CN-121840601-B - Water-light-storage multi-energy complementary cooperative scheduling method based on double peak-staggering compensation

CN121840601BCN 121840601 BCN121840601 BCN 121840601BCN-121840601-B

Abstract

The invention discloses a water-light accumulation multi-energy complementary cooperative scheduling method based on double peak-staggering compensation, which comprises the steps of constructing a plurality of types of preset scheduling models including a water-light independent scheduling model, a water-light complementary scheduling model and a water-light accumulation complementary scheduling model, collecting photovoltaic predicted output data, hydropower warehouse-in flow data and initial state data of a pumped storage power station in a current period in real time, determining a target scheduling mode in the current period from the plurality of types of preset scheduling models by utilizing a preset conversion efficiency evaluation criterion based on the photovoltaic predicted output data and the initial state data, solving the preset scheduling model corresponding to the target scheduling mode based on the photovoltaic predicted output data and the hydropower warehouse-in flow data, and generating a day-ahead scheduling instruction comprising an output plan of each power station. According to the invention, the photovoltaic absorption rate and the system power generation increase are improved through a dynamic adjustment complementary strategy of sensing the pumping and accumulating charge state and the power grid pressure.

Inventors

XU BIN
WANG XINRONG
ZHU LINGWEI
TAN JIAYING
YANG XUESONG
FENG QIANGLONG
Ren Zichen
YAO YAO
ZHOU SIYING

Assignees

河海大学

Dates

Publication Date: 20260512
Application Date: 20260313

Claims (8)

1. The water-light-storage multi-energy complementary cooperative scheduling method based on double peak-staggering compensation is characterized by comprising the following steps of: based on the preconfigured physical parameters of each power station, constructing a plurality of types of preset scheduling models including a water-light independent scheduling model, a water-light complementary scheduling model and a water-light storage complementary scheduling model; collecting photovoltaic predicted output data, hydropower storage flow data and initial state data of a pumped storage power station in the current period in real time; Determining a target scheduling mode of the current period from a plurality of types of preset scheduling models by utilizing a preset conversion efficiency evaluation criterion based on the photovoltaic predicted output data and the initial state data; Solving a preset scheduling model corresponding to a target scheduling mode based on photovoltaic predicted output data and hydropower warehouse-in flow data, and generating a day-ahead scheduling instruction containing each power station output plan; the conversion efficiency evaluation criterion is used for evaluating conversion relations between hydropower regulation losses and photovoltaic absorption increments under different regulation modes; The photovoltaic consumption increment estimated by a preset conversion efficiency estimation criterion is determined by calculating the complementary increased power generation amount of the system; the complementary power generation amount of the system is defined as the increment of the total power generation amount of the system, which is obtained by solving a preset scheduling model corresponding to a target scheduling mode and is obtained relative to the total power generation amount of the system which is obtained by solving a water-light independent scheduling model; Solving a preset scheduling model corresponding to the target scheduling mode, including executing dual peak-staggering compensation: Based on the photovoltaic predicted output data, controlling the cascade hydropower station to change the output time distribution, so that the hydropower generation peak avoids the photovoltaic output peak; controlling the pumped storage power station to operate under a pumping working condition to consume the photovoltaic power curtailment in a period when the photovoltaic power curtailment exists; the hydroelectric adjustment loss involved in the conversion efficiency evaluation criterion is quantized into the electric quantity loss rate of the hydroelectric compensation photovoltaic; The electric quantity loss rate of the hydro-electric compensation photovoltaic is defined as a hydro-electric power generation amount reduction value caused by the consumption of unit photovoltaic power curtailment, and the calculation mode is the ratio of the hydro-electric power generation reduction amount of the complementary scheduling mode relative to the independent scheduling mode to the photovoltaic power supply increase amount; determining a target scheduling pattern of a current period from a plurality of types of preset scheduling models, including applying static decision logic based on a preset fixed threshold value: Judging whether the electric quantity loss rate of the hydro-electric compensation photovoltaic is lower than a preset fixed threshold value or not; If yes, determining the target scheduling mode as a water-light complementary scheduling model; if not, or if the photovoltaic power discarding exists in the current period, determining the target scheduling mode as a water-light accumulation complementary scheduling model.
2. The method of claim 1, wherein the plurality of classes of preset scheduling models includes a water-light independent scheduling model, a water-light complementary scheduling model, and a water-light complementary scheduling model; the hydraulic independent scheduling model takes the self-generated energy of the hydropower station as an objective function; the water-light complementary scheduling model takes the maximum total power generation amount of the system in a scheduling period as an objective function and comprises water balance constraint and electric power transmission channel constraint; The water-light storage complementary scheduling model is used for increasing energy balance constraint, water level constraint and output constraint of the pumped storage power station on the basis of the water-light complementary scheduling model.
3. The method of claim 1, wherein determining a target scheduling pattern for a current time period from a plurality of classes of preset scheduling models based on the photovoltaic predicted output data and the initial state data comprises performing a three-stage progressive scheduling procedure of pre-judgment-hierarchical optimization-collaborative adjustment: Step one, calculating the estimated hydropower marginal loss rate based on the photovoltaic predicted power data and the hydropower operation state under the hydropower independent dispatching mode obtained by solving the hydropower independent dispatching model; Step two, calculating a dynamic loss rate threshold value of the current period based on the initial state data, and determining a target scheduling mode based on a comparison result of the estimated hydropower marginal loss rate and the dynamic loss rate threshold value; and step three, solving a preset scheduling model corresponding to the target scheduling mode to obtain the output distribution result of each power station.
4. A method according to claim 3, wherein calculating an estimated rate of marginal loss of hydropower comprises: determining a photovoltaic power rejection prediction amount based on the photovoltaic predicted power output data and the power output channel capacity; calculating a force adjustment loss coefficient which represents the degree of deviation of the actual operation efficiency of the hydropower from the optimal operation efficiency based on the hydropower operation state and a prestored optimal operation efficiency curve of the hydropower; based on photovoltaic power discarding prediction, a power output adjustment loss coefficient and output data in a hydropower independent scheduling mode, the estimated hydropower marginal loss rate is calculated, and a hydropower complementary scheduling model is not required to be operated.
5. The method of claim 4, wherein determining the target scheduling mode comprises performing the logic of: judging whether the estimated water and electricity marginal loss rate is smaller than a dynamic loss rate threshold value or not, and judging whether the sum of photovoltaic electricity discarding predicted amounts is smaller than the pumping and accumulating available capacity of the pumped storage power station or not at the same time; And if the two judging conditions are met at the same time, determining the target scheduling mode as a water-light complementary scheduling model, and otherwise, determining the target scheduling mode as a water-light storage complementary scheduling model.
6. The method according to claim 5, comprising: The dynamic loss rate threshold is obtained by carrying out double factor correction on the pumping and storage reference loss rate, and the calculation formula is the product of the pumping and storage reference loss rate, the capacity availability coefficient and the peak regulation urgent coefficient, wherein the pumping and storage reference loss rate is determined based on the comprehensive circulation efficiency of the pumping and storage power station; The capacity availability coefficient represents the available regulation capacity state of the pumped storage power station at the current moment, and the value of the capacity availability coefficient and the current energy storage quantity are in negative correlation, and the calculation mode is that the difference between the maximum energy storage quantity and the current energy storage quantity of the pumped storage power station is divided by the difference between the maximum energy storage quantity and the minimum energy storage quantity of the pumped storage power station; The peak regulation urgent coefficient characterizes the pressure state of the power grid receiving the photovoltaic output in the current period, and is calculated by dividing the part of the photovoltaic predicted output exceeding the capacity of the power output channel in the current period by the maximum pumping power of the pumped storage power station, and taking the minimum value of the obtained ratio and the value 1 as the peak regulation urgent coefficient.
7. A method according to claim 3, further comprising, after stage three, performing a co-adjustment step: Calculating the actual hydropower marginal loss rate based on the solving result of a preset scheduling model corresponding to the target scheduling mode; Judging whether the deviation between the actual water and electricity marginal loss rate and the estimated water and electricity marginal loss rate exceeds a preset tolerance deviation threshold value or not; if yes, the estimated water and electricity marginal loss rate is updated by using the actual water and electricity marginal loss rate, and the second execution stage is returned to determine the target scheduling mode again.
8. The method of claim 4, wherein the calculating the estimated hydroelectric marginal loss rate based on the photovoltaic power rejection prediction, the power take-off adjustment loss coefficient, and the power take-off data in the independent scheduling mode is performed by: Calculating the difference between the output force in the hydropower independent dispatching mode and the preset minimum allowable hydropower output force, and carrying out time period integration or accumulation by combining the output force adjustment loss coefficient to obtain the estimated hydropower generation reduction amount; Dividing the estimated hydroelectric power generation reduction amount by the sum of the photovoltaic power rejection prediction amounts to obtain the estimated hydroelectric marginal loss rate.

Description

Water-light-storage multi-energy complementary cooperative scheduling method based on double peak-staggering compensation Technical Field The invention relates to the technical field of power system dispatching, in particular to a water-light-storage multi-energy complementary cooperative dispatching method based on double peak-shifting compensation. Background The construction of new power systems based on new energy sources has become a trend. In various renewable energy sources, the hydropower station with strong regulation capability and the pumped storage power station with strong fluctuation photovoltaic are utilized to carry out multipotency complementation, so that the method is an important means for stabilizing the randomness of new energy sources and improving the digestion capability of a power grid. The efficient coordinated scheduling mechanism of the water-light-storage hybrid energy system is researched, and the method has important engineering value for guaranteeing safe and stable operation of a power grid and improving the utilization rate of clean energy. Currently, scheduling research for a water-light accumulation complementary system is mainly focused on capacity configuration and joint operation strategy optimization. The prior art generally builds a unified optimal scheduling model covering multiple types of power supplies based on deterministic or stochastic programming theory, and directly solves the problem through a large-scale nonlinear programming algorithm. At the operation control level, the force share between the hydropower, the photovoltaic and the pumped storage is distributed mainly by adopting a rule based on a fixed priority or a strategy based on a single economic index (such as a fixed electricity price difference or a fixed comprehensive circulation efficiency), so as to realize the power balance and the absorption target of the system. However, the conventional scheduling scheme has the common problems that a decision mechanism is hard to be adapted to dynamic working conditions and the uniform solving and calculating efficiency is low. In particular, existing mode switching logic relies on static fixed thresholds, such as considering only fixed cycle efficiency losses of pumping and accumulating, ignoring the time-varying impact of pumped-storage power station real-time state of charge (SOC) and grid peak regulation urgency on complementary benefits. The unified static criterion leads to the lack of self-adaptability of a scheduling strategy, namely, when the capacity of the pumping and storage reservoir is tension or the peak regulation pressure of the power grid is high, decision is still mechanically made based on fixed efficiency, so that the pumping and storage resources are idle or overdrawn, and the maximization of the power generation increase is difficult to realize. In addition, the traditional method lacks a rapid prejudging mechanism, and directly carries out integral optimization on the full-element complex model, so that the calculation dimension is high, the convergence speed is low, and the requirement of scheduling in the future on timeliness is difficult to meet. Disclosure of Invention The invention aims to provide a water light storage multi-energy complementary cooperative scheduling method based on double peak staggering compensation, which aims to solve at least one of the problems in the prior art. According to one aspect of the application, a water light storage multi-energy complementary cooperative scheduling method based on double peak staggering compensation comprises the following steps: based on the preconfigured physical parameters of each power station, constructing a plurality of types of preset scheduling models including a water-light independent scheduling model, a water-light complementary scheduling model and a water-light storage complementary scheduling model; collecting photovoltaic predicted output data, hydropower storage flow data and initial state data of a pumped storage power station in the current period in real time; Determining a target scheduling mode of the current period from a plurality of types of preset scheduling models by utilizing a preset conversion efficiency evaluation criterion based on the photovoltaic predicted output data and the initial state data; Solving a preset scheduling model corresponding to a target scheduling mode based on photovoltaic predicted output data and hydropower warehouse-in flow data, and generating a day-ahead scheduling instruction containing each power station output plan; The conversion efficiency evaluation criterion is used for evaluating the conversion relation between the hydropower regulation loss and the photovoltaic absorption increment under different regulation modes. According to one aspect of the application, the multiple types of preset scheduling models comprise a water-light independent scheduling model, a water-light complementary scheduling model and a wate