Devising policies for a low carbon city requires a careful understanding of the characteristics of urban residential lifestyle and consumption. The production-based accounting approach based on top-down statistical data has a limited ability to reflect the total greenhouse gas (GHG) emissions from residential consumption. In this paper, we present a survey-based GHG emissions accounting methodology for urban residential consumption, and apply it in Xiamen City, a rapidly urbanizing coastal city in southeast China. Based on this, the main influencing factors determining residential GHG emissions at the household and community scale are identified, and the typical profiles of low, medium and high GHG emission households and communities are identified. Up to 70% of household GHG emissions are from regional and national activities that support household consumption including the supply of energy and building materials, while 17% are from urban level basic services and supplies such as sewage treatment and solid waste management, and only 13% are direct emissions from household consumption. Housing area and household size are the two main factors determining GHG emissions from residential consumption at the household scale, while average housing area and building height were the main factors at the community scale. Our results show a large disparity in GHG emissions profiles among different households, with high GHG emissions households emitting about five times more than low GHG emissions households. Emissions from high GHG emissions communities are about twice as high as from low GHG emissions communities. Our findings can contribute to better tailored and targeted policies aimed at reducing household GHG emissions, and developing low GHG emissions residential communities in China.

The short-term forecasting of PV power generation ensures the scheduling and dispatching of electrical power, helps design a PV-integrated energy management system and enhances the security of grid operation. However, due to the randomness of solar energy, the output of the photovoltaic system will fluctuate, which will affect the safe operation of the grid. To solve this problem, a high-precision hybrid prediction model based on Variational Quantum Circuit (VQC) and Long Short-Term Memory (LSTM) network is developed to predict solar irradiance one hour in advance. VQC is embedded in LSTM to iteratively optimize the weight parameters of four gates (forgetting gate, input gate, cell state, and output gate) to improve prediction accuracy. To evaluate the prediction performance of this model, five solar radiation observatories located in China are selected, together with widely used models including Seasonal Autoregressive Integrated Moving Average (SARIMA), Convolution Neural Network (CNN), Recurrent Neural Network (RNN), Gate Recurrent Unit (GRU) and LSTM, comparisons are made under different seasons and months. The experimental results show that the annual average Root Mean Square Error (RMSE) of the Quantum Long Short-Term Memory (QLSTM) model is 61.756 $W/m2$ , which is reduced by 10.7%, 13.9%, 8.1%, 3.8%, and 3.4% respectively, compared with other models; the annual average Mean Absolute Error (MAE) is 24.257 $W/m2$ , which is reduced by 28.1%, 28.9%, 24.1%, 12.2%, and 12.8% respectively, compared with other models; the annual average R-Square ( $R^{2}$ ) is 0.946, which is improved by 1.5%, 1.9%, 1.2%, 0.4%, and 0.4% respectively, compared with other models.

Reliable photovoltaic(PV) forecasting can provide important data support for power system operation, which is the key to realize the large-scale consumption of solar energy resources. PV forecasting task becomes crucial to ensure power system stability and economic operation. This paper reviews the existing research of PV forecasting methods from the perspective of multi-temporal scale and multi-spatial scale. Firstly, according to the forecasting process, demand, temporal and spatial scale, the forecasting methods are classified and the evaluation indicators involved in the research are listed. Secondly, based on the temporal scale of PV power generation, the results are combed through the three kind of scale of ultra-short-term, short-term and medium and long-term prediction. Thirdly, on each kind of temporal scale, the results are subdivided into single-site prediction and regional prediction to sort out in detail. Finally, the results are analyzed on the basis of the predicted temporal scale, spatial scale and input data. It has been observed that most recent papers highlight the importance of short-term predictions. The machine learning method shows excellent nonlinear description ability in short-term prediction, the prediction results are satisfactory. The spatial average effect of regional prediction reduces the variability of solar energy, the prediction results are reliable.

This article proposes an adaptive, optimal, data-driven control approach based on reinforcement learning and adaptive dynamic programming to the three-phase grid-connected inverter employed in virtual synchronous generators (VSGs). This article takes into account unknown system dynamics and different grid conditions, including balanced/unbalanced grids, voltage drop/sag, and weak grids. The proposed method is based on value iteration, which does not rely on an initial admissible control policy for learning. Considering the premise that the VSG control should stabilize the closed-loop dynamics, the VSG outputs are optimally regulated through the adaptive, optimal control strategy proposed in this article. Comparative simulations and experimental results validate the proposed method's effectiveness and reveal its practicality and implementation.

In order to solve the problem of insufficient control performance of various traditional control strategies in the complex environment of grid-connected inverters. The active disturbance rejection control(ADRC) strategy based on virtual synchronous generator(VSG) is proposed. The mathematical model of grid-connected photovoltaic inverter based on VSG is built. The proposed control strategy provides the inverter with more disturbance attenuation and provides rotational inertia. The control strategy estimates and compensates the total disturbance, generates the reference active power and reactive power by ADRC. The control strategy converts the three-phase voltage and current outputs into positive and negative sequences on the dq reference frame. The VSG control module generates a reference voltage command, outputs it to the dual closed loop PI feedforward decoupling control. The PWM signal is finally obtained by the PI feedforward decoupling control. The ADRC strategy based on the VSG does not change the original control characteristics of the VSG, it retains the characteristics of the synchronous generator, it also provides inertia and damping for the power grid. Simulation show that ADRC strategy based on VSG applied to the inverter can attenuate disturbances. Under the unfavorable conditions of the unstable reference power, like unbalanced three-phase voltage, and random disturbance, the output power match the international electricity standard.