CN117117924B - Energy storage capacity configuration method, device and equipment considering clear market income - Google Patents

Energy storage capacity configuration method, device and equipment considering clear market income Download PDF

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CN117117924B
CN117117924B CN202311376800.9A CN202311376800A CN117117924B CN 117117924 B CN117117924 B CN 117117924B CN 202311376800 A CN202311376800 A CN 202311376800A CN 117117924 B CN117117924 B CN 117117924B
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frequency modulation
power
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CN117117924A (en
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万靖
方仍存
陈睿
陈艳波
陈泽宇
别芳玫
范玉宏
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North China Electric Power University
Economic and Technological Research Institute of State Grid Hubei Electric Power Co Ltd
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Economic and Technological Research Institute of State Grid Hubei Electric Power Co Ltd
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    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/28—Arrangements for balancing of the load in a network by storage of energy
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
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    • G06Q50/06—Energy or water supply
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/008—Circuit arrangements for ac mains or ac distribution networks involving trading of energy or energy transmission rights
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/24—Arrangements for preventing or reducing oscillations of power in networks
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
    • H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
    • H02J2300/20—The dispersed energy generation being of renewable origin
    • H02J2300/28—The renewable source being wind energy

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Abstract

An energy storage capacity configuration method, device and equipment considering market clear income, wherein the method comprises the following steps: establishing a depth peak shaver market clearing model, calculating market clearing expense, and establishing a depth peak shaver compensation item according to a depth peak shaver market clearing result; establishing an energy storage capacity configuration double-layer model considering clear market income, wherein the upper-layer model is an energy storage capacity configuration model, and the model takes daily average energy storage investment cost, total energy storage daily operation income and energy storage alleviation depth peak shaving compensation as an objective function; the lower model is an electric energy-frequency modulation combined market clearing model; and converting the energy storage capacity configuration double-layer model into a single-layer model by adopting KKT conditions and a dual theory to solve. The method can not only effectively configure the energy storage capacity according to the market clearing result, but also quickly find out the global optimal solution.

Description

Energy storage capacity configuration method, device and equipment considering clear market income
Technical Field
The invention relates to the technical field of power system dispatching automation, in particular to an energy storage capacity configuration method, device and equipment considering clear market income.
Background
With the continuous rise of the permeability of wind power and the continuous deepening of the reform of the electric power market, the safety and stability problems of the power grid are more and more remarkable. In order to better cope with the influence of electric quantity gaps and frequency fluctuation on the safe and stable operation of the power system, the power system is required to be additionally provided with energy storage equipment. Currently, multiple provinces allow energy storage to participate in spot market transactions as independent bodies, and thus the capacity allocation problem of energy storage is the focus of current research. The existing energy storage capacity configuration model generally adopts a single-layer optimization model, and the daily operation cost of energy storage is calculated by making a certain prediction on the market price. However, the difficulty of price prediction of the electric power market is large, the problem of inaccuracy of prediction data exists, and the predicted market price may deviate from the actual price greatly, so that the energy storage capacity cannot be effectively configured.
Disclosure of Invention
The invention aims to overcome the defect and the problem that the energy storage capacity cannot be effectively configured in the prior art, and provides an energy storage capacity configuration method, device and equipment capable of effectively configuring the energy storage capacity and considering clear market income.
In order to achieve the above object, the technical solution of the present invention is: an energy storage capacity configuration method considering market clear benefits, comprising:
establishing a depth peak shaver market clearing model, calculating market clearing expense, and establishing a depth peak shaver compensation item according to a depth peak shaver market clearing result;
establishing an energy storage capacity configuration double-layer model considering clear market income, wherein the upper-layer model is an energy storage capacity configuration model, and the model takes daily average energy storage investment cost, total energy storage daily operation income and energy storage alleviation depth peak shaving compensation as an objective function; the lower model is an electric energy-frequency modulation combined market clearing model;
and converting the energy storage capacity configuration double-layer model into a single-layer model by adopting KKT conditions and a dual theory to solve.
The objective function of the depth peak shaving market finding model is as follows:
；
in the method, in the process of the invention,、/>、/>respectively representing the dispatching cycle, the total number of the deep peak-shaving thermal power units and the peak-shaving output segmentation number of the units; />The running cost is the deep peak shaving market; />For the units involved in deep peak regulation->In the peak regulation interval without oil feedingPeak shaving quotation; />Is->Machine set participating in deep peak regulation at moment->In the peak regulating interval without adding oil>The power is marked in the depth peak regulation of (2); />For the units involved in deep peak regulation->In the oil adding peak regulating interval->Peak shaving quotation; />Is->Machine set participating in deep peak regulation at moment->In the oil adding peak regulating interval->The power is marked in the depth peak regulation of (2); />A relaxation variable of deep peak regulation and wind abandoning is adopted; />And is a unit wind abandoning punishment coefficient.
Calculating the market clearing marginal price by using the depth peak shaving market clearing model as a dual change, and then multiplying the market clearing marginal price by the market clearing middle scalar of each unit to obtain the market clearing expense and the depth peak shaving market clearing expenseThe expression of (2) is:
；
in the method, in the process of the invention,is->The clear electricity price is unified by time depth peak regulation; />Is a peak regulating unit->The total amount bid in the deep peak shaving market.
The method for establishing the depth peak shaving compensation term comprises the following steps:
according to the total standard sum of each unit in the deep peak shaving market, a first compensating interval is formulated, and the wind power consumption of the first interval is as follows:
；
in the method, in the process of the invention,representing the total number of wind power stations; />For wind power station->At->Output power at time;the total wind power consumption of the system when the energy storage is not configured;
and (3) formulating an abandoned wind punishment item according to an abandoned wind relaxation variable in the deep peak shaving market, and taking the abandoned wind punishment item as a compensated second interval, wherein the wind power consumption of the second interval is as follows:
；
in the method, in the process of the invention,is a peak regulating unit->The total amount bid in the deep peak shaving market.
The objective function of the energy storage capacity configuration model is as follows:
；
；
；
；
in the method, in the process of the invention,the total income of the operation of the energy storage day; />The daily energy storage investment cost is; />A depth peak regulation compensation term; />The price is measured for the electric energy; />To configure the energy storage discharge power; />Configuring energy storage charging power; />Clearing prices for the frequency modulation capacity; />The energy storage capacity is configured to be the winning frequency modulation capacity; />The price is cleared for the frequency modulation mileage; />The winning bid frequency modulation mileage for energy storage configuration; />Investment cost for configuring energy storage unit capacity; />Capacity configured for energy storage; />Investment cost for configuring energy storage unit power; />Power configured for energy storage; />Is the discount rate; />The energy storage operation period is; />Maintaining cost for configuring unit power of energy storage; />Clearing cost for the deep peak shaving market; />Is of depthPeak shaving penalty cost; />For wind power station->At->Output power at time;the total wind power consumption of the system when the energy storage is not configured; />Is a peak regulating unit->The total bid amount in the deep peak shaving market; />A relaxation variable of deep peak regulation and wind abandoning is adopted; />、/>The compensation conversion coefficients are respectively depth peak regulation compensation coefficients and wind abandon punishment compensation coefficients; />、/>And the identification is respectively a wind power depth peak regulation interval identification and a wind power wind discarding interval identification.
The constraint conditions of the energy storage capacity configuration model comprise identification of energy storage capacity configuration constraint, energy storage power configuration constraint, capacity-power ratio constraint, discharge price constraint, charge price constraint and frequency modulation price constraint.
The objective function of the electric energy-frequency modulation combined market finding model is as follows:
；
in the method, in the process of the invention,representing the conventional peak regulation output segmentation number of the thermal power generating unit; />Representing the amount of stored energy; />Is a thermal power generating unitFirst->Segment power generation quotation; />Is a thermal power generating unit->First->The section is->The amount of electricity is marked in time; />、/>Respectively is thermal power generating unit->Frequency-modulated capacity quotation and frequency-modulated mileage quotation; />、/>Respectively is thermal power generating unit->At->Marking frequency modulation capacity and frequency modulation mileage at any time; />、/>For storing energy->Charging and discharging quotation of (2); />、/>For storing energy->At->The charging and discharging amount is marked at the moment; />、/>For storing energy->Frequency-modulated capacity quotation and frequency-modulated mileage quotation; />、/>For storing energy->At->Marking frequency modulation capacity and frequency modulation mileage at any time; />For storing energy->The winning discharge price of (2); />To configure the energy storage discharge power; />For storing energy->Charging quotation of winning bid; />Configuring energy storage charging power; />For storing energy->Frequency modulation capacity quotation of (2); />The energy storage medium frequency modulation capacity is configured; />For storing energy->Frequency-modulated mileage quotation; />Frequency modulation mileage is marked for configuration of energy storage; />Quoting for wind power;/>for wind power station->At->Output power at time.
The constraints of the electric energy-frequency modulation combined market clearing model comprise system power balance constraint, frequency modulation capacity mileage constraint, thermal power generating unit output constraint, unit frequency modulation capacity-mileage constraint, climbing constraint and power transmission line power constraint.
An energy storage capacity configuration apparatus that considers market clear gains, comprising:
the depth peak regulation compensation item establishing module is used for establishing a depth peak regulation market clearing model, calculating market clearing cost and establishing a depth peak regulation compensation item according to a depth peak regulation market clearing result;
the energy storage capacity configuration double-layer model building module is used for building an energy storage capacity configuration double-layer model considering clear market income, wherein the upper-layer model is an energy storage capacity configuration model, and the model takes daily average energy storage investment cost, total daily operation income of energy storage and energy storage alleviation depth peak regulation compensation as objective functions; the lower model is an electric energy-frequency modulation combined market clearing model;
and the energy storage capacity configuration double-layer model solving module is used for converting the energy storage capacity configuration double-layer model into a single-layer model by adopting KKT conditions and a dual theory to solve.
An energy storage capacity configuration device that takes into account market revenue, comprising a memory and a processor;
the memory is used for storing computer program codes and transmitting the computer program codes to the processor;
the processor is configured to execute a method of energy storage capacity configuration that takes into account market revenue according to instructions in the computer program code.
Compared with the prior art, the invention has the beneficial effects that:
according to the method, a deep peak regulation economic compensation model is established according to a clear result of a deep peak regulation market, the peak regulation economic compensation model is used as a compensation item of an upper layer configuration model, an electric energy-frequency modulation combined market clear model is built again, the electric energy-frequency modulation combined market clear model is used as a lower layer objective function, the clear result is transmitted to an upper layer, an energy storage capacity configuration model is built, the energy storage capacity configuration model is used as a cost item of the upper layer objective function, a benefit item of the upper layer objective function is calculated according to the clear result, and then a double-layer model is converted into a single-layer model by adopting KKT conditions and a dual theory to solve. Therefore, the method and the device can not only effectively configure the energy storage capacity according to the market clearing result, but also quickly find the global optimal solution.
Drawings
FIG. 1 is a flow chart of a method of energy storage capacity configuration that takes into account market revenue.
Fig. 2 is a block diagram of an energy storage capacity allocation apparatus considering market clear benefits according to the present invention.
Fig. 3 is a block diagram of an energy storage capacity configuration apparatus according to the present invention in consideration of market clear gains.
Detailed Description
The invention is described in further detail below with reference to the accompanying drawings and detailed description.
Example 1:
referring to fig. 1, a method for configuring an energy storage capacity considering market clear benefits includes:
s1, establishing a depth peak shaving market clearing model, calculating market clearing cost, and establishing a depth peak shaving compensation item according to a depth peak shaving market clearing result;
firstly, collecting abandoned wind data of economic dispatch of a power grid in the future and unit quotation data of deep peak shaving market transactions; then, according to the current electric energy and the clear result of the frequency modulation market, a unit which can participate in deep peak regulation is determined; then, boundary conditions are set according to the steps, and the clear calculation of the depth peak shaving market is carried out; the objective function of the depth peak shaving market finding model is as follows:
；
in the method, in the process of the invention,、/>、/>respectively representing the dispatching cycle, the total number of the deep peak-shaving thermal power units and the peak-shaving output segmentation number of the units; />The running cost is the deep peak shaving market; />For the units involved in deep peak regulation->In the peak regulation interval without oil feedingPeak shaving quotation; />Is->Machine set participating in deep peak regulation at moment->In the peak regulating interval without adding oil>The power is marked in the depth peak regulation of (2); />For the units involved in deep peak regulation->In the oil adding peak regulating interval->Peak shaving quotation; />Is->Machine set participating in deep peak regulation at moment->In the oil adding peak regulating interval->The power is marked in the depth peak regulation of (2); />A relaxation variable of deep peak regulation and wind abandoning is adopted; />And is a unit wind abandoning punishment coefficient.
Calculating the market clearing marginal price by using the depth peak shaving market clearing model as a dual change, and then multiplying the market clearing marginal price by the market clearing scalar of each unit to obtain the market clearing expenseThe expression of (2) is:
；
in the method, in the process of the invention,is->The clear electricity price is unified by time depth peak regulation; />Is a peak regulating unit->The total amount bid in the deep peak shaving market.
The method for establishing the depth peak shaving compensation term comprises the following steps:
according to the total standard sum of each unit in the deep peak shaving market, a first compensating interval is formulated, and the wind power consumption of the first interval is as follows:
；
in the method, in the process of the invention,representing the total number of wind power stations; />For wind power station->At->Output power at time;the total wind power consumption of the system when the energy storage is not configured;
and (3) formulating an abandoned wind punishment item according to an abandoned wind relaxation variable in the deep peak shaving market, and taking the abandoned wind punishment item as a compensated second interval, wherein the wind power consumption of the second interval is as follows:
；
in the method, in the process of the invention,is a peak regulating unit->The total amount bid in the deep peak shaving market.
S2, establishing an energy storage capacity configuration double-layer model considering clear market income, wherein the upper-layer model is an energy storage capacity configuration model, and the model takes daily average energy storage investment cost, total daily operation income of energy storage and energy storage relieving depth peak shaving compensation as objective functions; the lower model is an electric energy-frequency modulation combined market clearing model;
the objective function of the energy storage capacity configuration model is as follows:
；
；
；
；
in the method, in the process of the invention,for the total income of the daily operation of energy storage, the clear income of the electric energy market and the clear income of the frequency modulation market are considered; />For daily energy storage investment cost, depreciation coefficient and maintenance cost are considered; />The method comprises the steps of representing a depth peak shaving compensation item after a power grid is started to a depth peak shaving market; />The price is measured for the electric energy; />To configure the energy storage discharge power; />Configuring energy storage charging power; />Clearing prices for the frequency modulation capacity; />The energy storage capacity is configured to be the winning frequency modulation capacity; />The price is cleared for the frequency modulation mileage; />The winning bid frequency modulation mileage for energy storage configuration; />Investment cost for configuring energy storage unit capacity; />Capacity configured for energy storage; />Investment cost for configuring energy storage unit power; />Power configured for energy storage; />Is the discount rate; />The energy storage operation period is; />Maintaining cost for configuring unit power of energy storage; />Clearing cost for the deep peak shaving market; />Punishment costs for depth peak shaving;for wind power station->At->Output power at time; />The total wind power consumption of the system when the energy storage is not configured; />Is a peak regulating unit->The total bid amount in the deep peak shaving market; />A relaxation variable of deep peak regulation and wind abandoning is adopted; />、/>The compensation conversion coefficients are respectively depth peak regulation compensation coefficients and wind abandon punishment compensation coefficients; />、/>And the identification is respectively a wind power depth peak regulation interval identification and a wind power wind discarding interval identification.
The constraint conditions of the energy storage capacity configuration model comprise identification of energy storage capacity configuration constraint, energy storage power configuration constraint, capacity-power ratio constraint, discharge price constraint, charge price constraint and frequency modulation price constraint.
Identifying energy storage capacity configuration constraints:；
in the method, in the process of the invention,configuring a maximum capacity for the stored energy;
energy storage power configuration constraints:；
in the method, in the process of the invention,configuring maximum power for the stored energy;
capacity-power ratio constraint:；
in the method, in the process of the invention,、/>a lower power limit and an upper power limit of the energy storage configuration respectively;
discharge price constraint:；
in the method, in the process of the invention,the energy storage average discharge cost; />Configuring the maximum discharge price of the energy storage;
charging price constraint:；
in the method, in the process of the invention,the lowest price of wind power is obtained; />Configuring a maximum charge price for the stored energy;
frequency modulation price constraint:；
in the method, in the process of the invention,the average cost is frequency modulation; />Providing a basic price for configuring the frequency modulation capacity of the stored energy;the highest bid for the frequency modulation capacity to configure the stored energy.
The objective function of the electric energy-frequency modulation combined market finding model is as follows:
；
in the method, in the process of the invention,representing the conventional peak regulation output segmentation number of the thermal power generating unit; />Representing the amount of stored energy; />Is a thermal power generating unitFirst->Segment power generation quotation; />Is a thermal power generating unit->First->The section is->The amount of electricity is marked in time; />、/>Respectively is thermal power generating unit->Frequency-modulated capacity quotation and frequency-modulated mileage quotation; />、/>Respectively is thermal power generating unit->At->Marking frequency modulation capacity and frequency modulation mileage at any time; />、/>For storing energy->Charging and discharging quotation of (2); />、/>For storing energy->At->The charging and discharging amount is marked at the moment; />、/>For storing energy->Frequency-modulated capacity quotation and frequency-modulated mileage quotation; />、/>For storing energy->At->Marking frequency modulation capacity and frequency modulation mileage at any time; />For storing energy->The winning discharge price of (2); />To configure the energy storage discharge power; />For storing energy->Winning charge of (C)Quotation; />Configuring energy storage charging power; />For storing energy->Frequency modulation capacity quotation of (2); />The energy storage medium frequency modulation capacity is configured; />For storing energy->Frequency-modulated mileage quotation; />Frequency modulation mileage is marked for configuration of energy storage; />Quoting for wind power; />For wind power station->At->Output power at time.
The constraints of the electric energy-frequency modulation combined market clearing model comprise system power balance constraint, frequency modulation capacity mileage constraint, thermal power generating unit output constraint, unit frequency modulation capacity-mileage constraint, climbing constraint and power transmission line power constraint;
system power balance constraint: the total power generation of the running unit needs to meet the system load requirement; the network loss is ignored by adopting direct current power flow, and the sum of the output of the thermal power unit, the wind power unit and the energy storage power station is balanced with the total load;
；
in the method, in the process of the invention,representing the total number of load nodes; />The thermal power unit is powered; />For node->A load;
frequency modulation capacity mileage constraint:
；
in the method, in the process of the invention,、/>the frequency modulation capacity requirement and the frequency modulation mileage requirement of the system are respectively; />、/>The dual variables of the respective constraints;
thermal power generating unit output constraint: the output of the thermal power generating unit is higher than the minimum power generation and lower than the maximum power generation;
；
in the method, in the process of the invention,、/>representing the start-stop variable of the thermal power generating unit; />The upper limit of the sectional output of the unit;、/>respectively the minimum and maximum output power of the thermal power generating unit; />、/>、/>、/>The dual variables of the respective constraints;
unit frequency modulation capacity-mileage constraint:
；
in the method, in the process of the invention,、/>respectively corresponding constraint dual variables;
climbing constraint: the difference between the output of the machine set in the front and back time periods cannot exceed the upper limit of the adjusting capacity of the machine set;
；
；
in the method, in the process of the invention,、/>respectively is thermal power generating unit->The operating state climbing rate and the start-stop state climbing rate; />、The dual variables of the respective constraints;
power constraint of transmission line:
；
；
in the method, in the process of the invention,transmitting power for the line; />And->For being +.>Phase angle of the connected node; />Phase angle for balancing node; />For line->Is a reactance of (2); />Is the maximum transmission power capacity of the line.
S3, converting the energy storage capacity configuration double-layer model into a single-layer model by adopting KKT conditions and a dual theory, and solving;
for the nonlinear term in the depth peaking compensation term, the nonlinear term is linearized by introducing an auxiliary variable:
；
；/>
；
in the method, in the process of the invention,is an auxiliary variable of wind power cost;
by usingAnd replacing the product term of the integer variable and the continuous variable to realize the bilinear term linearization step.
Deriving a KKT condition and a stability condition of the market-clearing model, comprising the steps of:
(1) And deriving the sectional output of the thermal power generating unit until the corresponding stability condition is obtained:
；
in the method, in the process of the invention,as a lagrangian function; />The price is measured for the electric energy; />Is a thermal power generating unit->First->Segment power generation quotation; />、/>The dual variables are the sectional output of the unit respectively; />、/>The dual variables of the electric energy-frequency coupling constraint are respectively; />、/>The dual variables are respectively the climbing constraint of the unit;
(2) The charge and discharge power of the energy storage power station is derived until the corresponding stability condition is obtained:
；
in the method, in the process of the invention,、/>and->、/>The dual variables are respectively energy storage charging constraint and discharging constraint;is charge and discharge efficiency; />、/>、/>The dual variables are respectively energy storage capacity constraint;
(3) And deriving the frequency modulation capacity of the thermal power and the energy storage until obtaining the corresponding stability condition:
；
in the method, in the process of the invention,a dual variable that is a balance constraint of frequency modulation capacity; />Quoting the frequency modulation capacity of the thermal power generating unit;clearing prices for the frequency modulation capacity; />The historical mileage coefficient is the thermal power frequency modulation; />The method comprises the steps of marking frequency modulation capacity for a thermal power generating unit; />、/>A pair multiplier constrained for unit energy-frequency coupling; />Scalar in energy storage frequency modulation capacity; />Quoting for the energy storage frequency modulation capacity; />The historical mileage coefficient is the energy storage frequency modulation; />、/>The dual variables are thermal power frequency modulation capacity-mileage constraint; />、/>Constraining dual variables for energy storage charge and discharge power;、/>the dual variable is the energy storage frequency modulation capacity; />、/>The dual variable is constrained for the frequency modulation capacity of the thermal power generating unit; />
(4) And deriving the frequency modulation mileage of the thermal power and the energy storage until obtaining the corresponding stability condition:
；
in the method, in the process of the invention,frequency modulation mileage is achieved for the thermal power generating unit; />The method comprises the steps of (1) quoting the frequency modulation mileage of the thermal power generating unit; />Frequency modulation mileage is marked for energy storage; />Quoting for energy-storage frequency-modulation mileage; />The dual variable is the balance constraint of the frequency modulation mileage;、/>and the energy storage frequency modulation capacity and mileage constraint dual variables are respectively adopted.
And (3) a lower layer combined market clearing model is rewritten by using KKT conditions and stability conditions, and an upper layer energy storage capacity configuration model is added on the basis, so that the construction of a double-layer model is completed. The objective function of the bilayer model is bilinear terms, and the objective function needs to be converted into a linear term acceleration model through KKT conditions and stability conditions to solve.
The double-layer model objective function linearization unified solving method based on KKT conditions and dual theory is used for linearizing the proposed energy storage capacity configuration double-layer model taking clear market income into consideration. Based on the derived stability constraint, the electric energy balance dual variables in the constraintFrequency modulation capacity balance dual variable->Frequency modulation mileage balance dual variable>With the remaining items being replaced. In addition, by deriving the balance constraint, the electric energy balance dual variable +.>Frequency modulation capacity balance dual variable->Frequency modulation mileage balance dual variable>The trouble of solving the dual variables is saved, and the model is converted into a mixed integer balance planning problem; the proposed model can be solved with Gurobi.
The double-layer optimization theory is applied to the dispatching field and achieves good effects. The two-tier optimization theory involves two tiers of optimization problems, where the parameters of the first tier problem are the optimal solutions to the second tier problem. In particular, a two-layer optimization theoretical model may be described as an optimization process of one optimization problem under the constraint or influence of another optimization problem. When solving the double-layer optimization problem, because the internal optimization problem is generally difficult to find a closed solution, an iterative optimization method is generally adopted to continuously and iteratively update model parameters until a certain convergence condition is met. In addition, optimization algorithms such as gradient descent, genetic algorithm, particle swarm optimization and the like can be adopted to solve the double-layer optimization problem. The traditional double-layer optimization adopts the form of solving by an upper heuristic algorithm and invoking a solver by a lower layer. This faces the problem that the global search capability of the heuristic algorithm is poor and it is difficult to find a globally optimal solution. Heuristic algorithms are a technique to find a problem solution in a short time based on limited knowledge. It is an analytical behavior based on limited knowledge and hypothesis about the system to draw conclusions about the system. At an acceptable cost (referring to computation time and space), a heuristic can give one feasible solution for each instance of the combinatorial optimization problem to be solved, but the degree of deviation of the feasible solution from the optimal solution cannot generally be predicted. Because of its heuristic nature, the quality of the solution of an algorithm often depends on the specific nature of the problem and the choice of the initial solution. Furthermore, since the heuristic may only consider the locally optimal solution of the problem in the solution process, the resulting solution may not be a globally optimal solution. In order to overcome the defects, a double-layer optimization method based on KKT conditions and a dual theory is developed, and the method can quickly find a global optimal solution and overcome the calculation difficulty of a double-layer model. The double-layer model can improve the accuracy of price calculation by calculating the price of the market at the lower layer, thereby improving the energy storage capacity configuration result.
The effectiveness of the energy storage capacity configuration method considering clear market income is verified based on the actual measurement data of the power grid. The tested power grid system comprises 9 thermal power units, and one 40MW/80MWh energy storage unit. And calculating a depth peak shaving result to construct a depth peak shaving compensation term, and verifying an energy storage capacity configuration model considering market clear income. The type of the battery for energy storage adopts a vanadium redox flow battery in electrochemical energy storage, the investment cost of unit power and capacity is 100.5 ten thousand yuan and 250.4 ten thousand yuan respectively, the maintenance cost of annual unit power is 12.5 ten thousand yuan, and the energy storage charging and discharging efficiency is 95%. To illustrate the effectiveness and advantages of the model of the present invention, the following scenario was subjected to comparative analysis. The depth peak shaving and clearing results are shown in table 1;
TABLE 1 deep peak shaving market clearing results
(1) Four seasons typical configuration result and configuration benefit comparison
As can be seen from table 2, by configuring the energy storage, the system improves the ability to consume wind power, and the energy storage also achieves profitability by participating in market transactions. The four typical days are configured to be similar in scale, wherein the typical winter days are configured to be maximum in energy storage regulation. Although the total load is maximum in a typical day in summer, the wind power output in the day is smaller than that in other typical days, the corresponding deep peak shaving demand is also minimum, and the configured capacity is minimum; the load on the typical day in winter is only next to the load on the typical day in summer, the deep peak shaving demand is only next to the autumn, and the maximum configuration capacity is obtained. The results of the combined electric energy-frequency modulation market clearing are shown in tables 3 and 4. The analysis of the electric energy-frequency modulation service combined market gain can show that the energy storage obtains the most benefit in the frequency modulation auxiliary service market. By improving the wind power consumption rate of the system, the deep peak shaving cost borne by a wind power place is reduced, and the maximum profit of energy storage in a wind power consumption contract is 63.5%. By wind power consumption contracts, energy storage power stations can basically recover most of the investment costs.
TABLE 2 energy storage optimization configuration results based on wind power consumption contracts and market clear profits for typical days of each season
Table 3 electric energy market clearing results
TABLE 4 frequency modulation market clearing results
(2) Configuration of energy storage for improving deep peak regulation operation interval
As shown in Table 5, the multiple units are adjusted from the original oil feeding depth peak-shaving state to the non-oil feeding depth peak-shaving state, and the running loss and environmental pollution generated in the depth peak-shaving process of the units are improved. Before energy storage is configured, the thermal power generating unit needs to bear more than 60% of frequency modulation requirements of the system, part of units cannot participate in the peak shaving market because the units participate in the frequency modulation market, and part of output frequency modulation units need to be reserved in the frequency modulation market so that output cannot be suppressed, and the factors cause the system to have larger deep peak shaving requirements. After energy storage is configured, the newly built energy storage and the original energy storage bear most of the frequency modulation requirements of the system, so that more units can participate in the deep peak shaving market. The thermal power generating unit not participating in the frequency modulation market can also lower the self-output, and the deep peak regulation pressure of the system is reduced. The wind-discarding phenomenon does not exist in the system after energy storage is configured, and the wind power absorption task is well completed.
Table 5 shows improvement of the energy storage to the deep peak shaver operation interval
Example 2:
referring to fig. 2, an energy storage capacity configuration apparatus considering market clear benefits, which is applied to the method described in embodiment 1, the apparatus comprising:
the depth peak regulation compensation item establishing module is used for establishing a depth peak regulation market clearing model, calculating market clearing cost and establishing a depth peak regulation compensation item according to a depth peak regulation market clearing result;
the energy storage capacity configuration double-layer model building module is used for building an energy storage capacity configuration double-layer model considering clear market income, wherein the upper-layer model is an energy storage capacity configuration model, and the model takes daily average energy storage investment cost, total daily operation income of energy storage and energy storage alleviation depth peak regulation compensation as objective functions; the lower model is an electric energy-frequency modulation combined market clearing model;
and the model solving module is used for converting the energy storage capacity configuration double-layer model into a single-layer model by adopting KKT conditions and a dual theory to solve.
Example 3:
referring to fig. 3, an energy storage capacity configuration apparatus, including a memory and a processor, that considers market revenue;
the memory is used for storing computer program codes and transmitting the computer program codes to the processor;
the processor is configured to execute the method of embodiment 1 according to instructions in the computer program code.
A computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements the method of embodiment 1.
In general, the computer instructions to implement the methods of the present invention may be carried in any combination of one or more computer-readable storage media. The non-transitory computer-readable storage medium may include any computer-readable medium, except the signal itself in temporary propagation.
The computer readable storage medium can be, for example, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples (a non-exhaustive list) of the computer-readable storage medium would include the following: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a random access memory (RAn), a read-only memory (ROn), an erasable programmable read-only memory (EKROn or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROn), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this document, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.
The computer program code for carrying out operations of the present invention may be written in one or more programming languages, or combinations thereof, including an object oriented programming language such as Java, snalltalk, C ++ and conventional procedural programming languages, such as the "C" language or similar programming languages, particularly Kython languages suitable for neural network computing and TensorFlow, kyTorch-based platform frameworks may be used. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any number of types of networks, including a Local Area Network (LAN) or a Wide Area Network (WAN), or be connected to an external computer (for example, through the Internet using an Internet service provider).
The foregoing apparatus and non-transitory computer readable storage medium may refer to a specific description of a method for configuring energy storage capacity and beneficial effects that consider market clear benefits, and will not be described in detail herein.
While embodiments of the present invention have been shown and described above, it should be understood that the above embodiments are illustrative and not to be construed as limiting the invention, and that variations, modifications, alternatives, and variations may be made to the above embodiments by one of ordinary skill in the art within the scope of the invention.

Claims (6)

1. A method of energy storage capacity configuration that takes into account market revenue, comprising:
establishing a depth peak shaver market clearing model, calculating market clearing expense, and establishing a depth peak shaver compensation item according to a depth peak shaver market clearing result;
the objective function of the depth peak shaving market finding model is as follows:
；
in the method, in the process of the invention,、/>、/>respectively representing the dispatching cycle, the total number of the deep peak-shaving thermal power units and the peak-shaving output segmentation number of the units;the running cost is the deep peak shaving market; />For the units involved in deep peak regulation->In the peak regulating interval without adding oil>Peak shaving quotation; />Is->Machine set participating in deep peak regulation at moment->In the peak regulating interval without adding oil>The power is marked in the depth peak regulation of (2); />For the units involved in deep peak regulation->In the oil adding peak regulating interval->Peak shaving quotation; />Is->Machine set participating in deep peak regulation at moment->In the oil adding peak regulating interval->The power is marked in the depth peak regulation of (2); />A relaxation variable of deep peak regulation and wind abandoning is adopted; />The unit wind abandon punishment coefficient;
calculating the market clearing marginal price by using the depth peak shaving market clearing model as a dual change, and then multiplying the market clearing marginal price by the market clearing middle scalar of each unit to obtain the market clearing expense and the depth peak shaving market clearing expenseThe expression of (2) is:
；
in the method, in the process of the invention,is->The clear electricity price is unified by time depth peak regulation; />Is a peak regulating unit->The total bid amount in the deep peak shaving market;
the method for establishing the depth peak shaving compensation term comprises the following steps:
according to the total standard sum of each unit in the deep peak shaving market, a first compensating interval is formulated, and the wind power consumption of the first interval is as follows:
；
in the method, in the process of the invention,representing the total number of wind power stations; />For wind power station->At->Output power at time; />The total wind power consumption of the system when the energy storage is not configured;
and (3) formulating an abandoned wind punishment item according to an abandoned wind relaxation variable in the deep peak shaving market, and taking the abandoned wind punishment item as a compensated second interval, wherein the wind power consumption of the second interval is as follows:
；
in the method, in the process of the invention,is a peak regulating unit->The total bid amount in the deep peak shaving market;
establishing an energy storage capacity configuration double-layer model considering clear market income, wherein the upper-layer model is an energy storage capacity configuration model, and the model takes daily average energy storage investment cost, total energy storage daily operation income and energy storage alleviation depth peak shaving compensation as an objective function; the lower model is an electric energy-frequency modulation combined market clearing model;
the objective function of the electric energy-frequency modulation combined market finding model is as follows:
；
in the method, in the process of the invention,representing the conventional peak regulation output segmentation number of the thermal power generating unit; />Representing the amount of stored energy; />Is a thermal power generating unit->First, theSegment power generation quotation; />Is a thermal power generating unit->First->The section is->The amount of electricity is marked in time; />、/>Respectively is thermal power generating unit->Frequency-modulated capacity quotation and frequency-modulated mileage quotation; />、/>Respectively is thermal power generating unit->At->Marking frequency modulation capacity and frequency modulation mileage at any time; />、/>For storing energy->Charging and discharging quotation of (2); />、/>For storing energy->At->The charging and discharging amount is marked at the moment; />、/>For storing energy->Frequency-modulated capacity quotation and frequency-modulated mileage quotation; />、/>To store energyAt->Marking frequency modulation capacity and frequency modulation mileage at any time; />For storing energy->The winning discharge price of (2); />To configure the energy storage discharge power; />For storing energy->Charging quotation of winning bid; />Configuring energy storage charging power; />For storing energy->Frequency modulation capacity quotation of (2); />The energy storage medium frequency modulation capacity is configured; />For storing energy->Frequency-modulated mileage quotation;frequency modulation mileage is marked for configuration of energy storage; />Quoting for wind power; />For wind power station->At->Output power at time;
and converting the energy storage capacity configuration double-layer model into a single-layer model by adopting KKT conditions and a dual theory to solve.
2. The method for energy storage capacity allocation considering market clear benefits according to claim 1, wherein,
the objective function of the energy storage capacity configuration model is as follows:
；
；
；
；
in the method, in the process of the invention,the total income of the operation of the energy storage day; />The daily energy storage investment cost is; />A depth peak regulation compensation term; />The price is measured for the electric energy; />To configure the energy storage discharge power; />Configuring energy storage charging power;clearing prices for the frequency modulation capacity; />The energy storage capacity is configured to be the winning frequency modulation capacity; />The price is cleared for the frequency modulation mileage; />The winning bid frequency modulation mileage for energy storage configuration; />Investment cost for configuring energy storage unit capacity; />Capacity configured for energy storage; />Investment cost for configuring energy storage unit power; />Power configured for energy storage; />Is the discount rate;the energy storage operation period is; />Maintaining cost for configuring unit power of energy storage; />Clearing cost for the deep peak shaving market; />Punishment costs for depth peak shaving; />For wind power station->At->Output power at time;the total wind power consumption of the system when the energy storage is not configured; />Is a peak regulating unit->The total bid amount in the deep peak shaving market; />A relaxation variable of deep peak regulation and wind abandoning is adopted; />、/>The compensation conversion coefficients are respectively depth peak regulation compensation coefficients and wind abandon punishment compensation coefficients; />、/>And the identification is respectively a wind power depth peak regulation interval identification and a wind power wind discarding interval identification.
3. The energy storage capacity allocation method according to claim 2, wherein the constraint conditions of the energy storage capacity allocation model include identification of energy storage capacity allocation constraint, energy storage power allocation constraint, capacity-power ratio constraint, discharge price constraint, charge price constraint and frequency modulation price constraint.
4. The energy storage capacity configuration method considering market clear income according to claim 1, wherein the constraints of the electric energy-frequency modulation combined market clear model comprise system power balance constraints, frequency modulation capacity mileage constraints, thermal power generating unit output constraints, unit frequency modulation capacity-mileage constraints, climbing constraints and power transmission line power constraints.
5. An energy storage capacity allocation device for consideration of market clear earnings, characterized in that the device is applied to the method of claim 1, said device comprising:
the depth peak regulation compensation item establishing module is used for establishing a depth peak regulation market clearing model, calculating market clearing cost and establishing a depth peak regulation compensation item according to a depth peak regulation market clearing result;
the energy storage capacity configuration double-layer model building module is used for building an energy storage capacity configuration double-layer model considering clear market income, wherein the upper-layer model is an energy storage capacity configuration model, and the model takes daily average energy storage investment cost, total daily operation income of energy storage and energy storage alleviation depth peak regulation compensation as objective functions; the lower model is an electric energy-frequency modulation combined market clearing model;
and the energy storage capacity configuration double-layer model solving module is used for converting the energy storage capacity configuration double-layer model into a single-layer model by adopting KKT conditions and a dual theory to solve.
6. An energy storage capacity allocation device considering market clear benefits, characterized in that,
comprising a memory and a processor;
the memory is used for storing computer program codes and transmitting the computer program codes to the processor;
the processor is configured to perform the method according to any of claims 1 to 4 according to instructions in the computer program code.
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