成本效益研究有助于了解各市场主体的效益情况和交易意愿,从而指导市场制定更加合理的交易机制。文章提出了一种需求响应(demand response,DR)下计及电碳交易市场耦合的多个市场主体的成本效益分析方法。首先将碳交易中的单位碳排放成本计入各发电机组的报价函数中进行统一出清,体现碳交易与现货市场运营的耦合;其次,考虑了需求响应下电碳耦合市场中各个主体的交易机制,提出了发电侧、用户侧、负荷聚合商、储能运营商及电网侧的成本效益分析模型。最后,对中国某省电网某实际运行日进行了算例分析,结果表明,碳排放权交易市场运作将抬高电力市场出清价格,影响相关主体的成本与效益。

Cost-benefit research helps to understand the benefits and willingness to trade of various market players, so as to guide the market to formulate a more reasonable trading mechanism. This paper proposes a cost-benefit analysis method for multiple market players considering the coupling of electricity and carbon trading markets under demand response (DR). First, the unit carbon emission cost in carbon trading is included in the quotation function of each generating unit for unified clearing, which reflects the coupling of carbon trading and spot market operations. Secondly, the transactions of various entities in the electricity-carbon coupling market under demand response are considered. A cost-benefit analysis model for the generation side, the user side, the load aggregator, the energy storage operator and the grid side is proposed. Finally, an example analysis is carried out on an actual operation day of a certain provincial power grid in China. The results show that the operation of the carbon emission trading market will raise the clearing price of the electricity market and affect the cost and benefit of the relevant entities.

电力系统低碳转型要求“多线出击”,仅依靠消纳高比例新能源难度日益增大,只有做到电、碳多元发展,才能实现全链条优化。在综合考虑碳配额与电量之间的关联度、匹配度和相对自由度等协同关系的基础上,研究“源网荷”各环节的各主体参与碳配额和电量的协同交易模式,构建碳配额与电量的双因素交易场景模型;结合具备弱中心化、分布式交易等特征的区块链技术与电碳市场构建的天然吻合性,研究碳配额交易链与电量交易链的耦合交易模型;以源端火力发电厂和清洁能源电厂为例,提出了双链下省级区域内各电厂间碳配额和发电量指标的交易模型架构。通过智能合约,将基于粒子群算法的多目标搜索优化算法进行程序化的脚本运行,实现对发电量指标交易模型的仿真和验证,同时对碳配额交易模型进行可行性分析,证实了所提交易方案的适应性和有效性,为电碳市场的构建提供参考。

The low-carbon transformation of the power system requires a“multi-line attack”and it is increasingly difficult to rely only on the consumption of a high proportion of new energy, primarily because the entire chain can only be optimized by achieving the diversified development of electricity and carbon. On the basis of comprehensively considering the synergistic relationship between carbon quotas and electricity, such as the correlation, matching degree, and relative degree of freedom, the collaborative trading mode of carbon quotas and electricity is studied by all entities in each link of the“source-grid-load”and a two-factor trading scenario model of carbon quotas and electricity is developed. Combining the natural compatibility of blockchain technology with the characteristics of weak centralization, distributed trading, and the development of the electric carbon market, the transaction model of the carbon quota trading chain and electricity trading chain is studied. Considering source-end thermal and clean energy power plants as examples, a trading model framework for carbon quotas and power generation indicators between power plants in the provincial area under a double chain is proposed. Through the smart contract, a multi-objective search optimization algorithm based on the particle swarm algorithm is programmed to execute the script to realize the simulation and verification of the trading model of the power generation index. A feasibility analysis of the carbon quota trading model is performed, which confirms the adaptability and effectiveness of the proposed trading scheme and provides a reference for the development of the electric carbon market.

Understanding electricity consumption and production patterns is a necessary first step toward reducing the health and climate impacts of associated emissions. In this work, the economic input-output model is adapted to track emissions flows through electric grids and quantify the pollution embodied in electricity production, exchanges, and, ultimately, consumption for the 66 continental US Balancing Authorities (BAs). The hourly and BA-level dataset we generate and release leverages multiple publicly available datasets for the year 2016. Our analysis demonstrates the importance of considering location and temporal effects as well as electricity exchanges in estimating emissions footprints. While increasing electricity exchanges makes the integration of renewable electricity easier, importing electricity may also run counter to climate-change goals, and citizens in regions exporting electricity from high-emission-generating sources bear a disproportionate air-pollution burden. For example, 40% of the carbon emissions related to electricity consumption in California's main BA were produced in a different region. From 30 to 50% of the sulfur dioxide and nitrogen oxides released in some of the coal-heavy Rocky Mountain regions were related to electricity produced that was then exported. Whether for policymakers designing energy efficiency and renewable programs, regulators enforcing emissions standards, or large electricity consumers greening their supply, greater resolution is needed for electric-sector emissions indices to evaluate progress against current and future goals.Copyright © 2019 the Author(s). Published by PNAS.

Decarbonization is an urgent global policy priority, with increasing movement towards zero-carbon targets in the United States and elsewhere. Given the joint decarbonization strategies of electrifying fossil fuel-based energy uses and decarbonizing the electricity supply, understanding how electricity emissions might change over time is of particular value in evaluating policy sequencing strategies. For example, is the electricity system likely to decarbonize quickly enough to motivate electrification even on relatively carbon-intensive systems? Although electricity sector decarbonization has been widely studied, limited research has focused on evaluating emissions factors at the utility level, which is where the impact of electrification strategies is operationalized. Given the existing fleet of electricity generators, ownership structures, and generator lifespans, committed emissions can be modeled at the utility level. Generator lifespans are modeled using capacity-weighted mean age-on-retirement for similar units over the last two decades, a simple empirical outcome variable reflecting the length of time the unit might reasonably be expected to operate. By also evaluating generators in wholesale power markets and designing scenarios for new-build generation, first-order annual average emissions factors can be projected forward on a multidecadal time scale at the utility level. This letter presents a new model of utility-specific annual average emissions projections (greenhouse gases and air pollutants) through 2050 for the United States, using a 2019 base year to define existing asset characteristics. Enabling the creation and evaluation of scenario-based projections for dynamic environmental intensity metrics in a decarbonizing electricity sector can inform life cycle and other environmental assessment studies that evaluate impact over time, in addition to highlighting particular opportunities and risks associated with the timing and location of long-lived capital investments as the fossil fuel electricity generator fleet turns over. Model results can also be used to contextualize utilities’ decarbonization commitments and timelines against their asset bases.