Energy scheduling method, device, equipment and computer-readable storage medium

Abstract

The present application discloses an energy scheduling method, device, equipment and computer-readable storage medium, the method comprising: constructing a power balance equation and obtaining target parameters, establishing a price model using the conditional value at risk method; constructing a day-ahead market target function according to the price model and target parameters corresponding to the day-ahead market; obtaining a deviation settlement model according to the target parameters, spot market trading rules and the output of the integrated energy system in the day-ahead market and the real-time market, constructing a real-time market target function according to the day-ahead market revenue, the price model corresponding to the real-time market and the deviation settlement model, and obtaining a real-time operation curve, and controlling the integrated energy system to perform operation scheduling according to the real-time operation curve. The above technical solution disclosed in the present application takes into account the day-ahead market price fluctuations, the real-time market price fluctuations and the spot market transactions, therefore, the rationality of operation scheduling can be improved, so that the economic efficiency of the integrated energy system can be optimized.

Description

Technical Field

[0001] The present application relates to the technical field of energy scheduling, and more specifically, to an energy scheduling method, apparatus, device and computer-readable storage medium. Background Art

[0002] With the continuous deepening of electricity market reform, the construction of the spot market has become the main task of the electricity market in the future. As the main form of energy consumption on the user side in the future, the economic optimization of the integrated energy system participating in spot market transactions has become a key issue that needs to be solved urgently.

[0003] At present, the energy dispatch process of the integrated energy system is implemented on the basis of fixed prices, and the trading rules of the electricity spot market are not taken into consideration. Due to the drastic price fluctuations in the spot market and the increasingly complex trading rules, the existing energy dispatch method for the integrated energy system to participate in the spot market cannot make the various energy sources in the integrated energy system operate under the optimal conditions, and thus cannot achieve better economic efficiency.

[0004] In summary, how to achieve reasonable scheduling of various energy sources in an integrated energy system in order to achieve better economic efficiency is a technical problem that needs to be urgently solved by technical personnel in this field. Summary of the invention

[0005] In view of this, the purpose of this application is to provide an energy scheduling method, device, equipment and computer-readable storage medium for realizing the reasonable scheduling of various energy sources in an integrated energy system so as to achieve better economic efficiency.

[0006] In order to achieve the above objectives, this application provides the following technical solutions:

[0007] An energy scheduling method, comprising:

[0008] Constructing a power balance equation of the integrated energy system according to the operating characteristics of each energy source in the integrated energy system, obtaining target parameters according to the power balance equation, and establishing a price model corresponding to the day-ahead market and a price model corresponding to the real-time market by using the conditional value-at-risk method;

[0009] According to the price model corresponding to the day-ahead market and the target parameter, a day-ahead market target function is constructed with the goal of maximizing the day-ahead market revenue, and the day-ahead market revenue is obtained according to the day-ahead market target function;

[0010] A settlement model is performed according to the target parameters, the spot market trading rules and the output of the integrated energy system in the day-ahead market and the real-time market to obtain a deviation settlement model, and a real-time market objective function is constructed with the goal of maximizing the difference between the real-time operation income and the day-ahead market income according to the day-ahead market income, the price model corresponding to the real-time market and the deviation settlement model;

[0011] The real-time market objective function is solved to obtain a real-time operation curve of the integrated energy system participating in the spot market, and the integrated energy system is controlled to perform operation scheduling according to the real-time operation curve.

[0012] Preferably, the conditional value at risk method is used to establish a price model, including:

[0013] The price model is established using the conditional value-at-risk method:

[0014]

[0015] Among them, F β (x, α) is the CVaR calculation value, β is the confidence level, α is the VaR value under the confidence level β, x is the price, y is the time, R is the time series, f(x, y) is the data distribution function, in the day-ahead market, f(x, y) is the day-ahead market price series, in the real-time market, f(x, y) is the real-time market price series, ρ(y) is the probability density function, [f(x, y)-α] + Indicates selecting the maximum value, when f(x,y)-α is greater than 0 [f(x,y)-α] + is f(x,y)-α, when f(x,y)-α is not greater than 0 [f(x,y)-α] + is 0.

[0016] Preferably, after the price model is established by using the conditional value at risk method, the method further includes:

[0017] Modify the price model to

[0018] in, is the CVaR calculation value under discrete data, m is the total number of data groups, y k is the y variable data sequence under discrete data.

[0019] Preferably, the day-ahead market objective function is:

[0020]

[0021] Among them, g da represents the day-ahead market objective function, R da(k) is the profit of the day-ahead market optimization operation in the kth price scenario, C da (k) is the operating cost of the day-ahead market in the kth price scenario, k = 1, 2, ..., N, N is the number of price scenarios involved, μ da The risk preference coefficient optimized for the day-ahead market, l k and ξ is an auxiliary variable, β da is the day-ahead market confidence, for l k :ξ-(R da (k)-C da (k))≤l k And l k ≥0, The amount of electricity sold by the integrated energy system to the grid in the day-ahead market. is the price of selling electricity to the grid, is the operating cost of energy storage, is the operating cost of CHP, The day-ahead market clearing price, The amount of electricity purchased from the grid by the integrated energy system in the day-ahead market.

[0022] Preferably, obtaining the day-ahead market revenue according to the day-ahead market objective function includes:

[0023] Solving the day-ahead market objective function to obtain a day-ahead market declaration curve;

[0024] The day-ahead market revenue is obtained according to the day-ahead market declaration curve and the day-ahead market clearing electricity price.

[0025] Preferably, the deviation settlement model is: Among them, C dev (k) represents the output deviation in the kth price scenario, abs is the absolute value function, P da (t) is the day-ahead market price at time t, P re (t) is the real-time market price at time t, n k (t) is an auxiliary variable, sign is a sign judgment function, which is 1 if it is greater than 0 and -1 if it is not greater than 0. buy,re (t) is the real-time market purchase volume of the integrated energy system, P buy,da (t) is the day-ahead market purchase volume of the integrated energy system;

[0026] The real-time market objective function is: Among them, g re represents the real-time market objective function, R re (k) is the profit of real-time market optimization running in the kth price scenario, Cre (k) is the operating cost of the real-time market in the kth price scenario, μ re Risk appetite coefficient optimized for real-time market, β re is the day-ahead market confidence, q k is an auxiliary variable, for q k :ξ-(R re (k)-C re (k)-C dev (k))≤q k And q k ≥0.

[0027] Preferably, solving the real-time market objective function includes:

[0028] The real-time market objective function is solved using a CPLEX solver.

[0029] An energy scheduling device, comprising:

[0030] The first construction module is used to construct a power balance equation of the integrated energy system according to the operating characteristics of each energy source in the integrated energy system, obtain target parameters according to the power balance equation, and use the conditional value at risk method to establish a price model corresponding to the day-ahead market and a price model corresponding to the real-time market;

[0031] A second construction module is used to construct a day-ahead market objective function based on the price model corresponding to the day-ahead market and the target parameter with the goal of maximizing the day-ahead market revenue, and obtain the day-ahead market revenue according to the day-ahead market objective function;

[0032] A third construction module is used to perform settlement modeling according to the target parameters, the spot market trading rules and the output of the integrated energy system in the day-ahead market and the real-time market to obtain a deviation settlement model, and to construct a real-time market objective function according to the day-ahead market income, the price model corresponding to the real-time market and the deviation settlement model with the goal of maximizing the difference between the real-time operation income and the day-ahead market income;

[0033] The operation scheduling module is used to solve the real-time market objective function, obtain the real-time operation curve of the integrated energy system participating in the spot market, and control the integrated energy system to perform operation scheduling according to the real-time operation curve.

[0034] An energy scheduling device, comprising:

[0035] Memory for storing computer programs;

[0036] A processor is used to implement the steps of the energy scheduling method as described in any one of the above items when executing the computer program.

[0037] A computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the energy scheduling method as described in any one of the above items are implemented.

[0038] The present application provides an energy scheduling method, device, equipment and computer-readable storage medium, wherein the method includes: constructing a power balance equation of the integrated energy system according to the operating characteristics of each energy source in the integrated energy system, obtaining target parameters according to the power balance equation, and using the conditional value at risk method to establish a price model corresponding to the day-ahead market and a price model corresponding to the real-time market; constructing a day-ahead market target function with the goal of maximizing the day-ahead market revenue according to the price model and target parameters corresponding to the day-ahead market, and obtaining the day-ahead market revenue according to the day-ahead market target function; performing settlement modeling according to the target parameters, spot market trading rules and the output of the integrated energy system in the day-ahead market and the real-time market to obtain a deviation settlement model, and constructing a real-time market target function with the goal of maximizing the difference between the real-time operation revenue and the day-ahead market revenue according to the day-ahead market revenue, the price model corresponding to the real-time market and the deviation settlement model; solving the real-time market target function to obtain a real-time operation curve of the integrated energy system participating in the spot market, and controlling the integrated energy system to perform operation scheduling according to the real-time operation curve.

[0039] The above-mentioned technical scheme disclosed in the present application constructs a power balance equation of the integrated energy system according to the operating characteristics of each energy source in the integrated energy system, obtains target parameters according to the constructed power balance equation, and adopts the conditional value at risk method to establish a price model corresponding to the day-ahead market and a price model corresponding to the real-time market, so as to solve the price fluctuation of the corresponding market through the corresponding price model, and constructs the day-ahead market objective function with the goal of maximizing the day-ahead market return according to the price model and target parameters corresponding to the day-ahead market, that is, the construction of the day-ahead market objective function takes the day-ahead market price fluctuation into account, and after the day-ahead market objective function is constructed, it is solved to obtain the day-ahead market return, and then, according to the target parameters obtained by solving the power balance equation of the integrated energy system, the spot market trading rules and the integrated energy The system's output in the day-ahead market and the real-time market is modeled for settlement to obtain a deviation settlement model, and based on the solved day-ahead market revenue, the constructed price model corresponding to the real-time market, and the constructed deviation settlement model, a real-time market objective function is constructed with the goal of maximizing the difference between the real-time operating revenue and the day-ahead market revenue. Since the above process takes into account the day-ahead market price fluctuations, the real-time market price fluctuations, and the spot market trading rules, when the integrated energy system is controlled to perform operation scheduling based on the real-time operation curve obtained by solving the real-time objective function, the rationality of the operation scheduling can be improved, that is, each energy in the integrated energy system can be operated in a more reasonable manner, that is, each device in the integrated energy system can be operated under optimal conditions, so that the economy of the integrated energy system can be optimized. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying any creative work.

[0041] Figure 1 A flow chart of an energy scheduling method provided in an embodiment of the present application;

[0042] Figure 2 A framework diagram of the energy scheduling method provided in the embodiment of the present application;

[0043] Figure 3 A schematic diagram of the total operation curve of each energy source of the comprehensive energy system provided in the embodiment of the present application;

[0044] Figure 4 A schematic diagram of an operating curve of a gas turbine provided in an embodiment of the present application;

[0045] Figure 5 A schematic diagram of the output power of the electric energy storage provided in the embodiment of the present application;

[0046] Figure 6 A schematic diagram of a wind power output operation curve provided in an embodiment of the present application;

[0047] Figure 7 A schematic diagram of the structure of an energy scheduling device provided in an embodiment of the present application;

[0048] Figure 8 A schematic diagram of the structure of an energy scheduling device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0049] The integrated energy system is an effective means of utilizing multiple energy sources including distributed renewable energy. At present, with the further deepening of the power market reform, the construction of the spot market has become one of the main tasks of the power market in the future. In the power market environment, prices fluctuate violently and trading rules become more and more complicated. The economic optimization of the integrated energy system participating in spot market transactions has become one of the key issues to be solved urgently. In the prior art, after the integrated energy system participates in the power spot market, its energy dispatching process is implemented on the basis of a fixed price, and the trading rules of the spot market are not considered in the energy dispatching process. Therefore, the energy dispatching method of the integrated energy system participating in the spot market cannot make the various energy sources therein operate under the optimal conditions, resulting in the inability to achieve the optimal economic efficiency of the integrated energy system participating in spot market transactions.

[0050] To this end, the present application provides a method that can realize the reasonable scheduling of various energy sources in an integrated energy system, so that the economic efficiency of the integrated energy system participating in spot market transactions can reach the optimal economic efficiency.

[0051] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0052] See also Figure 1 and Figure 2 ,in, Figure 1 A flow chart of an energy scheduling method provided in an embodiment of the present application is shown. Figure 2 The energy scheduling method provided in the embodiment of the present application is shown in the framework diagram. An energy scheduling method provided in the embodiment of the present application may include:

[0053] S11: Construct the power balance equation of the integrated energy system according to the operating characteristics of each energy source in the integrated energy system, obtain the target parameters according to the power balance equation, and use the conditional value at risk method to establish a price model corresponding to the day-ahead market and a price model corresponding to the real-time market.

[0054] First, models corresponding to each energy source can be established based on the operating characteristics of wind power, photovoltaics, demand response, diesel generation, energy storage, cogeneration, etc. in the integrated energy system.

[0055] Specifically, a wind power output model corresponding to wind power can be established: Among them, P w Indicates the fan output, P r Indicates the rated output of the fan, v ci 、v r 、v co are the cut-in wind speed, rated wind speed and cut-out wind speed of the wind turbine respectively, and v is the current wind speed of the wind turbine; and a photovoltaic output model corresponding to photovoltaic is established: P pv =r1Aη, where r1, A, and η are respectively the light radiation, the photovoltaic module area, and the photoelectric conversion efficiency, P pv Output for photovoltaic power; establish a demand response processing model and constraints corresponding to demand response: Among them, P dem (t) is the demand response output at time t, d i (t), γ i (t) are the demand response call volume and response credibility of the i-th user, d i,j (t) is the demand response of the jth device of the i-th user; the energy storage charging and discharging model is constructed: Among them, P st (t), P cha (t), P dis (t) are the output of energy storage at time t, the charging power determined in the model, and the discharging power determined in the model, respectively. st is the energy storage charging power, δ cha (t),δ dis (t) are energy storage charging efficiency, charging decision 0-1 variable, and discharging decision 0-1 variable, respectively. S oc (t) is the SOC (State of Charge) of the energy storage at time t, S ca is the energy storage installed capacity, and the energy storage charging and discharging constraints and power constraints are: Among them, P st,max , S oc,max They are the maximum charging and discharging power of energy storage and the maximum allowable SOC of energy storage respectively; and the cogeneration output model and constraints corresponding to cogeneration are established: Among them, P cchp (t) is the output of cogeneration at time t, σ and δ are the electricity conversion efficiency and the combustion calorific value of gas, Q cchp (t), Q heat (t) are the heat output of the cogeneration unit and the heat demand at time t, and F(t) is the fuel input.

[0056] After establishing the models corresponding to each energy source, the power balance equation of the comprehensive energy system can be established based on the models of each energy source:

[0057] P cha (t)δ cha (t)+P load (t)-P dem (t)+P buy (t) = P pv (t)+P w (t)+P cchp (t)+P dis (t)δ dis (t)+P sell (t)

[0058] Among them, P load (t), P buy (t), P sell (t) are the internal load power consumption of the integrated energy system at time t, the power purchased by the integrated energy system from the power grid, and the power sold by the integrated energy system to the power grid.

[0059] After constructing the power balance equation of the integrated energy system, the power balance equation can be solved to obtain the target parameters, specifically P buy (t) and P sell (t) The specific situation in the day-ahead market and the real-time market, that is, the day-ahead market is obtained according to the corresponding power balance equation. and And according to the corresponding power balance equation, we can get P in the real-time market. buy,re (t) and P sell,re (t).

[0060] At the same time, the conditional value-at-risk method can be used to recommend a price model corresponding to the day-ahead market, and the conditional value-at-risk method can be used to establish a price model corresponding to the real-time market, so as to quantify the risk of price fluctuations in the electricity spot market through the corresponding price model, thereby solving the uncertainty problem of unpredictable prices.

[0061] S12: According to the price model and target parameters corresponding to the day-ahead market, a day-ahead market objective function is constructed with the goal of maximizing the day-ahead market revenue, and the day-ahead market revenue is obtained according to the day-ahead market objective function.

[0062] After executing step S11, a day-ahead market objective function can be constructed with the goal of maximizing the day-ahead market revenue based on the price model corresponding to the day-ahead market and the target parameters obtained by solving the power balance equation (specifically, the target parameters corresponding to the day-ahead market), and the constructed day-ahead market objective function can be solved to obtain the day-ahead market revenue.

[0063] S13: Settlement modeling is performed based on target parameters, spot market trading rules, and the output of the integrated energy system in the day-ahead market and the real-time market to obtain a deviation settlement model, and a real-time market objective function is constructed based on the day-ahead market revenue, the price model corresponding to the real-time market, and the deviation settlement model with the goal of maximizing the difference between the real-time operating revenue and the day-ahead market revenue.

[0064] In addition, after step S11, a settlement model can be built based on the target parameters obtained by solving the power balance equation (specifically, the target parameters corresponding to the day-ahead market and the target parameters corresponding to the real-time market), the spot market trading rules (recovering the arbitrage portion of the day-ahead market and the real-time market that exceeds the allowable range, and taking into account the regulatory measures of market rules), and the output of the integrated energy system in the day-ahead market and the real-time market to obtain a deviation settlement model.

[0065] Afterwards, the real-time market objective function can be constructed based on the day-ahead market income obtained in step S12, the price model corresponding to the real-time market established in step S11, and the deviation settlement model established above, with the goal of maximizing the difference between the real-time operating income and the day-ahead market income.

[0066] S14: Solve the real-time market objective function to obtain the real-time operation curve of the integrated energy system participating in the spot market, and control the integrated energy system to perform operation scheduling according to the real-time operation curve.

[0067] Solve the constructed real-time market objective function to obtain the real-time operation curve of the integrated energy system participating in the spot market, and control the integrated energy system to operate and dispatch according to the real-time operation curve. It should be noted that the real-time operation curve of the integrated energy system participating in the spot market obtained above not only includes the total operation curve of the integrated energy system, but also includes the operation curves of multiple internal devices. For details, please refer to Figure 3-Figure 6 , among which, among which, Figure 3 The schematic diagram of the total operation curve of each energy source of the comprehensive energy system provided in the embodiment of the present application is shown. Figure 4 A schematic diagram of an operating curve of a gas turbine provided in an embodiment of the present application is shown. Figure 5 A schematic diagram of the output power of the electric energy storage provided in the embodiment of the present application is shown. Figure 6The wind power output operation curve diagram provided in the embodiment of the present application is shown. Figure 3-Figure 6 In the figure, the horizontal axis represents time (h) and the vertical axis represents power (MW). Figure 3 The solid line in is the total operation curve of each energy source in the comprehensive energy system in the real-time market. Figure 4 The solid line in is the operating curve of the gas turbine in the real-time market. Figure 5 The filled column structure in the figure represents the output power of electric energy storage in the real-time market. Figure 6 The solid line in the figure is the operating curve of wind power in the real-time market.

[0068] Through the above process, a real-time operation curve that maximizes the difference between the real-time operation income and the day-ahead market income can be obtained. Therefore, when the integrated energy system is controlled to perform operation scheduling according to the real-time operation curve, the rationality of the operation scheduling can be improved, so that each energy in the integrated energy system can be operated in a more reasonable manner, thereby achieving the optimal economic efficiency of the integrated energy system participating in the spot market.

[0069] The above-mentioned technical scheme disclosed in the present application constructs a power balance equation of the integrated energy system according to the operating characteristics of each energy source in the integrated energy system, obtains target parameters according to the constructed power balance equation, and adopts the conditional value at risk method to establish a price model corresponding to the day-ahead market and a price model corresponding to the real-time market, so as to solve the price fluctuation of the corresponding market through the corresponding price model, and constructs the day-ahead market objective function with the goal of maximizing the day-ahead market return according to the price model and target parameters corresponding to the day-ahead market, that is, the construction of the day-ahead market objective function takes the day-ahead market price fluctuation into account, and after the day-ahead market objective function is constructed, it is solved to obtain the day-ahead market return, and then, according to the target parameters obtained by solving the power balance equation of the integrated energy system, the spot market trading rules and the integrated energy The system's output in the day-ahead market and the real-time market is modeled for settlement to obtain a deviation settlement model, and based on the solved day-ahead market revenue, the constructed price model corresponding to the real-time market, and the constructed deviation settlement model, a real-time market objective function is constructed with the goal of maximizing the difference between the real-time operating revenue and the day-ahead market revenue. Since the above process takes into account the day-ahead market price fluctuations, the real-time market price fluctuations, and the spot market trading rules, when the integrated energy system is controlled to perform operation scheduling based on the real-time operation curve obtained by solving the real-time objective function, the rationality of the operation scheduling can be improved, that is, each energy in the integrated energy system can be operated in a more reasonable manner, that is, each device in the integrated energy system can be operated under optimal conditions, so that the economy of the integrated energy system can be optimized.

[0070] An energy scheduling method provided in an embodiment of the present application uses a conditional value-at-risk method to establish a price model, which may include:

[0071] The price model is established using the conditional value-at-risk method:

[0072]

[0073] Among them, F β (x,a) is the CVaR calculation value, β is the confidence level, α is the VaR value under the confidence level β, x is the price, y is the time, R is the time series, f(x,y) is the data distribution function, in the day-ahead market, f(x,y) is the day-ahead market price series, in the real-time market, f(x,y) is the real-time market price series, ρ(y) is the probability density function, [f(x,y)-α] + Indicates selecting the maximum value, when f(x,y)-α is greater than 0 [f(x,y)-α] + is f(x,y)-α, when f(x,y)-α is not greater than 0 [f(x,y)-α] + is 0.

[0074] When using the conditional value-at-risk method to establish a price model corresponding to the day-ahead market and a price model corresponding to the real-time market, the conditional value-at-risk method can be used to construct the following price model:

[0075]

[0076] In the above model, F β (x, α) is the calculated value of CVaR (Conditional Value at Risk), β is the confidence level, α is the VaR (Value at Risk) value under the confidence level β, x is the price, y is the time, R is the time series, f(x, y) is the data distribution function, which in this application specifically refers to the day-ahead market price series and the real-time market price series. Specifically, in the day-ahead market, x is the day-ahead price corresponding to time y, f(x, y) is the day-ahead market price series, in the real-time market, x is the real-time price corresponding to time y, f(x, y) is the real-time market price series, ρ(y) is the probability density function, [f(x, y)-α] + Indicates selecting the maximum value, when f(x,y)-α is greater than 0 [f(x,y)-α] + is f(x,y)-α, when f(x,y)-α is not greater than 0 [f(x,y)-α] + is 0.

[0077] An energy scheduling method provided in an embodiment of the present application may further include, after establishing a price model using the conditional value at risk method:

[0078] Modify the price model to

[0079] in, is the CvaR calculation value under discrete data, m is the total number of data groups, y k is the y variable data sequence under discrete data.

[0080] Considering that price is discrete data, in order to adapt to the price data type, the price model constructed Can be modified to in, is the CvaR calculation value under discrete data, m is the total number of data groups, y k It is the y variable data sequence under discrete data, so as to facilitate the accuracy of the price model corresponding to the day-ahead market and the price model corresponding to the real-time market.

[0081] An energy scheduling method provided in an embodiment of the present application, the day-ahead market objective function is:

[0082] Among them, g da represents the day-ahead market objective function, R da (k) is the profit of the day-ahead market optimization operation in the kth price scenario, C da (k) is the operating cost of the day-ahead market in the kth price scenario, k = 1, 2, ..., N, N is the number of price scenarios involved, μ da The risk preference coefficient optimized for the day-ahead market, l k and ξ is an auxiliary variable, β da is the day-ahead market confidence, for l k :ξ-(R da (k)-C da (k))≤l k And l k ≥0, The amount of electricity sold by the integrated energy system to the grid in the day-ahead market. is the price of selling electricity to the grid, is the operating cost of energy storage, is the operating cost of CHP, The day-ahead market clearing price, The amount of electricity purchased from the grid by the integrated energy system in the day-ahead market.

[0083] In this application, the day-ahead market objective function constructed based on the price model and target parameters corresponding to the day-ahead market and aiming at maximizing the day-ahead market revenue is specifically: in, Corresponding to the price model constructed corresponding to the day-ahead market, and β da is the day-ahead market confidence, l k and ξ are auxiliary variables. For l in the above objective function, k , and its specific calculation model is ξ-(R da (k)-C da (k))≤l k And l k ≥0, the profit R of the day-ahead market optimization operation in the kth price scenario da (k) Specific adoption Calculate the operating cost C of the day-ahead market in the kth price scenario da (k) Specific adoption Perform calculations.

[0084] An energy scheduling method provided in an embodiment of the present application, which obtains a day-ahead market revenue according to a day-ahead market objective function, may include:

[0085] Solve the day-ahead market objective function to obtain the day-ahead market declaration curve;

[0086] The day-ahead market revenue is obtained based on the day-ahead market declaration curve and the day-ahead market clearing electricity price.

[0087] After constructing the day-ahead market objective function, the day-ahead market objective function can be solved to obtain the day-ahead market declaration curve. For details, see Figure 3-Figure 6 , Figure 3 The dotted line in is the overall operation curve of the integrated energy system in the day-ahead market. Figure 4 The dotted line in is the operating curve of the gas turbine in the day-ahead market. Figure 5 The unfilled column structure in the figure represents the output power of electric energy storage in the day-ahead market. Figure 6 The dotted line in the figure is the operating curve of wind power in the day-ahead market. When the day-ahead market declaration curve is obtained, the day-ahead risk return can be obtained so that the staff can timely understand the day-ahead wind power revenue. After obtaining the day-ahead market declaration curve, the day-ahead market revenue can be calculated based on the day-ahead market declaration curve and the day-ahead market clearing electricity price (specifically, the data in the day-ahead market declaration curve can be multiplied by the day-ahead market clearing price to obtain the day-ahead market revenue), so that it can participate in the construction of the real-time market objective function.

[0088] An energy scheduling method provided in an embodiment of the present application, wherein the deviation settlement model is: Among them, C dev (k) represents the output deviation in the kth price scenario, abs is the absolute value function, P da (t) is the day-ahead market price at time t, Pre (t) is the real-time market price at time t, n k (t) is an auxiliary variable, sign is a sign judgment function, which is 1 if it is greater than 0 and -1 if it is not greater than 0. buy,re (t) is the real-time market purchase volume of the integrated energy system, P buy,da (t) is the day-ahead market purchase volume of the integrated energy system;

[0089] The real-time market objective function is: Among them, g re represents the real-time market objective function, R re (k) is the profit of real-time market optimization running in the kth price scenario, C re (k) is the estimated cost of the real-time market running in the kth price scenario, μ re Risk appetite coefficient optimized for real-time market, β re is the day-ahead market confidence, q k is an auxiliary variable, for q k :ξ-(R re (k)-C re (k)-C dev (k))≤q k And q k ≥0.

[0090] In this application, the deviation settlement model obtained by settlement modeling based on the target parameters, spot market trading rules and the output of the integrated energy system in the day-ahead market and the real-time market can be specifically Among them, C dev (k) represents the output deviation in the kth price scenario, k = 1, 2, ..., N, N is the number of price scenarios involved, t represents the time and t = 1, 2, ..., 24, that is, it represents 24 hours in a day, P da (t) is the day-ahead market price at time t, P re (t) is the real-time market price at time t, n k (t) is an auxiliary variable, sign is a sign judgment function, which is 1 if it is greater than 0 and -1 if it is not greater than 0.

[0091] Accordingly, according to the day-ahead market return, the price model corresponding to the real-time market and the deviation settlement model, the real-time market objective function is constructed with the goal of maximizing the difference between the real-time operating return and the day-ahead market return as the specific value: in, Corresponding to the price model constructed corresponding to the real-time market, and β re is the real-time market confidence, q k is an auxiliary variable, and for q k , and its specific calculation model is ξ-(Rre (k)-C re (k)-C dev (k))≤q k q k ≥0.

[0092] An energy scheduling method provided in an embodiment of the present application solves a real-time market objective function and may include:

[0093] The real-time market objective function is solved using the CPLEX solver.

[0094] In this application, the real-time market objective function constructed by the CPLEX solver can be used to solve the real-time operation curve of the integrated energy system participating in the spot market, and the real-time market income can be obtained at the same time, so that the staff can obtain the real-time market income in a timely manner. In addition, the CPLEX solver can also be used to solve the power balance equation of the integrated energy system and the day-ahead market objective function to obtain the required parameter information.

[0095] Of course, other solvers may also be used to solve the real-time market objective function, and this application does not impose any limitation on this.

[0096] The present application also provides an energy scheduling device, see Figure 7 , which shows a schematic diagram of the structure of an energy scheduling device provided in an embodiment of the present application, which may include:

[0097] The first construction module 21 is used to construct a power balance equation of the integrated energy system according to the operating characteristics of each energy source in the integrated energy system, obtain target parameters according to the power balance equation, and use the conditional value at risk method to establish a price model corresponding to the day-ahead market and a price model corresponding to the real-time market;

[0098] The second construction module 22 is used to construct a day-ahead market objective function based on a price model and target parameters corresponding to the day-ahead market with the goal of maximizing the day-ahead market revenue, and obtain the day-ahead market revenue based on the day-ahead market objective function;

[0099] The third construction module 23 is used to perform settlement modeling according to the target parameters, the spot market trading rules and the output of the integrated energy system in the day-ahead market and the real-time market to obtain a deviation settlement model, and to construct a real-time market objective function according to the day-ahead market income, the price model corresponding to the real-time market and the deviation settlement model with the goal of maximizing the difference between the real-time operation income and the day-ahead market income;

[0100] The operation scheduling module 24 is used to solve the real-time market objective function, obtain the real-time operation curve of the integrated energy system participating in the spot market, and control the integrated energy system to perform operation scheduling according to the real-time operation curve.

[0101] An energy scheduling device provided in an embodiment of the present application, the first building module 21 may include:

[0102] Set up the unit for building a price model using the conditional value-at-risk approach:

[0103]

[0104] Among them, F β (x, α) is the CVaR calculation value, β is the confidence level, α is the VaR value under the confidence level β, x is the price, y is the time, R is the time series, f(x, y) is the data distribution function, in the day-ahead market, f(x, y) is the day-ahead market price series, in the real-time market, f(x, y) is the real-time market price series, ρ(y) is the probability density function, [f(x, y)-α] + Indicates selecting the maximum value, when f(x,y)-α is greater than 0 [f(x,y)-α] + is f(x,y)-α, when f(x,y)-α is not greater than 0 [f(x,y)-α] + is 0.

[0105] In an energy scheduling device provided in an embodiment of the present application, the first building module 21 may further include:

[0106] The modification unit is used to modify the price model after the price model is established by using the conditional value at risk method. in, is the CVaR calculation value under discrete data, m is the total number of data groups, y k is the y variable data sequence under discrete data.

[0107] An energy scheduling device provided in an embodiment of the present application has a day-ahead market objective function as follows:

[0108] Among them, g da represents the day-ahead market objective function, R da (k) is the profit of the day-ahead market optimization operation in the kth price scenario, C da (k) is the operating cost of the day-ahead market in the kth price scenario, k = 1, 2, ..., N, N is the number of price scenarios involved, μ da The risk preference coefficient optimized for the day-ahead market, l k and ξ is an auxiliary variable, β da is the day-ahead market confidence, for lk :ξ-(R da (k)-C da (k))≤l k And l k ≥0, The amount of electricity sold by the integrated energy system to the grid in the day-ahead market. is the price of selling electricity to the grid, is the operating cost of energy storage, is the operating cost of CHP, The day-ahead market clearing price, The amount of electricity purchased from the grid by the integrated energy system in the day-ahead market.

[0109] In an energy scheduling device provided in an embodiment of the present application, the second building module 22 may include:

[0110] The first solving unit is used to solve the day-ahead market objective function to obtain the day-ahead market declaration curve;

[0111] A day-ahead market revenue unit is obtained, which is used to obtain the day-ahead market revenue according to the day-ahead market declaration curve and the day-ahead market clearing electricity price.

[0112] An energy scheduling device provided in an embodiment of the present application has a deviation settlement model as follows: Among them, C dev (k) represents the output deviation in the kth price scenario, abs is the absolute value function, P da (t) is the day-ahead market price at time t, P re (t) is the real-time market price at time t, n k (t) is an auxiliary variable, sign is a sign judgment function, which is 1 if it is greater than 0 and -1 if it is not greater than 0. buy,re (t) is the real-time market purchase volume of the integrated energy system, P buy,da (t) is the day-ahead market purchase volume of the integrated energy system;

[0113] The real-time market objective function is: Among them, g re represents the real-time market objective function, R re (k) is the profit of real-time market optimization running in the kth price scenario, C re (k) is the operating cost of the real-time market in the kth price scenario, μ re Risk appetite coefficient optimized for real-time market, β re is the day-ahead market confidence, q k is an auxiliary variable, for q k :ξ-(R re (k)-Cre (k)-C dev (k))≤q k And q k ≥0.

[0114] An energy scheduling device provided in an embodiment of the present application, the operation scheduling module 24 may include:

[0115] The second solving unit is used to solve the real-time market objective function by using the CPLEX solver.

[0116] The present application also provides an energy scheduling device, see Figure 8 , which shows a schematic diagram of the structure of an energy scheduling device provided in an embodiment of the present application, which may include:

[0117] A memory 31, used for storing computer programs;

[0118] The processor 32 can implement the following steps when executing the computer program stored in the memory 31:

[0119] According to the operating characteristics of each energy source in the integrated energy system, the power balance equation of the integrated energy system is constructed, the target parameters are obtained according to the power balance equation, and the price model corresponding to the day-ahead market and the price model corresponding to the real-time market are established by the conditional value at risk method; according to the price model and target parameters corresponding to the day-ahead market, the day-ahead market target function is constructed with the goal of maximizing the day-ahead market revenue, and the day-ahead market revenue is obtained according to the day-ahead market target function; according to the target parameters, the spot market trading rules and the output of the integrated energy system in the day-ahead market and the real-time market, the settlement model is modeled to obtain the deviation settlement model, and according to the day-ahead market revenue, the price model corresponding to the real-time market and the deviation settlement model, the real-time market target function is constructed with the goal of maximizing the difference between the real-time operation revenue and the day-ahead market revenue; the real-time market target function is solved to obtain the real-time operation curve of the integrated energy system participating in the spot market, and the integrated energy system is controlled to perform operation scheduling according to the real-time operation curve.

[0120] The present application also provides a computer-readable storage medium, in which a computer program is stored. When the computer program is executed by a processor, the following steps can be implemented:

[0121] According to the operating characteristics of each energy source in the integrated energy system, the power balance equation of the integrated energy system is constructed, the target parameters are obtained according to the power balance equation, and the price model corresponding to the day-ahead market and the price model corresponding to the real-time market are established by the conditional value at risk method; according to the price model and target parameters corresponding to the day-ahead market, the day-ahead market target function is constructed with the goal of maximizing the day-ahead market revenue, and the day-ahead market revenue is obtained according to the day-ahead market target function; according to the target parameters, the spot market trading rules and the output of the integrated energy system in the day-ahead market and the real-time market, the settlement model is modeled to obtain the deviation settlement model, and according to the day-ahead market revenue, the price model corresponding to the real-time market and the deviation settlement model, the real-time market target function is constructed with the goal of maximizing the difference between the real-time operation revenue and the day-ahead market revenue; the real-time market target function is solved to obtain the real-time operation curve of the integrated energy system participating in the spot market, and the integrated energy system is controlled to perform operation scheduling according to the real-time operation curve.

[0122] The computer-readable storage medium may include: a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and other media that can store program codes.

[0123] For the description of the relevant parts of an energy scheduling device, equipment and computer-readable storage medium provided in the embodiments of the present application, reference can be made to the detailed description of the corresponding parts of an energy scheduling method provided in the embodiments of the present application, which will not be repeated here.

[0124] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply that there is any such actual relationship or order between these entities or operations. Moreover, the term "include", "comprise" or any other variant thereof is intended to cover non-exclusive inclusion, so that the process, method, article or equipment that includes a series of elements are inherent to the elements. In the absence of more restrictions, the elements limited by the sentence "comprise one..." do not exclude the presence of other identical elements in the process, method, article or equipment that includes the elements. In addition, the above-mentioned technical solution provided in the embodiment of the present application is consistent with the corresponding technical solution in the prior art in principle, and the part is not described in detail, so as not to repeat too much.

[0125] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. An energy scheduling method, characterized in that: include: Constructing a power balance equation of the integrated energy system according to the operating characteristics of each energy source in the integrated energy system, obtaining target parameters according to the power balance equation, and establishing a price model corresponding to the day-ahead market and a price model corresponding to the real-time market by using the conditional value-at-risk method; According to the price model corresponding to the day-ahead market and the target parameter, a day-ahead market target function is constructed with the goal of maximizing the day-ahead market revenue, and the day-ahead market revenue is obtained according to the day-ahead market target function; A settlement model is performed according to the target parameters, the spot market trading rules and the output of the integrated energy system in the day-ahead market and the real-time market to obtain a deviation settlement model, and a real-time market objective function is constructed with the goal of maximizing the difference between the real-time operation income and the day-ahead market income according to the day-ahead market income, the price model corresponding to the real-time market and the deviation settlement model; Solving the real-time market objective function to obtain a real-time operation curve of the integrated energy system participating in the spot market, and controlling the integrated energy system to perform operation scheduling according to the real-time operation curve; Wherein, using the conditional value at risk method to establish a price model includes: using the conditional value at risk method to establish a price model: Among them, F β (x, α) is the CVaR calculation value, β is the confidence level, α is the VaR value under the confidence level β, x is the price, y is the time, R is the time series, f(x, y) is the data distribution function, in the day-ahead market, f(x, y) is the day-ahead market price series, in the real-time market, f(x, y) is the real-time market price series, ρ(y) is the probability density function, Indicates selecting the maximum value, when f(x,y)-α is greater than 0 [f(x,y)-α] + is f(x,y)-α, when f(x,y)-α is not greater than 0 [f(x,y)-α] + is 0; The day-ahead market objective function is: Among them, g da represents the day-ahead market objective function, R da (k) is the profit of the day-ahead market optimization operation in the kth price scenario, C da (k) is the operating cost of the day-ahead market in the kth price scenario, k = 1, 2, …, N, N is the number of price scenarios involved, μ da The risk preference coefficient optimized for the day-ahead market, l k and ξ is an auxiliary variable, β da is the day-ahead market confidence, for l k :ξ-(R da (k)-C da (k))≤l k And l k ≥0, The amount of electricity sold by the integrated energy system to the grid in the day-ahead market. is the price of selling electricity to the grid, is the operating cost of energy storage, is the operating cost of CHP, The day-ahead market clearing price, The amount of electricity purchased from the grid by the integrated energy system in the day-ahead market; The deviation settlement model is: Among them, C dev (k) represents the output deviation in the kth price scenario, abs is the absolute value function, P da (t) is the day-ahead market price at time t, P re (t) is the real-time market price at time t, n k (t) is an auxiliary variable, sign is a sign judgment function, which is 1 if it is greater than 0 and -1 if it is not greater than 0. buy,re (t) is the real-time market purchase volume of the integrated energy system, P buy,da (t) is the day-ahead market purchase volume of the integrated energy system; The real-time market objective function is: Among them, g re represents the real-time market objective function, R re (k) is the profit of real-time market optimization running in the kth price scenario, C re (k) is the operating cost of the real-time market in the kth price scenario, μ re Risk appetite coefficient optimized for real-time market, β re is the day-ahead market confidence, q k is an auxiliary variable, for q k :ξ-(R re (k)-C re (k)-C dev (k))≤q k And q k ≥0.

2. The energy scheduling method according to claim 1, characterized in that: After the price model is established by using the conditional value at risk method, it also includes: Modify the price model to in, is the CVaR calculation value under discrete data, m is the total number of data groups, y k is the y variable data sequence under discrete data.

3. The energy scheduling method according to claim 2, characterized in that: The day-ahead market revenue is obtained according to the day-ahead market objective function, including: Solving the day-ahead market objective function to obtain a day-ahead market declaration curve; The day-ahead market revenue is obtained according to the day-ahead market declaration curve and the day-ahead market clearing electricity price.

4. The energy scheduling method according to claim 1, characterized in that: Solving the real-time market objective function includes: The real-time market objective function is solved using a CPLEX solver.

5. An energy scheduling device, characterized in that: include: The first construction module is used to construct a power balance equation of the integrated energy system according to the operating characteristics of each energy source in the integrated energy system, obtain target parameters according to the power balance equation, and use the conditional value at risk method to establish a price model corresponding to the day-ahead market and a price model corresponding to the real-time market; A second construction module is used to construct a day-ahead market objective function based on the price model corresponding to the day-ahead market and the target parameter with the goal of maximizing the day-ahead market revenue, and obtain the day-ahead market revenue according to the day-ahead market objective function; A third construction module is used to perform settlement modeling according to the target parameters, the spot market trading rules and the output of the integrated energy system in the day-ahead market and the real-time market to obtain a deviation settlement model, and to construct a real-time market objective function according to the day-ahead market income, the price model corresponding to the real-time market and the deviation settlement model with the goal of maximizing the difference between the real-time operation income and the day-ahead market income; An operation scheduling module is used to solve the real-time market objective function, obtain the real-time operation curve of the integrated energy system participating in the spot market, and control the integrated energy system to perform operation scheduling according to the real-time operation curve; The first building block is specifically used to establish a price model using the conditional value-at-risk method: Among them, F β (x, α) is the CVaR calculation value, β is the confidence level, α is the VaR value under the confidence level β, x is the price, y is the time, R is the time series, f(x, y) is the data distribution function, in the day-ahead market, f(x, y) is the day-ahead market price series, in the real-time market, f(x, y) is the real-time market price series, ρ(y) is the probability density function, [f(x, y)-α] + Indicates selecting the maximum value, when f(x,y)-α is greater than 0 [f(x,y)-α] + is f(x,y)-α, when f(x,y)-α is not greater than 0 [f(x,y)-α] + is 0; The day-ahead market objective function is: Among them, g da represents the day-ahead market objective function, R da (k) is the profit of the day-ahead market optimization operation in the kth price scenario, C da (k) is the operating cost of the day-ahead market in the kth price scenario, k = 1, 2, ..., N, N is the number of price scenarios involved, μ da The risk preference coefficient optimized for the day-ahead market, l k and ξ is an auxiliary variable, β da is the day-ahead market confidence, for l k :ξ-(R da (k)-C da (k))≤l k And l k ≥0, The amount of electricity sold by the integrated energy system to the grid in the day-ahead market. is the price of selling electricity to the grid, is the operating cost of energy storage, is the operating cost of CHP, The day-ahead market clearing price, The amount of electricity purchased from the grid by the integrated energy system in the day-ahead market; The deviation settlement model is: Among them, C dev (k) represents the output deviation in the kth price scenario, abs is the absolute value function, P da (t) is the day-ahead market price at time t, P re (t) is the real-time market price at time t, n k (t) is an auxiliary variable, sign is a sign judgment function, which is 1 if it is greater than 0 and -1 if it is not greater than 0. buy,re (t) is the real-time market purchase volume of the integrated energy system, P buy,da (t) is the day-ahead market purchase volume of the integrated energy system; The real-time market objective function is: Among them, g re represents the real-time market objective function, R re (k) is the profit of real-time market optimization running in the kth price scenario, C re (k) is the operating cost of the real-time market in the kth price scenario, μ re Risk appetite coefficient optimized for real-time market, β re is the day-ahead market confidence, q k is an auxiliary variable, for q k :ξ-(R re (k)-C re (k)-C dev (k))≤q k And q k ≥0.

6. An energy scheduling device, characterized in that: include: Memory for storing computer programs; A processor, configured to implement the steps of the energy scheduling method as described in any one of claims 1 to 4 when executing the computer program.

7. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the energy scheduling method according to any one of claims 1 to 4 are implemented.

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