Search

Cities and nations worldwide are pledging to energy and carbon neutral objectives that imply a huge contribution from buildings. High-performance targets, either zero energy or zero carbon, are typically difficult to be reached by single buildings, but groups of properly-managed buildings might reach these ambitious goals. For this purpose we need tools and experiences to model, monitor, manage and optimize buildings and their neighborhood-level systems. The paper describes the activities pursued for the deployment of an advanced energy management system for a multi-carrier energy grid of an existing neighborhood in the area of Milan. The activities included: (i) development of a detailed monitoring plan, (ii) deployment of the monitoring plan, (iii) development of a virtual model of the neighborhood and simulation of the energy performance. Comparisons against early-stage energy monitoring data proved promising and the generation system showed high efficiency (EER equal to 5.84), to be further exploited. [ABSTRACT FROM AUTHOR]

The importance of CO2 removal from the atmosphere has long been an essential topic due to climate change. In this paper, the authors aim to demonstrate the suitability of the underground reservoirs for CO2 storage based on their geological characteristics. The research addressed the potential of geological formations for fossil CO2 storage in the Baltic States to support the goal of achieving carbon neutrality in the region. The geological, technical, and economic feasibility for CO2 storage has been assessed in terms of carbon sequestration in geological structures and the legal framework for safe geological storage of fossil CO2. Results indicate that prospective structural traps in the Baltic States, with reasonable capacity for CO2 storage, occur only in Southwestern Latvia (onshore) and in the Baltic Sea (offshore), whilst other regions in the Baltics either do not meet basic geological requirements, or have no economically feasible capacity for CO2 storage. Based on the examination of geological characteristics, the most fitting is the middle Cambrian reservoir in the Baltic sedimentary basin, and one of the most prospective structural traps is the geological structure of Dobele, with an estimated storage capacity of 150 Mt CO2. This study revealed that the storage capacity of the middle Cambrian reservoir (up to 1000 Mt CO2) within the borders of Southwestern Latvia is sufficient for carbon capture and safe storage for the whole Baltic region, and that geological structures in Latvia have the capacity to store all fossil CO2 emissions produced by stationary sources in the Baltic States for several decades. [ABSTRACT FROM AUTHOR]

Green bonds have increasingly been utilized around the world as a source of financing for renewable energy development, designed with compliance requirements and measurable economic returns to investors, while mitigating climate change. However, the efficacy of green bond arranged in the emerging economies for financing renewable energy assets and how the underlying risks are managed have remained to be explored. The paper aims to examine the evolving green financial system sponsored by both public and private institutions in managing such risks within China's emerging economy. A case study of green financing for a bundle of wind power assets led by a state-owned enterprise (SOE) reveals an alternative approach by structuring public–private collaboration while stipulating market-based financial incentives to institutional stakeholders under a political economy. This institutional consortium is composed of a state development bank, a commercial bank, credit rating agencies, institutional and private investors, regional power purchasers, and carbon trading entities. Financial stakeholders' risk in such emerging sustainable investment is moderated by these participating institutions and structured "upsides" from carbon trading aligned with the framework of green finance and standards for green bond development. The results reveal the potentials of scaling up the development of renewable energy by adequately managing and sharing key risks, while allocating substantial funding into renewable energy projects under such a green financial system that is to be complementary with a scalable post COVID-19 economic recovery. [ABSTRACT FROM AUTHOR]

To mitigate global warming, the Chinese government has successively set carbon intensity targets for 2020 and 2030. Energy restructuring is critical for achieving these targets. In this paper, a combined forecasting model is utilized to predict primary energy consumption in China. Subsequently, the Markov model and non-linear programming model are used to forecast China's energy structure in 2020 and 2030 in three scenarios. Carbon intensities were forecasted by combining primary energy consumption, energy structure and economic forecasting. Finally, this paper analyzes the contribution potential of energy structure optimization in each scenario. Our main research conclusions are that in 2020, the optimal energy structure will enable China to achieve its carbon intensity target under the conditions of the unconstrained scenario, policy-constrained scenario and minimum external costs of carbon emissions scenario. Under the three scenarios, the carbon intensity will decrease by 42.39%, 43.74%, and 42.67%, respectively, relative to 2005 levels. However, in 2030, energy structure optimization cannot fully achieve China's carbon intensity target under any of the three scenarios. It is necessary to undertake other types of energy-saving emission reduction measures. Thus, our paper concludes with some policy suggestions to further mitigate China's carbon intensities. [ABSTRACT FROM AUTHOR]

Rapid increases in energy demand and international drive to reduce carbon emissions from fossil fuels have led many oil-rich countries to diversify their energy portfolio and resources. Libya is one of these countries, and it has recently become interested in utilizing its renewable-energy resources in order to reduce financial and energy dependency on oil reserves. This paper introduces an original multicriteria decision-making Pairwise-CODAS model in which the modification of the CODAS method was made using Linguistic Neutrosophic Numbers (LNN). The paper also suggests a new LNN Pairwise (LNN PW) model for determining the weight coefficients of the criteria developed by the authors. By integrating these models with linguistic neutrosophic numbers, it was shown that it is possible to a significant extent to eliminate subjective qualitative assessments and assumptions by decision makers in complex decision-making conditions. The LNN PW-CODAS model was tested and validated in a case study of the selection of optimal Power-Generation Technology (PGT) in Libya. Testing of the model showed that the proposed model based on linguistic neutrosophic numbers provides objective expert evaluation by eliminating subjective assessments when determining the numerical values of criteria. A sensitivity analysis of the LNN PW-CODAS model, carried out through 68 scenarios of changes in the weight coefficients, showed a high degree of stability of the solutions obtained in the ranking of the alternatives. The results were validated by comparison with LNN extensions of four multicriteria decision-making models. [ABSTRACT FROM AUTHOR]

Accurate and timely macro forecasting requires new and powerful predictors. Carbon emissions data with high trading frequency and short releasing lag could play such a role under the framework of mixed data sampling regression techniques. This paper explores the China case in this regard. We find that our multiple autoregressive distributed lag model with mixed data sampling method setup outperforms either the auto-regressive or autoregressive distributed lag benchmark in both in-sample and out-of-sample nowcasting for not only the monthly changes of the purchasing managers' index in China but also the Chinese quarterly GDP growth. Moreover, it is demonstrated that such capability operates better in nowcasting than h-step ahead forecasting, and remains prominent even after we account for commonly-used macroeconomic predictive factors. The underlying mechanism lies in the critical connection between the demand for carbon emission in excess of the expected quota and the production expansion decision of manufacturers. [ABSTRACT FROM AUTHOR]

Pathways leading to a carbon neutral future for the German energy system have to deal with the expected phase-out of coal-fired power generation, in addition to the shutdown of nuclear power plants and the rapid ramp-up of photovoltaics and wind power generation. An analysis of the expected impact on electricity market, security of supply, and system stability must consider the European context because of the strong coupling—both from an economic and a system operation point of view—through the cross-border power exchange of Germany with its neighbors. This analysis, complemented by options to improve the existing development plans, is the purpose of this paper. We propose a multilevel energy system modeling, including electricity market, network congestion management, and system stability, to identify challenges for the years 2023 and 2035. Out of the results, we would like to highlight the positive role of innovative combined heat and power (CHP) solutions securing power and heat supply, the importance of a network congestion management utilizing flexibility from sector coupling, and the essential network extension plans. Network congestion and reduced security margins will become the new normal. We conclude that future energy systems require expanded flexibilities in combination with forward planning of operation. [ABSTRACT FROM AUTHOR]

Future carbon management during energy production will rely on carbon capture and sequestration technology and carbon sequestration methods for offsetting non-capturable losses. The present study quantifies carbon sequestration via reforestation using measurements and modeling for recent and legacy surface coal mining grasslands that are re-restored through tree planting. This paper focuses on a case study of legacy coal mining sites in the southern Appalachia the United States. This five million-hectare region has a surface mining footprint of approximately 12% of the land area, and the reclamation method was primarily grassland. The results of the soil carbon sequestration rates for restored forest soils approach 2.0 MgC ha−1 y−1 initially and average 1.0 MgC ha−1 y−1 for the first fifty years after reclamation. Plant, coarse root and litter carbon sequestration rates were 2.8 MgC ha−1 y−1 with plant carbon estimated to equilibrate to 110 MgC ha−1 after forty years. Plant, root and litter carbon stocks are projected to equilibrate at an order of magnitude greater carbon storage than the existing conditions, highlighting the net carbon gain. Reforestation of legacy mine sites shows carbon sequestration potential several orders of magnitude greater than typical land sequestration strategies for carbon offsets. Projections of future scenarios provide results that show the study region could be carbon neutral or a small sink if widespread reforesting during reclamation was implemented, which is contrary to the business-as-usual projections that result in a large amount of carbon being released to the atmosphere in this region. [ABSTRACT FROM AUTHOR]

In the challenge for a sustainable society, carbon-neutrality is a critical objective for all cities in the coming decades. In the EU City-zen project, academic partners collaborate to develop an urban energy transition methodology, which supports cities in making the energy transition to sustainable lifestyles and carbon neutrality. As part of the project, so-called Roadshows are organised in cities that wish to take the first step toward zero-energy living. Each Roadshow is methodologically composed to allow sustainability experts from across Europe to co-create designs, strategies and timelines with local stakeholders in order to reach this vital goal. Following a precursory investigative student workshop (the SWAT Studio), Dubrovnik was the third city to host the Roadshow in November 2016. During these events the characteristics of Dubrovnik, and the district of Gruž in particular, were systematically analysed, leading to useful insights into the current problems and potentials of the city. In close collaboration with local stakeholders, the team proposed a series of interventions, validated by the calculation of carbon emission, to help make Gruž, and in its wake the whole city of Dubrovnik, net zero energy and zero carbon. The vision presented to the inhabitants and its key city decision makers encompassed a path towards an attainable sustainable future. The strategies and solutions proposed for the Dubrovnik district of Gruž were able to reduce the current carbon sequestration compensation of 1200 hectares of forestland to only 67 hectares, an area achievable by urban reforestation projects. This paper presents the City-zen methodology of urban energy transition and that of the City-zen Roadshow, the analysis of the city of Dubrovnik, proposed interventions and the carbon impact, as calculated by means of the carbon accounting method discussed in the paper. [ABSTRACT FROM AUTHOR]

The targets of global CO2 reduction outline the importance of decarbonizing the heating and cooling sector, which consume half of the final energy in the European Union (EU). Consequently, heating network operators must adapt to growing requirements for carbon neutrality. Energy system modeling allows the simulation of individual network compositions and regulations, while considering electricity market signals for a more efficient plant operation. The district heating model, programmed for this work, covers a measured heat demand with peak load boiler, biomass-fired combined heat and power (CHP) plant, and biomass heating plant supply. The CHP plant reacts to electricity prices of the European Power Exchange market and uses a long-term heat storage to decouple heat and electricity production. This paper presents the results of three annual simulation scenarios aimed at carbon neutrality for the analyzed heating network. Two scenarios achieve a climate-neutral system by replacing the peak load boiler generation. The exclusive storage capacity expansion in the first scenario does not lead to the intended decarbonization. The second scenario increases the output of the CHP plant, while the third simulation uses the biomass heating plant supply. This additional heat producer enables a significant reduction in storage capacity and a higher CHP plant participation in the considered electricity market. [ABSTRACT FROM AUTHOR]

The achievement of carbon neutrality requires a deep transformation of the Polish power sector. This paper analyses the impact of increased electricity generation from wind and solar technologies envisaged in the newest version of the Energy Policy of Poland until 2040 on the operation of dispatchable generators in 2030. The analysis was carried out using the Model of Economic Dispatch and Unit commitment for System Analysis (MEDUSA) model, which solves a mixed integer problem related to unit commitment and economic dispatch in electrical power production. At first, the model was validated based on the real operation data from 2018. Next, five scenarios were built to analyse the operation of the system in 2030. The overall result of the study is that the safest solution from the point of view of power system stability is to extend the decommissioning of coal units of 200 and 300 MW classes, to invest in renewable energy sources (RES) according to the energy policy, to build new gas power plants with the total capacity of ca. 4 GW, and to enforce Demand Side Management (DSM) programs for shifting the electrical load. The proposed framework for the optimization of power system planning helps to avoid wrong investment decisions that would have a negative impact on energy prices. [ABSTRACT FROM AUTHOR]

Finland has adopted a high profile in climate change mitigation. A national target of achieving carbon neutrality by 2035 has been declared. As a part of this, the use of coal for energy purposes has been banned from May 2029 onwards. The Nordic electricity market was a world fore-runner in creating a liberalized, multi-national electricity market in the 1990s. At present, the electricity systems of Finland, Sweden, and Norway are already very low-carbon. The Baltic countries Estonia, Latvia, and Lithuania joined the Nordic market about a decade ago. Estonian electricity production is the most carbon-intensive of all the EU countries due to the extensive use of domestic oil shale. Especially Lithuania still suffers from capacity deficit created by the closure of the Soviet time nuclear reactor Ignalina in Lithuania. This paper presents the ambitions of the EU and national level energy and climate policies and models the multi-national impacts of Finland's forthcoming closure of coal-fired generation. We also take into account Sweden's planned decrease in nuclear generation. We find that these national-level policies have an impact on the Baltic countries as reduced import possibilities and increasing electricity prices, and the expected rise of the EU CO2 allowance prices amplifies these. We further find that the abandonment of coal and nuclear power plants increases the net import and increases CO2 emissions in neighboring regions. [ABSTRACT FROM AUTHOR]

A carbon market was introduced for the first time in January 2005, when the EU assigned carbon emission allowances to approximately 15,000 enterprises in 25 countries and established a market for emissions trading. In Korea, the carbon emission trading system started from January 2015 with three phases running up to 2025. As many countries have introduced carbon markets, new evaluation models that consider not only fossil energy prices but also carbon emission costs are necessary because additional costs of using fossil energy might have been incurred due to carbon emissions. The purpose of this paper is to develop a real option model that considers not only the uncertainty of existing fossil energy prices, but also the uncertainty of carbon emission rights prices, in evaluating the economic value of renewable energy R&D. Using the real option model, we attempted to assess the effects of the uncertainty of newly appearing carbon markets on the economic value of renewable energy R&D in Korea empirically. Furthermore, we derived an optimal decision path according to the uncertain future situations of fossil energy and carbon markets by analyzing a trinomial lattice model in which the optimal timings of R&D and deployment are identified. [ABSTRACT FROM AUTHOR]

Palm oil's utilization as a renewable energy (RE) source has led the government to intervene by introducing emission reduction projects. Carbon trading projects are part of the strategic direction adopted within the climate mitigation plan and sustainability drive in the palm oil industry. The perquisites and opportunities encountered within emissions trading are expected to aid palm oil producers economically, environmentally, and socially. This study addresses and analyses how the carbon trading projects' targets in Malaysia can be achieved, the problematic, and pressing issues around their implementation and whether these projects are sustainable and create a positive impact. This paper is based on literature reviews and semi-structured interviews with expert palm oil producers in Malaysia. The findings have revealed that carbon trading implementation in Malaysia has delivered new insights towards the international climate policy approach on the feasibility and impact of long-term sustainability goals. However, the impact of the implementation needs support from the government for further development. In conclusion, the major contribution of this study is that the carbon trading implementation in Malaysia complies with the objectives and principles of sustainable development and creates a significant influx in investment for Malaysia's economic growth. [ABSTRACT FROM AUTHOR]

As a responsible world power, China has established quantitative carbon emission reduction targets and started to establish carbon trading pilots in 2013. Successfully connection and a certain size of the carbon market can further enhance the liquidity of carbon allowances and, to a certain extent, contribute to the enthusiasm of enterprises to participate. This paper gives an overview of the past operational development of China's eight carbon market pilots, the current problems in the national carbon market, the elements that need to be improved during the establishment process, and the feasibility of future connection between China's carbon market. In addition, the international carbon market was summarized and analyzed. We found that, compared with the already mature carbon market in the world, there is still a big gap between China and these countries' carbon market in various respects, such as the carbon price formation mechanism, the carbon quota allocation, carbon emission coverage sources, and legal and policy systems. Thus, China should manifest the pricing mechanism, unify the allocation of carbon quotas, cover the sources of emissions, introduce clear laws and regulations in the construction of the carbon market, all these will ensure the smooth operation of the national carbon market. [ABSTRACT FROM AUTHOR]

Accurately predicting the carbon price sequence is important and necessary for promoting the development of China’s national carbon trading market. In this paper, a multiscale ensemble forecasting model that is based on ensemble empirical mode decomposition (EEMD-ADD) is proposed to predict the carbon price sequence. First, the ensemble empirical mode decomposition (EEMD) is applied to decompose a carbon price sequence, SZA2013, into several intrinsic mode functions (IMFs) and one residual. Second, the IMFs and the residual are restructured via a fine-to-coarse reconstruction algorithm to generate three stationary and regular frequency components that high frequency component, low frequency component, and trend component. The fluctuation of each component can effectively reveal the factors that influence market operation. Third, extreme learning machine (ELM) is applied to forecast the trend component, support vector machine (SVM) is applied to forecast the low frequency component and the high frequency component is predicted via PSO-ELM, which means extreme learning machine whose input weights and bias threshold were optimized by particle swarm optimization. Then, the predicted values are combined to form a final predicted value. Finally, using the relevant error-type and trend-type performance indexes, the proposed multiscale ensemble forecasting model is shown to be more robust and accurate than the single format models. Three additional emission allowances from the Shenzhen Emissions Exchange are used to validate the model. The empirical results indicate that the established model is effective, efficient, and practical in terms of its statistical measures and prediction performance. [ABSTRACT FROM AUTHOR]

Under the carbon trading mechanism, renewable energy projects can gain additional benefits through Chinese Certified Emission Reduction transactions. Due to the uncertainty of carbon trading system, carbon prices will fluctuate randomly, which will affect the investment timing of renewable energy projects. Thus, the value of the option will be generated. Therefore, renewable energy power generation project investment has the right of option. However, the traditional investment decision-making method can no longer meet the requirements of renewable energy investment in the current stage. In this paper, a real option model considering carbon price fluctuation is proposed as a tool for renewable energy investment. Considering optimal investment timing and carbon price, the model introduces a carbon price fluctuation as part of the optimization, studies the flexibility of enterprises’ delayed investment under the fluctuation of carbon price. A case study is carried out to verify the effectiveness of the proposed real option model by selecting a wind farm in North China. The model is expected to help investors to assess the volatility and risk of renewable energy projects more accurately, and help investors to make a complete plan for the project investment, thus promoting the efficient allocation of resources in the energy industry. [ABSTRACT FROM AUTHOR]

As the low-carbon economy continues to expand, wind power, as one form of clean energy, promotes the low-carbon power development process. In this paper, a multi-objective environmental economic dispatch (EED) model is proposed considering multiple uncertainties of the system. Carbon trading costs and green certificate trading costs are introduced into the economic costs. Meanwhile, the objective function of pollutant emissions is taken into account in the model, which can further promote the reduction of pollutant emissions in the system scheduling. The output of wind turbines is uncertain and volatile, so it brings new challenges to the power system EED once the large-scale wind power accesses the power grid. For the multiple uncertainties of the system, fuzzy chance-constrained programming is introduced, and the output of the wind turbines and the load are regarded as fuzzy variables. We use the clear equivalence forms to clarify the fuzzy chance constraints. The improved multi-objective standard particle swarm optimization (SPSO) algorithm is used to solve the optimization problem effectively. The feasibility and effectiveness of the proposed model and algorithm are verified by an example of a 10-unit system with two wind farms. [ABSTRACT FROM AUTHOR]