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Decision-making methodologies in offshore wind power investments: A review

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Abstract

The deterioration of the environment and the depletion of resources are promoting the development of clean, renewable energy. Offshore wind is characterized by its sustainability and cleanliness, and is one of the fastest-growing renewable energy in recent years. Various methodologies have been therefore utilized to support offshore wind power investment decision-making. However, the existing literature lacks a comprehensive analysis and summary of these methods aimed at improving investment efficiency. To this end, this paper undertakes a systematic literature review of methodologies and theories commonly used in offshore wind power investment decision-making, following with the characteristics, applicability of various methods discussed and discussion of representative literature. Then, the selected papers were classified by the year of publication (2010-2020), journals, country of author affiliation, method consideration perspectives and application fields. These classifications are presented to highlight the trends, which aim to provide broad, systematic approaches and tools for assessment of power investment, and to give suggestions on which method to use for each situation. It can be seen that the popularity and applicability of these methods have improved after 2015. They cannot replace but complement each other and should be implemented in a parallel or better comprehensive way. The outputs of this review will map appropriate analytical techniques to specific investment applications and perspectives, provide researchers with guidance on future investment decision-making research, and point out any possible gaps. Specifically, through this review, decision-makers would be able to choose the best-suited or hybrid methodology, according to different fields and objects, for investment viability and effectiveness. Finally, untapped issues recognized in recent research approaches are discussed along with suggestions for future research.

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Distributed photovoltaic (DPV) projects have been rapidly proposed in China due to policy promotion, and investment decisions immensely decide the success of DPV projects. This paper aims to propose an investment decision framework for DPV projects under the energy performance contracting (EPC) business model which is currently vigorously promoted in China, thereby improving the efficiency and accuracy of decision making. Firstly, the distinctive criteria system for DPV project investment decision is established, including natural, market, technical, policy, competitive and economic factors. Secondly, the weights of criteria are determined by integrating subjective and objective weights to obtain more accurate weights. Then, the TODIM (an acronym in Portuguese of interactive and multicriteria decision making) approach is utilized to rank the alternative DPV projects, taking into account investors’ psychological behavior. Finally, a case study in central and eastern China is carried out to illustrate the rationality and feasibility of the proposed framework. The results show that the Project A4 located in Nanchang City is the optimal project, and the rank of alternatives is sensitive to the recession coefficient. This paper provides insightful information for the DPV investors with different risk preferences to evaluate the investment performance of EPC projects and select the most appropriate one under uncertain environment.
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The development of low-speed wind farms (LSWFs) has been recognized as the most promising option for onshore wind power project in the future. However, existing research on hilly LSWFs is still insufficient, and site selection is a vital in the construction of LSWF projects. This paper proposes a decision framework to provide investors with guidance for LSWF site selection in hilly areas. First, an indicator system that complies with the peculiarities of hilly LSWF projects has been established from the sustainability perspective. Second, the indicator data are collected by adopting hesitant fuzzy linguistic term sets (HFLTSs) to ensure the completeness of the data. Third, considering the impact of subjective preferences and objective fairness on the indicator weights, a method combining the extended triangular intuitionistic fuzzy number-decision making trial and evaluation laboratory (TIFN-DEMATEL) method and the entropy weight method for multiple data types is used to determine the indicator weights. Fourth, an acronym in Portuguese for interactive multi-criteria decision-making (TODIM), which takes the psychological characteristics of decision makers (DMs) into account, is applied to rank alternative sites. Finally, a case study in the eastern China is illustrated to demonstrate the rationality of the decision framework.
Article
Developments in wind energy projects have received much interest in the last decade due to the encouragement of sustainable development policies. Consequently, the number of wind energy projects has rapidly increased so that wind energy is an important part of an integrated power system. The success of an offshore wind energy project depends on the selection of the optimal offshore wind power station (OWPS) location, which is often determined through the use of multi-criteria decision-making (MCDM). There are, however, a number of shortcomings in the use of MCDM methods for site selection: 1) the incomplete utilization of information and loss of data in the decision-making process; and 2) the interaction issue in the neutrosophic vicinity is neglected. In this study, to address those shortcomings, we propose a new hybrid methodology for the selection of offshore wind power station location combining the Analytical Hierarchy Process (AHP) and Preference Ranking Organization Method for Enrichment Evaluations (PROMETHEE)-II methods in the neutrosophic environment. First, a comprehensive index system of evaluation criteria is constructed of OWPS site selection. Then, the neutrosophic set is utilized in the specialist committee decision to express incomplete information. Furthermore, by gathering opinions of specialists, we take into consideration the interaction problem. Through the development of the hybrid method, this research presents rigorous methodological support for site selection in order to achieve benefits in coastal management. The proposed methodology for OWPS site selection is validated through the use of a case study from Egypt.
Article
In recent years, the profit growth of wind power enterprises in China is generally weak, and the phenomenon of “wind curtailment” in some areas is relatively common. The comprehensive evaluation of wind power performance in various regions of China and the study of the driving forces of wind power performance differences in different regions are of great significance for scientific planning and layout of wind power investment. Based on the panel data, this paper combines Data Envelopment Analysis (DEA) and Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) to establish an evaluation model to evaluate the wind power performance of China's 29 provinces and cities from 2011 to 2018. Then uses the evaluation results as input data of Adaptive Neuro-Fuzzy Inference System (ANFIS) prediction model to predict the wind power performance of 2019 in various regions of China under the current policy and environment. Finally, the main factors affecting the performance of China's wind power generation are explored by establishing a regression model. The research results show that at present, the main factors affecting the performance of China's wind power generation are local power consumption capacity, economic development degree and the rate of wind abandonment. On the premise of solving the problem of wind curtailment, appropriately promoting power consumption and renewable energy policy reform, from Feed-in-Tariff (FiT) to a Renewable Portfolio Standard (RPS), are effective means to promote the development of wind power generation in China.
Article
Brazil has a different energy matrix from the world, from electricity generation with renewable resources. With the increase in energy demand, environmental problems of hydroelectric plants show a need to increase the installed capacity of renewable sources. Investments in renewable generation are required, decision affected by different aspects, such as variability in generation, uncertainties in the energy market and investor risk aversion, the company's current portfolio. The paper presents a stochastic decision support model for renewable energy investments, maximizing the expected return for a given level of risk aversion. To assess uncertainties, the Conditional Value-at-Risk (CVaR) is used. Scenarios are generated based on past generation and NEWAVE output data. Simulations with the investment option are related in this energy complementation. The risk for intermittent source involves the company using CVaR assessment. The results conclude that the diversification of the firm's assets and the complementary composition of the sources of generation reduce the investor's portfolio's financial risks and the risk aversion level of the decision maker influences the market position that the company must adopt. Model tending to more conservative solutions when risk aversion is higher, confirming the literature, the existence of a trade-off between risk aversion and expected return.
Article
Site selection is one of the most significant decision making activities for development of wind energy resource. In this study, a novel method integrating Geographic Information System (GIS), interval Analytic Hierarchy Process (IAHP) and stochastic VIKOR was proposed to tackle site selection issue of wind farms in the Wafangdian region, China. Two major factors, i.e. biodiversity conservation and production safety, were utilized to determine feasible areas. Afterwards, the weight of evaluation criteria, including the social impact, economic benefit, terrain and eco-environment protection, were identified by using IAHP. Finally, the suitability indexes of various alternatives were calculated by stochastic VIKOR and their ranking was used to determine high-suitability areas for wind farm locations. The obtained results indicated that 30.2% of studied region were suitable for installing the wind-power facilities, but only 3.36% were determined to be highly suitable. By comparing the optimized results to the actual locations of existing wind farms, it was revealed that the ideological framework of this study were practical and effective in guiding site selection of wind farms, and they were also useful for applications in site selection of other renewable energy forms involving complex spatial analysis and multiple criteria evaluation, including solar, hydroelectric, geothermal and biomass.
Article
This study investigates the degree of importance of criteria affecting the optimal site selection of offshore wind farms. Firstly, forty two different influential criteria have been selected by reviewing the scientific literature on offshore wind farm site selection. Secondly, a survey has been conducted receiving a response from thirty four internationally renowned experts across seventeen countries. Each participant is asked to indicate the importance and relevance of each criterion based on their experience. Finally, the importance of each criterion for offshore wind farm site selection is determined using a novel Decision Making-Level Based Weight Assessment (LBWA) approach based on interval-valued fuzzy-rough numbers (IVFRN). The proposed method allows exploitation of the uncertainties and subjectivity that exist in the decision-making process. The results from this study improve our understanding of the importance and impact of each criterion which we believe would be invaluable for the future studies on the site selection of offshore wind farms.
Article
Site suitability problems in the renewable energy studies have taken a new turn since the advent of geographical information system (GIS). GIS has been used for site suitability analysis for renewable energy due to its prowess in processing and analyzing attributes with geospatial components. Multi-criteria decision making (MCDM) tools are further used for criteria ranking in the order of influence on the study. Upon location of most appropriate sites, the need for intelligent resource forecast to aid in strategic and operational planning becomes necessary if viability of the investment will be enhanced and resource variability will be better understood. One of such intelligent models is the adaptive neuro-fuzzy inference system (ANFIS) and its variants. This study presents a mini-review of GIS-based MCDM facility location problems in wind and solar resource site suitability analysis and resource forecast using ANFIS-based models. We further present a framework for the integration of the two concepts in wind and solar energy studies. Various MCDM techniques for decision making with their strengths and weaknesses were presented. Country specific studies which applies GIS-based method in site suitability were presented with criteria considered. Similarly, country-specific studies in ANFIS-based resource forecast for wind and solar energy were also presented. From our findings, there has been no technically valid range of values for spatial criteria and analytical hierarchical process (AHP) has been commonly used for criteria ranking leaving other techniques less explored. Also, hybrid ANFIS models are more effective compared to standalone ANFIS models in resource forecast and ANFIS optimized with population-based models has been mostly used. Finally, we present a roadmap for integrating GIS-MCDM site suitability studies with ANFIS-based modeling for improved strategic and operational planning.
Article
Offshore wind farms have been rapidly proposed owing to the promotion of sustainable development policies. Site selection contributes to the success of offshore wind farm projects and is a complex multi-criteria group decision-making problem. This paper proposes a multi-criteria group decision-making method based on the intuitionistic linguistic aggregation operators and applies it to the site selection decision-making process of offshore wind farm. To begin with, the intuitionistic linguistic numbers are introduced to deal with the uncertainty and fuzziness in decision-making. An optimization weighting model that comprehensively considers subjective and objective factors is then established, which can effectively reflect the correlations among the criteria. Furthermore, in view of the limited rational behavior of the decision-makers, an extended intuitionistic linguistic aggregation operator is defined, according to which the criteria values can be aggregated into a numerical value for comparison. Finally, a case study in China is conducted to verify the rationality and effectiveness of this method. The research results show that the offshore wind farm located in Laizhou, Dongying is the optimal site, and the ranking results of the alternatives are sensitive to the absolute risk aversion coefficient.
Article
The objective of this article is to apply a method to determine the economic feasibility of floating offshore wind farms at Portugal continental coast. The method proposed has several phases: geographic, economic and restrictions. The objective of the geographic phase is to estimate the input variables that will be afterwards used to calculate the economic parameters in the economic phase. The restriction of bathymetry, the last stage, is then added to the economic maps previously calculated, whose value will be different depending on the floating offshore wind device chosen. In this study, two scenarios for electric tariff have been taken into consideration as well several floating offshore wind substructures: semisubmersible, TLP and spar. Results indicate what is the best floating offshore wind platform in terms of economic parameters and where is the best area, in the study region, to implement floating offshore wind farms.
Article
As a momentous energy policy innovation endowed with the highest level of political support in China, the solar PV poverty alleviation project (PPAP) combines the development of clean energy with poverty alleviation, which was promoted together with the other five targeted poverty alleviation methods intensively. Nevertheless, is solar PV efficient for poverty alleviation in rural China? How much does PPAP contribute to poor villages compared with other methods? In this paper, we explored the PPAPs performance on the improvement of economic, social, ecological and infrastructure construction, by using the field survey data in 52 poor villages in 8 provinces throughout China and adopting methodologies of principal component analysis (PCA), data envelopment analysis (DEA) and grey relation analysis (GRA). The results indicate that: (1) The six poverty alleviation models have made positive contributions to poverty alleviation, and the PPAP ranks the last but one; (2) PPAP plays a minor role in poverty alleviation, suggesting that greater investment in PPAP will have little impact on the efficiency of poverty reduction in rural China; (3) limiting factors in achieving high poverty alleviation efficiency are insufficient scales and unreasonable investment distribution, and (4) Great regional differences exist in poverty reduction efficiency.
Article
The objective of this study was to identify ideal sites to locate utility-scale wind and solar farms inSongkhla, a province in southern Thailand. Geographic Information System (GIS) and analytical hierarchyprocess (AHP) were used to assess various physiographic, environmental and economic siting criteria.The data used in this work were primarily obtained from governmental organizations. Additionally, aGlobal Horizontal Irradiation (GHI) solar map with a spatial resolution of 1km/pixel for the years 2007e2015 was obtained from Solargis as well as a 200 m resolution wind resource map of 100 m aboveground level obtained from previous research conducted in the study area. The results of the studyindicate that Songkhla has potential land areas of up to 66.113 km2and 844.93 km2available for windand solar farms respectively, though only areas of 38.749 km2and 69.509 km2respectively were judgedas being“highly suitable”. Most of these highly suitable areas were located in the Ranot District. Theresults of this study provide an important starting point for stakeholders interested in investing inrenewable energy in Southern Thailand. Knowledge of the suitability of sites will provide a greater levelof confidence and therefore likely expedite the renewable energy investment process.
Article
Offshore wind energy is recognized as an important source of renewable energy and has experienced rapid growth in recent years especially in north-western European countries. In this paper, the efficiency of 71 offshore wind farms across five north-western European countries is assessed using the Data Envelopment Analysis (DEA) Method. The number of turbines, cost, distance to shore, and area of the wind farms are selected as the inputs and the connectivity to population centres, the produced electricity and the water depth are considered as the outputs. The results show that the average CCR efficiency score of all offshore wind farms considered in this study is 87%, and the relative median efficiency of offshore wind farms in different countries is not statistically different. This study offers a practical and holistic performance assessment to the offshore wind stakeholders and policy makers via including economic, environmental, technical and social inputs and outputs in the analysis.
Article
The expressway service area photovoltaic (ESAPV) projects have been greatly promoted due to the increasing passenger volume and the current development of electric vehicle industry. Site selection based on sustainability perspective is critical to the future construction of ESAPV. This paper puts forward a decision-making framework to ensure the validity of the ESAPV site selection. First, the index system meeting the characteristics of the ESAPV project is established. Second, in order to take into account the influence of subjective preference and objective justice on the results, this study chooses the Decision Making Trial and Evaluation Laboratory (DEMATEL) method to determine the weights of first-level indicators that lack objective data support and employs an integrated weighting method to determine the weights of second-level indicators. Third, the TODIM (an acronym in Portuguese of interactive and multi-criteria decision making) method which considers decision makers’ risk aversion psychology is extended to triangular intuitionistic fuzzy environment combined with group decision-making theory. Finally, a case study in Hebei of China is carried out to verify the practicability of the systematic framework. The results indicate that the optimal project is located in Shijiazhuang.
Article
Constructing an economical wind-PV-seawater pumped storage (SPS) plant is crucial to promote the complementarity of wind and PV resources in time and space dimensions and to reduce energy abandonment caused by voltage instability. It is also beneficial to realizing long-time sustainable development objectives for offshore areas and even for the whole country. To select the optimal site of wind-PV-SPS power plants with massive difficulties lying in different attitudes of decision makers to loss risk, uncertainties of decision-making environment and various attributes of evaluation alternatives, a fuzzy multi criteria decision making based a two-stage evaluation mode is proposed. Firstly, two sets of criteria systems, representing veto selection of regional resources and the conditions of sustainable development described by triangular intuitionistic fuzzy numbers (TIFNs), are established to ensure the scientificality and comprehensiveness of the evaluation process. Then, alternatives are preliminarily selected through veto identification. Afterwards, criteria weights of the second evaluation stage determined by the entropy method are incorporated within TODIM (an acronym in Portuguese of interactive and multiple attribute decision making) to rank potential wind-PV-SPS objects. Finally, to validate the effectiveness of the proposed model, a case study in China is conducted and the calculation result shows that Qingtian Bay in Zhou Shan is the best.
Article
This study puts forward a real options model and uses it to evaluate the investment value of offshore wind power project under market co-movement effect. The main purpose is to check investment benefit of offshore wind power project, as an investor. Several uncertainties are taken into account, including investment costs, feed-in tariffs, carbon prices and policy subsidy. Moreover, an additional uncertain factor, i.e. the market linkage of investment costs, is considered. As a case study, Jiangsu Xiangshui offshore wind park is used to illustrate the model in scenario analysis. Using a least-squares Monte Carlo simulation method, we obtain that the project value is negative. Therefore investors should abandon or postpone investment until better conditions prevail. Furthermore, this paper shows sensitivity analysis of the impact of uncertain factors on the project value. Especially sensitivity analysis of variable costs, it shows a certain impact on project value in here, which has been ignored in previous real options studies. The research results would be helpful for renewable energy project assessment and the decision-making process associated with it.
Article
Energy policy making is one of the most significant issues for countries and it can be evaluated by using multi-criteria decision making (MCDM) methods. The energy decision and policy-making problems include selecting among energy alternatives, evaluating energy supply technologies, determining energy policy and energy planning. There is a wide range of studies about energy decision-making problems in the literature and different types of energy alternatives are considered in these studies. The MCDM methods are used as effective tools in order to solve energy decision-making problems since they evaluate alternatives with different perspectives in terms of several conflicting criteria. In this context, the fuzzy set theory (FST) that expresses uncertainties in human opinions, can be successfully used together with the MCDM methods to get more sensitive, concrete and realistic results. This paper aims to present a comprehensive review and bring together existing literature and the most recent advances to lead researchers about the methodologies and applications of fuzzy MCDM in the energy field. For this aim, a large number of papers that use fuzzy MCDM methods to solve energy policy and decision making problems have been analyzed with respect to some characteristics such as types of fuzzy sets, year, journal, fuzzy MCDM method, country and document type. The results of this study indicate that fuzzy Analytic Hierarchy Process (AHP), as an individual tool or by integrating with another MCDM method, is the most applied MCDM method and type-1 fuzzy sets are the most preferred type of fuzzy sets. Additionally, Turkey and China are countries which have the highest number of publications related to fuzzy MCDM methods in energy-related problems.
Article
The use of renewable energy sources instead of conventional energy sources is at the core of policy actions to reduce dependency on fossil fuels worldwide. As a result, especially during the last decade, the cost of renewable energy has significantly decreased, enriching renewable energy cost-competitiveness. Due to the spatial nature of renewable energy sector-related decisions, the synergy of geographical information systems (GIS) and Multiple Criteria Decision Analysis (MCDA) models can enrich the quality of the related decisions given their ability to effectively support land management considerations. Moreover, their implementation significantly enriches the performance of the traditional capital projects evaluation methods (CPEM) by providing physical data to the sizing process in a quick and accurate manner. Thus, decision-making frameworks that combine GIS-based suitability analysis with traditional financial evaluation techniques can significantly enrich the planning phase to achieve efficient installations in terms of required area reduction, power generation maximization and local characteristics examination. With respect to the realization of wind energy exploitation projects, the paper at hand proposes a framework capable of expanding the use of the traditional GIS-based derived suitability index to establishing portfolios. Moreover, the proposed framework is enriched by robust analysis using Monte Carlo Simulation (MCS), which provides significant insights regarding the stability of the derived portfolios and the projects that they comprise. The proposed framework is illustrated through a case study in the Thrace region in northeastern Greece.
Article
Multi-criteria decision-making (MCDM) method has a widely application in management and energy field. Considering the broad development prospects of offshore wind power and deficiency of integrated coastal management, a decision framework combining triangular intuitionistic fuzzy numbers (TIFNs), Analytic Network Process (ANP) and Preference Ranking Organization Method for Enrichment Evaluations (PROMETHEE) is proposed and applied in site selection of offshore wind power station (OWPS). The aim of this study is to provide theoretical and methodological support for the site selection decision-making of coastal wind power projects and to improve the benefits of integrated coastal management. Taking six criteria (wind resources, construction, economic, environment, society, risk) and the related sub-criteria into consideration, an evaluation system of OWPS site selection is established. The optimal location scheme is determined by the decision framework in current paper. After a sensitivity analysis and a comparative analysis, the result shows that decision framework has strong robustness and feasibility. Thus, the evaluation criteria and methodology in this paper can provide a theoretical reference for the development of coastal management and offshore wind power.
Article
Industrial manufacturing is the largest end-use sector in terms of both final energy demand and greenhouse gas emissions (more than 30% of the total); its increase is rapidly altering the world climate. The need to mitigate the environmental impacts of manufacturing processes makes energy efficiency a key success factor for sustainable production. Accordingly, the scientific community's interest in energy management has grown considerably, resulting in several literature reviews on energy modelling and production systems analysis, emissions calculation, sustainability tools and benchmarking techniques. However, a comprehensive analysis of methods and tools aimed at improving energy awareness and assessing their effects on energy efficiency is lacking. To address this gap, this paper undertakes a systematic literature review of energy assessment methods and tools. From the 1367 papers retrieved by searching scientific literature databases, 64 scientific articles met the inclusion criteria and were analysed in detail. The study aims to provide scholars with a picture of the current state of scientific research and to identify the scientific works that could help industry practitioners in energy management. Following the ISO 50001 framework, the methods and tools were divided into three main groups (i.e. energy analysis, energy evaluation and energy-saving measures methods) and the specific findings relating to each group were synthesized. Finally, the paper addresses unresolved issues and challenges and makes suggestions for future research directions.
Article
The study uses the two-stage bias-corrected DEA approach of Simar and Wilson (2007) to assess the efficiency of the EU countries in terms of their wind power investment in 2015. The set of input variables includes installed wind power capacity and average wind power density, while output variables include wind-generated electricity and three additional aspects: environmental, economic and energy security. Next, the study examines the effect of renewable energy policy regarding wind energy, the energy mix, and the offshore wind power utilisation on the wind power efficiency of the analysed countries. The results obtained reveal that the United Kingdom, Sweden, Denmark, and Ireland are the most efficient countries in terms of wind power investment. The inclusion of additional aspects demonstrates the greatest improvement of efficiency in Belgium, Cyprus, the Netherlands, Estonia and Germany. The results seem to indicate that economic instruments used within renewable energy policy have a positive effect on wind power efficiency, while policy support and regulatory instruments might negatively impact. Moreover, the results show that the energy mix explains the variation of the efficiency of the EU countries when their economic and environmental aspects are considered. The analysis of the geographic location indicates that countries with a high share of offshore wind capacity are the most efficient.
Article
The utilization of renewable energy sources has come into prominence especially over the last two decades. In the literature, various methods have been utilized for the evaluation of renewable energy sources. In particular, multi-attribute decision making (MADM) methods have been widely used throughout the renewable energy literature for several purposes such as evaluation of energy policies, selection of the most suitable renewable energy source for electricity generation, evaluation of renewable energy sources, identification of the optimal site for a renewable energy facility, and selection of the best one among energy alternatives. In the scope of this paper, the studies employing MADM methods in renewable energy applications have been taken into consideration. The main aim of the study is to determine the reasons and factors explaining why these methods have been employed. It can be concluded that Analytic Hierarchy Process (AHP), Analytic Network Process (ANP), ELimination Et Choix Traduisant la REalité (ELECTRE) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) have come into the forefront as the most widely employed methods in the literature. However, there are a few studies employing outranking methods, ELECTRE and Preference Ranking Organization Method for Enrichment Evaluations (PROMETHEE) for the purpose of evaluating renewable energy investments/projects. Furthermore, in the literature, there are limited papers considering utilization of renewable energy sources such as geothermal, hydro, and waste. Finally, this study indicates that renewable energy exploitation is a quite suitable topic to use MADM methods.
Article
Tapping into a large amount of renewable generation considering the inherent variability of renewable energy sources (RES) can greatly increase the risk of supply and demand imbalances in electric power delivery. One of the major components of this risk is the intermittency of both wind and solar power generation. In this paper, we show that by strategically planning for geographical and technological diversification of renewable generation capacity it is possible to reduce such risk in a RES-only US energy portfolio. We consider wind and solar as the sole sources of generation and use risk-averse stochastic optimization with Conditional Value-at-Risk (CVaR) to optimize energy generation locations and capacities in an idealized case study. The optimal RES portfolios demonstrate a significant improvement in generation profile compared to non-pooled or non-optimized alternatives. This confirms that with smart policy planning one can push the limits of the risk of imbalances in RES-only portfolios within continental United States, and highlights the need for system-wide thinking when designing a large-scale energy portfolio.
Article
Photovoltaic with its main characteristics of clean and abundant reserves has been widely used. This paper investigates how to select a satisfactory industrial and commercial rooftop distributed photovoltaic (ICR-DPV) project to invest from the small and medium-sized enterprises’ (SMEs) view. Flaws and inadequacies existing in the current decision-making process may cause an inaccurate investment result. Therefore, this paper establishes a cloud-TODIM framework to deal with the problems. First, criteria covering the economy, resource, risk factors and engineering feasibility are established. Second, hesitant fuzzy linguistic term set (HFLTS) and cloud model are applied to describe the indeterminate information so that the hesitation and randomness of linguistic variables can be fully expressed. Third, the analytic network process (ANP) method and entropy method are combined to gain the criteria weights, which can not only avoid too much subjectivity in weight determination but also measure the mutual influence between the various criteria simultaneously. Furthermore, the TODIM method considers the psychological behavior of investors, so it is utilized to rank alternatives to make the framework more applicable for practical evaluation. Finally, a case in Shandong province validates the applicability of the proposed framework. This paper provides a more rational and scientific decision-making framework for investors.
Article
Simulation and modelling allows a range of offshore wind farm stakeholders to test and improve a project’s viability in a cost-effective and safe manner. This paper presents a model developed to conduct detailed financial analysis of an offshore wind farm, extending the current state-of-the-art. The model was designed for versatility and can consider both fixed and floating technologies, a wide variety of strategies, and any site specified by the user. Stochastic time-series simulation modules perform in-depth analysis of the technologies, strategies and procedures applied during the installation, operation and maintenance, and decommissioning phases of a wind farm lifecycle. Results include energy production, costs and the duration of activities at each stage. These populate financial spreadsheets, which calculate key performance indicators including the Levelised Cost of Energy. The paper successfully validates the model against real-life case-studies where possible; published data; and uses sensitivity analysis to ensure the model is working as expected. Through a case-study, the paper demonstrates how 1) the model enables the identification of key cost and time drivers, facilitating scenario optimisation; 2) the stochastic nature of the model considers the impact of uncertain variables on results such as weather conditions and wind turbine failure rates; 3) the model can be used to assess different business models and financing structures. This comprehensive range of abilities means that the model is suited to a variety of end-users and meets the demands of a growing industry, striving to achieve further cost-reductions across a range of site conditions, technologies and markets.
Article
Designing policies to achieve a more sustainable electricity system requires policy-makers to weigh different electricity futures against a wide range of societal, economic, environmental, and technical implications. There is controversy on multiple fronts, as no technology satisfies all the demands of sustainability. Moreover, electricity systems include combinations of interacting technologies, meaning it is not enough to analyze technologies individually. We present a methodology for evaluating the sustainability of a region's electric generation portfolio, using multi-criteria decision analysis. Our framework focuses on long-term capacity planning for resource adequacy and sustainability. We used a regional electricity model and pay close attention to controversies involving offshore wind, natural gas pipelines, and the retirement of nuclear plants. We evaluate a set of generation portfolios under nine illustrative stakeholder preference scenarios across seven sustainability metrics. We find that under many stakeholder preferences, increasing offshore wind from 1.6 to 10 GW and eliminating oil generation scores well. If stakeholders are concerned about the full range of sustainability metrics – including costs, climate change, pollution, land-use, jobs, and safety alongside water conservation and nuclear concerns – then the most sustainable solution is to increase nuclear (to 9.2 GW from 3.5) alongside wind, and back them up with base levels of natural gas and hydro (18.7 and 3.3 GW respectively).
Article
As conventional energy resources are limited and polluting, new energy resources, being renewable and environmentally friendly, have been receiving increasing attention in recent years. However, no study on new energy investment, which acts a significant role in promoting the development and use of new energy resources, has been conducted. To cover this gap, an applicable decision support model is established by integrating Z-numbers, regret theory and elimination and choice translating reality III (ELECTRE III) to address new energy investment risk evaluation problems. In this way, Z-numbers are used to describe the decision-making information involved in the problems, a suggested method is combined with regret theory to determine the utility, rejoice and regret values of Z-information, and ELECTRE III is introduced to handle multiple criteria evaluation comprehensively. To elucidate and validate the application of the established model, a case study for new energy investment in Qingshuitang Industrial Zone is conducted and in-depth results analysis and discussion are implemented. The study shows that solar energy is the best investment project and environment is the most important investment factor. Moreover, the results demonstrate that the established model can effectively support new energy investment decision-making and it performs better than other existing methods.
Article
Sustainability is a concept that integrates at least three dimensions: environmental, economic and social. Energy systems are usually evaluated as a key contributor for sustainable development, needing the methodology used for their evaluation to address many indicators, some are quantitative, while others are qualitative. It is therefore a challenge to choose the best methodology to accomplish this task. In this article, a comprehensive literature review has been performed to analyse which tools have been used by the scientific community for the sustainability evaluation of renewable energy systems during the past ten years (2007-2017). The purpose of this work focuses on verifying that the methodological framework integrated by the Life Cycle Analysis (LCA) and Multi-Criteria Decision Making (MCDM) combination is the right tool for the sustainability evaluation of renewable energy systems and obtaining a set of sustainable indicators, evaluation methods and the context where they are applied (such energy policies, electrical supply and evaluation of projects). A knowledge database has been built from the scientific experience of 154 cases of sustainability evaluation in renewable energy systems, with special focus on photovoltaic systems. The results of this revision show that LCA and MCDM applied individually do not achieve a comprehensive sustainability evaluation, due to their intrinsic high degree of uncertainty and the different kinds of analysed parameters. The hybrid framework of LCA and MCDM applied in combination appears as the most appropriate approach for this purpose, and specifically the combination of LCA and Analytic Herarchic Process (AHP), the most frequently used by the scientific community, for its simplicity and robustness for sustainable evaluation in energy systems.
Article
China has begun to promote offshore photovoltaic in coastal areas taking its advantages of saving land resources and proximity to load centers. However, the projects are bound to face a series of risk factors as the industry is in its infancy. This paper conducts a risk assessment on offshore photovoltaic power generation projects in China based on a fuzzy framework. Firstly, 16 risk factors affecting offshore photovoltaic power generation projects in China are identified and classified into 4 groups. Secondly, a risk assessment model is constructed involving Hesitant Fuzzy Linguistic Term Sets, Triangular Fuzzy Number and Fuzzy Synthetic Evaluation. Thirdly, this paper conduct an empirical study of China, and the result shows that the risk level of offshore photovoltaic power generation projects in China is medium high. Finally, some response measures are proposed. The risk index system and corresponding countermeasures can provide a reference for project managers to allocate resources to prevent risk events. Besides, the risk assessment model can help project investors to avoid too risky projects. In addition, the risk assessment on offshore photovoltaic power generation projects in China has not been discussed by scholars yet. Thus, this paper contributes to the literature and expand the knowledge.
Article
Onshore and offshore wind turbines may have different environmental sustainability due to their own characteristics, and this information is important for future growth of wind power. The paper uses life cycle assessment (LCA) to estimate the life-cycle greenhouse gas (GHG) emissions of onshore and offshore wind turbines with the nominal capacity of 2 MW, to advance our understanding of onshore and offshore wind energy and to inform policy, planning, and investment decisions for future growth of wind power. Results show that the life-cycle GHG emission intensity is 0.082 kg CO2-equivalent (eq)/Megajoule (MJ) for onshore wind turbine and is 0.130 kg CO2-eq/MJ for offshore wind turbine, respectively. Offshore wind turbine has larger life-cycle GHG emissions than onshore wind turbine, owing to the floating platform fixed in sea. Onshore and offshore wind turbines have much smaller life-cycle GHG emission intensity than coal power plants. If the installed wind turbines in 2014 displace coal, the saved GHG emissions can roughly reach 5.08 × 10⁷ t CO2-eq, accounting for 0.09% of global GHG emissions in 2012. The sensitive analysis shows that the lifetime and energy production of wind turbine have large influences on the GHG emission intensity of both onshore and offshore wind turbines, implying that it is an effective way to prolong the lifetime of wind turbine and increase the energy production of wind turbine to reduce the GHG emission intensity of wind turbine. The sensitivity analysis further shows that the distance from wind turbine factory to wind farm site has more significant influence on the life-cycle GHG emission intensities of both onshore and offshore wind turbines than that from wind farm site to the recycling and landfill locations, suggesting that the wind farm site and the wind turbine factory should be as close as possible.
Article
In this paper, a sensitivity analysis is performed on the levelized cost of energy (LCOE) for floating offshore wind farms (FOWFs). The analysis is carried out for three floating wind turbine concepts and three different offshore sites. At first, a methodology is presented for calculating the LCOE for a specific FOWF. Afterwards, the base LCOE values for each of the floating wind turbine concepts and sites are obtained. The sensitivity analysis includes over 325 input parameters that are studied in order to identify the ones that most influence the LCOE. Furthermore, a complementary sensitivity analysis is performed by varying the input parameters based on uncertainty ranges provided by each of the concept designers. This serves to obtain maximum and minimum LCOE variation limits and possible cost reduction potentials. It has been observed that the capital cost related parameters such as turbine, substructure and mooring system manufacturing cost as well as power cable cost are some of the most influencing parameters besides common parameters such as the discount rate and energy losses. The LCOE variation limits obtained in this study vary between 67 €/MWh and 135 €/MWh among the different concepts and offshore sites including offshore transmission costs.
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Wind power supplies clean energy, but it is vulnerable to climate change. As the impacts of climate change increase, economic assessment methods of wind power projects are required to capture climate uncertainties. The study proposes a decision-making model to analyze the economic feasibility of offshore wind farm projects considering the impacts of climate change using real options analysis (ROA). The model can consider project volatility using the wind speed projected from climate scenarios that affect wind power production. A case study of an offshore wind farm in South Korea was conducted to confirm the validity of the proposed model. The case study proved that the managerial flexibility provided by the proposed real options effectively reduces risks and increases the long-term profitability of offshore wind farm projects.
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In 2015, the United Nations announced the new Sustainable Development Goals (SDGs) to safeguard the earth and end poverty as the new global sustainable development agenda. One of these SDGs, Goal #7, is about affordable and clean energy. Despite the importance, there are few tools that guide policy-makers in aligning their domestic policies with these SDGs. The paper addresses this research gap and introduces a numerical decision-support method for identifying the most suitable renewable energy source (RES). RES selection according to SDGs can be a challenge for decision makers. This article presents an integrated multi-criteria decision-making (MCDM) method that is based on hesitant fuzzy linguistic (HFL) term set. The decision criteria are weighed with HFL Analytic Hierarchy Process (AHP), and the most appropriate RES alternative is chosen with the HFL COmplex PRoportional ASsessment (COPRAS) technique. The value of the method is demonstrated on a case from Turkey, and a comparative analysis. This approach constitutes a novelty by proposing a numerical model for SDGs that combines AHP and COPRAS in a HFL environment with group decision-making for the first time. The method can help policy-makers in better structuring local energy policies with regard to global efforts in a developing country setting.
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Developing net-zero energy communities powered by renewable energy (RE) resources has become a popular concept. To make the best choices for community-level net-zero energy systems, it is necessary to identify the best energy technologies at local level. Evaluation of RE technologies has to be extended from technical and economic aspects to include environmental and social wellbeing. It is possible to identify the true costs and benefits of energy use by taking a cradle-to-grave life cycle perspective. In this study, a RE screening and multi-stage energy selection framework was developed. A fuzzy multi-criteria decision making approach was used in ranking the technologies to incorporate the conflicting requirements, stakeholder priorities, and uncertainties. Different scenarios were investigated to reflect different decision maker priorities. Under a pro-environment scenario, small hydro, onshore wind, and biomass combustion technologies perform best. Under a pro-economic decision scenario, biomass combustion, small hydro, and landfill gas have the best rankings. Triple bottom line sustainability was combined with technical feasibility through a ruled-based approach to avoid the theoretical pitfalls inherent in energy-related decision making. This assessment is geared towards providing decision makers with flexible tools, and is expected to aid in the pre-project planning stage of RE projects.
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The offshore wind (OW) industry has reached reasonable maturity over the past decade and the European market currently consists of a diverse pool of investors. Often equity investors buy and sell stakes at different phases of the asset service life with a view to maximize their return on investment. A detailed assessment of the investment returns taking into account the technical parameters of the problem, is pertinent towards understanding the value of new and operational wind farms. This paper develops a high fidelity lifecycle techno-economic model, bringing together the most up-to-date data and parametric equations from databases and literature. Subsequently, based on a realistic case study of an OW farm in the UK, a sensitivity analysis is performed to test how input parameters influence the model output. Sensitivity analysis results highlight that the NPV is considerably sensitive to FinEX and revenue parameters, as well as to some OPEX parameters, i.e. the mean time to failure of the wind turbine components and the workboat significant wave height limit. Application of the model from the perspective of investors with different entry and exit timings derives the temporal return profiles, revealing important insights regarding the potential minimum asking and maximum offered price.
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China has attached great importance to offshore wind power development due to rich offshore wind resources and the proximity to load centers. However, hindered by various barriers, the growth of offshore wind installed capacity is much slower than the government's expectation. This study proposes a barrier-analysis framework for offshore wind power based on the grey decision-making trial and evaluation laboratory (DEMATEL) method. Firstly, four categories of barriers affecting offshore wind power in China are identified through a literature survey and interaction with experts, including economic, technological, environmental and social-political barriers. Secondly, this study investigates the prominence of these barriers and the cause-effect relationships among them using the grey DEMATEL approach. Then, a sensitive analysis is conducted by considering different scenarios of experts' weights to verify the robustness of analysis results. Finally, this study further validates the results with experts' feedback and the existing literature. According to the results, six barriers are regarded as key barriers to Chinese offshore wind power industry, and corresponding strategic measures are suggested to eliminate these key barriers and promote its sustainable development. This study can provide a valuable insight into barriers to China's offshore wind power industry and help stakeholders to develop more effective barrier-removal strategies.
Chapter
Engineering economics deals with the investment decisions, where the investment parameters are very hard to estimate exactly. In the cases where we do not have the required data for parameter estimation, possibilistic approaches may be used. In this chapter, a brief literature review on wind energy investments is first presented. Later, the chapter gives present worth analysis (PWA) methods extended to fuzzy sets. The chapter introduces ordinary fuzzy PWA, type-2 fuzzy PWA, intuitionistic fuzzy PWA, and hesitant fuzzy PWA. A numerical application for each extension is presented. © 2018, Springer International Publishing AG, part of Springer Nature.
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Many irreversible long-run capital investments entail opportunities for managers to respond flexibly to changes in the economic environment. However, common levelized cost measures used to guide decision-making, such as the levelized cost of electricity, implicitly assume that the values of random economic variables are known with certainty when investment decisions are made. This assumption implies, often incorrectly, that managerial flexibility carries zero value. This paper improves levelized cost measures by deriving an expansion that accounts for both uncertainties in relevant variables and the value of managerial flexibility in responding to them. This method is applied to quantify the value of flexibility in two example decision problems. In one, an operator of a natural gas electricity generation facility evaluates whether to invest in carbon capture capabilities. Another considers retirement decisions for U.S. nuclear plants. These examples illustrate that simplified cost metrics can inaccurately guide decision-making by inflating cost estimates relative to the proposed levelized cost measure that accounts for uncertainty and flexibility.