With the continuous advancement of the reform of the power system and the further development of renewable energy, the problems of renewable energy consumption such as abandoning wind and solar power and subsidy gaps are gradually exposed. In order to promote the consumption of renewable energy, it is of great research value and practical significance to let all parties on the power generation and consumption side jointly fulfill the responsibility of consumption through market-oriented means. This paper summarizes the foreign market mechanisms for promoting renewable energy consumption, and focuses on investigating the operating rules, transaction organizations, and transaction timing of foreign renewable energy consumption markets. On the basis of fully learning from the experience and lessons of foreign renewable energy consumption, combined with the characteristics of China's power system and the actual situation of the market, the difficulties and suggestions for the development of the renewable energy consumption market under the current dual carbon goals are put forward.
The new power system with new energy as the major energy resources has become the development direction of the future power grid. In the distribution system, it is manifested in the high penetration of distributed generation (DG), e.g., photovoltaic, wind power and electric vehicles (EVs). The carrying capacity of the distribution system for DG and EV is of great significance to the planning of the system. This paper firstly analyzes and summarizes the theoretical basis for evaluating the carrying capacity of distribution systems for DG and EV from four perspectives: the modeling method for DG and EV uncertainty, the site selection strategy of DG and EV charging pile/station, the evaluation index of distribution system’s carrying capacity, and the carrying capacity evaluation method. Then the paper analyzes the key technologies for improving the carrying capacity of the distribution system from the four aspects of source, network, load and storage. Finally, combined with the development trend and characteristics of China's new power system, the research on the carrying capacity of distribution systems is prospected. This review provides a useful reference for the planning of large-scale distributed new energy and new loads in distribution systems.
With large-scale penetration of wind power into power systems, the dynamic interaction between wind power generators and synchronous generators is intensified. The negative impact and positive control effect of wind power generators on the low-frequency oscillation (LFO) in power systems is increasingly obvious. This paper investigates the LFO damping in power systems with doubly-fed induction generators (DFIGs). The mechanism of DFIG influencing the LFO is described. The damping measures from the wind farm or power system are compared. LFO damping strategies with parameter tuning and control strategy design to the DFIG power oscillation damper (POD) are discussed. The parameter tuning based on traditional methods, optimization, robust design, etc., are compared. And the control design methods such as linear quadratic regulator (LQR), active disturbance rejection control (ADRC), sliding mode control (SMC), fuzzy control are also compared. Suggestions to the future research on the LFO damping in DFIG integrated power systems are given.
With the proposal of "double carbon target" and the increase of renewable energy penetration rate, it becomes more and more important to ensure the flexible operation of power system. In order to aggregate multiple types of distributed flexible resources such as power generators, load and storage, this paper constructs an operation optimization and two-layer benefit allocation models for virtual power plants (VPP), which considers the uncertainty of wind and solar power and shared energy storage under the environment of electricity market. Firstly, a VPP system structure including distributed PV and wind power generators, shared energy storage and flexible load is constructed, and a two-level power market trading mechanism is established. Secondly, the method of scenario generation and reduction is used to deal with the uncertainty of wind and solar power output. On this basis, the VPP operation optimization model considering day-ahead and real-time trades is constructed with the goal of maximizing VPP operation income. Thirdly, in order to guarantee the fairness and rationality of participants' income, the income distribution strategies for VPP participants and shared energy storage investors are established on the basis of the Owen value method of two-layer cooperative game. Finally, an example analysis is carried out to verify the effectiveness of the proposed model. The example results show that VPP with shared energy storage can effectively reduce the interference caused by wind and solar power uncertainty and increase the income of VPP in two-level market. The income distribution of each investment subject through Owen value method is conducive to ensuring the fairness and rationality of the system.
Carbon peak and carbon neutralization targets are momentous strategical decisions issued by the CPC Central Committee and the State Council to coordinate the two overall situations at home and abroad. As the main form of clean energy utilization, electrification is one of the main pathways to achieve the goal of carbon neutralization in China. This paper reviews the development of electrification in China and abroad, and summarizes the challenges in China according to the development status. In view of the lack of theoretical and analytical tools in the current planning for electrical energy substitution path, a macro and micro coupled integrated assessment model framework of electrification is proposed in this paper. The first is to build a database of typical industries to provide a basis for the analysis of electrical energy substitution potential in each industry. The second is to obtain the Chinese parameters of shared socioeconomic pathways (SSPs) scenarios at the macro level and export them as socio-economic development scenarios. The third is to obtain the Chinese technical parameters to achieve parameter calibration at the micro level. The fourth is to couple the macro and micro markets to establish a dynamic feedback and two-wheel drive mechanism. This study is helpful to improve the theoretical system of electric energy substitution and provide scientific methodology support for the steady and orderly implementation of electrification in the future.
In order to fully explore the ability of various types of integrated demand response (IDR) to participate in the coordination of integrated energy system (IES) optimal operation, this paper firstly analyzes the multi-time scale characteristics of IDR, establishes various types of IDR models and formulates multi-time scale response strategy of IDR. Then, on the basis of the strategy, a multi-time scale optimization operation model of IES aimed at economic optimization is established. The model is divided into three stages: day-ahead, intra-day upper and intra-day lower stages. In the day-ahead stage, an economically optimal day-ahead scheduling plan is formulated. In the intra-day stage, the output of cooling/heat, electricity/gas related equipment is managed in different layers by dividing the long and short time scales. Finally, the analysis of a calculation example shows that the proposed model can effectively reduce the operating cost and give full play to the potential of equipment with different response capacities to participate in intra-day operation.
Integrated demand response (IDR) is the main means to exploit the load-side regulation potential of the integrated energy system (IES). However, IDR users’ energy consumption behaviors are characterized by random-fuzzy mixed uncertainties, which will not only bring challenges to the formulation of scheduling strategy, but also affect the reliability of the system, and may even lead to load outage. To solve the above problem, firstly, a model based on the improved PMV-PPD index to evaluate user participation willingness is proposed. Secondly, according to the cloud model theory, a price IDR model considering mixed uncertainties is established. On the basis of these, the IES optimal scheduling strategy considering the uncertainties of various energy response boundaries and price elasticity coefficients is proposed. The results of examples show that the proposed IES optimal scheduling strategy can better deal with the load fluctuation caused by the uncertainties of IDR and improve system reliability effectively.
A hierarchical energy system management framework based on the demand response of electric vehicles (EVs) and temperature-controlled loads (TCLs) is developed to address the impact of large-scale EVs on the power system. Stimulated EV clusters and TCL clusters can quickly respond to the scheduling strategies of load aggregators to reduce the impact on the grid caused by the large number of flexible loads connected to the grid. Firstly, a hybrid model of convolutional neural network and long-and short-term memory network is used to predict each part of the load, and the load aggregator dispatches controllable flexible loads to maximize the fit of the predicted load profile. The load aggregator performs peer to peer (P2P) power trading with the power operator according to the current scheduling strategy and applies distributed optimization to solve the maximum benefit for both parties. For the remaining energy demand after local energy trading, a multi-objective optimization model for system operating cost, carbon emission, and wind energy spillover is considered. The Pareto frontier of this model is solved using NSGA-II with centralized optimization and verified by arithmetic cases in the IEEE 30-node system. The simulation results show that the proposed optimal energy dispatch strategy can not only meet the power requirements of EVs and TCLs, but also bring good economic and environmental benefits to the power system.
Aiming at the problem that large-scale grid-connected renewable energy and high-proportion power electronic equipment access in new power system, which leads to the decrease of the system inertia level and affects the safe and stable operation of the power grid, a new power system multi-energy inertia dynamic optimization control model is proposed. Firstly, the inertia characteristics of electricity, heat and gas are analyzed, and the inertia models of energy transfer in the power, heat and gas systems are established, respectively. Secondly, considering the coupling and coordination relationship under multi-energy transfer, an event-driven dynamic inertia optimization control method for power, thermal and gas systems is proposed. The modified IEEE 39-node power system, 6-node thermal system and 7-node gas system are simulated as examples. The simulation results show that the proposed control method can effectively improve the frequency response of the new power system and maintain the robustness of the system operation.
Large scale integration of intermittent distributed generation (DG) challenges the reliable and secure operation of distribution systems. Therefore, this paper proposes a two-stage robust scheduling method for flexible interconnected distribution systems considering energy storage and dynamic reconfiguration. Firstly, a two-stage robust scheduling model for flexible interconnected distribution systems is established, considering flexible distribution switches (FDSs), energy storage and dynamic reconfiguration. In the first stage, the network topologies and energy storage outputs are globally optimized considering their temporal correlation and day-ahead forecasting uncertainties of the DG outputs. In the second stage, the FDS power in each time slot is dispatched according to the decisions of the first stage and ultra-short-term DG forecast. Then, an improved column and constraint generation algorithm is adopted to solve the robust model, where an alternating direction process is used to accelerate the sub-problem. Finally, the effectiveness of the proposed robust scheduling model and algorithm is verified using the 33-node and 69-node distribution systems with promising results.
At present, a large amount of new energy is connected to the user side, and the actual power load minus the power generated by the new energy (hereinafter referred to as “net load”) is usually used for prediction research. Due to the strong randomness of new energy power generation, net load has strong uncertainty and poor regularity, which makes it difficult to predict accurately. To this end, this paper proposes a net load prediction method based on the feature-weighted Stacking ensemble algorithm. Firstly, through the analysis of the prediction performance and difference of different prediction models, this paper chooses Long Short-Term Memory Network, Elman Neural Network, Random Forest Tree and Least Squares Support Vector Machine as stacking ensemble learners. Secondly, because the traditional Stacking ensemble prediction model ignores the differences between learners, the model’s prediction ability is insufficient. Therefore, this paper weights the features of the learners according to the prediction accuracy to correct the prediction error introduced by different learners. Finally, the measured data in the German TENNET area is analyzed as an example. The simulation results show that, compared with the single forecasting model and the traditional Stacking integrated forecasting method, the payload prediction method based on feature-weighted stacking ensemble learning has higher forecasting accuracy in sunny, cloudy, rainy, snowy and other weather conditions.
Due to the influence of the abrupt change-point of temperature, the load sequence has a threshold effect, which leads to poor load forecasting effects of traditional linear time series models. This paper uses the abrupt change-point of the temperature as the threshold and establishes a threshold autoregressive moving average model with temperature as the exogenous variable (TARMAX). The forecasting accuracy is improved. In this paper, the Markov Chain Monte Carlo (MCMC) method is firstly applied to search for the abrupt change-point of the temperature, and the model parameters are obtained. Then, the method of random search variables is used to quickly select the optimal model, which effectively reduces the amount of calculation for selecting the time series model. Finally, the residential daily power load in different seasons is forecasted. The example shows that, compared with the linear time series models, the long short-term memory network (LSTM), and the multi-layer perceptron (MLP), the TARMAX model improves the forecasting accuracy of the power load.
With the increase of the proportion of new energy such as photovoltaic and wind power, the inertia of the receiving-end power grid is gradually diluted, and the rate of change of frequency caused by the inertia response will increase after being disturbed, which will threaten the safety and stability of frequency. There is spatial coupling between the change of frequency in the inertia response and the position of the power disturbance, which is related to the topology of the power grid. To cope with the spatial difference of the inertia response, this paper quantifies the spatial coupling relationship between generator inertia response and active power disturbance by calculating the relative gain matrix of the multi-machine system inertia response model. Simulation results show that the relative gain can intuitively characterize the influence of different positions and types of power disturbances on the generator inertia response. This method is of great significance to the analysis and control of the frequency safety and stability of low inertia systems under power accidents.
The virtual synchronous generator (VSG) control strategy enables the power electronic converter to have the moment of rotational inertia and damping coefficient of the synchronous generator, but the relationship between the two parameters and the frequency in the regulation process is a nonlinear function, which is regarded as a linear relationship in the traditional methods. The control strategy can only roughly adjust two parameters and the adjustment frequency is too high. Therefore, this paper proposes an adaptive control strategy of inertia and damping for VSG, which is based on Radial Basis Function (RBF), using the artificial intelligence algorithm to improve the control strategy. Firstly, this paper analyzes the control methods of moment of inertia and damping coefficient from the perspectives of VSG mathematical model, output characteristics and small signal model, and gives the value ranges of corresponding parameters. Secondly, according to the unique nonlinear relationship of VSG, a double-input and double-output RBF neural network control strategy is established. Finally, the transient response of the traditional control strategy and the control strategy proposed in this paper are compared by Matlab / Simulink simulation to verify the effectiveness of the proposed control strategy.
Frequent wind curtailment and load shedding caused by the uncertainty of both source and load seriously affects the economy of microgrid operation. To effectively deal with the uncertainty of both source and load, this paper proposes a two-stage adjustable robust microgrid economic dispatch model based on the mRMR-XGboost-IDM model. Firstly, aiming at the problem that the uncertain ambiguity set based on the IDM model is highly dependent on the amount of historical data, mRMR-XGboost prediction method is introduced, and the volume of historical data is enlarged to improve the accuracy of the obtained uncertainty interval. Secondly, on the basis of the obtained uncertainty interval, a two-stage robust economic dispatch model of the microgrid is constructed, and adjustable robust parameters are introduced to coordinate economy and robustness. Finally, the column-and-constraint generation algorithm, duality theory, and big-M method are used to solve the optimal economic dispatch strategy. The experimental cases verify that the proposed model can improve the accuracy of the uncertainty interval description, effectively deal with the uncertainty of source and load and improve the economy of system operation.
