Harvard-China Project Papers funded by the Energy Foundation China

Offshore wind power, with accelerated declining levelized costs, is emerging as a critical building-block to fully decarbonize the world’s largest CO2 emitter, China. However, system integration barriers as well as system balancing costs have not been quantified yet. Here we develop a bottom-up model to test the grid accommodation capabilities and design the optimal investment plans for offshore wind power considering resource distributions, hourly power system simulations, and transmission/storage/hydrogen investments. Results indicate that grid integration barriers exist currently at the provincial level. For 2030, optimized offshore wind investment levels should be doubled compared with current government plans, and provincial allocations should be significantly improved considering both resource quality and grid conditions. For 2050, offshore wind capacity in China could reach as high as 1500 GW, prompting a paradigm shift in national transmission structure, favoring long-term storage in the energy portfolio, enabling green hydrogen production in coastal demand centers, resulting in the world’s largest wind power market.

Countries such as China are facing a bottleneck in their paths to carbon neutrality: abating emissions in heavy industries and heavy-duty transport. There are few in-depth studies of the prospective role for clean hydrogen in these ‘hard-to-abate’ (HTA) sectors. Here we carry out an integrated dynamic least-cost modelling analysis. Results show that, first, clean hydrogen can be both a major energy carrier and feedstock that can significantly reduce carbon emissions of heavy industry. It can also fuel up to 50% of China’s heavy-duty truck and bus fleets by 2060 and significant shares of shipping. Second, a realistic clean hydrogen scenario that reaches 65.7 Mt of production in 2060 could avoid US$1.72 trillion of new investment compared with a no-hydrogen scenario. This study provides evidence of the value of clean hydrogen in HTA sectors for China and countries facing similar challenges in reducing emissions to achieve net-zero goals.

The paper explores options for a 2050 carbon free energy future for India. Onshore wind and solar sources are projected as the dominant primary contributions to this objective. The analysis envisages an important role for so-called green hydrogen produced by electrolysis fueled by these carbon free energy sources. This hydrogen source can be used to accommodate for the intrinsic variability of wind and solar complementing opportunities for storage of power by batteries and pumped hydro. The green source of hydrogen can be used also to supplant current industrial uses of gray hydrogen produced in the Indian context largely from natural gas with important related emissions of CO2. The paper explores further options for use of green hydrogen to lower emissions from otherwise difficult to abate sectors of both industry and transport. The analysis is applied to identify the least cost options to meet India’s zero carbon future.

China, 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.

The 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.

Hydrogen 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.

With 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.