Regional Renewable Energy Installation Optimization Strategies with Renewable Portfolio Standards in China
Abstract
:1. Introduction
2. Literature Review
3. Regional Power Installation Optimization Model
3.1. Symbol Definition (Nomenclature)
3.1.1. Subscript
3.1.2. Parameters
3.1.3. Variables
3.2. Objective Function
3.3. Constraints
3.3.1. RE Resource Potential Constraint
3.3.2. Regional RPS Target Constraint
3.3.3. Constraints on Inter-Regional Transmission
3.3.4. Flexibility Constraint
4. Algorithm Design
Algorithm Performance Analysis
5. Data Sources and Scenarios Setting
5.1. Scenario Design
5.2. Data Sources
6. Results and Discussion
6.1. Regional Renewable Energy Installation Optimization Results
6.2. Comparative Analysis of Different Scenarios
- (1)
- By the end of 2030, under the pessimistic scenario, in which the pandemic cannot be effectively controlled, the newly installed capacity of non-hydropower RE power generation will be about 1655.54 GW, of which the largest newly installed generation capacity of wind power in the North China Power Grid will be about 150.14 GW. The region with the largest newly installed capacity of solar and biomass power generation will be the Northwest Power Grid, 429.98 and 6.93 GW, respectively.
- (2)
- Under the neutral scenario from 2021 to 2030, the total newly installed capacity of non-hydropower RE power generation will be about 1661.01 GW, of which the newly installed capacity of wind power generation in the North China Power Grid will be the largest at about 150.62 GW. The top-two regions with newly installed solar power generation capacity are the Northwest and North China Power Grids, with new capacities of 431.57 and 253.52 GW, respectively. Regarding biomass power generation, the Northwest Power Grid will add 6.96 GW of installed capacity.
- (3)
- Under the optimistic scenario, in which the pandemic has been effectively controlled, the newly installed capacity of non-hydropower RE power generation during the study period will be about 1667.39 GW, and the total installed capacity will be at least 18.78 GW more than that under the pessimistic scenario. As can be seen from Figure 6, the region with the largest newly installed wind power generation capacity is the North China Power Grid, while the region with the largest newly installed solar and biomass power generation capacity is still the Northwest.
7. Conclusions
- (1)
- Under the RPS system, the newly installed capacity of renewable energy will be mainly non-hydropower. By 2030, China’s installed capacity of non-hydropower renewable energy will reach 2.2 TW. Therefore, we should make full use of existing resources, explore new and clean energy, and decompose and arrange indicators for each province and city according to the allocation results of regional renewable energy, which can further optimize the energy layout of each region.
- (2)
- The results of the optimal allocation model established in this paper show that in non-hydropower renewable energy, the installed capacity of wind power generation accounts for about 31.9% of total installed non-hydropower RE capacity, and the capacity of solar power generation accounts for 65.7% of total installed capacity. The implementation of the RPS policy is to encourage the redevelopment of renewable energy in various regions. Therefore, the effects of implementing it can be determined by comparing the installed power generation capacity under different scenarios of the pandemic.
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
- Johnson, J.X.; Novacheck, J. Emissions Reductions from Expanding State-Level Renewable Portfolio Standards. Environ. Sci. Technol. 2015, 49, 5318–5325. [Google Scholar] [CrossRef] [PubMed]
- Koltsaklis, N.E.; Gioulekas, I.; Georgiadis, M.C. Optimal scheduling of interconnected power systems. Comput. Chem. Eng. 2018, 111, 164–182. [Google Scholar] [CrossRef]
- Gulagi, A.; Bogdanov, D.; Breyer, C. A Cost Optimized Fully Sustainable Power System for Southeast Asia and the Pacific Rim. Energies 2017, 10, 583. [Google Scholar] [CrossRef]
- Wang, G.; Zhang, Q.; Mclellan, B.C.; Li, H. Multi-region optimal deployment of renewable energy considering different interregional transmission scenarios. Energy 2016, 108, 108–118. [Google Scholar] [CrossRef]
- Kwon, T.H. Rent and rent-seeking in renewable energy support policies: Feed-in tariff vs. renewable portfolio standard. Renew. Sustain. Energy Rev. 2015, 44, 676–681. [Google Scholar] [CrossRef]
- Dong, Y.; Shimada, K. Evolution from the renewable portfolio standards to feed-in tariff for the deployment of renewable energy in Japan. Renew. Energy 2017, 107, 590–596. [Google Scholar] [CrossRef]
- Fan, J.; Wang, Q.; Zhang, X. A bibliometric analysis of the water-energy-food nexus based on the SCIE and SSCI database of the Web of Science. Mitig. Adapt. Strateg. Glob. Change 2021, 8, 26. [Google Scholar] [CrossRef]
- Bhattacharya, S.; Giannakas, K.; Schoengold, K. Market and welfare effects of renewable portfolio standards in United States electricity markets. Energy Econ. 2017, 64, 384–401. [Google Scholar] [CrossRef]
- Bao, X.; Zhao, W.; Wang, X.; Tan, Z. Impact of policy mix concerning renewable portfolio standards and emissions trading on electricity market. Renew. Energy 2018, 135, 761–774. [Google Scholar] [CrossRef]
- Zhao, X.; Ren, L.; Zhang, Y.; Wan, G. Evolutionary game analysis on the behavior strategies of power producers in renewable portfolio standard. Energy 2018, 162, 505–516. [Google Scholar] [CrossRef]
- Xu, J.; Lv, T.; Hou, X.; Deng, X.; Liu, F. Provincial allocation of renewable portfolio standard in China based on efficiency and fairness principles. Renew. Energy 2021, 179, 1233–1245. [Google Scholar] [CrossRef]
- Yu, B.; Zhao, Z.; Zhao, G.; An, R.; Sun, F.; Li, R.; Peng, X. Provincial renewable energy dispatch optimization in line with Renewable Portfolio Standard policy in China. Renew. Energy 2021, 174, 236–252. [Google Scholar] [CrossRef]
- Wang, H.; Su, B.; Mu, H.; Li, N.; Gui, S.; Duan, Y.; Kong, X. Optimal way to achieve renewable portfolio standard policy goals from the electricity generation, transmission, and trading perspectives in southern China. Energy Policy 2020, 139, 111319. [Google Scholar] [CrossRef]
- Zhou, D.; Hu, F.; Zhu, Q.; Wang, Q. Regional allocation of renewable energy quota in China under the policy of renewable portfolio standards. Resour. Conserv. Recycl. 2021, 176, 105904. [Google Scholar] [CrossRef]
- Yang, D.X.; Jing, Y.Q.; Wang, C.; Nie, P.Y.; Sun, P. Analysis of renewable energy subsidy in China under uncertainty: Feed-in tariff vs. renewable portfolio standard. Energy Strategy Rev. 2021, 34, 100628. [Google Scholar] [CrossRef]
- Song, X.-H.; Han, J.-J.; Zhang, L.; Zhao, C.-P.; Wang, P.; Liu, X.-Y.; Li, Q.-C. Impacts of renewable portfolio standards on multi-market coupling trading of renewable energy in China: A scenario-based system dynamics model. Energy Policy 2021, 159, 112647. [Google Scholar] [CrossRef]
- Joshi, J. Do renewable portfolio standards increase renewable energy capacity? Evidence from the United States. J. Environ. Manag. 2021, 287, 112261. [Google Scholar] [CrossRef]
- Liu, Z.; Wang, S.; Lim, M.Q.; Kraft, M.; Wang, X. Game theory-based renewable multi-energy system design and subsidy strategy optimization. Adv. Appl. Energy 2021, 2, 100024. [Google Scholar] [CrossRef]
- Cosic, A.; Stadler, M.; Mansoor, M.; Zellinger, M. Mixed-integer linear programming based optimization strategies for renewable energy communities. Energy 2021, 237, 121559. [Google Scholar] [CrossRef]
- Fan, J.-L.; Wang, J.-X.; Hu, J.-W.; Wang, Y.; Zhang, X. Optimization of China’s provincial renewable energy installation plan for the 13th five-year plan based on renewable portfolio standards. Appl. Energy 2019, 254, 113757. [Google Scholar] [CrossRef]
- Memon, S.A.; Patel, R.N. An overview of optimization techniques used for sizing of hybrid renewable energy systems. Renew. Energy Focus 2021, 39, 1–26. [Google Scholar] [CrossRef]
- Makhloufi, S.; Khennas, S.; Bouchaib, S.; Arab, A.H. Multi-objective cuckoo search algorithm for optimized pathways for 75 % renewable electricity mix by 2050 in Algeria. Renew. Energy 2021, 185, 1410–1424. [Google Scholar] [CrossRef]
- Deveci, K.; Güler, Ö. A CMOPSO based multi-objective optimization of renewable energy planning: Case of Turkey. Renew. Energy 2020, 155, 578–590. [Google Scholar] [CrossRef]
- Hori, K.; Kim, J.; Kawase, R.; Kimura, M.; Matsui, T.; Machimura, T. Local energy system design support using a renewable energy mix multi-objective optimization model and a co-creative optimization process. Renew. Energy 2019, 156, 1278–1291. [Google Scholar] [CrossRef]
- Elkadeem, M.; Younes, A.; Sharshir, S.W.; Campana, P.E.; Wang, S. Sustainable siting and design optimization of hybrid renewable energy system: A geospatial multi-criteria analysis. Appl. Energy 2021, 295, 117071. [Google Scholar] [CrossRef]
- Martelli, E.; Freschini, M.; Zatti, M. Optimization of renewable energy subsidy and carbon tax for multi energy systems using bilevel programming. Appl. Energy 2020, 267, 115089. [Google Scholar] [CrossRef]
- Liu, Z.; Li, Y.; Fan, G.; Wu, D.; Guo, J.; Jin, G.; Zhang, S.; Yang, X. Co-optimization of a novel distributed energy system integrated with hybrid energy storage in different nearly zero energy community scenarios. Energy 2022, 247, 123553. [Google Scholar] [CrossRef]
- Hannan, M.A.; Tan, S.Y.; Al-Shetwi, A.Q.; Jern, K.P.; Begum, R.A. Optimized controller for renewable energy sources integration into microgrid: Functions, constraints and suggestions. J. Clean. Prod. 2020, 256, 120419. [Google Scholar] [CrossRef]
- Lei, Y.; Wang, D.; Jia, H.; Li, J.; Chen, J.; Li, J.; Yang, Z. Multi-stage stochastic planning of regional integrated energy system based on scenario tree path optimization under long-term multiple uncertainties. Appl. Energy 2021, 300, 117224. [Google Scholar] [CrossRef]
- Sobhani, H.; Shahmoradi, F.; Sajadi, B. Optimization of the renewable energy system for nearly zero energy buildings: A future-oriented approach. Energy Convers. Manag. 2020, 224, 113370. [Google Scholar] [CrossRef]
- Li, J.; Xu, W.; Cui, P.; Qiao, B.; Feng, X.; Xue, H.; Wang, X.; Xiao, L. Optimization configuration of regional integrated energy system based on standard module. Energy Build. 2020, 229, 110485. [Google Scholar] [CrossRef]
- Zhou, Y.; Zhou, Z. Integrated inexact optimization for hybrid renewable energy systems. In Renewable-Energy-Driven Future; Academic Press: Cambridge, MA, USA, 2020; pp. 203–231. [Google Scholar]
- Kakodkar, R.; He, G.; Demirhan, C.; Arbabzadeh, M.; Baratsas, S.; Avraamidou, S.; Mallapragada, D.; Miller, I.; Allen, R.; Gençer, E.; et al. A review of analytical and optimization methodologies for transitions in multi-scale energy systems. Renew. Sustain. Energy Rev. 2022, 160, 112277. [Google Scholar] [CrossRef]
- Li, S.; Zhu, J.; Dong, H.; Zhu, H.; Fan, J. A novel rolling optimization strategy considering grid-connected power fluctuations smoothing for renewable energy microgrids. Appl. Energy 2022, 309, 118441. [Google Scholar] [CrossRef]
- Wei, T.; Chu, X.; Yang, D.; Ma, H. Power balance control of RES integrated power system by deep reinforcement learning with optimized utilization rate of renewable energy. Energy Rep. 2022, 8, 544–553. [Google Scholar] [CrossRef]
- Atabaki, M.S.; Mohammadi, M.; Aryanpur, V. An integrated simulation-optimization modelling approach for sustainability assessment of electricity generation system. Sustain. Energy Technol. Assess. 2022, 52, 102010. [Google Scholar] [CrossRef]
- Liu, L.; Wang, D.; Jia, H.; Wang, W.; Meng, Z.; Zhi, Y.; Zhang, S.; Du, W. Analysis of Tie-line Power Optimization in Integrated Energy Distribution System. Energy Procedia 2019, 158, 6484–6489. [Google Scholar] [CrossRef]
- Al-Shahri, O.A.; Ismail, F.B.; Hannan, M.A.; Lipu, M.H.; Al-Shetwi, A.Q.; Begum, R.A.; Al-Muhsen, N.F.O.; Soujeri, E. Solar photovoltaic energy optimization methods, challenges and issues: A comprehensive review. J. Clean. Prod. 2021, 284, 125465. [Google Scholar] [CrossRef]
- Tang, D.; Dong, C.; Wu, X.; Qian, H.; Wang, H.; Jiang, H.; Zhang, Z.; Chen, Y.; Deng, X.; Lin, Z.; et al. Allocation strategy of regulated consumption quota ratio of renewable energy based on game theory. Energy Rep. 2021, 7, 67–74. [Google Scholar] [CrossRef]
- Vitor, T.S.; Vieira, J.C.M. Operation planning and decision-making approaches for Volt/Var multi-objective optimization in power distribution systems. Electr. Power Syst. Res. 2020, 191, 106874. [Google Scholar] [CrossRef]
- Ding, Y.; Wei, X. Bi-level optimization model for regional energy system planning under demand response scenarios. J. Clean. Prod. 2021, 323, 129009. [Google Scholar] [CrossRef]
- Potrč, S.; Čuček, L.; Martin, M.; Kravanja, Z. Sustainable renewable energy supply networks optimization–The gradual transition to a renewable energy system within the European Union by 2050. Renew. Sustain. Energy Rev. 2021, 146, 111186. [Google Scholar] [CrossRef]
- Ogunmodede, O.; Anderson, K.; Cutler, D.; Newman, A. Optimizing design and dispatch of a renewable energy system. Appl. Energy 2021, 287, 116527. [Google Scholar] [CrossRef]
- Yu, S.; Zhou, S.; Qin, J. Layout optimization of China’s power transmission lines for renewable power integration considering flexible resources and grid stability. Int. J. Electr. Power Energy Syst. 2021, 135, 107507. [Google Scholar] [CrossRef]
- Hou, S.; Fan, Y.; Yi, B.-W. Long-term renewable electricity planning using a multistage stochastic optimization with nested decomposition. Comput. Ind. Eng. 2021, 161, 107636. [Google Scholar] [CrossRef]
- Cho, S.; Kim, J. Multi-site and multi-period optimization model for strategic planning of a renewable hydrogen energy network from biomass waste and energy crops. Energy 2019, 185, 527–540. [Google Scholar] [CrossRef]
- Daraei, M.; Avelin, A.; Thorin, E. Optimization of a regional energy system including CHP plants and local PV system and hydropower: Scenarios for the County of Västmanland in Sweden. J. Clean. Prod. 2019, 230, 1111–1127. [Google Scholar] [CrossRef]
- Deng, X.; Lv, T. Power system planning with increasing variable renewable energy: A review of optimization models. J. Clean. Prod. 2019, 246, 118962. [Google Scholar] [CrossRef]
- Li, T.; Li, Z.; Li, W. Scenarios analysis on the cross-region integrating of renewable power based on a long-period cost-optimization power planning model. Renew. Energy 2020, 156, 851–863. [Google Scholar] [CrossRef]
- Zakaria, A.; Ismail, F.B.; Lipu, M.H.; Hannan, M. Uncertainty models for stochastic optimization in renewable energy applications. Renew. Energy 2019, 145, 1543–1571. [Google Scholar] [CrossRef]
- She, C.; Jia, R.; Hu, B.-N.; Zheng, Z.-K.; Xu, Y.-P.; Rodriguez, D. Life cycle cost and life cycle energy in zero-energy building by multi-objective optimization. Energy Rep. 2021, 7, 5612–5626. [Google Scholar] [CrossRef]
- Wang, Y.; Song, F.; Ma, Y.; Zhang, Y.; Yang, J.; Liu, Y.; Zhang, F.; Zhu, J. Research on capacity planning and optimization of regional integrated energy system based on hybrid energy storage system. Appl. Therm. Eng. 2020, 180, 115834. [Google Scholar] [CrossRef]
- Yang, J.; Liu, C.; Mi, Y.; Zhang, H.; Terzija, V. Optimization operation model of electricity market considering renewable energy accommodation and flexibility requirement. Glob. Energy Interconnect. 2021, 4, 227–238. [Google Scholar] [CrossRef]
- Li, X.; Wang, W.; Wang, H. A novel bi-level robust game model to optimize a regionally integrated energy system with large-scale centralized renewable-energy sources in Western China. Energy 2021, 228, 120513. [Google Scholar] [CrossRef]
- Bayatloo, F.; Bozorgi-Amiri, A. A novel optimization model for dynamic power grid design and expansion planning considering renewable resources. J. Clean. Prod. 2019, 229, 1319–1334. [Google Scholar] [CrossRef]
- Niu, D.; Ji, Z.; Li, W.; Xu, X.; Liu, D. Research and application of a hybrid model for mid-term power demand forecasting based on secondary decomposition and interval optimization. Energy 2021, 234, 121145. [Google Scholar] [CrossRef]
- Yang, H.; Chen, B.; Xiang, S.; Liu, J.; Ackom, E. Distributionally robust optimal dispatch modelling of renewable-dominated power system and implementation path for carbon peak. Comput. Ind. Eng. 2021, 163, 107797. [Google Scholar] [CrossRef]
- Cheng, R.; Xu, Z.; Liu, P.; Wang, Z.; Li, Z.; Jones, I. A multi-region optimization planning model for China’s power sector. Appl. Energy 2015, 137, 413–426. [Google Scholar] [CrossRef]
- Yi, B.; Xu, J.; Fan, Y. Optimal pathway and impact of achieving the mid-long term renewable portfolio standard in China. J. Syst. Eng. 2017, 32, 313–324. [Google Scholar]
- Wang, Z.H. Economy and security analysis of AC UHV in China. Electr. Power Autom. Equip. 2007, 27, 1–4. [Google Scholar]
- Sullivan, P.; Krey, V.; Riahi, K. Impacts of considering electric sector variability and reliability in the MESSAGE model. Energy Strat. Rev. 2013, 1, 157–163. [Google Scholar] [CrossRef]
Algorithm | Target Mean Value | Optimal Value | Worst Value | Average Deviation |
---|---|---|---|---|
IGA | 0.278 | 0.214 | 0.354 | 0.020 |
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. |
© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
He, Y.; Wan, L.; Zhang, M.; Zhao, H. Regional Renewable Energy Installation Optimization Strategies with Renewable Portfolio Standards in China. Sustainability 2022, 14, 10498. https://doi.org/10.3390/su141710498
He Y, Wan L, Zhang M, Zhao H. Regional Renewable Energy Installation Optimization Strategies with Renewable Portfolio Standards in China. Sustainability. 2022; 14(17):10498. https://doi.org/10.3390/su141710498
Chicago/Turabian StyleHe, Yuanyuan, Luxin Wan, Manli Zhang, and Huijuan Zhao. 2022. "Regional Renewable Energy Installation Optimization Strategies with Renewable Portfolio Standards in China" Sustainability 14, no. 17: 10498. https://doi.org/10.3390/su141710498