The carbon market plays a critical role in promoting the transition toward renewable energy sources and reducing greenhouse gas emissions in the electricity generation and transmission. Extant research has overlooked the dynamic bilateral causality that exists between electricity and carbon markets. Moreover, these studies have frequently treated the macroeconomic effect as exogenous. To bridge this research gap, this paper presents a holistic modeling framework that comprehensively captures the intertwined nature of electricity and carbon markets and their concomitant interactions with the overarching economy. The suggested modeling framework is an integration of three principal modules, namely, a carbon market, an electricity market, and economic system. This synergistic blend provides an exhaustive understanding of the entire market operation cycle. It offers detailed clearance rules, and most importantly, it adopts a macroeconomic systematic modeling approach for evaluating the impact emanating from the interconnected electricity and carbon markets. To illustrate the practicality and effectiveness of the proposed approach, a case study anchored on empirical data sourced from the electricity and carbon markets in China is conducted. The empirical findings underscore the fact that incorporating a green certificate market into the modeling framework can precipitate a reduction in greenhouse gas emissions. Additionally, the results indicate that expanding the scale of the green certificate market from 1.9% in 2021 to 33% by 2023 will increase the generation of green electricity by 10%.

Designing customized dynamic pricing is a promising way to incent consumers to adjust their daily energy consumption behaviors. It helps manage flexible demand response resources on peak load. However, it is insufficiently investigated in previous studies from the individual behavior perspective. To tackle the gap, this paper proposes a graph deep learning-based retail dynamic pricing mechanism. First, a graph attention network-based temporal price elasticity perceptron model is proposed. It explores a novel path to learn price elasticity by using graph deep learning, and can accurately assess consumers’ energy consumption behaviors under different prices. Then, to avoid unfair evaluation of demand response, two indexes are proposed as auxiliary measures to assess energy consumption behavior learning models. At last, a customized dynamic pricing model based on the temporal price elasticity perceptron model is proposed. It can develop consumer’s time-varying demand response potential. This potential is first defined in this paper to measure what potentials of shifting/curtailing energy during a period a consumer has. By the pricing, the consumer could be incented to engage in demand response. The numerical studies validate the feasibility and superiority of the proposed methods, meanwhile price risks from the price change can be hedged effectively.

In response to the imperative climate objectives delineated by the Intergovernmental Panel on Climate Change (IPCC), the carbon emission market has been established as a pivotal mechanism to enforce emission limitations on electricity companies. In this paper, we propose a novel simulation and analysis method for the coupled electricity and carbon market that leverages the power of multi-agent-based modeling. Rec-ognizing the intricate inter-dependencies between the trading behavior of different types of entities in the electricity market and the carbon market, our method integrates these elements to provide a comprehensive view of unified m arket dynamics, and conditional generative adversarial networks (CGAN) are used to generate the bidding strategies of generators who participate in both electricity and carbon market. The proposed framework is tested using the IEEE-39 bus system. In this simulation system, different market participants' types and behavior patterns are examined to show the impact on the market outcomes. This study found that market participants' behavior diversity significantly affects their profit in these markets.

Preliminary assessment of energy policies holds significant importance in policy formulation and investment decision-making. The input-output (IO) table portrays an economy's production structure and possesses immense potential for conducting energy policy assessment. However, the limitations arising from the sector aggregation during Input-Output Table construction often hinder such potential. To address this issue, in this paper, we first present an approach to disaggregate the energy sectors in China's IO Table, followed by proposing an environmental assessment method for energy policies. Applying this method, we assess energy-related policies from China's 14th Five-Year Plan across three scenarios. The assessment reveals that, although these policies effectively reduce emissions, they fail to meet the carbon emission intensity reduction targets, even under the most optimistic scenario. The findings suggest that relying solely on the energy policy is inadequate to attain the desired carbon emission intensity reduction goals.

随着我国电力体制市场化改革的深入,市场化的运营为电力市场参与者带来了风险.而在碳达峰、碳中和战略背景下,电力市场参与者在面对传统风险外,还需要面对低碳政策与碳市场带来的碳风险.作为重要的风险管理工具,电力期货在各大成熟的电力市场中有着广泛的应用,同时也引起了我国电力市场参与者的广泛关注.为定量研究引入电力期货对现货市场带来的可能影响,文章提出了一个双层电力市场仿真模型.该模型第一层对电力期货的市场交易行为进行了建模,第二层对电力期货背景下的发电企业最优报价策略进行了建模.基于所提模型,文章在一个六机组的电力市场环境下,针对两种合约签订方法进行了数值仿真实验.仿真结果显示,在两种合约签订方法下,电力期货的引入都有助于现货市场平衡每年高峰用电月份过高的现货电价与每年低峰用电月份过低的现货电价,从而帮助电力市场参与者进行风险管控.

The rapid development of the smart energy system promotes bidirectional communications between the supply-side and demand-side. End users can handily receive real-time prices and adjust their electric energy consumption behaviors. Acquiring the time-varying price elasticity of demand (PED) of electricity can help utility companies to understand the time-varying electricity consumption behavior affected by price, thereby facilitating demand-side management. However, estimating time-varying PED is rarely considered in existing studies. This paper bridges the gap, proposing a time-varying PED estimation algorithm. To analyze PEDs more precisely, the proposed algorithm is applied to each appliance based on the advanced non-intrusive load monitoring (NILM) technology. Moreover, a demand-side smart dynamic pricing mechanism is also proposed to provide decision support of individually optimal dynamic pricing for utility companies to encourage end users to participate in the demand response (DR) program. Comprehensive experiments have been conducted to validate the practical feasibility of the proposed mechanism. Numerical simulations show that the proposed mechanism can facilitate the DR program by reducing the peak-to-average ratio (PAR) in electricity consumption without suffering from the price risk.

Objective:To investigate the clinical value of combined sound touch elastography (STE) and ultrasonography (US) score in staging liver fibrosis in chronic hepatitis B (CHB) patients.Methods:A total of 153 CHB patients who underwent liver biopsy were enrolled into liver fibrosis groups (F1-F4 groups) according to the METAVIR grading standard, and 53 healthy volunteers were included as a control group (F0 group). All subjects received STE/STQ, two-dimensional ultrasound, and liver function biochemical index detection. Logistic regression was used to analyze whether STE/STQ and US quantitative scores were in the same order of magnitude. Receiver operating curve (ROC) analysis of STE, STQ, STE combined with US, and STQ combined with US in the diagnosis of liver fibrosis at each stage was performed.Results:There were statistical differences in the liver STE value and liver STQ value among the F0-F4 groups [liver STE value: (5.71±0.68) kPa vs (6.64±0.96) kPa vs (8.00±1.59) kPa vs (10.14±1.82) kPa vs (13.94±2.83) kPa, F=166.28, P=0.002; liver STQ value: (5.98±1.09) kPa vs (7.01±1.42) kPa vs (8.40±2.54) kPa vs (10.14±1.99) kPa vs (14.91±3.09) kPa, F=123.77, P=0.003]. The spleen STE value only had statistical difference between the liver cirrhosis (F4) group and other groups [(25.69±5.31) kPa vs (16.30±4.29) kPa, (17.04±3.37) kPa, (17.00±3.79) kPa, and (17.41±5.31) kPa; P<0.05]. The ratio of?μUS/μSTE was close to 1, which means that STE and US quantitative scores were at the same level. According to the area under the ROC (AUROC), STE combined with US quantitative scoring showed the best diagnostic performance: ≥F1 stage liver fibrosis (AUROC: 0.944); ≥F2 stage liver fibrosis (AUROC: 0.955); ≥F3 stage liver fibrosis (AUROC: 0.976). For F4 liver cirrhosis, STQ combined with US quantitative scoring had the best diagnostic performance (AUROC: 0.979).Conclusion:STE combined with US quantitative scoring shows the best diagnostic ability in different stages of liver fibrosis, while STQ combined with US quantitative scoring has the best performance in diagnosing liver cirrhosis.

This paper proposes a bilevel optimization model to find the equilibrium of an electricity market where each GENeration COmpany (GENCO) takes the optimal investment and bidding decision. The upper level determines the optimal investment and bidding strategy of each GENCO, while the lower level represents the market clearing process. One highlight of this paper is that both fuel-fired and renewable generators are considered in the model. Although the proposed model is too complicated to solve, based on some relaxation assumptions, it can be reformulated to a trilevel model and solved by the primal-dual method. The proposed model and solution technique are tested in IEEE 14-bus system.

The wake effect is the major obstacle to reaching the maximum power generation for wind farms, since choosing the suitable wake model that satisfies both computational cost and accuracy is a difficult task. Deep Reinforcement Learning (DRL) is a powerful data-driven method that can learn the optimal control policy without modeling the environment. However, the “trial and error” mechanism of DRL may cause high costs during the learning process. To address this issue, we propose an ensemble-based DRL wind farm control framework. Under this framework, a new algorithm called Actor Bagging Deep Deterministic Policy Gradient (AB-DDPG) is proposed, which combines the actor-network bagging method with the Deep Deterministic Policy Gradient. The gradient of the proposed method is proved to be consistent with the DDPG method. The experiment results in WFSim show that AB-DDPG can learn the optimal control policy with lower learning cost and a more robust learning process.

The rapid development of the Industrial Internet-of-Things extends demand response (DR) research to the aspect of low-carbon emission in smart grids. This study proposed the concept of low-carbon DR (LCDR) in the electricity market as well as the price-based LCDR mechanism and its model. First, carbon cost conduction from the generation side to the demand side was analyzed, and then conduction function was quantifiably deduced. Second, the mechanism and model of price-based LCDR were proposed by considering three DR signals, namely, the electricity price, carbon price, and carbon emission intensity of the demand side, based on the traditional price-based DR (PBDR) mechanism. Third, the proposed LCDR mechanism was applied to the environmental–economic dispatch optimization problem. At last, case studies on the modified IEEE 39-bus system verified that the LCDR mechanism can reduce carbon emissions while maintaining the function of the traditional PBDR. Meanwhile, the applicability of LCDR was illustrated based on carbon emission sensitivity to LCDR model parameters. The proposed mechanism can guide participants in the electricity market in reducing electricity carbon emissions.