Integrated energy systems (IESs) provide a synergistic supply of electricity and heat, which offers a new approach for district heating. However, the operational risk of IESs increases continuously as the extent of renewable energy source (RES) penetration continues to increase. This condition negatively impacts the benefits of IESs to the participants in the integrated energy market (IEM) and increases the costs of the system. This paper addresses these issues by proposing a two-stage stochastic programming model that considers RES penetration for IESs operated independently of electricity grids (i.e., islanded IESs). Then, a two-stage clearing model of the IEM is established, and a market settlement process is designed. The simulation results demonstrate that the two-stage clearing model of the IEM can effectively alleviate the operational risk associated with high RES penetration by allowing market participants to make timely adjustments corresponding to their interests according to changes in RES generation. INDEX TERMS Real-time market, integrated electricity and heat system, integrated energy market, market clearing, market settlement, two-stage stochastic programming.
State estimation has been widely used in power system energy management systems. However, the application of state estimation for integrated electrical and heating networks (IEHNs) remains in a preliminary stage. This paper addresses this issue by proposing a robust state estimation method for IEHNs based on the weighted least absolute value in conjunction with equality constraints. The robust performance of the proposed estimator resolves the disadvantages of existing combined state estimators. A heating load pseudo-measurement model based on an artificial neural network and real-time measurements is developed to suppress the negative effects of measurements that contain bad data, and thereby ensure an adequate basis for accurate state estimation and guarantee the observability of the heating network. The effectiveness of the proposed state estimation method and its robustness to bad data are verified by comparison with the performance of the conventional largest normalized residual test based on the equality-constrained weighted least squares state estimation of IEHNs in numerical simulations employing a simple IEHN and/or the Barry Island IEHN as case studies. INDEX TERMS Integrated electrical and heating network, state estimation, weighted least squares absolute value, pseudo-measurement model, bad data identification.
At this stage, due to the increasing use of electric vehicles, the position of electric vehicle load scheduling in grid power scheduling is becoming more and more important. Effective electric vehicle power dispatching can balance the peak-valley difference of power dispatching, increase the power supply utilization rate of power grid dispatching, and reduce the power supply pressure of line transformer. The load forecast can describe the user’s electricity consumption habits in the next period of time, and can provide important data basis for power dispatching. This paper summarizes the research status of electric vehicle charging load, analyzes traditional charging load research methods, propose a charging load forecasting method combining XGBoost(Extreme Gradient Boosting) and LSTM (Long Short Term Memory Network), And use the data of a charging station in Jiangsu to verify the calculation example. The proposed method is based on the prediction results of the XGBoost model for feature engineering, extracting data features using phase space reconstruction techniques and statistical methods. In addition, training the LSTM model for load prediction. Based on the charging record data of domestic charging stations, this paper applies the artificial intelligence method to the charging load forecast of domestic charging stations for the first time. The charging station load forecasting method studied in this paper can support the regional load forecasting research of electric vehicles with high permeability, and further optimize power dispatching.
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