As the subsidies of distributed resources decrease, to bid in day-ahead market by forming virtual power plant becomes a new income channel for demand-side entities. This paper designs a complete transaction mechanism from three parts to realize this mode such as bidding strategy, profit distribution and the formation of virtual power plant competing by demand-side entities. The bidding strategy model of price-maker virtual power plant is proposed, and the uncertainty is modeled on the basis of interval optimization. In addition, a generation quantity and uncertainty contribution based allocation mechanism is designed to distribute the profit. Then, this paper analyzes the stability of each coalition status to study the formation of virtual power plant. Finally, an example is given to verify the effectiveness of the proposed method. The results show that the proposed transaction mechanism conforms to the characteristics of the user-side stakeholders, and its allocation mechanism takes into account the stabilizing effect of participants on uncertainty, which can provide auxiliary decision-making for the user side stakeholders.
The uncertainty of wind and photovoltaic power output poses a threat to the stable operation of power system, and the power-gas coupling system including power to gas (P2G) can solve this problem. The paper integrates power to gas into virtual power plant (VPP), presents the basic concepts, general modeling, and dispatching optimization model of virtual power plant connected with power-to-gas. In terms of the uncertainty of wind and photovoltaic power, the conditional value-at-risk (CVaR) method and robust stochastic optimization method are introduced to characterize the uncertain variables in the objective function and constraint conditions, respectively. Then, the maximum total emission allowances (MTEA) is applied to achieve the near-zero carbon dispatching scheme of virtual power plant. To solve the multi-objective model, a model solving algorithm based on the input-output table of objective function and the check of weight deviation is constructed, and a 9-node energy hub system is selected for example analysis. The results show that: the virtual power plant connected with power-to-gas can realize the complementary utilization of distributed energy and form the electricity-gas-electricity recycling mode. The proposed model can take into account the high economic benefits and uncertainty risks of distributed energy.This work is supported by National Natural Science Foundation of China (No.71573084).
Virtual power plants (VPP) can effectively aggregate decentralized demand-side resources. It is an effective way for demand-side resources to participate in electricity market. However, the market behavior of VPP cannot be expressed by the traditional bidding model of the thermal power plant, meanwhile the transmission constraints will also impact the interior resource management strategy of the VPP. Under the aforementioned background, a robust bidding model of multi-type resources in virtual power plant in power market is proposed and it takes into account renewable energy output uncertainty and the adjusting ability of aggregated units. The virtual power plant studied in this paper consists of wind power, energy storage, gas turbine and demand response load. The model takes Karush-Kuhn-Tucker (KKT) equivalent condition of market liquidation model as constraints to represent the relationship between market liquidation process and VPP decision-making. The results show that the model can optimize internal resource combination of VPP according to the market situation, provide an economic and reliable bidding strategy, and effectively improve the economic benefits of VPP.
With the rapid growth of installed capacity of wind and photovoltaic power, wind farms and photovoltaic power stations equipped with energy storage have the conditions to be black-start power supply. Using PV-wind-battery system as black-start power supply can improve the black-start capacity of regional power grid. Therefore, this paper designs a feasible evaluation method for the PV-wind-battery system to be black-start power supply. First of all, the output power of the PV-wind-battery system is analyzed from the perspective of load demand of black start, and the support probability and existing problems of black start with PV-wind-battery system are obtained. Then, on the basis of the power prediction method and power evaluation index, the feasibility evaluation method for the PV-wind-battery system to be black-start power supply is designed. The reference value of output power of PV-wind-battery system is obtained by the power prediction method. The feasibility of the PV-wind-battery system to be black-start power supply is determined by the evaluation index of output power of PV-wind-battery system. Finally, using MATLAB simulation software, different scenarios are designed to verify the effectiveness of the feasibility evaluation. The results show that the output power of wind-solar-storage power generation system can meet the power requirements of black-start load when the execution probability inclination is greater than 1.
The uncertainties of daily interval flow and the restriction of the transmission section are important factors that affect the day-ahead dispatching plan of large-capacity cascade hydropower stations. In order to improve the adaptability of the daily dispatching to different inflow scenarios, the probability density function of daily interval flow is obtained on the basis of the forecast daily average interval flow firstly, combining historical data analysis and probability distribution fitting. Through accurately describing the rule of interval flow in each period, the deviation of cascade power generation under different scenarios is established to reflect the uncertainty of the interval flow at each period. Secondly, fuzzy variables are established to express the complex section constraint relationship in the day-ahead dispatching, and the section constraint is converted to the generator’s active power constraint through off-line simulation. The multi-core parallel dynamic programming algorithm is adopted to solve the day-ahead dispatching model to ensure the convergence and efficiency of the solution. Finally, the rationality and feasibility of the proposed algorithm are verified through the example of cascade hydropower stations in southwest China connected to IEEE 39-node system, and the water level and transmission section power are not out of limits in the four typical inflow scenarios.
Aiming at the black-start scheme of blackouts in areas with abundant wind and solar resources, this paper firstly establishes a black-start control model for wind-solar-storage power stations to study the process of supporting the black-start of the power grid. The energy storage system is used as the main power source to ensure the system voltage and frequency stable. Secondly, a coordinated control strategy for load tracking power of wind-solar units is proposed. When wind-solar power output is insufficient, wind-solar power unit adopts maximum power point tracking (MPPT) algorithm, and the energy storage system acts as the main power source to balance the power of microgrid. When wind-solar output is sufficient, wind-solar units effectively track load power changes to avoid excessive power charging to energy storage. This control strategy can not only reduce the times of charge-discharge conversion of the energy storage system, but also greatly reduce the configuration capacity of the energy storage system in black start, which can provide economic benefits. Finally, on the DIgSILENT/PowerFactory simulation platform, a control model for wind-solar-storage power station is built, and the simulation verifies the feasibility of wind-solar-storage power supply as power grid black-start source and the effectiveness of the control strategy proposed in this paper.
The flexibility retrofit of thermal power has been proposed as a highlight in the 13th Five-Year Power plan, but the development trend during the 13th Five-Year Plan period in different regions is diverse. In the long run, thermal power, as the main body of China’s power generation capacity, will still provide the most important regulation capacity in most regions. Thus, this article comprehensively sorts out the domestic and foreign development history of flexibility retrofit of thermal power, summarizes the key technical, economic, and mechanism factors that affect its progress, proposes typical flexibility retrofit models, and discusses its future development prospects and key research issues. The investigation shows that, with the establishment of various flexibility markets, flexibility retrofit of thermal power in China will transit from policy-oriented to market-oriented, and flexibility will also become the core indicator of thermal power and even the entire power system from plan, design to operation. It will be an important cornerstone of the successful transformation for China’s power system building the technical standards and planning system with Chinese characteristics for thermal-power flexibility retrofit.
In order to improve the frequency anti-interference of the island microgrid, a coordinated frequency control method based on reinforcement learning for island microgrid is proposed. The proposed control method performs Q-learning on the basis of the frequency deviation of the microgrid, and dynamically adjusts the droop control parameters of multiple distributed power sources to change their output power, to realize multi-source coordinated active frequency control in the microgrid. Firstly, the principle of Q-learning algorithm is introduced. Secondly, a frequency recovery control method based on Q-learning is proposed, and a controller based on the Q-learning algorithm is set up. The Q-learning algorithm is used to dynamically correct the droop parameters and coordinate multiple distributed power sources in the microgrid for frequency recovery control. Finally, MATLAB is used to establish a typical microgrid simulation model, and on the basis of S-function, a self-defined reinforcement learning controller is established to verify the effectiveness and adaptability of the proposed method from the aspects of the Q-learning training process and frequency control response characteristics.
In the islanded microgrid integrated with high proportion of photovoltaic power, the change of solar irradiance will lead to obvious photovoltaic power fluctuation, and then affect the trend of fault current of lines in microgrid. For the photovoltaic power with different irradiance, how to accurately identify the microgrid fault is important for microgrid protection. To this issue, a typical microgrid model with photovoltaic power generation is firstly established in this paper, and the influence of solar irradiance variation on the fault current in the islanded microgrid mode is analyzed. Then, the fast wavelet energy entropy (WEE) algorithm is used to extract the transient characteristics of fault current, and the concise transient characteristics are selected to construct the fault comprehensive sample set. Finally, considering photovoltaic power intermittence, this paper forms a new microgrid fault identification method by training Kernel-based extreme learning machine with typical fault sample set. The simulation result shows that the proposed method can not only accurately extract the transient characteristics of microgrid fault under different irradiance, but also accurately identify the fault, which provides technical support for the fault analysis and protection of microgrid.
In the VSC-HVDC system, the unbalanced power in DC power grid can lead to DC over-voltage, which may damage IGBT, capacitor and other devices. For the problem of DC over-voltage under the fault of DC power grid, this paper proposes a control strategy to suppress the rise of DC voltage. Firstly, the mechanism of the DC overvoltage is studied, and the relationship between the rise of DC voltage and the unbalanced power in DC power grid is quantitatively obtained. Then a virtual modulation control strategy to suppress the rising of DC voltage is proposed, and different control strategies are adopted for different faults. Finally, a simulation model of a four-terminal VSC-HVDC is built on PSCAD / EMTDC, and the effectiveness of the proposed control strategy is verified by simulation of three typical faults: short-circuit fault, single station blocking fault and monopolar blocking fault at the receiving end.
Once blocking fault occurs in large-capacity ultra-high-voltage direct current (UHVDC) transmission system, a series of control measures are implemented to fill the power shortage of the receiving-end power grid. Focus on the problem of overloaded transmission sections after primary and secondary frequency modulation, this paper proposes an overloaded-section adjustment strategy based on the improved Apriori algorithm. Firstly, the blocking fault is simulated by the dynamic power flow algorithm and the transmission sections are adjusted by sensitivity method. Then, the adjustment information is extracted. After data processing, the database of unit, load adjustment strategy and section state change is set up. The Apriori algorithm is improved that fore-part item-set is restricted to be the unit and load adjustment strategy and rear-part item-set is restricted to be the change of the section state. The strong association rules between the adjustment strategy and the change of the section state are analyzed by improved Apriori algorithm. Finally, the simulation results of Jinping-Suzhou DC fault and association analysis show that the improved Apriori algorithm greatly reduces the generation of invalid rules, and the strong association rules effective for section adjustment are extracted from association analysis, which provides decision-making basis for overloaded transmission section adjustment of the power grid.
In the process of applying AC distribution network planning technology to DC distribution network, it is necessary to consider not only the changes of traditional power supply planning objectives and topology, but also the changes of multi-objective and multiple constraint conditions, as well as the changes caused by the access of a large number of active loads, and the development trend of DC distribution network technology and application. First of all, this paper summarizes the four aspects of power grid planning, including the integration of power grid planning, secondary planning and demand planning. Then the topology of DC distribution network is analyzed, and the topology classification based on bus, structure and connection mode is proposed, and the typical application scenarios of DC distribution network are analyzed. Then, the DC distribution network planning method considering multiple constraints and uncertainties is discussed, including the analysis and summary of uncertain model, strong coupling between planning and operation, and various uncertain factors. The DC distribution network planning considering (quasi) real-time monitoring demand is discussed in two aspects of active management / demand-side management and protection control. Finally, from the technical level, information level and equipment level, the problems to be solved in the DC distribution network planning are sorted out and prospected, which provides reference for the future planning and research of DC distribution network.
In order to improve the calculation efficiency of small-signal stability analysis of DC transmission system, a small-signal reduced-order model of DC transmission system is established. Taking the two-terminal flexible DC transmission system as the research object, this paper firstly establishes the small-signal model of the two-terminal flexible DC system, and then selects the order of the system state variables according to the number of Hankel singular values on the basis of the equilibrium theory, and realizes the reduction of the small-signal model through matrix transformation. By calculating the dominant eigenvalue trajectories of the full-order system and the reduced-order system, it is verified that the reduced-order system and the full-order system have the same small-disturbance stability. The simulation results show that there is a similar dynamic response before and after the order reduction, which verifies the accuracy of the reduced-order model. In the face of complex networks, the order reduction method based on equilibrium theory can not only reduce the complexity of the model and improve the computational efficiency, but also predict the stability of the system.
In order to realize fast and precise perception of the frequency response performance of the power system under the background of massive anticipated faults, this paper proposes a method for predicting the multi-dimensional frequency indicators based on regularized greedy forests (RGF). This method establishes the non-linear mapping relationship between input features and multi-dimensional indicators through RGF. By optimizing global parameters and introducing three regularization mechanisms to the decision-making forest, the RGF can effectively represent complex functions and prevent overfitting. To ensure the performance of the model, the combinations of parameters are traversed by the grid search to find the best parameter of the constructed RGF model. Case studies on the modified IEEE RTS-79 system demonstrate the high precision, rapidity, and well generalization ability of the proposed method.
With the development of integrated energy system and power market reform, integrated energy servicer is expected to become a new market member in power market transaction. In order to solve the problem that the limited reference information in the declaration stage restricts the formulation of the declaration strategy, a declaration strategy based on Q-learning for integrated energy servicer is proposed to improve the ideal degree of the declaration strategy. The core idea of the proposed strategy is to make full use of the huge historical operation information and train the declaration strategy agent by artificial intelligence algorithms to establish the inherent relationship between the limited reference information grasped by integrated energy servicer during the market bidding process and its optimal declaration strategy. The declaration agent can realize automatic generation and intelligent improvement of declaration policies, which takes energy market public information, social public information and enterprise private information as environment variables. Finally, a case study based on the actual data of a provincial power grid shows that the proposed method can better match the declaration strategy under the cooperative game and has the characteristics of fast convergence, high ideal degree and high computational efficiency, which is more suitable for the actual needs of integrated energy servicer.
