Renewable energy excess consumption and electricity market coupling analysis method and system

Abstract

The invention relates to a renewable energy excess consumption and power market coupling analysis method and system, and belongs to the field of power market and new energy consumption, and the method comprises the steps: selecting coupling analysis participants, and constructing a renewable energy integration model based on the coupling analysis participants; optimizing the renewable energy integration model, determining a model problem boundary and defining assumed conditions of a renewable energy excess consumption market and an electricity market, obtaining a causal loop diagram, and determining variables and constants of the optimized renewable energy integration model; and based on variables and constants of the optimized renewable energy integration model, establishing a function relationship between the variables and the constants, drawing a stock flow diagram to obtain a renewable energy excess consumption and power market coupling model, and performing renewable energy consumption maximization and power cost minimization analysis by using the coupling model to promote power system transformation. The problems that renewable energy excessive consumption and power market lack coupling, and quantitative analysis and research models do not conform to reality are solved.

Description

Technical Field

[0001] The present invention relates to the field of power market and new energy consumption technology, and in particular to a method and system for analyzing the coupling between excess consumption of renewable energy and power market. Background Art

[0003] System dynamics is a scientific modeling tool that integrates relevant theories and methods such as control theory, information theory, and decision theory. It uses electronic computers as tools to simulate and obtain experimental results by establishing system dynamics models. System dynamics is easy to build and learn, and has the characteristics of combining multivariable, qualitative and quantitative analysis. It can handle multi-loop, nonlinear, time-varying complex system problems, facilitate the definition of the causal relationship of relevant factors in the dynamic process, and help researchers quantitatively analyze the impact of relevant factors on the dynamic process. Therefore, some researchers use system dynamics to study related topics of renewable energy consumption trading. However, the relevant research of existing scholars mainly focuses on the innovative research of renewable energy consumption measurement methods, and less research on the accounting methods of renewable energy consumption measurement in different scenarios (such as carbon market, electricity market, etc.), and most of them are combined with carbon market, lacking quantitative analysis of the impact of the mutual coupling between renewable energy consumption trading market and electricity market. At the same time, the construction of some related research models is not in line with the actual situation, and the analysis of the impact effect is not accurate enough. The construction and transformation of the new power system is inseparable from the connection between various trading markets and various green policies, and explores the coupling analysis of excess renewable energy consumption and electricity market. Summary of the invention

[0004] In view of the above analysis, the embodiments of the present invention aim to provide a method and system for analyzing the coupling between excess consumption of renewable energy and the electricity market. Based on the research method of system dynamics, the mutual influence between the excess consumption trading market of renewable energy and the electricity market is fully considered, the interaction mechanism between the RES-E consumption guarantee mechanism and the electricity market is studied, and the low-carbon transformation of the electricity market is actively promoted to solve the technical problems of the lack of coupling analysis and quantitative analysis between the existing excess consumption of renewable energy and the electricity market, and the research model is not in line with reality.

[0005] The present invention provides a method for analyzing excess consumption of renewable energy and coupling with the electricity market, comprising the following steps:

[0006] Step S1, selecting coupling analysis participants and constructing a renewable energy integration model based on the coupling analysis participants;

[0007] Step S2, optimizing the renewable energy integration model, determining the model problem boundary and clarifying the assumptions of the renewable energy excess consumption market and the power market, obtaining a causal loop diagram, and determining the variables and constants of the optimized renewable energy integration model;

[0008] Step S3, based on the variables and constants of the optimized renewable energy integration model, establish a functional relationship between the variables and the constants, draw a stock flow diagram to obtain a renewable energy excess consumption and electricity market coupling model, and use the coupling model to maximize the renewable energy consumption and minimize the electricity cost analysis to promote the transformation of the power system.

[0009] Furthermore, the coupling analysis participants include:

[0010] Thermal power generators, renewable energy generators, grid companies and power users in the power market are multi-objective optimization participants;

[0011] The thermal power generators are divided into carbon quota buyer thermal power generators and carbon quota seller thermal power generators.

[0012] Furthermore, the renewable energy integration model includes a renewable energy excess consumption market subsystem and a power market subsystem;

[0013] The attributes of the renewable energy excess consumption market subsystem and the electricity market subsystem include state variables, rate variables, auxiliary variables and constants.

[0014] Furthermore, the step S2 comprises:

[0015] Step S21, determining an optimized renewable energy integration model problem boundary based on the state variables, rate variables, auxiliary variables and constants of the renewable energy excess consumption market subsystem and the power market subsystem;

[0016] Step S22, clarifying the assumptions of the renewable energy excess consumption market and the electricity market;

[0017] Step S23: drawing a causal loop diagram based on the renewable energy integration model problem boundary and the assumptions;

[0018] Step S24: Determine variables and constants of the optimized renewable energy integration model based on the causal loop diagram.

[0019] Furthermore, the problem boundary of the renewable energy integration model optimized in step S21 includes:

[0020] The factors within the boundary are divided into endogenous factors and exogenous factors;

[0021] The endogenous factors include the endogenous factors of the renewable energy excess consumption market subsystem and the endogenous factors of the power market subsystem;

[0022] The exogenous factors include exogenous factors of the renewable energy excess consumption market subsystem and exogenous factors of the electricity market subsystem.

[0023] Furthermore, the assumptions in step S22 include:

[0024] Hypothesis 1: Market entities that bear the consumption responsibility mainly fulfill their consumption responsibility by actually consuming renewable energy electricity, and the shortfall is made up by purchasing excess consumption capacity;

[0025] Assumption 2: In the renewable energy excess consumption market, the trading entities of excess consumption are electricity users, which are divided into sellers and buyers;

[0026] Assumption 3: Electricity suppliers in the electricity market only consider electricity generated by renewable energy.

[0027] Furthermore, the functional relationship between the variables and the constants in the optimized integrated model includes:

[0028] Total electricity consumption = INTEG (5949*electricity demand growth rate / 12, 5949 / 12);

[0029] Renewable energy access to the grid = renewable energy power generation * (1-grid loss);

[0030] Profit margin of renewable energy manufacturers = (electricity price + excess consumption price - marginal cost of renewable energy);

[0031] Renewable energy generation = renewable energy installed capacity * 2441.18 / 12;

[0032] Renewable energy capacity under construction = INTEG (renewable energy capacity started - renewable energy capacity completed, 0);

[0033] Renewable energy completed construction = DELAY FIXED (renewable energy started construction, 12, 0);

[0034] Renewable energy construction starts = 838.6*(1+renewable energy demand growth rate) / 12 / 2441.18*renewable energy manufacturer's profit margin;

[0035] Minimum renewable energy consumption = annual renewable energy consumption weight * total electricity consumption in Beijing, Tianjin and Hebei;

[0036] Renewable energy consumption weight growth rate = 0.013;

[0037] Excess demand for renewable energy electricity = electricity price * (renewable energy demand - renewable energy access) / renewable energy access;

[0038] Renewable energy installed capacity = INTEG (renewable energy completed construction, 0.36385);

[0039] Excess renewable energy consumption = renewable energy conversion consumption - minimum renewable energy consumption;

[0040] Renewable energy is converted into consumption = renewable energy power generation;

[0041] Marginal cost of renewable energy = 0.5;

[0042] Renewable energy demand = INTEG (838.6*renewable energy demand growth rate / 12, 838.6 / 12);

[0043] Renewable energy demand growth rate = 0.095;

[0044] Annual renewable energy consumption weight = INTEG (0.15*renewable energy consumption weight growth rate / 12, 0.15);

[0045] Electricity price = IF THEN ELSE(SMOOTH3I(Electricity price change, 3, 0.39408)>0.75, 0.75, IF THEN ELSE(SMOOTH3I(Electricity price change, 3, 0.39408)<0.16, 0.16, SMOOTH3I(Electricity price change, 3, 0.39408)));

[0046] Electricity price change = INTEG(excess demand for renewable energy electricity / 140, 0.39408);

[0047] Electricity demand growth rate = 0.095;

[0048] Network loss = 0.06;

[0049] Excess consumption volume buyer holdings = INTEG (excess consumption volume trading volume - excess consumption volume converted into quota, 0);

[0050] Excess consumption transaction volume = IF THEN ELSE (MIN (expected sales of excess consumption, expected purchases of excess consumption) < 0, 0, MIN (expected sales of excess consumption, expected purchases of excess consumption));

[0051] Excess consumption price = MIN (excess consumption price upper limit, MAX (excess consumption price change, excess consumption price lower limit));

[0052] The price ceiling for excess consumption = 0.31;

[0053] The lower limit of the price of excess consumption = 0.08;

[0054] Excess consumption price change = INTEG (excess consumption excess demand, 0.16);

[0055] Excess consumption volume seller holdings = INTEG (excess consumption volume trading volume - excess consumption volume of renewable energy, 4.43);

[0056] Excess consumption excess demand = IF THEN ELSE ((Expected purchase of excess consumption - expected sale of excess consumption) / 100000<-0.05,-0.05,IF THEN ELSE ((Expected purchase of excess consumption - expected sale of excess consumption) / 100000>0.05,0.05,(Expected purchase of excess consumption - expected sale of excess consumption) / 100000));

[0057] Excess consumption is converted into quota = minimum consumption of renewable energy;

[0058] Expected sales volume of excess consumption = price of excess consumption / 0.31*excess consumption seller holdings / 100;

[0059] Expected purchase volume of excess consumption = IF THEN ELSE (excess consumption buyer's holdings > excess consumption converted into quota, 0, 0.06 / excess consumption price*(excess consumption converted into quota - excess consumption buyer's holdings)).

[0060] Furthermore, the endogenous factors of the renewable energy excess consumption market subsystem include renewable energy installed capacity under construction, renewable energy installed capacity, renewable energy demand, minimum renewable energy consumption, excess consumption price changes, excess consumption seller holdings, excess consumption buyer holdings, excess demand for excess consumption, excess renewable energy consumption, excess consumption transaction volume, conversion of excess consumption into quotas, profit margin of renewable energy manufacturers, renewable energy demand, conversion of renewable energy into consumption, excess consumption price, expected excess consumption sales, expected excess consumption purchases, renewable energy consumption weight, minimum renewable energy consumption, and renewable energy power generation;

[0061] The exogenous factors of the renewable energy excess consumption market subsystem include the renewable energy demand growth rate, renewable energy consumption weight growth rate, excess consumption price upper limit, and excess consumption price lower limit;

[0062] The endogenous factors of the power market subsystem include changes in power prices, total power consumption; excess demand for renewable energy power, power prices, and renewable energy power generation;

[0063] The exogenous factors of the power market subsystem include network losses and power demand growth rate.

[0064] Furthermore, the renewable energy power generation types include wind power generation, photovoltaic power generation and biomass power generation.

[0065] This specification also discloses a renewable energy excess consumption and power market coupling analysis system, which is characterized by comprising:

[0066] An integration model building module M1 is used to select coupling analysis participants and build a renewable energy integration model based on the coupling analysis participants;

[0067] The integration model optimization module M2 is used to optimize the renewable energy integration model, determine the model problem boundary and clarify the assumptions of the renewable energy excess consumption market and the power market, obtain a causal loop diagram, and determine the variables and constants of the optimized renewable energy integration model;

[0068] A coupling model establishment module M3 is used to establish a functional relationship between the variables and the constants based on the variables and constants of the optimized renewable energy integration model, draw a stock flow diagram to obtain a coupling model of excess renewable energy consumption and electricity market;

[0069] The coupling analysis module M4 is used to analyze the maximum renewable energy consumption and the minimum electricity cost based on the coupling model to promote the transformation of the power system.

[0070] Compared with the prior art, the present invention can achieve at least one of the following beneficial effects:

[0071] 1. The present invention uses multi-objective optimization theory to seek the optimal decision-making behavior of power generation enterprises, power grid enterprises and power users under the coupling mechanism of excess consumption transaction and power market, and constructs the profit function of each subject under the coupling mechanism. In the subject construction, the present invention considers more comprehensive subjects, can simulate the coupling effect of excess consumption transaction market and power market more specifically, and is more in line with the actual situation;

[0072] 2. Based on the existing development scale of the power market and the policy support for excess consumption transaction, the present invention studies the impact of excess consumption transaction on the decision-making behavior of various entities in the power market. The excess consumption transaction situation considered is the excess consumption market transaction mechanism currently being implemented, which is more realistic.

[0073] 3. The present invention takes into account the conversion of excess consumption and renewable energy power generation, and considers the coupling effect and linkage relationship of different market mechanisms, which is more in line with the actual situation;

[0074] 4. The simulation results of the basic situation using the model of the present invention are helpful to explore the impact of the implementation of the renewable energy consumption guarantee mechanism on the power market, thereby adjusting the decision-making behavior of each consumption entity under relevant policies, and helping each entity to maximize its benefits while meeting the emission reduction requirements. At the same time, it promotes the coordinated development of the renewable energy consumption transaction market and the power market, and promotes the transformation of the new power system.

[0075] In the present invention, the above-mentioned technical solutions can also be combined with each other to achieve more preferred combination solutions. Other features and advantages of the present invention will be described in the subsequent description, and some advantages can become obvious from the description, or can be understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained through the contents particularly pointed out in the description and the drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0076] The drawings are only for the purpose of illustrating particular embodiments and are not to be considered limiting of the present invention. Like reference symbols denote like components throughout the drawings.

[0077] Figure 1 This is a flow chart of a method for analyzing excess consumption of renewable energy and coupling with the electricity market in one embodiment of the present invention;

[0078] Figure 2 A causal loop diagram formed by the interaction between the excess consumption volume transaction of renewable energy and the power market in one embodiment of the present invention;

[0079] Figure 3 A stock-flow diagram formed by the interaction between the excess consumption volume transaction of renewable energy and the power market in one embodiment of the present invention;

[0080] Figure 4 It is a diagram showing the excess consumption price and the excess consumption price change trend obtained by simulating in a basic scenario according to an embodiment of the present invention;

[0081] Figure 5 It is a trend diagram of the excess consumption seller holdings and the excess consumption buyer holdings obtained by simulation under a basic scenario in one embodiment of the present invention;

[0082] Figure 6 A graph showing a change trend of the expected sales volume of excess consumption and the expected purchase volume of excess consumption obtained by simulation under a basic scenario in one embodiment of the present invention;

[0083] Figure 7This is a trend diagram of excess consumption obtained by simulation under different renewable energy consumption weight growth rate scenarios according to an embodiment of the present invention;

[0084] Figure 8 It is a graph of converting excess consumption obtained by simulation under different renewable energy consumption weight growth rate scenarios into a quota change trend diagram according to an embodiment of the present invention;

[0085] Fig. 9 This is a trend diagram of excess consumption transaction volume obtained by simulation under different renewable energy consumption weight growth rate scenarios according to an embodiment of the present invention;

[0086] Fig.10 This is a graph showing a trend of excess renewable energy consumption obtained by simulation under different renewable energy demand growth rate scenarios according to an embodiment of the present invention;

[0087] Fig.11 This is a price trend diagram of excess consumption obtained by simulation under different renewable energy demand growth rate scenarios according to an embodiment of the present invention;

[0088] Fig.12 A graph showing a trend of profit margin changes of renewable energy manufacturers obtained by simulation under different renewable energy demand growth rate scenarios according to an embodiment of the present invention;

[0089] Fig.13 This is a schematic diagram of a renewable energy excess consumption and electricity market coupling analysis system according to an implementation mode of the present invention. DETAILED DESCRIPTION

[0090] The preferred embodiments of the present invention are described in detail below in conjunction with the accompanying drawings, wherein the accompanying drawings constitute a part of this application and are used together with the embodiments of the present invention to illustrate the principles of the present invention, but are not used to limit the scope of the present invention.

[0091] The purpose of the present invention is to provide a coupling analysis method and system for excess consumption of renewable energy and electricity market. Based on the research method of system dynamics, the present invention fully considers the impact of the implementation of the excess consumption guarantee mechanism of renewable energy RES-E (Renewable Energy Sources for Electricity) on the electricity market, and aims to further quantify the coupling effect between the RES-E consumption guarantee mechanism and the electricity market trading mechanism, and actively promote the transformation of the new power system.

[0092] The present invention draws the causal loop diagram of the renewable energy excess consumption market subsystem and the power market subsystem to obtain the causal relationship and feedback loop between the system internal variables and constants under the operation of the power market and the renewable energy excess consumption market. Then, by determining the properties of the model variables and constants and establishing the mathematical relationship between the variables and constants, the system dynamics model is obtained, which intuitively shows the internal logical relationship and interaction mechanism of the power market system and the excess consumption market.

[0093] Embodiment 1:

[0094] A specific embodiment of the present invention discloses a method for analyzing excess consumption of renewable energy and coupling with the electricity market, wherein steps S1-S3:

[0095] Step S1, selecting coupling analysis participants and constructing a renewable energy integration model based on the coupling analysis participants;

[0096] Step S2, optimizing the renewable energy integration model, determining the model problem boundary and clarifying the assumptions of the renewable energy excess consumption market and the power market, obtaining a causal loop diagram, and determining the variables and constants of the optimized renewable energy integration model;

[0097] Step S3, based on the variables and constants of the optimized renewable energy integration model, establish a functional relationship between the variables and the constants, draw a stock flow diagram to obtain a renewable energy excess consumption and electricity market coupling model, and use the coupling model to maximize the renewable energy consumption and minimize the electricity cost analysis to promote the transformation of the power system.

[0098] Specifically, step S1 includes steps S11 - S12 .

[0099] Step S11, selecting coupling analysis participants.

[0100] Select thermal power generators, renewable energy generators, power grid companies and power users in the power market as multi-objective optimization participants;

[0101] The thermal power generators are divided into carbon quota buyer thermal power generators and carbon quota seller thermal power generators.

[0102] Step S12, constructing a renewable energy integration model based on the coupling analysis participants;

[0103] The renewable energy integration model is composed of a renewable energy excess consumption trading market subsystem and a power market subsystem. The attributes of the renewable energy excess consumption trading market subsystem and the power market subsystem include state variables, rate variables, auxiliary variables and constants.

[0104] (1) The state variables, rate variables, auxiliary variables and constants of the excess consumption trading market subsystem are as follows:

[0105] The state variables include: the price change of excess consumption, the holdings of excess consumption buyers, and the holdings of excess consumption sellers;

[0106] Rate variables include: excess consumption and excess demand, excess consumption trading volume, excess consumption of renewable energy, and conversion of excess consumption into quotas;

[0107] Auxiliary variables include: expected sales of excess consumption, expected purchases of excess consumption, and excess consumption price;

[0108] The constants include: the upper limit of the price for excess consumption and the lower limit of the price for excess consumption.

[0109] (2) The state variables, rate variables, auxiliary variables and constants of the power market subsystem are as follows:

[0110] State variables include: changes in electricity prices, demand for renewable energy, renewable energy capacity under construction, renewable energy installed capacity, annual renewable energy consumption weight, and total electricity consumption.

[0111] The rate variables include: excess demand for renewable energy electricity, completion of renewable energy construction, and commencement of renewable energy construction.

[0112] Auxiliary variables include: electricity price, profit margin of renewable energy manufacturers, access to the grid for renewable energy, renewable energy power generation, conversion of renewable energy into consumption, and minimum renewable energy consumption.

[0113] The constants include: renewable energy demand growth rate, renewable energy consumption weight growth rate, electricity demand growth rate, network loss, and renewable energy marginal cost.

[0114] Tables 1 to 4 show the names, properties and unit descriptions of various variables and constants.

[0115] Table 1 Explanation of variables and constants in the renewable energy excess consumption trading market subsystem

[0116] Variable and constant names Variable properties Variable units Changes in the price of excess consumption State variables Yuan / kWh Excess consumption buyer holdings State variables 100 million kWh / month Excess consumption amount held by sellers State variables 100 million kWh / month Excess consumption Excess demand Rate Variable 100 million kWh / month Excess consumption volume trading volume Rate Variable 100 million kWh / month Excess consumption of renewable energy Rate Variable 100 million kWh / month Excess consumption converted into quotas Rate Variable 100 million kWh / month Expected sales volume of excess consumption Auxiliary variables 100 million kWh / month Expected purchase volume of excess consumption Auxiliary variables 100 million kWh / month Excess consumption price Auxiliary variables Yuan / kWh Price ceiling for excess consumption constant Yuan / kWh Excess consumption price floor constant Yuan / kWh

[0117] Table 2 Description of variables and constants of the electricity market subsystem

[0118]

[0119]

[0120] Table 3 Parameter settings of excess consumption trading market subsystem

[0121] Price ceiling for excess consumption 0.31 yuan / kWh Excess consumption price floor 0.08 yuan / kWh

[0122] Table 4 Parameter settings in the power market subsystem

[0123]

[0124]

[0125] Specifically, step S2 includes steps S21-S23.

[0126] Step S21, determining an optimized renewable energy integration model problem boundary based on the state variables, rate variables, auxiliary variables and constants of the renewable energy excess consumption market subsystem and the power market subsystem.

[0127] Based on the state variables, rate variables, auxiliary variables and constants, a model problem boundary is determined.

[0128] The problem boundary of the renewable energy integration model contains many research objects, scopes and problems that are expected to be solved, and the division into endogenous factors and exogenous factors is an important part of it.

[0129] Endogenous factors are located within the boundaries and are directly related to the research question. Researchers can control or observe endogenous factors. Exogenous factors are located outside the boundaries and are external factors that are not directly controlled by researchers. Researchers need to consider the impact of exogenous factors, but cannot directly manipulate them.

[0130] The endogenous and exogenous factors are determined as follows:

[0131] (1) The endogenous factors of the renewable energy excess consumption market subsystem include renewable energy installed capacity under construction, renewable energy installed capacity, renewable energy demand, minimum renewable energy consumption, excess consumption price changes, excess consumption sellers' holdings, excess consumption buyers' holdings, excess demand for excess consumption, renewable energy excess consumption, excess consumption transaction volume, conversion of excess consumption into quotas, profit margin of renewable energy manufacturers, renewable energy demand, conversion of renewable energy into consumption, excess consumption price, expected excess consumption sales, expected excess consumption purchases, renewable energy consumption weight, minimum renewable energy consumption, and renewable energy power generation;

[0132] (2) The exogenous factors of the renewable energy excess consumption market subsystem include the renewable energy demand growth rate, renewable energy consumption weight growth rate, excess consumption price upper limit, and excess consumption price lower limit;

[0133] (3) The endogenous factors of the power market subsystem include changes in power prices, total power consumption; excess demand for renewable energy power, power prices, and renewable energy power generation;

[0134] (4) The exogenous factors of the electricity market subsystem include network losses and electricity demand growth rate.

[0135] Step S22, clarifying the assumptions of the renewable energy excess consumption market and the electricity market.

[0136] Assumptions include:

[0137] Assumption 1: Market entities that bear the responsibility of consumption mainly fulfill their consumption responsibilities by actually consuming renewable energy electricity, and the shortfall is made up by purchasing excess consumption.

[0138] Assumption 2: In the renewable energy excess consumption market, the trading entities of excess consumption are electricity users, which are divided into sellers and buyers.

[0139] Assumption 3: Electricity suppliers in the electricity market only consider electricity generated by renewable energy.

[0140] Renewable energy generation types include: wind power generation, photovoltaic power generation and biomass power generation.

[0141] Step S23: Draw a causal loop diagram based on the renewable energy integration model problem boundary and the assumptions.

[0142] Determine the causal relationship between each endogenous and exogenous factor in the power market subsystem and the excess consumption subsystem, and draw a causal loop diagram between the excess consumption market and the power market;

[0143] Figure 2 This is a causal loop diagram formed by the interaction between renewable energy excess consumption trading and the power market. The main feedback loops in this causal loop diagram are as follows:

[0144] ① Renewable energy installed capacity construction → (+) renewable energy installed capacity → (+) renewable energy power generation → (+) power supply → (-) power price → (+) renewable energy manufacturer profit margin → (+) renewable energy installed capacity construction;

[0145] The construction of renewable energy installations increases the installed capacity of renewable energy, which in turn increases the amount of renewable energy generated, increases electricity supply, reduces electricity prices, and reduces the profit margins of renewable energy manufacturers. Driven by profit, renewable energy manufacturers will reduce the construction of renewable energy installations.

[0146] ② Renewable energy installed capacity construction → (+) Renewable energy installed capacity → (+) Renewable energy power generation → (+) Renewable energy conversion to consumption → (+) Excess consumption seller holdings → (+) Excess consumption expected sales → (-) Excess consumption excess demand → (+) Excess consumption price → (+) Renewable energy manufacturer profit margin → (+) Renewable energy installed capacity construction;

[0147] The construction of renewable energy installed capacity increases renewable energy installed capacity, renewable energy power generation increases, and then the conversion of renewable energy into consumption increases, the sellers' holdings of excess consumption increase, the expected sales of excess consumption increase, the excess demand for excess consumption decreases, the price of excess consumption decreases, the profit margins of renewable energy manufacturers decreases, and driven by profits, the construction of renewable energy installed capacity decreases.

[0148] ③ Renewable energy installed capacity construction → (+) Renewable energy installed capacity → (+) Renewable energy power generation → (+) Renewable energy conversion to consumption → (+) Excess consumption seller holdings → (+) Excess consumption buyer holdings → (-) Excess consumption expected purchase volume → (+) Excess consumption excess demand → (+) Excess consumption price → (+) Renewable energy manufacturer profit margin → (+) Renewable energy installed capacity construction;

[0149] The construction of renewable energy installed capacity increases the installed capacity of renewable energy, increases the amount of renewable energy power generation, and then increases the amount of renewable energy converted into consumption. The sellers' holdings of excess consumption increase, the buyers' holdings of excess consumption increase, the expected purchase volume of excess consumption decreases, the excess demand for excess consumption decreases, the price of excess consumption decreases, the profit margins of renewable energy manufacturers decrease, and renewable energy manufacturers reduce installed capacity.

[0150] ④ Renewable energy installed capacity construction → (+) Renewable energy installed capacity → (+) Renewable energy power generation → (+) Renewable energy conversion to consumption → (+) Excess consumption demand → (+) Excess consumption buyer holdings → (-) Excess consumption expected purchase → (+) Excess consumption excess demand → (+) Excess consumption price → (+) Renewable energy manufacturer profit margin → (+) Renewable energy installed capacity construction;

[0151] The construction of renewable energy installed capacity increases the installed capacity of renewable energy, increases the amount of renewable energy power generation, and then increases the amount of renewable energy converted into consumption, increases the demand for excess consumption, increases the amount of excess consumption held by buyers, decreases the expected purchase of excess consumption, decreases the excess demand for excess consumption, decreases the price of excess consumption, decreases the profit margins of renewable energy manufacturers, and causes renewable energy manufacturers to reduce installed capacity.

[0152] ⑤ Renewable energy installed capacity construction → (+) Renewable energy installed capacity → (+) Renewable energy power generation → (+) Renewable energy conversion to consumption → (+) Excess consumption demand → (+) Excess consumption seller holdings → (+) Excess consumption expected sales → (-) Excess consumption excess demand → (+) Excess consumption price → (+) Renewable energy manufacturer profit margin → (+) Renewable energy installed capacity construction;

[0153] The construction of renewable energy installed capacity increases the installed capacity of renewable energy, increases the amount of renewable energy power generation, and then increases the amount of renewable energy converted into consumption, increases the demand for excess consumption, increases the amount of excess consumption sellers hold, increases the expected sales volume of excess consumption, reduces the excess demand for excess consumption, reduces the price of excess consumption, reduces the profit margins of renewable energy manufacturers, and causes renewable energy manufacturers to reduce installed capacity.

[0154] Step S24: Determine variables and constants of the optimized renewable energy integration model based on the causal loop diagram.

[0155] The endogenous and exogenous factors in the electricity market subsystem and the excess consumption subsystem are classified, and the state variables, rate variables, auxiliary variables and constants of the electricity market subsystem and the excess consumption subsystem in the renewable energy integration model are determined.

[0156] (1) The state variables, rate variables, auxiliary variables and constants of the excess consumption market subsystem are as follows:

[0157] State variables include: renewable energy installed capacity under construction, renewable energy installed capacity, renewable energy demand, minimum renewable energy consumption, price changes of excess consumption, excess consumption sellers’ holdings, and excess consumption buyers’ holdings;

[0158] Rate variables: renewable energy construction started, renewable energy construction completed, excess consumption exceeding demand, renewable energy excess consumption, excess consumption trading volume, excess consumption conversion to quota;

[0159] Auxiliary variables: profit margin of renewable energy manufacturers, renewable energy power generation, renewable energy demand, conversion of renewable energy into consumption, price of excess consumption, expected sales of excess consumption, expected purchase of excess consumption, renewable energy consumption weight, minimum renewable energy consumption;

[0160] Constants: growth rate of renewable energy demand, growth rate of renewable energy consumption weight, upper limit of excess consumption price, lower limit of excess consumption price, marginal cost of renewable energy.

[0161] (2) The state variables, rate variables, auxiliary variables and constants of the power market subsystem are as follows:

[0162] State variables include: electricity price changes, total electricity consumption;

[0163] Rate variables include: excess demand for renewable electricity;

[0164] Auxiliary variables include: electricity price, renewable energy generation;

[0165] The constants include: network losses, electricity demand growth rate;

[0166] Table 1-2 shows the name, properties and unit description of each variable and constant in the optimized renewable energy integration model.

[0167] Table 1 Explanation of variables and constants in the excess consumption market subsystem

[0168]

[0169]

[0170] Table 2 Description of variables and constants of the electricity market subsystem

[0171]

[0172]

[0173] Specifically, step S3 includes steps S31-S33.

[0174] Step S31, based on the variables and constants of the renewable energy integration model, establishing a functional relationship between the variables and constants in the model.

[0175] Establish the functional relationship between variables and constants in the excess consumption market and electricity market subsystems, and determine the cross-functional relationship between variables and constants in the excess consumption market subsystem and electricity market subsystem.

[0176] The functional relationship between the variables and constants established in this step is as follows:

[0177] Total electricity consumption = INTEG (5949*electricity demand growth rate / 12, 5949 / 12);

[0178] Renewable energy access to the grid = renewable energy power generation * (1-grid loss);

[0179] Profit margin of renewable energy manufacturers = (electricity price + excess consumption price - marginal cost of renewable energy);

[0180] Renewable energy generation = renewable energy installed capacity * 2441.18 / 12;

[0181] Renewable energy capacity under construction = INTEG (renewable energy capacity started - renewable energy capacity completed, 0);

[0182] Renewable energy completed construction = DELAY FIXED (renewable energy started construction, 12, 0);

[0183] Renewable energy construction starts = 838.6*(1+renewable energy demand growth rate) / 12 / 2441.18*renewable energy manufacturer's profit margin;

[0184] Minimum renewable energy consumption = annual renewable energy consumption weight * total electricity consumption in Beijing, Tianjin and Hebei;

[0185] Renewable energy consumption weight growth rate = 0.013;

[0186] Excess demand for renewable energy electricity = electricity price * (renewable energy demand - renewable energy access) / renewable energy access;

[0187] Renewable energy installed capacity = INTEG (renewable energy completed construction, 0.36385);

[0188] Excess renewable energy consumption = renewable energy conversion consumption - minimum renewable energy consumption;

[0189] Renewable energy is converted into consumption = renewable energy power generation;

[0190] Marginal cost of renewable energy = 0.5;

[0191] Renewable energy demand = INTEG (838.6*renewable energy demand growth rate / 12, 838.6 / 12);

[0192] Renewable energy demand growth rate = 0.095;

[0193] Annual renewable energy consumption weight = INTEG (0.15*renewable energy consumption weight growth rate / 12, 0.15);

[0194] Electricity price = IF THEN ELSE(SMOOTH3I(Electricity price change, 3, 0.39408)>0.75, 0.75, IF THEN ELSE(SMOOTH3I(Electricity price change, 3, 0.39408)<0.16, 0.16, SMOOTH3I(Electricity price change, 3, 0.39408)));

[0195] Electricity price change = INTEG(excess demand for renewable energy electricity / 140, 0.39408);

[0196] Electricity demand growth rate = 0.095;

[0197] Network loss = 0.06;

[0198] Excess consumption volume buyer holdings = INTEG (excess consumption volume trading volume - excess consumption volume converted into quota, 0);

[0199] Excess consumption transaction volume = IF THEN ELSE (MIN (expected sales of excess consumption, expected purchases of excess consumption) < 0, 0, MIN (expected sales of excess consumption, expected purchases of excess consumption));

[0200] Excess consumption price = MIN (excess consumption price upper limit, MAX (excess consumption price change, excess consumption price lower limit));

[0201] The price ceiling for excess consumption = 0.31;

[0202] The lower limit of the price of excess consumption = 0.08;

[0203] Excess consumption price change = INTEG (excess consumption excess demand, 0.16);

[0204] Excess consumption volume seller holdings = INTEG (excess consumption volume trading volume - excess consumption volume of renewable energy, 4.43);

[0205] Excess consumption excess demand = IF THEN ELSE ((Expected purchase of excess consumption - expected sale of excess consumption) / 100000<-0.05,-0.05,IF THEN ELSE ((Expected purchase of excess consumption - expected sale of excess consumption) / 100000>0.05,0.05,(Expected purchase of excess consumption - expected sale of excess consumption) / 100000));

[0206] Excess consumption is converted into quota = minimum consumption of renewable energy;

[0207] Expected sales volume of excess consumption = price of excess consumption / 0.31*excess consumption seller holdings / 100;

[0208] Expected purchase volume of excess consumption = IF THEN ELSE (excess consumption buyer's holdings > excess consumption converted into quota, 0, 0.06 / excess consumption price*(excess consumption converted into quota - excess consumption buyer's holdings)).

[0209] Step S32, based on the functional relationship between the optimized renewable energy integration model variables and constants and on the basis of the causal loop diagram, a stock flow diagram is drawn to obtain a renewable energy excess consumption and electricity market coupling model.

[0210] Based on the causal loop diagram of the system formed by the interaction between the renewable energy excess consumption market subsystem and the electricity market subsystem in step S2, combined with the classification of variables and constants, and the functional relationship between each variable and constant. By using different symbols to represent different types of variables and different types of arrows to represent different functional relationships, a stock flow diagram of the interaction between the renewable energy excess consumption market and the electricity market is drawn, such as Figure 3 As shown, at this point, the establishment of the coupling model of renewable energy excess consumption market and electricity market has been completed.

[0211] The coupling model can be used to solve the technical problems of the lack of coupling analysis and quantitative analysis between the existing renewable energy excess consumption and the power market, as well as the research model not being in line with reality. Further quantification can be made on the mutual coupling between the renewable energy consumption guarantee mechanism and the power market trading mechanism, and actively promote the transformation of the new power system.

[0212] Step S33 is used to analyze the maximum renewable energy consumption and the minimum electricity cost based on the coupling model to promote the transformation of the power system.

[0213] (1) Data collection and model parameter setting;

[0214] Collect electricity market and renewable energy data, and set the variables and constants of the coupled model.

[0215] (2) Setting the objective function based on the coupled analysis participants;

[0216] Based on the coupling analysis participants and the coupling model, an objective function is set, such as maximizing the amount of renewable energy consumption and minimizing electricity costs.

[0217] (3) Solve and analyze the model.

[0218] Based on the functional relationship between the variables and constants, the model is solved, and through sensitivity analysis, the sensitivity of the coupling model to different parameters and assumptions is evaluated to determine which factors have the greatest impact on the transformation of the power system.

[0219] Analyze the solution results to understand the relationship between excess renewable energy consumption and the electricity market under different scenarios.

[0220] (4) Analysis of the interaction between the renewable energy excess consumption market and the electricity market;

[0221] Based on the model solution results, the interactive effect between the renewable energy excess consumption market and the electricity market is analyzed;

[0222] Assess the operation of the renewable energy excess consumption market, including the price of excess consumption and the holdings of buyers and sellers;

[0223] Analyze electricity price fluctuations, renewable energy demand, and generation in electricity markets.

[0224] (5) Formulate policies and strategies;

[0225] Formulate policies and strategies based on the results of coupled model analysis to promote the transformation of the power system;

[0226] This may include formulating incentive policies to encourage the increase of renewable energy and improve the market competitiveness of excess consumption;

[0227] It is possible to consider introducing policies such as renewable energy power generation subsidies and carbon emission quota markets to reduce carbon emissions.

[0228] (6) Model verification and iteration, monitoring and feedback, and continuous optimization.

[0229] Apply the developed policies and strategies to the power system and monitor the actual effects;

[0230] Continuously optimize and iterate the coupling model, and make adjustments and improvements based on actual results to better promote the transformation of the power system;

[0231] Based on the monitoring results, policies and strategies are adjusted in a timely manner to ensure the sustainability of the power system and the consumption of clean energy.

[0232] The Vensim software is used to simulate the coupling model of excess renewable energy consumption and electricity market. Figure 4-Figure 12 This is the simulation result. Figure 4 The excess consumption price and excess consumption price change trend chart obtained by simulation under the basic scenario; Figure 5 The trend chart of the excess consumption sellers’ holdings and the excess consumption buyers’ holdings obtained by simulation under the basic scenario; Figure 6 The expected sales volume of excess consumption and the expected purchase volume of excess consumption obtained by simulation under the basic scenario are shown in the trend chart; Figure 7 The trend diagram of excess consumption obtained by simulation under different renewable energy consumption weight growth rate scenarios; Figure 8 The excess consumption obtained by simulation under different renewable energy consumption weight growth rate scenarios is converted into a quota change trend chart; Fig. 9 The trend chart of excess consumption transaction volume obtained by simulation under different renewable energy consumption weight growth rate scenarios; Fig.10The trend diagram of excess renewable energy consumption obtained by simulation under different renewable energy demand growth rate scenarios; Fig.11 The price trend chart of excess consumption obtained by simulation under different renewable energy demand growth rate scenarios; Fig.12 The profit margin change trend of renewable energy manufacturers obtained by simulation under different renewable energy demand growth rate scenarios.

[0233] Embodiment 2:

[0234] Another embodiment of the present invention discloses a system for analyzing the coupling between excess consumption of renewable energy and the electricity market, thereby realizing the method for analyzing the coupling between excess consumption of renewable energy and the electricity market in Embodiment 1. The specific implementation of each module refers to the corresponding description in Embodiment 1.

[0235] like Fig.13 As shown, the system includes an integrated model construction module M1, an integrated model parameter optimization module M2, a coupling model establishment module M3 and a coupling analysis module M4.

[0236] An integration model building module M1 is used to select coupling analysis participants and build a renewable energy integration model based on the coupling analysis participants;

[0237] The integration model optimization module M2 is used to optimize the renewable energy integration model, determine the model problem boundary and clarify the assumptions of the renewable energy excess consumption market and the power market, obtain a causal loop diagram, and determine the variables and constants of the optimized renewable energy integration model;

[0238] A coupling model establishment module M3 is used to establish a functional relationship between the variables and the constants based on the variables and constants of the optimized renewable energy integration model, draw a stock flow diagram to obtain a coupling model of excess renewable energy consumption and electricity market;

[0239] The coupling analysis module M4 is used to analyze the maximum renewable energy consumption and the minimum electricity cost based on the coupling model to promote the transformation of the power system.

[0240] Since the system in this embodiment and the method in Embodiment 1 are related in some respects, they can be mutually referenced, and are not described here in detail. Since the system embodiment and the above method embodiment have the same principle, the system embodiment also has the corresponding technical effects of the above method embodiment.

[0241] The beneficial effects brought about by the technical solution of the present invention are as follows:

[0242] 1. The present invention uses multi-objective optimization theory to seek the optimal decision-making behavior of power generation enterprises, power grid enterprises and power users under the coupling mechanism of excess consumption transaction and power market, and constructs the profit function of each subject under the coupling mechanism. In the subject construction, the present invention considers more comprehensive subjects, can simulate the coupling effect of excess consumption transaction market and power market more specifically, and is more in line with the actual situation;

[0243] 2. Based on the existing development scale of the power market and the policy support for excess consumption transaction, the present invention studies the impact of excess consumption transaction on the decision-making behavior of various entities in the power market. The excess consumption transaction situation considered is the excess consumption market transaction mechanism currently being implemented, which is more realistic.

[0244] 3. The present invention takes into account the conversion of excess consumption and renewable energy power generation, and considers the coupling effect and linkage relationship of different market mechanisms, which is more in line with the actual situation;

[0245] 4. The simulation results of the basic situation using the model of the present invention are helpful to explore the impact of the implementation of the renewable energy power (RES-E) consumption guarantee mechanism on the power market, so as to adjust the decision-making behavior of each consumption subject under relevant policies, and help each subject to maximize its benefits while meeting the emission reduction requirements. At the same time, it promotes the coordinated development of the RES-E consumption volume trading market and the power market, and promotes the transformation of the new power system.

[0246] Those skilled in the art will appreciate that all or part of the processes of the above-mentioned embodiments can be implemented by instructing related hardware through a computer program, and the program can be stored in a computer-readable storage medium, wherein the computer-readable storage medium is a disk, an optical disk, a read-only storage memory, or a random access memory, etc.

[0247] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by any technician familiar with the technical field within the technical scope disclosed by the present invention should be covered within the protection scope of the present invention.

Claims

1. A coupling analysis method for renewable energy excess consumption and electricity market, characterized in that: The steps include: Step S1, selecting coupling analysis participants and constructing a renewable energy integration model based on the coupling analysis participants; Step S2, optimizing the renewable energy integration model, determining the model problem boundary and clarifying the assumptions of the renewable energy excess consumption market and the power market, obtaining a causal loop diagram, and determining the variables and constants of the optimized renewable energy integration model; Step S3, based on the variables and constants of the optimized renewable energy integration model, establish a functional relationship between the variables and the constants, draw a stock flow diagram to obtain a renewable energy excess consumption and electricity market coupling model, and use the coupling model to maximize the renewable energy consumption and minimize the electricity cost analysis to promote the transformation of the power system.

2. The coupling analysis method according to claim 1, characterized in that: The coupling analysis participants include: Thermal power generators, renewable energy generators, grid companies and power users in the power market are multi-objective optimization participants; The thermal power generators are divided into carbon quota buyer thermal power generators and carbon quota seller thermal power generators.

3. The coupling analysis method according to claim 2, characterized in that: The renewable energy integration model includes a renewable energy excess consumption market subsystem and a power market subsystem; The attributes of the renewable energy excess consumption market subsystem and the electricity market subsystem include state variables, rate variables, auxiliary variables and constants.

4. The coupling analysis method according to claim 3, characterized in that: The step S2 comprises: Step S21, determining an optimized renewable energy integration model problem boundary based on the state variables, rate variables, auxiliary variables and constants of the renewable energy excess consumption market subsystem and the power market subsystem; Step S22, clarifying the assumptions of the renewable energy excess consumption market and the electricity market; Step S23: drawing a causal loop diagram based on the renewable energy integration model problem boundary and the assumptions; Step S24: Determine variables and constants of the optimized renewable energy integration model based on the causal loop diagram.

5. The coupling analysis method according to claim 4, characterized in that: The problem boundary of the renewable energy integration model optimized in step S21 includes: The factors within the boundary are divided into endogenous factors and exogenous factors; The endogenous factors include the endogenous factors of the renewable energy excess consumption market subsystem and the endogenous factors of the power market subsystem; The exogenous factors include exogenous factors of the renewable energy excess consumption market subsystem and exogenous factors of the electricity market subsystem.

6. The coupling analysis method according to claim 5, characterized in that: The assumptions in step S22 include: Hypothesis 1: Market entities that bear the consumption responsibility mainly fulfill their consumption responsibility by actually consuming renewable energy electricity, and the shortfall is made up by purchasing excess consumption capacity; Assumption 2: In the renewable energy excess consumption market, the trading entities of excess consumption are electricity users, which are divided into sellers and buyers; Assumption 3: Electricity suppliers in the electricity market only consider electricity generated by renewable energy.

7. The coupling analysis method according to claim 6, characterized in that: The functional relationship between the variables and constants in the optimized integrated model includes: Total electricity consumption = INTEG (5949*electricity demand growth rate / 12, 5949 / 12); Renewable energy access to the grid = renewable energy power generation * (1-grid loss); Profit margin of renewable energy manufacturers = (electricity price + excess consumption price - marginal cost of renewable energy); Renewable energy generation = renewable energy installed capacity * 2441.18 / 12; Renewable energy capacity under construction = INTEG (renewable energy capacity started - renewable energy capacity completed, 0); Renewable energy completed construction = DELAY FIXED (renewable energy started construction, 12, 0); Renewable energy construction starts = 838.6*(1+renewable energy demand growth rate) / 12 / 2441.18*renewable energy manufacturer's profit margin; Minimum renewable energy consumption = annual renewable energy consumption weight * total electricity consumption in Beijing, Tianjin and Hebei; Renewable energy consumption weight growth rate = 0.013; Excess demand for renewable energy electricity = electricity price * (renewable energy demand - renewable energy access) / renewable energy access; Renewable energy installed capacity = INTEG (renewable energy completed construction, 0.36385); Excess renewable energy consumption = renewable energy conversion consumption - minimum renewable energy consumption; Renewable energy is converted into consumption = renewable energy power generation; Marginal cost of renewable energy = 0.5; Renewable energy demand = INTEG (838.6*renewable energy demand growth rate / 12, 838.6 / 12); Renewable energy demand growth rate = 0.095; Annual renewable energy consumption weight = INTEG (0.15*renewable energy consumption weight growth rate / 12, 0.15); Electricity price = IF THEN ELSE(SMOOTH3I(Electricity price change, 3, 0.39408)>0.75, 0.75, IF THEN ELSE(SMOOTH3I(Electricity price change, 3, 0.39408)<0.16, 0.16, SMOOTH3I(Electricity price change, 3, 0.39408))); Electricity price change = INTEG(excess demand for renewable energy electricity / 140, 0.39408); Electricity demand growth rate = 0.095; Network loss = 0.06; Excess consumption volume buyer holdings = INTEG (excess consumption volume trading volume - excess consumption volume converted into quota, 0); Excess consumption transaction volume = IF THEN ELSE (MIN (expected sales of excess consumption, expected purchases of excess consumption) < 0, 0, MIN (expected sales of excess consumption, expected purchases of excess consumption)); Excess consumption price = MIN (excess consumption price upper limit, MAX (excess consumption price change, excess consumption price lower limit)); The price ceiling for excess consumption = 0.31; The lower limit of the price of excess consumption = 0.08; Excess consumption price change = INTEG (excess consumption excess demand, 0.16); Excess consumption volume seller holdings = INTEG (excess consumption volume trading volume - excess consumption volume of renewable energy, 4.43); Excess consumption excess demand = IF THEN ELSE ((Expected purchase of excess consumption - expected sale of excess consumption) / 100000<-0.05,-0.05,IF THEN ELSE ((Expected purchase of excess consumption - expected sale of excess consumption) / 100000>0.05,0.05,(Expected purchase of excess consumption - expected sale of excess consumption) / 100000)); Excess consumption is converted into quota = minimum consumption of renewable energy; Expected sales volume of excess consumption = price of excess consumption / 0.31*excess consumption seller holdings / 100; Expected purchase volume of excess consumption = IF THEN ELSE (excess consumption buyer's holdings > excess consumption converted into quota, 0, 0.06 / excess consumption price*(excess consumption converted into quota - excess consumption buyer's holdings)).

8. The coupling analysis method according to claim 5, characterized in that: include: The endogenous factors of the renewable energy excess consumption market subsystem include renewable energy installed capacity under construction, renewable energy installed capacity, renewable energy demand, minimum renewable energy consumption, excess consumption price changes, excess consumption seller holdings, excess consumption buyer holdings, excess demand for excess consumption, excess renewable energy consumption, excess consumption transaction volume, conversion of excess consumption into quotas, profit margin of renewable energy manufacturers, renewable energy demand, conversion of renewable energy into consumption, excess consumption price, expected excess consumption sales, expected excess consumption purchases, renewable energy consumption weight, minimum renewable energy consumption, and renewable energy power generation; The exogenous factors of the renewable energy excess consumption market subsystem include the renewable energy demand growth rate, renewable energy consumption weight growth rate, excess consumption price upper limit, and excess consumption price lower limit; The endogenous factors of the power market subsystem include changes in power prices, total power consumption; excess demand for renewable energy power, power prices, and renewable energy power generation; The exogenous factors of the power market subsystem include network losses and power demand growth rate.

9. The coupling analysis method according to any one of claims 1 to 8, characterized in that: The renewable energy power generation types include wind power generation, photovoltaic power generation and biomass power generation.

10. A renewable energy excess consumption and power market coupling analysis system, characterized in that: include: An integration model building module M1 is used to select coupling analysis participants and build a renewable energy integration model based on the coupling analysis participants; The integration model optimization module M2 is used to optimize the renewable energy integration model, determine the model problem boundary and clarify the assumptions of the renewable energy excess consumption market and the power market, obtain a causal loop diagram, and determine the variables and constants of the optimized renewable energy integration model; A coupling model establishment module M3 is used to establish a functional relationship between the variables and the constants based on the variables and constants of the optimized renewable energy integration model, draw a stock flow diagram to obtain a coupling model of excess renewable energy consumption and electricity market; The coupling analysis module M4 is used to analyze the maximum renewable energy consumption and the minimum electricity cost based on the coupling model to promote the transformation of the power system.

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