Significant high-quality heat is wasted in the vacuum thermionic generator (VTIG), which can be efficiently utilized as a prime mover of a bottoming system for cogeneration applications. For this purpose, a new environmental-friendly hybrid system composed of a heliostat solar field, VTIG, and looped multi-stage thermoacoustically driven cryocooler (LMTC) is established, in which the high-temperature heat source of the solar receiver runs the VTIG to generate power, and the LMTC recovers the waste heat of the VTIG to produce a cooling load. Thermodynamic, economic, and environmental analyses of the system are carried out based on exergy and emergy concepts. Moreover, a parametric study is performed to assess the effect of design parameters on the system's thermodynamic, economic, and environmental criteria. Finally, the multi-criteria salp swarm optimization algorithm and decision-making procedures are conducted to improve the exergetic performance and decrease the system's cost and monetary emergy rates along with the environmental impact and ecological emergy rate. Findings depict that at the reliable, optimal operation of the system, the exergetic efficiency can reach 29.36% with a maximum power of 17.2 MW and cooling load of 0.260 MW. The system's cost and monetary emergy rate can be reduced to 0.059 $/s and 5.94 × 1010 seJ/s, with 10.6% and 10% reductions, respectively. Moreover, the environmental impact and ecological emergy rates decline by 6% and 7.4%, respectively. The theoretical findings may offer guidance for the optimum designing and practical running of such a solar solid-state cogeneration system.
{"title":"A hybrid solar-driven vacuum thermionic generator and looped multi-stage thermoacoustically driven cryocooler system: Exergy- and emergy-based analysis and optimization","authors":"Yasaman Yousefi, A. Noorpoor, F. Boyaghchi","doi":"10.1063/5.0192008","DOIUrl":"https://doi.org/10.1063/5.0192008","url":null,"abstract":"Significant high-quality heat is wasted in the vacuum thermionic generator (VTIG), which can be efficiently utilized as a prime mover of a bottoming system for cogeneration applications. For this purpose, a new environmental-friendly hybrid system composed of a heliostat solar field, VTIG, and looped multi-stage thermoacoustically driven cryocooler (LMTC) is established, in which the high-temperature heat source of the solar receiver runs the VTIG to generate power, and the LMTC recovers the waste heat of the VTIG to produce a cooling load. Thermodynamic, economic, and environmental analyses of the system are carried out based on exergy and emergy concepts. Moreover, a parametric study is performed to assess the effect of design parameters on the system's thermodynamic, economic, and environmental criteria. Finally, the multi-criteria salp swarm optimization algorithm and decision-making procedures are conducted to improve the exergetic performance and decrease the system's cost and monetary emergy rates along with the environmental impact and ecological emergy rate. Findings depict that at the reliable, optimal operation of the system, the exergetic efficiency can reach 29.36% with a maximum power of 17.2 MW and cooling load of 0.260 MW. The system's cost and monetary emergy rate can be reduced to 0.059 $/s and 5.94 × 1010 seJ/s, with 10.6% and 10% reductions, respectively. Moreover, the environmental impact and ecological emergy rates decline by 6% and 7.4%, respectively. The theoretical findings may offer guidance for the optimum designing and practical running of such a solar solid-state cogeneration system.","PeriodicalId":16953,"journal":{"name":"Journal of Renewable and Sustainable Energy","volume":null,"pages":null},"PeriodicalIF":2.5,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140084521","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Offshore photovoltaic power stations (OPVPS) greatly help solve energy problems and land resource scarcity. A crucial phase of the OPVPS project lifecycle is site selection. To select the optimal location of OPVPS with many difficulties such as the uncertainty of the environment, the compensating relationships among criteria, and the different attributes of the alternatives, this paper proposed a fuzzy multi-criteria decision-making framework based on Pythagorean fuzzy Elimination et Choix Traduisant la Realité-III (ELECTRE-III) method. First, the comprehensive criteria system for siting OPVPS was constructed, which included veto and evaluation criteria. Second, the Pythagorean fuzzy set was used to express the uncertain evaluation of experts. Third, considering the actual situation that experts had different experiences and abilities, this paper proposed a novel expert weighting method. Fourth, entropy weighting method, best–worst method, and combination weighting of game theory were introduced to calculate the criteria weights. Fifth, considering the compensation between criteria, ELECTRE-III was used for ranking. Finally, to verify the applicability and robustness of the proposed framework, a China case study was conducted; the results showed that Haizhou Bay is the best alternative.
{"title":"A decision framework of offshore photovoltaic power station site selection based on Pythagorean fuzzy ELECTRE-III method","authors":"Qinghua Mao, Jiacheng Fan, Jian Lv, Yaqing Gao, Jinjin Chen, Mengxin Guo","doi":"10.1063/5.0191823","DOIUrl":"https://doi.org/10.1063/5.0191823","url":null,"abstract":"Offshore photovoltaic power stations (OPVPS) greatly help solve energy problems and land resource scarcity. A crucial phase of the OPVPS project lifecycle is site selection. To select the optimal location of OPVPS with many difficulties such as the uncertainty of the environment, the compensating relationships among criteria, and the different attributes of the alternatives, this paper proposed a fuzzy multi-criteria decision-making framework based on Pythagorean fuzzy Elimination et Choix Traduisant la Realité-III (ELECTRE-III) method. First, the comprehensive criteria system for siting OPVPS was constructed, which included veto and evaluation criteria. Second, the Pythagorean fuzzy set was used to express the uncertain evaluation of experts. Third, considering the actual situation that experts had different experiences and abilities, this paper proposed a novel expert weighting method. Fourth, entropy weighting method, best–worst method, and combination weighting of game theory were introduced to calculate the criteria weights. Fifth, considering the compensation between criteria, ELECTRE-III was used for ranking. Finally, to verify the applicability and robustness of the proposed framework, a China case study was conducted; the results showed that Haizhou Bay is the best alternative.","PeriodicalId":16953,"journal":{"name":"Journal of Renewable and Sustainable Energy","volume":null,"pages":null},"PeriodicalIF":2.5,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140274227","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Tomomi Uchiyama, Takeshi Seta, S. Iio, Toshihiko Ikeda, K. Takamure
The flow and output of a Savonius hydraulic turbine rotor were simulated using the lattice Boltzmann method (LBM). The rotor, characterized by a configuration featuring two semi-circular arc-shaped blades, operated at a Reynolds number of 1.1 × 105. The simulations were conducted in a two-dimensional domain, focusing on the incompressible flow within the cross-sectional area of the rotor perpendicular to its rotational axis. The LBM approach was coupled with a rotor rotation analysis. In the LBM framework, the non-orthogonal central moment model was employed for the precise computation of particle collisions. Additionally, the direct forcing method was used to consider the rotating blades and shaft. Consequently, the torque exerted on both advancing and returning blades and rotor output was successfully simulated. These simulations unveiled the inherently unsteady rotational behavior of the rotor, stemming from the variable torque acting upon the blades. Moreover, the computational results exhibited a notable agreement between the simulated flow pattern around the rotor and the experimental visualization. Furthermore, an approximately identical correlation between the rotor speed and power output was established, mirroring the experimental results. These findings underscore the robust applicability of LBM in facilitating the design and operational analysis of Savonius hydraulic turbines.
{"title":"Numerical simulation of the flow and output of a Savonius hydraulic turbine using the lattice Boltzmann method","authors":"Tomomi Uchiyama, Takeshi Seta, S. Iio, Toshihiko Ikeda, K. Takamure","doi":"10.1063/5.0189278","DOIUrl":"https://doi.org/10.1063/5.0189278","url":null,"abstract":"The flow and output of a Savonius hydraulic turbine rotor were simulated using the lattice Boltzmann method (LBM). The rotor, characterized by a configuration featuring two semi-circular arc-shaped blades, operated at a Reynolds number of 1.1 × 105. The simulations were conducted in a two-dimensional domain, focusing on the incompressible flow within the cross-sectional area of the rotor perpendicular to its rotational axis. The LBM approach was coupled with a rotor rotation analysis. In the LBM framework, the non-orthogonal central moment model was employed for the precise computation of particle collisions. Additionally, the direct forcing method was used to consider the rotating blades and shaft. Consequently, the torque exerted on both advancing and returning blades and rotor output was successfully simulated. These simulations unveiled the inherently unsteady rotational behavior of the rotor, stemming from the variable torque acting upon the blades. Moreover, the computational results exhibited a notable agreement between the simulated flow pattern around the rotor and the experimental visualization. Furthermore, an approximately identical correlation between the rotor speed and power output was established, mirroring the experimental results. These findings underscore the robust applicability of LBM in facilitating the design and operational analysis of Savonius hydraulic turbines.","PeriodicalId":16953,"journal":{"name":"Journal of Renewable and Sustainable Energy","volume":null,"pages":null},"PeriodicalIF":2.5,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140082802","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The electricity grid with high-penetration renewable energy sources has urged us to seek means to solve the mismatching between electricity supply and demand. Energy storage technology could accomplish the energy conversion process between different periods to achieve the efficient and stable utilization of renewable energy sources. In this paper, a hybrid energy storage system based on compressed air energy storage and reversible solid oxidation fuel cell (rSOC) is proposed. During the charging process, the rSOC operates in electrolysis cell (EC) mode to achieve the energy storage by converting the compression heat to chemical fuels. During the discharging process, the cell operates in fuel cell mode for electricity production and the gas turbine is conducted to recover the waste heat from cell. To evaluate the comprehensive performance of the proposed system, the energy, exergy, and exergoeconomic studies are conducted in this paper. Under the design condition, the results indicated that the proposed system is capable of generating 300.36 kW of electricity and 106.28 kW of heating; in the meantime, the energy efficiency, exergy efficiency, and total cost per unit exergy of product are 73.80%, 55.70%, and 216.78 $/MWh, respectively. The parametric analysis indicates that the increase in pressure ratio of air compressor, steam utilization factor of rSOC stack under EC mode and current density of the rSOC stack under EC mode reduce exergy efficiency and total cost per unit exergy of product simultaneously, while the increment of operating pressure of rSOC stack under FC mode enhances the exergy efficiency and decreases total cost per unit exergy of product. The multi-objective optimization is carried out to improve the comprehensive performance of the proposed system, and the results expressed that the best optimal solution has the exergy efficiency and total cost per unit exergy of product of 65.85% and 187.05 $/MWh, respectively. Compared to the basic operating condition, the improvement of the proposed system has led to the maximum enhancement of 20.32% in exergy efficiency and 18.60% in total cost per unit exergy of product.
{"title":"Assessment of a combined heating and power system based on compressed air energy storage and reversible solid oxide cell: Energy, exergy, and exergoeconomic evaluation","authors":"Hui Hui, Xinwen Chang, Xiaofei Ji, Jiaxue Hui","doi":"10.1063/5.0197046","DOIUrl":"https://doi.org/10.1063/5.0197046","url":null,"abstract":"The electricity grid with high-penetration renewable energy sources has urged us to seek means to solve the mismatching between electricity supply and demand. Energy storage technology could accomplish the energy conversion process between different periods to achieve the efficient and stable utilization of renewable energy sources. In this paper, a hybrid energy storage system based on compressed air energy storage and reversible solid oxidation fuel cell (rSOC) is proposed. During the charging process, the rSOC operates in electrolysis cell (EC) mode to achieve the energy storage by converting the compression heat to chemical fuels. During the discharging process, the cell operates in fuel cell mode for electricity production and the gas turbine is conducted to recover the waste heat from cell. To evaluate the comprehensive performance of the proposed system, the energy, exergy, and exergoeconomic studies are conducted in this paper. Under the design condition, the results indicated that the proposed system is capable of generating 300.36 kW of electricity and 106.28 kW of heating; in the meantime, the energy efficiency, exergy efficiency, and total cost per unit exergy of product are 73.80%, 55.70%, and 216.78 $/MWh, respectively. The parametric analysis indicates that the increase in pressure ratio of air compressor, steam utilization factor of rSOC stack under EC mode and current density of the rSOC stack under EC mode reduce exergy efficiency and total cost per unit exergy of product simultaneously, while the increment of operating pressure of rSOC stack under FC mode enhances the exergy efficiency and decreases total cost per unit exergy of product. The multi-objective optimization is carried out to improve the comprehensive performance of the proposed system, and the results expressed that the best optimal solution has the exergy efficiency and total cost per unit exergy of product of 65.85% and 187.05 $/MWh, respectively. Compared to the basic operating condition, the improvement of the proposed system has led to the maximum enhancement of 20.32% in exergy efficiency and 18.60% in total cost per unit exergy of product.","PeriodicalId":16953,"journal":{"name":"Journal of Renewable and Sustainable Energy","volume":null,"pages":null},"PeriodicalIF":2.5,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140402891","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Industrial integrated energy systems (IESs) and electric vehicles (EVs) provide new solutions for addressing the increasing challenges of the energy crisis and environmental pollution. With the increasing number of EVs and smart charging stations in industrial IES, the uncoordinated charging load of EVs imposes significant pressure on IES. Therefore, a well-designed dispatch scheme is crucial for reducing the economic cost for both parties, alleviating the energy supply pressure on IES, and promoting the development of a low-carbon society. To this end, given the load characteristics of EVs in industrial IES, we propose a dispatch framework based on the Stackelberg game theory, which includes the leader and the follower. The leader IES is responsible for formulating both unit dispatch and demand response plans, as well as determining the charging pricing for the smart charging station. The follower smart charging station optimizes EVs charging power by minimizing the charging cost in order to protect the interest of EV owners. Additionally, we introduce the carbon emission flow model into charging station pricing to shift the responsibility for carbon emissions from the generation side to the EV load side. Considering that the two-layer game model is difficult to solve, we use the Karush–Kuhn–Tucker condition and duality theorem to transform it into an equivalent single-layer optimization problem, which is easily solved. Simulation results demonstrate that the proposed game framework effectively reduces the economic cost of IES and the charging cost of EVs, alleviates the pressure from charging load, and reduces the carbon emissions of industrial IES.
{"title":"Economic and low-carbon dispatch of industrial integrated energy system with EV load based on Stackelberg game framework","authors":"Lingjie Chen, Chunyu Song, Wei Jiang, Jun Zhao","doi":"10.1063/5.0199685","DOIUrl":"https://doi.org/10.1063/5.0199685","url":null,"abstract":"Industrial integrated energy systems (IESs) and electric vehicles (EVs) provide new solutions for addressing the increasing challenges of the energy crisis and environmental pollution. With the increasing number of EVs and smart charging stations in industrial IES, the uncoordinated charging load of EVs imposes significant pressure on IES. Therefore, a well-designed dispatch scheme is crucial for reducing the economic cost for both parties, alleviating the energy supply pressure on IES, and promoting the development of a low-carbon society. To this end, given the load characteristics of EVs in industrial IES, we propose a dispatch framework based on the Stackelberg game theory, which includes the leader and the follower. The leader IES is responsible for formulating both unit dispatch and demand response plans, as well as determining the charging pricing for the smart charging station. The follower smart charging station optimizes EVs charging power by minimizing the charging cost in order to protect the interest of EV owners. Additionally, we introduce the carbon emission flow model into charging station pricing to shift the responsibility for carbon emissions from the generation side to the EV load side. Considering that the two-layer game model is difficult to solve, we use the Karush–Kuhn–Tucker condition and duality theorem to transform it into an equivalent single-layer optimization problem, which is easily solved. Simulation results demonstrate that the proposed game framework effectively reduces the economic cost of IES and the charging cost of EVs, alleviates the pressure from charging load, and reduces the carbon emissions of industrial IES.","PeriodicalId":16953,"journal":{"name":"Journal of Renewable and Sustainable Energy","volume":null,"pages":null},"PeriodicalIF":2.5,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140407567","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Chao Zhang, Jun Wang, Shu Hu, Yong Wu, Weidong Zhu
The high-quality development of new energy enterprises is of great significance to promote carbon peak and carbon neutrality and cope with the global warming crisis. However, with the increasing intensity of market competition and the appropriate weakening of the expected future subsidies, how to improve their performance through the fulfillment of the social responsibility of stakeholders has become a key scientific problem to be solved. Given the features of the new energy industry, including substantial initial investment, formidable technical barriers, and a pronounced reliance on policy support, this paper takes 182 new energy concept enterprises listed in China's A-shares in 2011–2020 as the research object. Employing qualitative comparative analysis, we extract four key rules for achieving high performance in new energy enterprises from the perspective of value co-creation of core stakeholders, including capital stakeholders (shareholders and creditors), technical stakeholders (employees), policy stakeholders (government and society), and upstream and downstream stakeholders (suppliers and customers). Then, we explore the performance improvement rules of typical cases. Our findings reveal that within the realm of new energy enterprises, capital-intensive enterprises with cost leadership and tax incentives, energy-manufacturing enterprises with suppliers dependence and saving environmental input, technology-innovation enterprises with cost leadership and talents dependence, and comprehensive-mature enterprises with suppliers dependence and tax incentives are more likely to achieve high performance. The findings can better guide management practice and promote the high-quality development of new energy enterprises.
新能源企业的高质量发展对于促进碳峰值和碳中和，应对全球变暖危机具有重要意义。然而，随着市场竞争的日趋激烈和未来补贴预期的适当弱化，如何通过履行利益相关者的社会责任来提高其绩效，已成为亟待解决的关键科学问题。鉴于新能源产业初期投入巨大、技术壁垒高、对政策支持依赖性强等特点，本文以 2011-2020 年在中国 A 股上市的 182 家新能源概念企业为研究对象。通过定性比较分析，从资本利益相关者（股东和债权人）、技术利益相关者（员工）、政策利益相关者（政府和社会）、上下游利益相关者（供应商和客户）等核心利益相关者价值共创的角度，提炼出新能源企业实现高绩效的四条关键法则。然后，我们探讨了典型案例的绩效改进规则。研究结果表明，在新能源企业中，具有成本领先和税收优惠政策的资本密集型企业、具有供应商依赖和节约环境投入的能源制造型企业、具有成本领先和人才依赖的技术创新型企业、具有供应商依赖和税收优惠政策的综合成熟型企业更有可能实现高绩效。研究结果可以更好地指导管理实践，促进新能源企业的高质量发展。
{"title":"Research on mining high performance path rules for new energy enterprises from the perspective of social responsibility—Empirical data from China","authors":"Chao Zhang, Jun Wang, Shu Hu, Yong Wu, Weidong Zhu","doi":"10.1063/5.0189232","DOIUrl":"https://doi.org/10.1063/5.0189232","url":null,"abstract":"The high-quality development of new energy enterprises is of great significance to promote carbon peak and carbon neutrality and cope with the global warming crisis. However, with the increasing intensity of market competition and the appropriate weakening of the expected future subsidies, how to improve their performance through the fulfillment of the social responsibility of stakeholders has become a key scientific problem to be solved. Given the features of the new energy industry, including substantial initial investment, formidable technical barriers, and a pronounced reliance on policy support, this paper takes 182 new energy concept enterprises listed in China's A-shares in 2011–2020 as the research object. Employing qualitative comparative analysis, we extract four key rules for achieving high performance in new energy enterprises from the perspective of value co-creation of core stakeholders, including capital stakeholders (shareholders and creditors), technical stakeholders (employees), policy stakeholders (government and society), and upstream and downstream stakeholders (suppliers and customers). Then, we explore the performance improvement rules of typical cases. Our findings reveal that within the realm of new energy enterprises, capital-intensive enterprises with cost leadership and tax incentives, energy-manufacturing enterprises with suppliers dependence and saving environmental input, technology-innovation enterprises with cost leadership and talents dependence, and comprehensive-mature enterprises with suppliers dependence and tax incentives are more likely to achieve high performance. The findings can better guide management practice and promote the high-quality development of new energy enterprises.","PeriodicalId":16953,"journal":{"name":"Journal of Renewable and Sustainable Energy","volume":null,"pages":null},"PeriodicalIF":2.5,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140092516","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
With the increasing application of wind energy, reliable wind speed prediction has become imperative. However, prior studies predominantly concentrated on single-model predictions, disregarding the inherent uncertainty in wind speed. This oversight resulted in inadequate deterministic and probabilistic forecasting outcomes across varying scenarios. To make up for these shortcomings, a novel forecasting system combining a data preprocessing technique, a sub-model selection method, and a modified multi-objective integrate optimization strategy is designed in this paper. According to the data obtained from China's wind farm, the forecasting efficiency of this system is verified from multiple perspectives. The findings show that the system takes advantage of each model to boost the precision and stability of point prediction successfully. Furthermore, it achieves higher interval coverage and narrower interval width under distinct confidence levels. These results highlight the system's potential as a reliable technical support for efficient dispatching of the entire power system.
{"title":"A novel combined wind speed forecasting system based on fuzzy granulation and multi-objective optimization","authors":"Chenglin Yang, Jianzhou Wang","doi":"10.1063/5.0175387","DOIUrl":"https://doi.org/10.1063/5.0175387","url":null,"abstract":"With the increasing application of wind energy, reliable wind speed prediction has become imperative. However, prior studies predominantly concentrated on single-model predictions, disregarding the inherent uncertainty in wind speed. This oversight resulted in inadequate deterministic and probabilistic forecasting outcomes across varying scenarios. To make up for these shortcomings, a novel forecasting system combining a data preprocessing technique, a sub-model selection method, and a modified multi-objective integrate optimization strategy is designed in this paper. According to the data obtained from China's wind farm, the forecasting efficiency of this system is verified from multiple perspectives. The findings show that the system takes advantage of each model to boost the precision and stability of point prediction successfully. Furthermore, it achieves higher interval coverage and narrower interval width under distinct confidence levels. These results highlight the system's potential as a reliable technical support for efficient dispatching of the entire power system.","PeriodicalId":16953,"journal":{"name":"Journal of Renewable and Sustainable Energy","volume":null,"pages":null},"PeriodicalIF":2.5,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140278742","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
To further promote the efficient use of energy storage and the local consumption of renewable energy in a multi-integrated energy system (MIES), a MIES model is developed based on the operational characteristics and profitability mechanism of a shared energy storage station (SESS), considering concentrating solar power (CSP), integrated demand response, and renewable energy output uncertainty. We propose a corresponding MIES model based on co-operative game theory and the CSP and an optimal allocation method for MIES shared energy storage. The model considers the maximum operating benefit of the SESS as the upper objective function and the minimum operating cost of the MIES as the lower objective function. First, the Karush–Kuhn–Tucker conditions of the lower-layer model are transformed into constraints of the upper-layer model, and the Big-M method is used to linearize the nonlinear problem and convert the two-layer nonlinear model into a single-layer linear model. Second, based on the Nash negotiation theory, the benefits of each IES in the MIES are allocated. Finally, the fuzzy chance constraints are used to relax the power balance constraints, and the trapezoidal fuzzy numbers are transformed into a deterministic equivalence class to assess the impact of renewable energy output uncertainty on system operation. The validity and rationality of the proposed two-layer model are verified through simulation, and the results demonstrate that the proposed shared storage capacity leasing model can effectively reduce the total operation cost, increase the profitability of the shared storage operator, and increase the utilization rate of the SESS.
为进一步促进多集成能源系统（MIES）中储能的高效利用和可再生能源的本地消纳，我们根据共享储能站（SESS）的运行特点和盈利机制，并考虑到聚光太阳能发电（CSP）、综合需求响应和可再生能源输出的不确定性，建立了一个多集成能源系统模型。我们基于合作博弈论和 CSP 提出了相应的 MIES 模型，并提出了 MIES 共享储能的优化分配方法。该模型以 SESS 的最大运营效益为上层目标函数，以 MIES 的最小运营成本为下层目标函数。首先，将下层模型的 Karush-Kuhn-Tucker 条件转化为上层模型的约束条件，并采用 Big-M 方法对非线性问题进行线性化处理，将双层非线性模型转化为单层线性模型。其次，根据纳什协商理论，分配 MIES 中各 IES 的收益。最后，利用模糊机会约束来放松电力平衡约束，并将梯形模糊数转化为确定性等价类，以评估可再生能源输出不确定性对系统运行的影响。通过仿真验证了所提两层模型的有效性和合理性，结果表明所提共享储能租赁模型能有效降低总运营成本，增加共享储能运营商的盈利能力，提高 SESS 的利用率。
{"title":"Optimal allocation method for MIES-based shared energy storage using cooperative game theory and CSP","authors":"Wei Chen, Haonan Lu, Zhanhong Wei","doi":"10.1063/5.0198282","DOIUrl":"https://doi.org/10.1063/5.0198282","url":null,"abstract":"To further promote the efficient use of energy storage and the local consumption of renewable energy in a multi-integrated energy system (MIES), a MIES model is developed based on the operational characteristics and profitability mechanism of a shared energy storage station (SESS), considering concentrating solar power (CSP), integrated demand response, and renewable energy output uncertainty. We propose a corresponding MIES model based on co-operative game theory and the CSP and an optimal allocation method for MIES shared energy storage. The model considers the maximum operating benefit of the SESS as the upper objective function and the minimum operating cost of the MIES as the lower objective function. First, the Karush–Kuhn–Tucker conditions of the lower-layer model are transformed into constraints of the upper-layer model, and the Big-M method is used to linearize the nonlinear problem and convert the two-layer nonlinear model into a single-layer linear model. Second, based on the Nash negotiation theory, the benefits of each IES in the MIES are allocated. Finally, the fuzzy chance constraints are used to relax the power balance constraints, and the trapezoidal fuzzy numbers are transformed into a deterministic equivalence class to assess the impact of renewable energy output uncertainty on system operation. The validity and rationality of the proposed two-layer model are verified through simulation, and the results demonstrate that the proposed shared storage capacity leasing model can effectively reduce the total operation cost, increase the profitability of the shared storage operator, and increase the utilization rate of the SESS.","PeriodicalId":16953,"journal":{"name":"Journal of Renewable and Sustainable Energy","volume":null,"pages":null},"PeriodicalIF":2.5,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140280289","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Accurate and reliable ultra-short-term wind power forecasting (WPF) is of great significance to the safe and stable operation of power systems, but the current research is difficult to balance the prediction accuracy, timeliness, and applicability at the same time. Therefore, this paper proposes a ultra-short-term WPF model based on feature weight analysis and cluster dynamic division. The model introduces an analytic hierarchy process and an entropy weight method to analyze the subjective and objective weight of the influencing features of wind power, respectively, then the subjective and objective weight ratio is determined by the quantum particle swarm optimization (QPSO) algorithm to obtain a more reasonable comprehensive weight of each feature. On this basis, it uses the K-Medoids algorithm to dynamically divide the wind power clusters into class regions by cycles. Then, the class region is used as the prediction unit to establish the TCN-BiLSTM model based on temporal convolutional networks (TCN) and bi-directional long short-term memory (BiLSTM) for training and prediction and optimizes the hyper-parameters of the model by the QPSO algorithm. Finally, the regional predictions are summed to obtain the final ultra-short-term power prediction. In addition, in order to verify the performance of the model, the actual operation data of a power field in Xinjiang, China, are selected for the example validation. The results show that the proposed model can ensure the prediction accuracy while minimizing the training time of the model and outperforms other existing methods in terms of prediction accuracy, timeliness, and applicability.
{"title":"Ultra-short-term wind power forecasting based on feature weight analysis and cluster dynamic division","authors":"Chen Chang, Yuyu Meng, J. Huo, Jihao Xu, Tian Xie","doi":"10.1063/5.0187356","DOIUrl":"https://doi.org/10.1063/5.0187356","url":null,"abstract":"Accurate and reliable ultra-short-term wind power forecasting (WPF) is of great significance to the safe and stable operation of power systems, but the current research is difficult to balance the prediction accuracy, timeliness, and applicability at the same time. Therefore, this paper proposes a ultra-short-term WPF model based on feature weight analysis and cluster dynamic division. The model introduces an analytic hierarchy process and an entropy weight method to analyze the subjective and objective weight of the influencing features of wind power, respectively, then the subjective and objective weight ratio is determined by the quantum particle swarm optimization (QPSO) algorithm to obtain a more reasonable comprehensive weight of each feature. On this basis, it uses the K-Medoids algorithm to dynamically divide the wind power clusters into class regions by cycles. Then, the class region is used as the prediction unit to establish the TCN-BiLSTM model based on temporal convolutional networks (TCN) and bi-directional long short-term memory (BiLSTM) for training and prediction and optimizes the hyper-parameters of the model by the QPSO algorithm. Finally, the regional predictions are summed to obtain the final ultra-short-term power prediction. In addition, in order to verify the performance of the model, the actual operation data of a power field in Xinjiang, China, are selected for the example validation. The results show that the proposed model can ensure the prediction accuracy while minimizing the training time of the model and outperforms other existing methods in terms of prediction accuracy, timeliness, and applicability.","PeriodicalId":16953,"journal":{"name":"Journal of Renewable and Sustainable Energy","volume":null,"pages":null},"PeriodicalIF":2.5,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140281954","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Consistency, in a narrow sense, denotes the alignment between the forecast-optimization strategy and the verification directive. The current recommended deterministic solar forecast verification practice is to report the skill score based on root mean square error (RMSE), which would violate the notion of consistency if the forecasts are optimized under another strategy such as minimizing the mean absolute error (MAE). This paper overcomes such difficulty by proposing a so-called “potential RMSE skill score,” which depends only on (1) the cross-correlation between forecasts and observations and (2) the autocorrelation of observations. While greatly simplifying the calculation, the new skill score does not discriminate inconsistent forecasts as much, e.g., even MAE-optimized forecasts can attain a high RMSE skill score.
从狭义上讲，一致性是指预报优化策略与核查指令之间的一致性。目前推荐的确定性太阳预报验证做法是根据均方根误差（RMSE）报告技能得分，如果预报是根据其他策略（如最小化平均绝对误差（MAE））优化的，则会违反一致性概念。本文提出了一种所谓的 "潜在 RMSE 技能得分"，它只取决于（1）预测与观测之间的交叉相关性和（2）观测的自相关性，从而克服了这一困难。虽然大大简化了计算，但新的技能分值对不一致预测的区分度并不高，例如，即使是 MAE 优化预测也能获得较高的 RMSE 技能分值。
{"title":"Potential root mean square error skill score","authors":"Martin János Mayer, Dazhi Yang","doi":"10.1063/5.0187044","DOIUrl":"https://doi.org/10.1063/5.0187044","url":null,"abstract":"Consistency, in a narrow sense, denotes the alignment between the forecast-optimization strategy and the verification directive. The current recommended deterministic solar forecast verification practice is to report the skill score based on root mean square error (RMSE), which would violate the notion of consistency if the forecasts are optimized under another strategy such as minimizing the mean absolute error (MAE). This paper overcomes such difficulty by proposing a so-called “potential RMSE skill score,” which depends only on (1) the cross-correlation between forecasts and observations and (2) the autocorrelation of observations. While greatly simplifying the calculation, the new skill score does not discriminate inconsistent forecasts as much, e.g., even MAE-optimized forecasts can attain a high RMSE skill score.","PeriodicalId":16953,"journal":{"name":"Journal of Renewable and Sustainable Energy","volume":null,"pages":null},"PeriodicalIF":2.5,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140521128","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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