基于我国2060年达到碳中和、实现零碳能源架构的需求，针对我国未来能源系统及能源转型现实路径展开研究。结合能源技术发展的趋势和我国目前以化石能源为主的能源体系的实际情况，分析我国实现能源转型的硬约束。通过对比研究碳中和能源解决方案不同技术选择的可行性、主要挑战及全球能源技术发展趋势，推演出在保证国家能源安全的前提下，我国2060年实现碳中和能源架构与零碳能源转型的现实路径。最后，提出未来构建以可再生能源为主体的新型能源系统亟需开展的关键技术及应采取的政策措施建议。

Based on the demand of China to achieve carbon neutrality and zero carbon energy architecture by 2060, this paper studied the future energy system and the realistic path of energy transformation in China. Combined with the trend of energy technology development and the actual situation of China’s current energy system dominated by fossil energy, the hard constraints of China’s energy transformation were analyzed. By comparing and studying the feasibility, the main challenges of different technology options for carbon neutrality energy solutions, and the global energy development trend, this paper deduced the realistic path of realizing carbon neutrality energy architecture and zero carbon energy transformation in 2060 under the premise of ensuring national energy security. Finally, the key technologies and the policy measures were put forward, which should be promoted to build a novel energy system with renewable energy as the main body for the future.

大规模应用储能技术是提高含可再生能源电网的运行经济性和安全性的有效途径。为了合理评估储能在电网中应用的经济性，采用全生命周期成本方法，根据抽水蓄能电站、压缩空气储能、铅酸电池、钠硫电池、液流电池、锂离子电池等储能成本和技术特性，测算了各类储能的投资、年费用、度电成本，比较了不同利用小时下各类储能的经济性。研究成果表明：抽水蓄能电站度电成本最低，其次是压缩空气储能，电池类储能度电成本最高。

The large-scale application of energy storage technology is an effective way to improve the economic performance and safety of the power grid containing renewable energy. In order to reasonably evaluate the economy of energy storage in the power grid, the life cycle cost method is adopted, according to the energy storage cost and technical characteristics of pumped storage power station, such as compressed air storage, lead-acid battery, sodium sulfur battery, liquid flow battery, lithium ion battery, etc. The investment, annual cost and electricity cost of various kinds of energy storage are calculated, and the economy of various types of energy storage under different utilization hours is compared. The research results show that the minimum cost of electricity storage for pumped storage power station is the lowest, followed by compressed air energy storage, and the highest energy cost of battery energy storage.

为降低源荷两侧不确定性对综合能源系统(integrated energy system, IES)安全性与经济性的影响，以及提升IES面对不确定性的灵活、稳定运行的能力，提出多种储能参与平抑不确定性波动的策略，建立多重不确定性下日前-实时两阶段协同优化的鲁棒模型。模型中加入鲁棒可调因子综合评估系统经济性与鲁棒性，在日前阶段，根据新能源及负荷预测功率确定预调度计划，以实现最小运行成本下的功率平衡；在实时阶段，根据新能源出力及负荷实际模拟功率确定参与二次灵活调整设备的调整功率，以最小成本实现功率再平衡。算例表明，电源侧、储能侧的实时调整能更好发挥IES应对不确定性的协同调节功能；引入鲁棒可调因子刻画不确实性，较好地均衡了系统运行的经济性和安全性。

In order to reduce the influence of uncertainty on both sides of the source and load on the security and economy of the integrated energy system (IES), and to improve the flexibility and stability of IES in the face of uncertainties, various strategies for energy storage participation in smoothing out the uncertainty fluctuations are proposed, and a robust model is established for the day-ahead and real-time two-stage cooperative optimization under multiple uncertainties. A robust adjustable factor is added to the model to comprehensively evaluate the system economy and robustness. In the day-ahead phase, a pre-dispatch plan is determined based on the predicted power of new energy and load to realize the power balance at the minimum operating cost. In the real-time phase, the adjustment power of the secondary flexible adjustment equipment is determined according to the new energy output and the actual simulated power of the load to realize power rebalancing at minimum cost. The case study shows that the real-time adjustment of power supply side and energy storage side can better play the synergistic adjustment function of IES to deal with uncertainty; the introduction of robust adjustable factor to portray the uncertainty better balances the economy and security of system operation.

在“双碳”目标的背景下，电力行业已成为碳减排中的重要组成部分。虚拟电厂(virtual power plant，VPP)通过整合和聚集分布式资源等参与碳市场，可进一步提升整体效益；然而，分布式新能源出力的不确定性给其运营管理带来了许多挑战。为此，在利用基于拉丁超立方抽样的场景生成与场景削减法处理风电、光伏出力不确定性问题的基础上，将多单元聚合并考虑到用户侧需求响应的VPP作为一个整体去参与电能量市场以及碳市场，构建VPP总成本最小的优化调度模型，然后利用改进灰狼优化算法对其进行求解。通过对不同场景算例进行对比分析，可以得出：碳市场及需求响应的存在，加强了风光等清洁能源的消纳，减少了温室气体的排放，也减少了VPP的运行成本，经济性和环保性得到了兼顾。

Against the backdrop of the "dual carbon" target, the power sector has become an important part of carbon reduction. Virtual power plants (VPP) can further improve their overall efficiency by integrating and aggregating distributed resources to participate in the carbon market. However, the uncertainty of distributed new energy output poses many challenges for their operation and management. Therefore, on the basis of using the scenario generation and scenario reduction method based on Latin hypercubic sampling to deal with the uncertainty problem of wind power and photovoltaic output of distributed energy, the VPP, which aggregates multiple units and takes into account the user-side demand response, participates in electric energy market as well as the carbon market as a whole, and the optimal scheduling model with the minimum total cost of the VPP is constructed, which is finally solved by using the improved gray wolf optimization algorithm. Through comparative analysis of different scenarios, it can be concluded that the existence of carbon market and demand response enhances the consumption of clean energy such as wind power and photovoltaic, and reduces greenhouse gas emissions, and reduces the operating cost of the VPPs, and takes into account its economy and environmental protection.

随着可再生能源渗透率的提高,分布式可再生能源带来的波动性、间歇性会传递至主网中,对系统安全运行造成影响,研究不确定性优化方法对系统实际运行具有一定的指导作用。传统的随机优化以及鲁棒优化方法不满足系统实际运行的非预期性要求。文章以日运行期望成本最小为目标,考虑分布式可再生能源发电不确定性,建立多阶段随机规划模型,可以根据之前不确定信息的实现在每个阶段确定预调度决策,不会受到未来不确定信息的影响,符合系统实际运行规律,满足非预期性。为了避免多阶段随机规划问题求解的维数灾难,采用随机对偶动态规划(stochastic dual dynamic programming, SDDP)算法进行求解。仿真结果表明,相比于传统的确定性模型,多阶段随机优化得到的最优调度决策树较之确定性优化得到的单一决策方案具有更广泛的决策空间,可以基于上一阶段不确定信息的实现和决策来更新调度决策,降低系统的运行成本。

With the increase of renewable energy penetration, the volatility and intermittence brought by distributed renewable energy will be transferred to the main network, which will affect the safe operation of the system. The study of uncertainty optimization method plays a certain guiding role in the actual operation of the system. However, the traditional stochastic optimization and robust optimization methods do not meet the unpredictable requirements of the actual operation of the system. In this paper, a multi-stage stochastic programming model considering the uncertainty of distributed renewable power generation is established with the goal of minimizing the expected cost of daily operation. The pre-scheduling decision can be made at each stage according to the realization of the previous uncertain information, which will not be affected by the future uncertain information, and is in line with the actual operation law of the system, and meets the unexpected requirements. In order to avoid the disaster of dimensionality in solving multi-stage stochastic programming problems, the stochastic dual dynamic programming algorithm is used to solve the problem. The simulation results show that, compared with the traditional deterministic model, the optimal scheduling decision tree obtained by multi-stage stochastic optimization has wider decision space than the single decision scheme obtained by deterministic optimization. The scheduling decision can be updated according to the implementation and decision of the uncertain information in the previous stage, and the operating cost of the system can be reduced.

冷热电综合能源系统是促进多种能源综合利用的重要载体，也是能源互联网发展的重要基础。为实现源荷双重不确定时冷热电综合能源系统的运行优化，并充分发挥源荷储各环节的调控潜力，本文提出了一种考虑源荷储协调的冷热电综合能源系统随机优化运行方法。首先采用非参数核密度估计与概率场景抽样相结合的方法对综合能源系统中源荷不确定性进行挖掘和表征，在此基础上，全面考虑储电、储冷、储热等多种能源存储形式及负荷需求响应措施，建立了源荷储协调的冷热电综合能源系统随机优化运行模型。针对模型中能源存储设备的运行时段耦合约束和日运行能量平衡约束等带来的复杂多阶段决策问题，提出基于序列二次规划算法的模型求解方法。以某典型冷热电综合能源系统为测试算例，对不考虑储能、不考虑需求响应及详细考虑源荷储协调的多种情形进行了对比分析，结果证明了所提模型和方法的有效性。

The integrated energy system including cooling, heating and power is an important carrier to promote the comprehensive utilization of various energy sources and an important foundation for the development of energy Internet. In order to realize the optimal operation of the integrated energy system of cooling, heating and power under the uncertainty of source and load, and give full play to the regulatory potential of source-load-storage, this paper proposes a stochastic optimal operation model of the integrated cooling, heating and power energy system with fully consideration of the coordination between the source-load-storage. The combined non-parametric kernel density estimation and probability scenario sampling method is introduced to excavate and characterize the uncertainty of source and load. A stochastic optimal operation model is established by comprehensively considering various energy storage forms such as electricity storage, cold storage, heat storage, and the coordination of source load storage is also taken into account in detail. In order to solve the complex multi-stage decision-making problem caused by the coupling constraints of the energy storage devices during the operation period and the daily operation energy balance constraints, the sequential quadratic programming algorithm is presented to solve the model. By taking a typical integrated cooling, heating and power energy system as a test example, a comparative analysis of various operating modes including no energy storage, no demand response, and source-load-storage coordination is conducted. The results show the effectiveness of the proposed model and method.