This paper presents the formulation and critical assessment of a novel type of demand response (DR) program targeting retail customers (such as small/medium size commercial, industrial, and residential customers) who are equipped with smart meters yet still face a flat rate. Enabled by pervasive mobile communication capabilities and smart grid technologies, load serving entities (LSEs) could offer retail customers coupon incentives via near-real-time information networks to induce demand response for a future period of time in anticipation of intermittent generation ramping and/or price spikes. This scheme is referred to as coupon incentive-based demand response (CIDR). In contrast to the real-time pricing or peak load pricing DR programs, CIDR continues to offer a flat rate to retail customers and also provides them with voluntary incentives to induce demand response. Theoretical analysis shows the benefits of the proposed scheme in terms of social welfare, consumer surplus, LSE profit, the robustness of the retail electricity rate, and readiness for implementation. The pros and cons are discussed in comparison with existing DR programs. Numerical illustration is performed based on realistic supply and demand data obtained from the Electric Reliability Council of Texas (ERCOT).

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.

Linear power flow models are widely used in power systems to simplify the nonlinear power flow equations. The DC power flow model is one of the representatives. There are many other linear power flow models that improve the DC power flow model with the inclusion of Q and $v$ . However, existing linear models are derived based on empirical mathematical approximation without a general methodology guidance. In this paper, we found that the fundamental difference among different linear power flow models lies in the formulation of “independent variables”. Based on this finding, a general formulation of linear power flow models is proposed. The linearization error is theoretically analyzed. In particular, the case when $\theta$ and $v^k$ are regarded as independent variables is thoroughly investigated. Method for finding the linear power flow with the minimum error is presented. The formulation of the independent variables associated with the minimum linearization error is determined by the distribution of state variables $v$ and $\theta$ . It is shown that the linearization error when $v^2$ is regarded as an independent variable is normally smaller than that for v because of the special properties of the distribution of $v$ in power grids.

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.

The technical virtual power plant (TVPP) is a promising paradigm to facilitate the integration of distributed energy resources (DERs) while incorporating operational constraints of both DERs and networks. Due to the limited predictability of DER generation and electric loads, the output capability of a TVPP is uncertain. In this regard, this paper proposes the robust capability curve (RCC) of a TVPP, which explicitly characterizes the allowable range of the scheduled power output that is executable for the TVPP under uncertainties. Implementing the RCC can secure the scheduling of the TVPP against unexpected fluctuation of operating conditions when the TVPP participates in the transmission-level dispatch. Mathematically, the RCC is the first-stage feasible set of an adjustable robust optimization problem. An uncertainty set model incorporating the variable correlation and uncertainty budget is employed, which makes the robustness and conservatism of the RCC adjustable. A novel methodology is proposed to estimate the RCC by the convex hull of points on its perimeter. Case studies based on several IEEE test feeders verify the effectiveness and computational efficiency of the proposed model and the proposed estimation method is also verified based on larger-scale test systems.