system, the low geothermal well operating lifetime and temperature is one of the main obstacles. Also, finding the optimum design parameters and operating conditions requires ...many experimental tests and intricate mathematical models. Besides, improving the energy efficiency and lacking technical feasibility of the combined geothermal systems are other challenges concerning geothermal systems. To cover the mentioned challenges, in the current study, a hybrid geothermal/absorption refrigeration system (ARS) incorporated with solar thermal collector, desalination unit and hydrogen storage system is designed and assessed. The proposed system is investigated by developing two methods of artificial intelligence (AI) as well as thermodynamic model. The intelligent methods are multilayer perceptron (MLP) neural network optimized with imperialist competitive algorithm (ICA), MLP-ICA, and MLP optimized with genetic algorithm (GA), MLP-GA. These methods are manufactured based on the solar irradiance, cooling water temperature difference, ambient temperature, pinch-point temperature, evaporating and condensing temperatures as independent parameters. These parameters are utilized to obtain the power generation, coefficient of performance of the ARS (COPchiller), heat exchanger area of the ARS, and cycle thermal efficiency.
The obtained results show that simulation of the system by MLP-ICA was successfully carried out and this model operates substantially better than the MLP-GA for simulating the behavior of the system. Also, the payback time for the proposed system (with the interest rate of 3%) was obtained around 8 years.
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•A hybrid system based on geothermal energy combined with solar system was designed.•Desalination unit and hydrogen storage system were integrated into the geothermal system.•MLP-ICA and MLP-GA were developed to simulate the behavior of the hybrid system.•MLP-ICA operates better than the MLP-GA for modeling the hybrid system.•The payback time of the hybrid system with the interest rate of 3% is around 8 years.
In this paper, the likely impacts of the EU emission trading system on the Nordic electricity market and on the position of various market actors are assessed. In its first phase, the EU CO"2 ...emission trading system includes power plants with thermal capacity greater than 20 MW, metals industry, pulp and paper industry, mineral industry and oil refineries. This paper describes the assessment done for the Finnish Minister of Trade and Industry, analysing the likely impacts on power plant operators, on energy-intensive industries, on other industries and on other consumer groups. The impacts of emissions trading were studied with the VTT electricity market model and with the TIMES energy system model. The annual average electricity price was found to rise 0.74 EUR MW h-1 for every 1 @? tonne CO"2-1 in the Nordic area. Large windfall profits were estimated to incur to electricity producers in the Nordic electricity market. In Finland, metals industry and private consumers were estimated to be most affected by the electricity market price increases. Expanded nuclear power generation could limit the increases in the prices of electricity to one-third compared to those in the base case. All rights reserved, Elsevier
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