China has enacted a series of policies since 2015 to substitute electricity for in-home combustion for rural residential heating. The Electric Heating Policy (EHP) has contributed to significant improvements in air quality, benefiting hundreds of millions of people. This shift, however, has resulted in a sharp increase in electric loads and associated carbon emissions. Here, we show that China’s EHP will greatly increase carbon emissions. We develop a theoretical model to quantify the carbon emissions from power generation and rural residential heating sectors. We found that in 2015, an additional 101.69–162.89 megatons of CO₂could potentially be emitted if EHP was implemented in 45–55% of rural residents in Northern China. In 2020, the incremental carbon emission is expected to reach 130.03–197.87 megatons. Fortunately, the growth of carbon emission will slow down due to China’s urbanization progress. In 2030, the carbon emission increase induced by EHP will drop to 119.19–177.47 megatons. Finally, we conclude two kinds of practical pathways toward low-carbon electric heating, and provide techno-economic analyses.

Solar and wind resources are vital for the sustainable energy transition. Although renewable potentials have been widely assessed in existing literature, few studies have examined the statistical characteristics of the inherent renewable uncertainties arising from natural randomness, which is inevitable in stochastic-aware research and applications. Here we develop a rule-of-thumb statistical learning model for wind and solar power prediction and generate a year-long dataset of hourly prediction errors of 30 provinces in China. We reveal diversified spatiotemporal distribution patterns of prediction errors, indicating that over 60% of wind prediction errors and 50% of solar prediction errors arise from scenarios with high utilization rates. The first-order difference and peak ratio of generation series are two primary indicators explaining the uncertainty distribution. Additionally, we analyze the seasonal distributions of the provincial prediction errors that reveal a consistent law in China. Finally, policies including incentive improvements and interprovincial scheduling are suggested.

Accurate solar power forecasting plays a critical role in ensuring the reliable and economic operation of power grids. Most of existing literature directly uses available weather conditions as input features, which might ignore some key weather factors and the coupling among weather conditions. Therefore, a novel solar power forecasting approach is proposed in this paper by exploring key weather factors from PV analytical modeling. The proposed approach is composed of three engines: i) analytical modeling of PV systems; ii) machine learning methods for mapping weather features with solar power; and iii) a deviation analysis for solar power forecast adjustment. In contrast to the existing research that directly uses available weather conditions, this paper explores the physical knowledge from PV models. Different irradiance components and PV cell temperatures are derived from PV analytical modeling. These weather features are used to reformulate the input of machine learning methods, which helps achieve a better forecasting performance. Moreover, based on the historical forecasting deviations, a compensation term is presented to adjust the solar power forecast. Case studies based on measured datasets from PV systems in Australia demonstrate that the forecasting performance can be highly improved by taking advantage of the key weather features derived from PV models.

With the popularity of plug-in electric vehicles (PEVs) and the development of vehicle to grid (V2G) technology, PEVs can be aggregated and behave as a controllable storage system via the internet of things. In this paper, we propose a dispatchable region formation approach of EV aggregation to capture its available flexibility in microgrid (MG) bidding. The dispatchable region of EV aggregation describes the feasible operation strategy as a single entity, characterized by its power and cumulative energy limits. Instead of scheduling an individual EV, the dispatchable region of large-scale EVs enables the MG operator to directly schedule the EV aggregation toward market revenue maximization. The MG bidding strategy is formulated as a risk-constrained stochastic programming, which maximizes day-ahead market profits considering real-time imbalance in a dual-pricing market. Case studies based on real-world datasets demonstrate that the proposed approach can significantly improve both computation efficiency and forecasting accuracy.

The increasing integration of renewable energy has highlighted the importance of interchange trading in multi-area power systems (MAPS). In some existing multi-area electricity markets, the marginal-pricing (MP) mechanism is adopted. However, this mechanism is known to lose market efficiency when profit-maximizing units strategically bid to manipulate electricity prices. To elicit truthful bids from generators and guarantee the optimality of MAPS, we propose an incentive mechanism based on Vickrey-Clarke-Groves (VCG) auction. A two-stage multi-area economic dispatch model for joint energy and reserve clearing is formulated with hybrid AC/DC links. We illustrate that when settled by the MP mechanism, thermal units have incentive to strategically bid on reserve prices, thereby preventing inter-area power exchange. The VCG-based mechanism sets up the payment to a unit as other generators’ incremental costs after removing it in the market clearing process, which reveals the externality induced by this unit and thus incentivizes truthful bids. To preserve each area's autonomy, a novel decentralized algorithm based on improved optimality condition decomposition is developed. Case studies based on a 2-area 60-bus system and a 3-area 354-bus system demonstrate the effectiveness and efficiency of the proposed mechanism and method.