Xinyu Chen, Yaxing Liu, Qin Wang, Jiajun Lv, Jinyu Wen, Xia Chen, Chongqing Kang, Shijie Cheng, and Michael McElroy. 2021. “
Pathway toward carbon-neutral electrical systems in China by mid-century with negative CO2 abatement costs informed by high-resolution modeling.” Joule, 5, 10 (20 October), Pp. 2715-2741.
Publisher's VersionAbstractChina, the largest global CO2 emitter, recently announced ambitious targets for carbon neutrality by 2060. Its technical and economic feasibility is unclear given severe renewable integration barriers. Here, we developed a cross-sector, high-resolution assessment model to quantify optimal energy structures on provincial bases for different years. Hourly power system simulations for all provinces for a full year are incorporated on the basis of comprehensive grid data to quantify the renewable balancing costs. Results indicate that the conventional strategy of employing local wind, solar, and storage to realize 80% renewable penetration by 2050 would incur a formidable decarbonization cost of $27/ton despite lower levelized costs for renewables. Coordinated deployment of renewables, ultra-high-voltage transmissions, storages, Power-to-gas and slow-charging electric vehicles can reduce this carbon abatement cost to as low as $−25/ton. Were remaining emissions removed by
carbon capture and sequestration technologies, achieving carbon neutrality could be not only feasible but also cost-competitive post 2050.
Shaojie Song, Haiyang Lin, Peter Sherman, Xi Yang, Chris P. Nielsen, Xinyu Chen, and Michael B. McElroy. 2021. “
Production of hydrogen from offshore wind in China and cost-competitive supply to Japan.” Nature Communications, 12, 6953.
Publisher's VersionAbstractThe Japanese government has announced a commitment to net-zero greenhouse gas emissions by 2050. It envisages an important role for hydrogen in the nation’s future energy economy. This paper explores the possibility that a significant source for this hydrogen could be produced by electrolysis fueled by power generated from offshore wind in China. Hydrogen could be delivered to Japan either as liquid, or bound to a chemical carrier such as toluene, or as a component of ammonia. The paper presents an analysis of factors determining the ultimate cost for this hydrogen, including expenses for production, storage, conversion, transport, and treatment at the destination. It concludes that the Chinese source could be delivered at a volume and cost consistent with Japan’s idealized future projections.
Haiyang Lin, Qiuwei Wu, Xinyu Chen, Xi Yang, Xinyang Guo, Jiajun Lv, Tianguang Lu, Shaojie Song, and Michael B. McElroy. 2021. “
Economic and technological feasibility of using power-to-hydrogen technology under higher wind penetration in China.” Renewable Energy, 173, Pp. 569-580.
Publisher's VersionAbstractHydrogen can play a key role in facilitating the transition to a future deeply decarbonized energy system and can help accommodate higher penetrations of renewables in the power system. Arguments to justify this conclusion are supported by an analysis based on real-world data from China’s Western Inner Mongolia (WIM). The economic feasibility and decarbonization potential of renewable-based hydrogen production are discussed through an integrated power-hydrogen-emission analytical framework. The framework combines a high-resolution wind resource analysis with hourly simulation for the operation of power systems and hydrogen production considering technical and economic specifications on selection of three different types of electrolyzers and two operating modes. The results indicate that using wind power to produce hydrogen could provide a cost-competitive alternative (<2 $kg-1) to WIM’s current coal-dominated hydrogen manufacturing system, contributing at the same time to important reductions in wind curtailment and CO2 emissions. The levelized cost for hydrogen production is projected to decrease in the coming decade consistent with increases in wind power capacity and decreases in capital costs for electrolyzers. Lessons learned from the study can be applied to other regions and countries to explore possibilities for larger scale economically justified and carbon saving hydrogen production with renewables.
Peter Sherman, Shaojie Song, Xinyu Chen, and Michael B. McElroy. 2021. “
Projected changes in wind power potential over China and India in high resolution climate models.” Environmental Research Letters, 16, 3, Pp. 034057.
Publisher's VersionAbstractAs more countries commit to emissions reductions by midcentury to curb anthropogenic climate change, decarbonization of the electricity sector becomes a first-order task in reaching this goal. Renewables, particularly wind and solar power, will be predominant components of this transition. How availability of the wind and solar resource will change in the future in response to regional climate changes is an important and underdiscussed topic of the decarbonization process. Here, we study changes in potential for wind power in China and India, evaluating prospectively until the year 2060. To do this, we study a downscaled, high-resolution multimodel ensemble of CMIP5 models under high and low emissions scenarios. While there is some intermodel variability, we find that spatial changes are generally consistent across models, with decreases of up to 965 (a 1% change) and 186 TWh (a 2% change) in annual electricity generation potential for China and India, respectively. Compensating for the declining resource are weakened seasonal and diurnal variabilities, allowing for easier large-scale wind power integration. We conclude that while the ensemble indicates available wind resource over China and India will decline slightly in the future, there remains enormous potential for significant wind power expansion, which must play a major role in carbon neutral aspirations.
Tianguang Lu, Xinyu Chen, Michael B. McElroy, Chris Nielsen, Qiuwei Wu, Hongying He, and Qian Ai. 2021. “
A reinforcement learning-based decision system for electricity pricing plan selection by smart grid end users.” IEEE Transactions on Smart Grid, 12, 3, Pp. 2176-2187.
Publisher's VersionAbstractWith the development of deregulated retail power markets, it is possible for end users equipped with smart meters and controllers to optimize their consumption cost portfolios by choosing various pricing plans from different retail electricity companies. This paper proposes a reinforcement learning-based decision system for assisting the selection of electricity pricing plans, which can minimize the electricity payment and consumption dissatisfaction for individual smart grid end user. The decision problem is modeled as a transition probability-free Markov decision process (MDP) with improved state framework. The proposed problem is solved using a Kernel approximator-integrated batch Q-learning algorithm, where some modifications of sampling and data representation are made to improve the computational and prediction performance. The proposed algorithm can extract the hidden features behind the time-varying pricing plans from a continuous high-dimensional state space. Case studies are based on data from real-world historical pricing plans and the optimal decision policy is learned without a priori information about the market environment. Results of several experiments demonstrate that the proposed decision model can construct a precise predictive policy for individual user, effectively reducing their cost and energy consumption dissatisfaction.