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This paper evaluates system aspects of biorefineries based on biomass gasification integrated with pulp and paper production. As a case the Billerud Karlsborg mill is used. Two biomass gasification concepts are considered: BIGDME (biomass integrated gasification dimethyl ether production) and BIGCC (biomass integrated gasification combined cycle). The systems analysis is made with respect to economic performance, global CO2 emissions and primary energy use. As reference cases. BIGDME and BIGCC integrated with district heating are considered. Biomass gasification is shown to be potentially profitable for the mill. The results are highly dependent on assumed energy market parameters, particularly policy support. With strong policies promoting biofuels or renewable electricity, the calculated opportunity to invest in a gasification-based biorefinery exceeds investment cost estimates from the literature. When integrated with district heating the BIGDME case performs better than the BIGCC case, which shows high sensitivity to heat price and annual operating time. The BIGCC cases show potential to contribute to decreased global CO2 emissions and energy use, which the BIGDME cases do not, mainly due to high biomass demand. As biomass is a limited resource, increased biomass use due to investments in gasification plants will lead to increased use of fossil fuels elsewhere in the system. Original Publication:Elisabeth Wetterlund, Karin Pettersson and Simon Harvey, Systems analysis of integrating biomass gasification with pulp and paper production - Effects on economic performance, CO2 emissions and energy use, 2011, ENERGY, (36), 2, 932-941.http://dx.doi.org/10.1016/j.energy.2010.12.017Copyright: Elsevier Science B.V., Amsterdam.http://www.elsevier.com/

Herein, we report on a combined process that incorporates hydrothermal liquefaction (HTL) and supercritical water gasification (SCWG) to improve energy recovered from algal biomass. Eight algal biomasses, including four microalgae and four macroalgae with a large difference in biochemical compositions, were screened for this dual process. The algal biomass feedstocks significantly affected the carbon and energy distribution in the product fractions (crude bio-oil, solid, gas, and water-soluble products). 62.50–71.34% energy of microalgae and 6.03–41.06% energy of macroalgae could be recovered as crude bio-oil. 11.86–21.55% carbon of the microalgae and 8.01–15.82% carbon of the macroalgae was distributed in the HTL process water in form of water soluble products after the HTL process. 14.3–33.7% energy of microalgae and 30.18–36.34% energy of macroalgae was retained in the HTL process water. SCWG could convert the organics in the HTL process water into fuel gases consisting mainly of H2 and CH4. 54–91% carbon of the HTL process water was transformed into the fuel gases, which correspond 5.53–18.30% energy of the algal biomass. Thus, this work shows that the integration of HTL and SCWG could improve energy recovery from algal biomass relative to the HTL process alone.

The pyrolysis of food waste has a double environmental advantage as it contributes to the management and treatment of waste and allows the production of renewable fuels. Spent coffee and tea grounds, were characterized by thermogravimetry to determine their composition and evaluating the pyrolysis kinetics of each lignocellulosic pseudocomponent and pyrolyzed in a fixed bed reactor. Tea grounds had about twice the cellulose and higher pyrolysis activation energy than the coffee grounds sample. At 673 K the pyrolysis of the coffee grounds led to a 42% bio-oil yield while the tea grounds produced only 18% of liquid product, which is compatible with its higher cellulose content and the higher activation energy for pyrolysis. The alkaline carbonates used as pyrolysis catalysts led to an increase in the production of a gaseous product, bio-gas, with a reduction in the production of bio-oil but accompanied by a significant increase in the volatile fraction of the produced bio-oils. Pyrolysis data shows that both coffee and tea residues can be used as raw materials to produce pyrolysis bio-oil and that low-value materials such as alkaline carbonates can be used as pyrolysis catalysts improving the characteristics of bio-oils produced such as acidity and volatility.

Microbial fuel cell (MFC) technology allows renewable energy production from the decomposition of organic matter present in wastewater. This technology, however, poses challenges that hinder scaling up of its usage in practical implementation. Also, resource recovery from the available waste sources is attracting attention due to the immense amount of valuable nutrients present in the wastewater. Herein, the present study focuses on investigating high-performance anodes, that is, using a stainless-steel brush (SSB) and carbon brush (CB) to accommodate the MFC in terms of energy production, leading to transformation into a microbial nutrient recovery system (MNRS) for nutrients’ recovery from wastewater. The present novel SSB anode system generated a stable voltage of ∼0.4V and a maximum power density of ∼400 mW/m2 whereas CB anode system produced the voltage and maximum power density of ∼0.5 V and ∼800 mW/m2, respectively. Besides, using the MNRS process, recovery of nutrients in terms of PO43− (80–83.4% for CB-MNRS and 60–69.5% for SSB-MNRS when using feed PO43− concentration of 10–20 ppm) and NH4+ (63–80.64% for the CB-MNRS and 52–70% for the SSB-MNRS when using feed NH4+ with a concentration of 10–20 ppm) was achieved.

This study aims to assess the torrefaction of biomass as alternative renewable energy fuel to coal during co-firing. It was evaluated that torrefaction improves biomass grindability to such an extent that it can be used in coal mills with coal in co-firing without capital intensive modification. Torrefaction of beech wood was performed on a batch scale reactor at three different temperatures (200, 250 and 300 °C) with 30 min of residence time. The chemical structural changes in torrefied biomass were investigated with binding energies and FTIR (Fourier transform infrared) analysis. Monocombustion and co-combustion tests were performed to examine the combustion behaviour regarding flue gas emissions (CO, NOx and SO2) at 0.5, 1.5 and 2.5 m distance from the burner opening along with fly ash analysis. The FTIR and binding energies showed that lignin hardly affected during light torrefaction while hemicellulosic material was significantly depleted. The Hardgrove grindability index (HGI) was calculated with three methods (DIN51742, IFK and ISO). The medium temperature torrefied biomass (MTTB) yields HGI value in the range of 32–37 that was comparable with HGI of El Cerrejon coal (36–41). A slight change in temperature enabled the torrefied beech wood to be co-milled with coal without capital intensive modification and improved grindability. Comparing the combustion behaviour of single fuels, low temperature torrefied biomass (LTTB) produces less amount of NOx (426 mg/m3), CO (0.002 mg/m3) and SO2 (2 mg/m3) as compared MTTB and raw beech wood. In the case of co-combustion, it was found that blending of coal with raw biomass does not show a stable behaviour. However, premixing of 50% of coal with 50% of torrefied biomasses (MTTB and LTTB) gives most stable behaviour and reduces NOx almost 30% and SOx up to almost 50% compared to coal. The fly ash contents analysis proved that K2O contents much decreased during co-firing of coal and torrefied fuels that could cause ash related issues during combustion of raw biomass.

© 2019 This study conducted a life cycle assessment of palm oil mill effluent (POME) based energy generation using the CML 2001 method and Gabi 8 software, focusing on two POME treatment technologies: the covered lagoon bio-digester (CLB) and the continuous stirred tank reactor (CSTR). The analysis determined the respective environmental impacts of the technologies, both of which are currently in use in Malaysia. The global warming potential (GWP) and acidification potential (AP) for CSTR were −4.48 kg CO2 eq/kWh and −2.21 kg SO2 eq/kWh respectively, while for CLB the values were −4.09 kg CO2 eq/kWh and −0.15 kg SO2 eq/kWh. Both technologies produced a negative result, which equates to a net environmental benefit. However, both systems had a negative impact in terms of eutrophication potential (EP). The CSTR nevertheless achieved a better EP result of 0.048 kg PO43− eq/kWh than the CLB with 0.054 kg PO43− eq/kWh. A sensitivity analysis was carried out in order to find a way to overcome the impacts of EP. The findings provide useful data to guide decision-makers in the sustainable management of POME, in Malaysia and globally where similar technologies are in use.

Wave energy has significant worldwide exploitable resource and its exploitation has attracted renewable energy investigator’ attention. Great progress on calculating device performance has been made by means of theoretical, numerical and model tests. This paper presents a method of calculating the energy extraction of a wave energy converter (WEC) based on Wave Height Take-off (WHTO). The method provides a means to improve the capture efficiency of designs, including demonstrating how well different kinds of WEC are optimized for certain wave conditions. Numerical simulations of a heaving buoy and a bottom-hinged pendulum in a 2D wave flume with different damping types (linear and nonlinear) are presented. The results show that the difference between the calculated energy extraction from the wave height reduction and from the model power take-off (PTO) was not significant in a 2D flume. Physical model tests were conducted using a simplified PTO consisting of a system of lifting weights, used to measure the energy extraction directly. Based on both numerical and physical model analyses, the article defines WHTO, which is equivalent to energy extracted by PTO, but determined without taking direct measurements. This paper aims to promote and validate the concept of the WHTO.

In a transition to 100% renewable energy, public regulation has to deal with, among other issues, price efficiency, security of supply, and the transition from sector based fossil fuel systems to sector integrated smart energy systems based on energy conservation and renewable energy. Most studies and practical policies only focus on building “green incentives” into the money flows. There is less research focus on the importance of citizen and consumer ownership models in the green energy transition process. This is problematic, as different ownership models have different influences on price efficiency as well as the ability of the smart energy system to integrate large amounts of fluctuating energy. The purpose of this paper is to analyze citizen and consumer ownership models both with regard to their influence on consumer prices and their capability to handle the multitude of coordination tasks in a transition from sector based to integrated smart energy systems. A consumer ownership model has positive potentials both in terms of maintaining low energy prices and securing low coordination transaction costs in smart energy systems. The realization of these positive potentials is dependent on the concrete institutional context and public regulation in which a given ownership construction is embedded. In a transition to 100% renewable energy, public regulation has to deal with, among other issues, price efficiency, security of supply, and the transition from sector based fossil fuel systems to sector integrated smart energy systems based on energy conservation and renewable energy. Most studies and practical policies only focus on building “green incentives” into the money flows. There is less research focus on the importance of citizen and consumer ownership models in the green energy transition process. This is problematic, as different ownership models have different influences on price efficiency as well as the ability of the smart energy system to integrate large amounts of fluctuating energy. The purpose of this paper is to analyze citizen and consumer ownership models both with regard to their influence on consumer prices and their capability to handle the multitude of coordination tasks in a transition from sector based to integrated smart energy systems. A consumer ownership model has positive potentials both in terms of maintaining low energy prices and securing low coordination transaction costs in smart energy systems. The realization of these positive potentials is dependent on the concrete institutional context and public regulation in which a given ownership construction is embedded.

This paper provides a perspective on the development of future district heating systems and technologies and their role in future smart energy systems. The reviewed papers elaborate on or otherwise contribute to the theoretical scientific understanding of how we can design and implement a suitable and least-cost transformation into a sustainable energy future. Focus is on the important role of the next generation of district heating and cooling technologies. The status of the scientific contributions demonstrates a high level of understanding of how to deal with the technical aspects. The primary current challenge seems to be the understanding of the implementation of these.

The transition to a 100% renewable energy system based on variable renewable energy raises technical but also institutional questions. The smart energy system concept integrates variable renewable energy by addressing the technical challenges through the integration of different energy sectors, but integration of variable renewable energy also entails a change in the cost structures, especially related to electricity. The effect of this change in cost structures on market prices is investigated. This is done through simulation of a 100% renewable energy system that utilises a large degree of cross-sector integration but maintaining the current electricity market structure. The paper uses a 100% renewable energy system scenario for a 2050 Danish energy system. This is reflected in the use of wind energy as the primary renewable energy source. It is concluded that the current electricity market structure is not able to financially sustain the amounts of wind power necessary for the transition to a 100% renewable energy system. Since earlier research shows that neither electricity production costs nor the total system costs is higher for the renewable path than the fossil-based alternatives, the conclusion in this paper points towards a need for reshaping the institutional structure of electricity trade. The transition to a 100% renewable energy system based on variable renewable energy raises technical but also institutional questions. The smart energy system concept integrates variable renewable energy by addressing the technical challenges through the integration of different energy sectors, but integration of variable renewable energy also entails a change in the cost structures, especially related to electricity. The effect of this change in cost structures on market prices is investigated. This is done through simulation of a 100% renewable energy system that utilises a large degree of cross-sector integration but maintaining the current electricity market structure. The paper uses a 100% renewable energy system scenario for a 2050 Danish energy system. This is reflected in the use of wind energy as the primary renewable energy source. It is concluded that the current electricity market structure is not able to financially sustain the amounts of wind power necessary for the transition to a 100% renewable energy system. Since earlier research shows that neither electricity production costs nor the total system costs is higher for the renewable path than the fossil-based alternatives, the conclusion in this paper points towards a need for reshaping the institutional structure of electricity trade.

Water management strategies can have considerable impacts on the regional climate and hydrology. It is usually the case that the construction and operation of hydropower reduce the river flow downstream due to the increase in evaporation. However, this paper shows that in humid regions, such as in Brazil, the hydropower storage reservoirs contribute to increase the flow of the river. This observation has been tested with historical reservoir levels and river flow data from several dams in Brazil. It was found that the operation of reservoirs in Brazil has a considerable impact on its river flows. The higher the storage level at the beginning of the humid period, the higher the river flow during the wet period. The paper proposes strategies to allow the reservoirs to fill up and to maintain the reservoirs filled in the future, with the intention of increasing hydropower generation and reducing the intermittency of other renewable energy sources.

Electric spring (ES) as a breakthrough in power electronics has led to a revolution in demand-side management. This paper presents flexible power management (FPM) of a networked microgrid (MG) in the presence of renewable energy sources (RESs) and flexibility sources (FSs). The FSs include the novel topology of the integrated unit of ES with electric vehicles (EVs) parking (IUEE) and incentive-based demand response program (DRP). The proposed FPM model is formulated as an optimization problem that minimizes the difference between the expected energy cost and the expected profit of FSs' flexibility subject to the AC optimal power flow (AC-OPF), RESs, FSs, and MG flexibility constraints. In the proposed model, flexibility pricing is performed for both upward and downward flexibility services in which the expected flexibility profit is proportional to the product of the FSs' flexibility and flexibility incentive price (FIP). Further, this paper uses a stochastic programming to model uncertain parameters associated with load demand, energy prices, maximum RES generation capacity, and EVs’ parameters. Finally, the potential of the proposed FPM model in improving the economic, operational and flexibility conditions of the MG is investigated through implementation of the proposed model on the 32-bus radial MG.

Small scale hybrid power systems gain popularity around the world as a viable way of reducing power generation environmental impact, reducing energy cost and increasing power supply reliability. Hybrid systems which are based on variable renewable sources usually utilize the effect of resources temporal, and to a smaller extent, spatial complementarity. Although there is already an extensive body of literature investigating the concept of resources complementarity, they rarely addressed the impact of complementarity on the reliability of a given hybrid system. In this paper we simulate the operation of wind and solar hybrid energy system (with and without battery) for evenly distributed 86 locations in Poland over the period 2010–2016 based on 15 min’ time step data. We analyze the impact of resources complementarity (on various time scales: 15 min, hourly and monthly) on the system reliability. To remove the capacity factor (resources availability depends on location) on the results, we select the installed capacity in wind and solar sources in such a way that on an annual scale they generate evenly 50% of the observed demand (which is assumed to be constant = 1 kW). We investigate the impact on complementarity in the system reliability for hybrids with and without energy storage. The second part of the paper deals with the problem of simulating the system reliability (in terms of Loss of Load Parameter) based on multiple linear regression and artificial neural networks. The results indicate that both temporal complementarity (expressed as coefficient of correlation) and storage capacity has non-linear impact on the hybrid system capacity to cover the load. Generated relations between mentioned factors show how complementarity indices may be used to size the solar-wind hybrids. The follow up studies should concentrate on analyzing the operation of hybrids utilizing more than two energy sources and juxtaposing complementarity based reliability assessment with other methods.

The transition in the energy market and the growing share of renewable energy sources have been boosting the research in new power cycles. For example, the concept of batch evaporation in the Misselhorn Cycle promises to increase the overall efficiency in low-temperature applications and therefore saves resources. In this paper, a dynamic evaporator model was extended in order to prove the feasibility of the Misselhorn Cycle despite its transient character. In this context, the thermal capacity of the wall material as well as the residence time of the heat source medium were added. The previous, underlying model predicted an improved system efficiency for the Misselhorn Cycle of about 50% compared to an Organic Rankine Cycle (ORC) at 100+C. Initially, the results of the extended model showed a negative influence of the inertial effects on the possible net power output (advantage over ORC only 10%). However, an unheated discharge phase and reduced dimensions of the heat exchanger could compensate these drawbacks and achieved results (about 40% better than ORC) in the same range as the previous, simple model predicted. These findings prove the general practical feasibility of the Misselhorn Cycle.The transition in the energy market and the growing share of renewable energy sources have been boosting the research in new power cycles. For example, the concept of batch evaporation in the Misselhorn Cycle promises to increase the overall efficiency in low-temperature applications and therefore saves resources. In this paper, a dynamic evaporator model was extended in order to prove the feasibility of the Misselhorn Cycle despite its transient character. In this context, the thermal capacity of the wall material as well as the residence time of the heat source medium were added. The previous, underlying model predicted an improved system efficiency for the Misselhorn Cycle of about 50% compared to an Organic Rankine Cycle (ORC) at 100+C. Initially, the results of the extended model showed a negative influence of the inertial effects on the possible net power output (advantage over ORC only 10%). However, an unheated discharge phase and reduced dimensions of the heat exchanger could compensate these drawbacks and achieved results (about 40% better than ORC) in the same range as the previous, simple model predicted. These findings prove the general practical feasibility of the Misselhorn Cycle.

Kazakhstan's electricity generation depends heavily on fossil fuels. Renewables and other non-fossil resources provide potential alternatives to diversify the electricity generation system. In this paper, various potential local non-fossil fuel resources, hydro, solar, wind, biomass and uranium are reviewed and an assessment framework for prioritizing these resources is established. A multi-criteria decision making approach, analytic hierarchy process (AHP) based on expert opinion, is utilized for developing the assessment model, using four main criteria of technical, economic, social and environmental aspects and thirteen sub-criteria. The review reveal that Kazakhstan has ample potential to develop a non-fossil fuel based electricity system. Furthermore, the model shows hydro to be the most favorable resource followed by solar; wind and nuclear are ranked third and fourth, respectively while biomass is found to be the least attractive option. It is also found that each resource is inclined towards a particular criterion; hydro towards social, solar towards economic, nuclear towards technical, with biomass and wind directed towards environmental. Besides reporting the use of the AHP model for the first time in the Kazakhstan context, the assessment carried out in this paper can assist decision-makers to articulate long-term energy policy for any country.

Natural gas (NG), as one fossil fuel with multiple applications, plays a major role in Europe. However, to face global climate change and accelerate the transition to a sustainable society, fossil fuels, such as NG have to be replaced in the long term. The aim of this paper is to evaluate the technical and economic aspects of emerging technology concepts for the production of renewable methane from biomass. Biochemical and thermochemical concepts are considered within the scope of this paper. To evaluate the different conversion technologies an adapted AHP (analytic hierarchy process) was developed, based on two methods AHP and utility value analysis. In total, 99 alternatives (bio- and thermochemical conversion) were evaluated. From the multi-criteria analysis, no single technology pathway was given preference. However, the choice of substrate was found to be a critical factor in comparing the technology options. Within biochemical conversion, organic waste alternatives were favoured, for the thermochemical conversion, residual forest wood. The goal, a comprehensive comparison of two different conversion pathways on a multi-criteria scale, was achieved, and has thus provided added value for the bioeconomy and biorefinery in research and policy aspects. Further, the methodology is expandable and transferable to other criteria and technologies.

In this paper a concept of an integrated energy system for residential applications has been presented. The prosumer system consists of a renewable source of electricity and an electrical energy storage. It is complemented with a reliable, gas-fueled source of heat and electricity which is a micro-cogeneration unit. The proposed solution presents a significant potential of covering the energy demand of the individual consumers while being considered as a natural replacement for traditional energy solutions which may increase low-stack emissions. A model of the analyzed micro-cogeneration system was previously developed by the authors and validated using experimental data. This paper proposes an original idea to use a reliable method of calculation for analyzing an innovative green-promoting energy system. By using the proposed integrated prosumer system, consumer electrical self-sufficiency may reach up to 99% (using 13 kWh battery), while covering total thermal energy demand. An economic assessment was made using dynamic methods and the areas of potential market competitiveness have been presented. The micro scale-generation in the context of electricity is characterized by significant investment costs, however, it may bring an important ecological effect in the future.

This paper focuses on a support management system for the management and operation planning of a microgrid by the new electricity market agent, the microgrid aggregator. The aggregator performs the management of microturbines, wind and photovoltaic systems, energy storage, electric vehicles, and usage of energy aiming at having the best participation in the market. Nowadays, the electricity market participation entails making decisions aided by a support and information system, which is an important part of a microgrid support management system. The microgrid support management system developed in this paper has a formulation based on a stochastic mixed-integer linear programming problem that depends on knowledge of the stochastic processes that describe the uncertain parameters. A set of plausible scenarios computed by Kernel Density Estimation sets the characterization of the random variables. But as commonly happen, a scenario reduction is necessary to avoid the need to have significant computational requirements due to the high degree of uncertainty. The scenario reduction carried out is a two-tier procedure, following a K-means clustering technique and a fast backward scenario reduction method. The case studies reveal the performance of the microgrid and validate the methodology basis conceived for the microgrid support management system.

This paper construct a CCHP and renewable energy based hybrid energy system driven by distributed energy resources (DERs CCHP). Then, the paper constructs performance indexes from energy, economic and environment. Thirdly, the paper proposes a multi-objective optimization model for DERs CCHP system under four optimization of energy rate (ER), total operation cost (TOC), carbon dioxide emission reductions (CER) and joint optimization. Finally, Guangzhou Higher Education Mega Center (GHEMC) in China is taken as the object for comparatively analyzing the operation performance of DERs CCHP system and CCHP system driven by natural gas (NG CCHP system). Results show: Joint optimization mode could balance the results of different optimization modes. DERs CCHP system shows better operation performance. ER of ER optimization, CER optimization and joint optimization are higher than that of NG CCHP system. NPV of all optimization modes is positive, IRR are bigger than the expected yield rate. DERs CCHP system could reduce CO 2 emission by utilizing wind energy and solar energy to replace NG. The sensitivity analysis indicates the performance of the DERs CCHP system will become better with the increase of chiller COP, decrease of NG price and wind-photovoltaic equipment cost.

In this paper, a small remote area which is located in Nigeria has been considered as a model to be tested by a managing scheme for providing both electricity and water. In this strategy, the groundwater is pumped into a water tank which can be later used for supplying required irrigation and drinking water. A PAT (Pump as Turbine) is used as a hybrid system for supplying electricity and water as well as storing water in the water tank. Also, a PV (photovoltaic plant), a package of batteries (BAT) in addition to a diesel ICE (internal combustion engine) are used and optimized along with the best size of devices and managing system, with the purpose of obtaining the maximum economical operating strategy. In this paper, two cases are considered to assess the effectiveness of the system under study. Firstly, all of the mentioned devices are used to show how internal combustion engine system dominates all the other components due to the low cost of fuel. In the second case, all renewable resources have been exploited and optimized in order to make a 100% renewable system with least possible cost. Having about 53 variables makes this problem very complicated which requires to be solved by an algorithm with more accuracy. Therefore, a MGSA (Modified Gravitational Search Algorithm) with an adapted mutation tactic is used to find the best cost and management strategy. In the first case, although the cost of diesel oil is very low, by using the PAT, about 5% of diesel oil consumption is reduced. In the second case, in order to make a 100% renewable system, the size of PV is enlarged approximately 16 times in comparison with the first case. The hybrid PV-PAT storing structure is capable to deliver the water for irrigation and domestic requirements as well as 9% of the electricity needed for the rural community.

bCHP (Biomass combined heat and power) systems are highly efficient at smaller-scales when a significant proportion of the heat produced can be effectively utilised for hot water, space heating or industrial heating purposes. However, there are many barriers to project development and this has greatly inhibited deployment in the UK. Project viability is highly subjective to changes in policy, regulation, the finance market and the low cost fossil fuel incumbent. The paper reviews the barriers to small-scale bCHP project development in the UK along with a case study of a failed 1.5MWel bCHP scheme. The paper offers possible explanations for the project's failure and suggests adaptations to improve the project resilience. Analysis of the project's: capital structuring contract length and bankability; feedstock type and price uncertainty, and plant oversizing highlight the negative impact of the existing project barriers on project development. The research paper concludes with a discussion on the effects of these barriers on the case study project and this industry more generally. A greater understanding of the techno-economic effects of some barriers for small-scale bCHP schemes is demonstrated within this paper, along with some methods for improving the attractiveness and resilience of projects of this kind.

This present paper deals with exergoeconomic results of the Wind Turbine Power Plants (WTPPs) for the first time to our knowledge. Ratio of exergy loss rate to capital cost-exergy efficiency graph and correlation are presented. Monthly exergoeconomic results are also presented for the three turbines. Ratio of exergy loss rate to capital cost and ratio of energy loss rate to capital cost depending on the wind speed are studied. And also as meteorological variables temperature (density of air) and humidity effect on ratio of exergy loss rate to capital cost are discussed and the results are given by the table and graphs clearly. The paper describes an exergoeconomic evaluation of wind turbine power plant. The analysis of the field data demonstrates the proposed system is a viable option for wind turbine power plant (WTTP) installations. (C) 2012 Elsevier Ltd. All rights reserved.

This paper investigates the integration of renewable electricity into the UK system in 2020. The purpose is to find the optimal wind generation that can be integrated based on total cost of supply. Using EnergyPLAN model and the Department of Energy and Climate Change (DECC) energy projections as inputs, this paper simulates the total cost of electricity supply with various levels of wind generation considering two systems: a reference and an alternative system. The results show that 80 TWh of wind electricity is most preferable in both systems, saving up to 0.9% of total cost when compared to a conventional system without wind electricity production. The alternative system, with decentralized generation and active demand management, brings relatively more cost saving, and higher wind utilisation, compared to the reference case. The sensitivity analysis with alternative fuel and capital costs again confirms the superiority of the alternative over the reference system.

It has been widely accepted worldwide that global warming is by far the greatest threat and challenge in the new millennium. In order to stop global warming and to promote sustainable development, renewable energy is a perfect solution to achieve both targets. Presently million hectares of land in Malaysia is occupied with oil palm plantation generating huge quantities of biomass. In this context, biomass from oil palm industries appears to be a very promising alternative as a source of raw materials including renewable energy in Malaysia. Thus, this paper aims to present current scenario of biomass in Malaysia covering issues on availability and sustainability of feedstock as well as current and possible utilization of oil palm biomass. This paper will also discuss feasibility of some biomass conversion technologies and some ongoing projects in Malaysia related to utilization of oil palm biomass as a source of renewable energy. Based on the findings presented, it is definitely clear that Malaysia has position herself in the right path to utilize biomass as a source of renewable energy and this can act as an example to other countries in the world that has huge biomass feedstock.

This paper challenges the view that improving the efficiency of energy use will lead to a reduction in national energy consumption, and hence is an effective policy for reducing national CO 2 emissions. It argues that improving energy efficiency lowers the implicit price of energy and hence make its use more affordable, thus leading to greater use—an effect termed the ‘rebound’ or ‘takeback’ effect. The paper presents the views of economists, as well as green critics of ‘the gospel of efficiency’. The paper argues that a more effective CO 2 policy is to concentrate on shifting to non-fossil fuels, like renewables, subsidized through a carbon tax. Ultimately what is needed, to limit energy consumption, is energy sufficiency (or conservation) rather than energy efficiency.

As the energy demand keeps growing with the world's increasing population, concerns have arisen about the carbon emissions that contribute to critical environmental issues. These environmental concerns drive many nations to commit themselves in pursuing sustainable energy resources. Different process integration techniques have been developed to achieve a viable energy planning sector. In reference to the literature review, the use of time-sliced based models in optimization problems is still sparse. Hence, this paper aims to develop a time-sliced based optimization model that can be integrated into energy integration models. With that, a multi-period pinch-based methodology (targeting, scheduling, and optimization) for electric power systems with the hybridization of renewables and fossil-based energy is proposed. The prospective framework is designed to meet the emissions limit, fulfil energy demand, and minimize the electricity bill. An illustrative case study in Malaysia incorporated with an actual billing system is used to demonstrate the effectiveness of the proposed framework. The results show that it is capable of reducing the total electricity bill by 35% and achieving a 71% reduction of emissions by using solar PV as the renewables.

This paper investigates the effect of renewable energy consumption on material consumption, considering the relationship between Material Footrprint and Gross Domestic Product (GDP), and testing the assumptions of the Environmental Kuznets Curve. A STIRPAT variation is used to specify a model relating the Material Footprint to renewable energy consumption and GDP. The effect is tested for the Material Footprint of fossil fuels and for the Material Footprint of the other categories. The analysis is applied to the seven European countries with the highest proportion of renewable energy consumption. The model estimation shows that the relationship between GDP and Material Footprint follows an inverted N-shaped form, and that the renewable energy favours the reduction of the material consumption of fossil fuels. However, there is a positive effect between the renewable energy consumption and the Material Footprint of the other categories beyond fossil fuels. These results must be interpreted considering the context, as the development of renewable energy coincides with the effects of the 2008 crisis, which may distort the relation between the variables. To pose dematerialization scenarios, it seems necessary to consider reducing energy consumption even if it comes from renewable sources SI

The optimal design of borehole thermal energy storage systems can ensure their techno-economical goals are met. Current design optimization methods either employ detailed modelling unsuitable for numerical optimization or use simplified models that do not consider operational conditions. This paper proposes an optimization-oriented model and a non-convex optimization formulation that, differently from other studies in the literature, can consider the influence of the seasonal storage size and temperature on its capacity, losses, heat transfer rate, and efficiency of connected heat pumps or chillers. This methodology was applied to a case study, considering two scenarios: storing only the rejected heat from cooling and integrating solar thermal generation. Results show that, with varying boundary conditions such as the electricity intensity profile, cooling demand, and price of carbon emissions, not only the optimal seasonal storage size changes but also its optimal operating conditions. The potential reduction of emissions was found, under standard boundary conditions, to be limited (up to 6.7%), but an increase in cooling demand and an enhancement of the intensity seasonal variation led to a reduction of 27.1%. Integration of solar generation further improved it to 43.7%, with a comparably small increase in annual cost, up to 6.1%.

One promising technology to manage and store renewable electricity from wind or solar sources is Power to Gas (PtG). PtG combines H2 from electrolysis –run by renewable electricity– with CO2 to produce synthetic CH4. A suitable option to get the required CO2 streams is the integration with carbon capture technologies, in particular with oxy-fuel combustion. The application proposed in this study is a cogeneration system that combines PtG, oxyfuel boiler, wind energy and photovoltaic solar production to be applied in buildings. This paper describes the concept and analyses the influence of equipment dimensions varying the relative size and the proportion of the solar-wind installed power for two locations representative of North and South European countries. Results Show that higher solar and wind powers are required in the Northern region to satisfy coverage of thermal demand. It results in longer time displacement of energy storage towards cold months. Accordingly to the generation patterns of wind and solar energies, higher solar proportion results in longer energy storage periods. Therefore, solar resource is more suitable than wind power to exploit the potential of PtG technology, although the size of the overall systems increases.

Battery Energy Storage Systems can provide various applications in the distribution networks including load leveling, expansion deferral, voltage profile improvement, frequency regulation, fast reserve, and renewable energy time shift. Despite offering various applications, the BESSs are characterized with high investment costs. An optimal investment plan can justify the cots by exploiting multiple simultaneous applications. To this end, this paper proposes a novel planning model for the BESSs in distribution network wherein both load leveling and voltage profile improvement applications are extracted. The load leveling application refers to storing cheap energy at off-peak periods to deliver at peak period with high energy costs. In this way, the load profile tends to be flat and resulting lower energy cost. The voltage profile improvement is achieved by the optimal reactive power control of the inverter located between the BESS and the grid. The idea is that the inverter can allocate rated power capacity to the reactive power exchange at periods with negligible or zero active power conversion. Simulation results demonstrate that the voltage profile can improve in some degrees without increasing planning and/or operation costs. Higher degrees of improvement can be achieved at the expanse of cost increase. Also, the simulations show that the conducted load leveling results to loss reduction, peak shaving, and network expansion deferral subsequently. - 2019 Elsevier Ltd This publication was made possible by NPRP Grant no. 11S-1125-170027 from the Qatar National Research Fund (a member of Qatar Foundation ). The statements made herein are solely the responsibility of the authors. Scopus

Growth of electricity generation from variable renewable resources like wind and solar has raised questions about future grid stability. This paper used several renewable energy penetration scenarios to determine when an electric grid might be more vulnerable to frequency contingencies, such as a generator outage. Unit commitment and dispatch modeling was used to quantify system inertia, an established proxy for grid stability. A case study of the Electric Reliability Council of Texas grid was used to illustrate the method. Results from the modeled scenarios showed that the Texas grid is resilient to major grid changes, even with relatively high penetrations ( ∼ 30% of annual energy generation compared to 18% in 2017) of renewable energy. However, retiring nuclear power plants and private-use networks in the model led to unstable inertia levels in our results. When the system inertia was constrained to meet a minimum threshold in our model, multiple coal and natural gas combined-cycle plants were dispatched at part-load or at their minimum operating level to maintain stable system inertia levels. This behavior is expected to expand with higher renewable energy penetrations and could occur on other electric grids that are reliant on synchronous generators for inertia support.

Future building energy management systems will have to be capable of adapting to variation in the rate of production of energy from renewable sources. Controllers employing a model predictive control (MPC) framework can optimise and schedule energy usage based on the availability of renewably generated energy. In this paper, an MPC using artificial neural networks (ANNs) was implemented in a residential building. The ANN-MPC was successfully tested and demonstrated good performance predicting the building's energy consumption. The controller was then modified to function as an economic MPC (EMPC) to optimise demand flexibility (i.e., the ability to adapt energy demands to fluctuations in supply). The operational costs of energy usage were associated with this demand flexibility, which was represented by three flexibility indicators: flexibility factor, supply cover factor, and load cover factor. The results from a day-long test showed that these flexibility indicators were maximised (flexibility factor ranged from −0.88 to 0.67, supply cover factor from 0.04 to 0.13, and load cover factor from 0.07 to 0.16) when the EMPC controller's demand flexibility was compared to that of a conventional proportional-integral (PI) controller. The EMPC framework for demand flexibility can be used to regulate on-site energy generation, grid consumption, and grid feed-in and can thus serve as a basis for overall optimisation of the operation of heating systems to achieve greater demand flexibility.

The present paper describes a quantitative method for preparing local heat supply strategies. Detailed spatial data on heat demand and supply are generated using combined top-down and bottom-up modelling for 14 member states of the European Union, which constitute 91% of its heat demand in buildings. Spatial analysis is used for zoning of heat supply into individual and collective heating. Continuous cost curves are used to model economically feasible district heating shares within prospective supply districts. Excess heat is appraised and allocated to prospective district heating systems by means of a two-stage network allocation process. Access to renewable energy sources such as geothermal, large-scale solar thermal, as well as sustainable biomass, is analysed. The result is a comprehensive and detailed set of heat supply strategies in a spatially discrete manner. The findings indicate that in the 14 European Union member states, up to 71% of building heat demand in urban areas can be met with district heating. Of this, up to 78% can be covered with excess heat, while the remainder can be covered with low enthalpy renewable energy sources. The conclusion shows the possibility of a largely de-carbonised heat sector as part of a smart energy system for Europe.

In many developing countries, there is a desire to switch from traditional fuel to renewable energy. However, the supply of renewable energy is often characterized by a severe lack of reliability. This paper seeks to answer if, and to what extent, power outages inhibit switching from fuelwood to hydropower based electricity supply, and factors that determine households' willingness to pay to reduce power outages using a unique combination of mixed logit and seemingly unrelated regression models. We find that frequency, duration, timing of power outages and advance notice are important characteristics determining whether households switch to electricity. The less reliable the electricity supply is the less likely households are to switch to it. Therefore, unreliability in electricity supply maintains the current use of fuelwood, resulting in continued environmental and health problems. Hence, policymakers should work to improve reliability to speed up the desired fuel switching process.

In the mobility sector Electric Vehicles represent one of the main opportunities to ensure strong reduction of local pollution. However, their higher costs compared to gas-fuelled cars are still a barrier for their large diffusions. One possible solutions to increase EVs penetration is their use as storage within households equipped with Renewable Energy Sources enabling a flexible energy management, for instance by the Vehicle-to-Grid and/or Vehicle-to-Home scheme. The aim of this paper is to investigate possible management of the EV battery through an optimization approach capable to minimize the electricity supply costs for an Italian residential end-user with PV, considering battery constraints, such as driving habits. A statistical approach of driver behavior is integrated within the optimization approach to define some possible daily driving patterns. The optimization takes into consideration mobility needs, prices for selling and purchasing electricity and hourly electrical, heating and cooling load profiles of the household. On the basis of these constraints the optimizer identifies when PV overproduction can be either used to charge batteries or to partially cover the load demand of the household. Finally, economic and energy evaluations are performed under Monte Carlo simulations to highlight reliability of potential benefits for the household case study.

Island energy systems should aim for a better integration of local resources and exploit their potential for local energy supply to increase their independence and security of supply besides other benefits. Two trends addressing this problem can be observed: On the one hand, increasing the local use of renewable electricity in the electricity sector by investing in Battery Energy Storage Systems (BESS). On the other hand, the integration of all energy sectors into a Smart Energy System (SES) with the conversion of renewable electricity to heat – thus enabling the usage of Thermal Energy Storage (TES). In this paper, these two potential approaches are investigated through energy systems analyses using EnergyPLAN for the Danish island Samso and the Orkney islands in Scotland. This investigation shows that BESS tend to address only the electricity sector, while TES furthermore improves issues in the heating sector and enables possibilities in the transport sector. The TES approach results in overall reduced energy system costs, while the BESS has a stronger effect on the exchange of electricity. Depending on the various energy systems, both approaches present potential solutions, while the SES approach with the use of TES demonstrates more advantages for the whole energy system.

The worldwide electricity mix has become diversified, mainly through the exploitation of endogenous and green resources. However, doubt has been cast on the much-vaunted advantages of renewables due to some of their characteristics, such as availability, security and affordability. In fact, growth in the installed capacity of renewable energy has increased electricity prices, which raises the question of how households have withstood the cost of energy transition. The main aim of this study is to empirically assess and discuss: (i) whether different types of household have suffered dissimilar effects from the promotion of renewables; (ii) the consequences of promoting renewables on household income; and (iii) if the promotion of renewables has reduced the risk of poverty and social exclusion. A panel data of European countries has been analysed using Kao's residual cointegration test, and an Autoregressive Distributed Lag approach, to assess the relationships. This paper proves that both income and risk of household poverty are directly linked with renewable energies, in both the short- and long-run. The energy transition to renewables has had negative consequences for households. Thus, the disadvantaged households should be helped to meet the increased cost arising from the energy transition.

The incorporation of renewable energy sources in isolated power systems is being significantly slower than in well-connected power systems. The intermittency and uncertainty of the power output of most renewable power technologies prevent a greater usage of these technologies in isolated power systems, in which the supply security is the major concern. In this paper we formulate a stochastic unit commitment problem that allows the participation of electric vehicles in energy, reserve capacity and primary frequency response markets in order to increase the flexibility of the power system operation. We explicitly consider the uncertainty in the power demand and renewable power availability, as well as accounting for the possibilities of contingencies of generating units using a N-1 security criterion. The proposed formulation is tested on an actual isolated power system comprising 38 generating units and 8 buses.

This paper proposes a multi-objective, bi-level optimization problem for cooperative planning between renewable energy sources and energy storage units in active distribution systems. The multi-objective upper level serves as the planning issues to determine the sizes, sites, and types of renewable energy sources and energy storage units. The fuzzy multi-objective lower level serves as the operation issues to formulate operation strategy and determine the schedules of energy storage units. By means of bi-level programming, the optimal operation strategy of energy storage units is incorporated into the upper level and optimized with planning issues cooperatively. Meanwhile, to address high-level uncertainties and simultaneously capture the temporal correlation related to renewable energy sources, electric vehicles, and load demands, the validity index of Davies Bouldin is adopted to develop sets of probabilistic scenarios with high quality and diversity. A hierarchical solving strategy based on modified particle swarm optimization is applied to solve the bi-level nonlinear, mixed integer optimization problem. Results and further analyses demonstrate that the proposed planning model and optimization methods have the ability to allocate renewable energy sources and energy storage units effectively for reducing costs, enhancing reliability, and promoting clean energy.

The ongoing reduction of greenhouse gas emissions entails increased use of intermittent renewable energy technologies such as wind and solar. This raises the need for cost-effective and efficient electricity storage. In particular seasonal variations in supply and demand will require tremendous storage capacity. In this paper we present a truly large-scale electricity storage system which uses pressurized reversible solid oxide cells combined with catalytic reactors to store electricity as synthetic natural gas. By storing the produced gas in existing natural gas grids the system can create a strong and efficient link between the electricity and gas markets. In addition, the system is able to operate reversibly using gas from the grid to satisfy the electric power demand.The system performance is analyzed with a component-based thermodynamic modeling tool which shows that electricity can be stored as synthetic natural gas with an energy efficiency of 89%. The gas to electricity efficiency is equally high, resulting in a round-trip storage efficiency of 80% (DC-to-DC).

A new analysis framework is developed and applied to assess the benefits of building energy efficiency policies and programs. One of the main advantages of the new energy productivity analysis is that it accounts for both economic and energy performances of energy efficiency actions using only one metric. Specifically, the approach applies the concept of energy productivity to the building sector and accounts for both value added and energy savings of energy efficiency measures. Moreover, the proposed analysis accounts for all quantifiable benefits of energy efficiency programs including economic, environmental, and social. In this paper, the general guidelines for the energy productivity analysis are first described. Then, the analysis is applied to evaluate energy efficiency renewable energy programs for both existing and new buildings in the Gulf Cooperation Council (GCC) countries. The analysis results indicate that retrofitting the existing building stock can provide significant benefits and can improve the energy productivity of the building sector in all GCC countries and free up large energy volumes and investment potentials to the development of other economic sectors. In particular, the analysis indicates that reduction in energy consumption, peak demand, and carbon emissions due to deep retrofit programs for the existing building stock can double the energy productivity of the GCC region.

This paper presents an evaluation of energy supply strategies for Egypt’s power sector and identifies prospects to meet rising electricity demand while addressing energy security and low-carbon development issues. We apply the TIMES energy system model to examine Egypt’s energy policy goals as reflected in Egypt’s Vision 2030, and specifically: (a) targeted power generation based on renewable energy under two different scenarios; (b) targeted carbon dioxide (CO2) emissions’ mitigation toward low-carbon society development; and (c) constraints on natural gas production for power generation. The quantitative results from the model suggest a need for diversification from predominantly natural gas to a mix of renewable and conventional energy sources in order to improve energy security, reduce dependency on fossil fuels, and reduce carbon dioxide emissions, with the level of diversification changing with different policy options. Although total energy system cost is projected to increase the effects on fossil-fuel dependency, diversity of energy supply-mix, marginal electricity generation price, and GHG mitigation indicate that it may be wise to target promotion of renewable energy for power generation and develop a low-carbon society.

Due to the increasing demand for energy conservation and the reduction of emissions, renewable energy applications for ships have attracted a great deal of attention. In this paper, a 5000-vehicle space pure car and truck carrier (PCTC) is selected as the research object. Then, on the basis of the existing power system, a unified grid-tied/stand-alone solar system is designed with a built-in battery energy storage system. The system includes a solar energy generation unit, a battery storage system, a diesel generating set, grid-tied/stand-alone controlled inverters, a battery management system (BMS) and an energy management system. According to an analysis of the experimental data, it can be concluded that the use of solar energy hybrid power, in theory, can reduce fuel consumption by 4.02% and carbon dioxide (CO2) emissions by 8.55% a year.

This paper proposes a methodology that is based on mixed-integer linear programming (MILP) to calculate the optimal sizing of a hybrid wind-photovoltaic power plant in an industrial area. The proposed methodology considers the: i) load requirements; ii) physical and geometric constraints for the renewable plants installation; iii) operating and maintenance costs of both wind and PV power plants; and the iv) electric energy absorbed by the public network. The power demand variation associated with the production cycles is considered by using a stochastic simulation tool. To consider both the load and seasonality variability, and to adapt the methodology to the actual operational use of the power plant, the optimization is performed separately for each month of the year. Then, an integrated economic analysis is discussed. The methodology is adopted to analyze an industrial plant in the Rome area used as a train depot and for maintenance purposes. The results, which combine the needs of the plant activity with the availability of renewable energy, enabled the determination of optimal solutions and the relevant savings achievable.

The Brazilian electricity system is an important example of a large country relying on a high renewable energy matrix with a major focus on hydropower, which has historically allowed for low carbon electricity production. However, the increase in the demand and climate change impacts on the availability of these renewable resources represent important challenges for long-term power planning. The contribution of this paper is twofold: Firstly, a first attempt to use the EnergyPLAN model for the analysis of the Brazilian electricity sector and in particular to study future scenarios is presented. Secondly, the possibility of achieving a 100% RES system is also addressed. The 100% RES scenario is found to be theoretically possible but a substantial increase in the overall installed capacity would be required, to support the grid mainly during the spring and summer season. The results underline the importance of seasonal complementarity of hydro and wind power and reveal how an increase in RES would add exportation potential, reducing also the Brazilian external energy dependency. The study identifies risk factors for these high RES scenarios and outlines several avenues for future research to address cost, environmental and technical uncertainties of the system., The authors wish to acknowledge the support of ALGORITMI research Centre at University of Minho. This work has been supported by COMPETE: POCI-01-0145-FEDER-007043 and FCT - Fundacao para a Ciencia e Tecnologia within the Project Scope: UID/CEC/00319/2013.

Solar aided coal-fired power system has been proven to be a promising way to utilise solar energy in large scale. In this paper, the performances of the solar tower aided coal-fired power (STACP) system at 100% load, 75% load, and 50% load for different days are investigated and the maximum solar power that the boiler can absorb under different plant loads are explored. Then, the effects of solar multiple (SM) and the thermal energy storage (TES) hour on the daily performance of STACP system are investigated. Results show that the maximum solar power that a 600 MWe boiler can absorb at 100% load, 75% load and 50% load are 76.4 MWth, 54.2 MWth and 23.0 MWth, respectively. Due to the augmented energy from the solar field, the maximum standard coal consumption rate is reduced by 13.53 g/kWh, 12.81 g/kWh and 8.22 g/kWh at 100% load, 75% load and 50% load, respectively. With an increase of solar power input, the boiler efficiency, overall system efficiency and solar thermal-to-electricity efficiency show a downward trend. In addition, the daily coal consumption of summer solstice is the lowest while the winter solstice is the highest for a particular SM and TES hour.

Coal plays a key role in China's economy as a dominant primary energy resource. In this paper, we provide probability density forecasts for weekly steam coal prices in China based on daily factors such as renewable energy source, Daqing oil, Japanese natural gas, Australia steam coal prices, coal mining industry index, A-share power sector index, A-share index, coal industry index, and temperature. The empirical results show that the influence of temperature lasts longer than other factors, while the Australia steam coal prices, renewable energy source and A-share index are the three best predictors for steam coal prices. It is also shown that the high-frequency factors are useful to forecast steam coal prices and that considering the nonlinearity of coal prices can improve the forecast accuracy by about 22%. We further provide the probability density forecasts for steam coal prices based on the influence of all the selected factors, the results suggest that our proposed method can provide accurate and satisfying probability density forecasts. Given these results, the policy-makers can make effective strategies which can not only adjust the energy structure but also ensure economic growth.

As the world seeks to become more sustainable, intelligent solutions are needed to increase the penetration of renewable energy. In this paper, the model-free deep reinforcement learning algorithm Rainbow Deep Q-Networks is used to control a battery in a small microgrid to perform energy arbitrage and more efficiently utilise solar and wind energy sources. The grid operates with its own demand and renewable generation based on a dataset collected at Keele University, as well as using dynamic energy pricing from a real wholesale energy market. Four scenarios are tested including using demand and price forecasting produced with local weather data. The algorithm and its subcomponents are evaluated against two continuous control benchmarks with Rainbow able to outperform all other method. This research shows the importance of using the distributional approach for reinforcement learning when working with complex environments and reward functions, as well as how it can be used to visualise and contextualise the agent's behaviour for real-world applications., 13 pages, 9 figures (17 counting each subfigure)

This paper contributes to literature by divulging the nature of scale and technique effects on renewable energy consumption, considering foreign direct investment (FDI) and financial development as considerable factors of renewable energy demand. The data for 39 countries over the period of 2000–2019 is used for empirical analysis. In doing so, second generation methodological approaches are applied to decompose scale and technique effects. The empirical results show the presence of cointegration between the model parameters, in the presence of cross-sectional dependence and structural breaks. Further, financial development is positively linked with renewable energy consumption. Foreign direct investment and renewable energy demand are positively linked. Composition effect has negative effect on renewable energy consumption. Economic growth and fossil fuel consumption have positive impact on renewable energy consumption. Long run estimation results indicate that renewable energy-FDI and renewable energy-financial development associations are U-shaped. It indicates that the scale effects exerted by FDI and financial development are overridden by technique and composition effects, and hence, the demand for renewable energy and consequential renewable energy consumption rises with the progression of economic growth. Based on this, policy suggestions are provided for these nations to ascertain sustainable development through bringing forth transformations in the energy policies.

Heat pumps and hot water tanks in local energy systems require sizing to increase on-site renewables self-consumption; decrease costs through variable electricity pricing; and utilise low-cost wind power. While detailed tools can capture these mechanisms, planning-level tools lack functionality and miss these benefits. In this paper an open-source planning-level modelling tool, PyLESA, is presented and applied to a sizing study to demonstrate the capturing of these benefits at the planning-level. Specific aims of the study were to investigate: (i) model predictive control vs. fixed order control, (ii) existing and future wind-influenced electricity tariffs, and (iii) optimal cost size combinations of heat pump and hot water tank. The lowest levelized cost of heat for the existing tariffs was for a time-of-use tariff, 750 kW heat pump and 500 m3 hot water tank combination. For the future wind-influenced tariff a 1000 kW heat pump and 2000 m3 hot water tank was cost optimal and showed model predictive control benefits over fixed order control with levelized heat costs reducing 41 %, and heat demand met by renewables increasing 18 %. These results demonstrate PyLESA as capable of capturing flexibility benefits at the planning stage of design and quantify the advantage of combining flexible tariffs with model predictive control.