High-resolution power system facing load response interaction under dual-carbon background

Abstract

The invention discloses a load response interaction-oriented high-resolution power system under a dual-carbon background, belongs to the field of power systems, and aims to solve the problem that a model of the power system under the dual-carbon background can safely face load response interaction with high resolution under source-load interaction scene simulation. The constraint conditions comprise an electric power balance constraint, an electric quantity balance constraint, an electric power system expansion constraint, a power supply output upper and lower limit constraint, a climbing constraint, a wind power and solar power generation constraint, a system adequacy constraint, a power grid operation reserve constraint and an energy storage equipment operation constraint; the method has the advantages that future transformation cost and capacity planning of the power system can be more practical.

Description

Technical Field

[0001] The present invention belongs to the field of power systems, and relates to a power system source-grid-load-storage planning model that couples capacity planning with operation simulation, and in particular to a high-resolution power system model for load response interaction under a dual-carbon background. Background Art

[0002] The dual carbon background is a combination of carbon peak and carbon neutrality. The "dual carbon" strategy advocates a green, environmentally friendly, and low-carbon lifestyle. Accelerating the pace of reducing carbon emissions will help guide green technology innovation and improve the global competitiveness of industry and economy. Under the dual carbon background, we will continue to promote the adjustment of industrial structure and energy structure, vigorously develop renewable energy, accelerate the planning and construction of large-scale wind power and photovoltaic base projects, and strive to balance economic development and green transformation.

[0003] The power system has an important impact on the realization of the dual carbon goals. According to the China Statistical Yearbook, China Energy Statistical Yearbook, Electricity Statistical Yearbook, State Grid Energy Research Institute, Global Energy Monitor, the relevant parameters are from the State Grid Energy Research Institute, Chen et al., Zhuo et al. and Zhang Xiliang et al. Based on the above data or models, it mainly includes the baseline scenario and the source-load interaction scenario. The baseline scenario is that the policy constraints such as the dual carbon goals and the proportion of clean energy are not considered during the transformation of the power system, and the future power and electricity balance is met by model optimization. The source-load interaction scenario takes into account the constraints of the dual carbon goals, but does not consider the role of demand-side management. However, in the context of dual carbon, it is necessary to consider the constraints of the dual carbon goals and the realization of source-load interaction through demand-side management, so that the power system's analysis of future transformation costs, capacity planning, and operation simulation can be more realistic. Summary of the invention

[0004] In order to solve the problem that the model of the power system in the dual-carbon background can be highly secure and load-oriented in response to interaction under the source-load interaction scenario simulation, according to some embodiments of the present application, a power system, the model of the power system includes

[0005] Technical modules, including cost parameters;

[0006] Energy module, including energy parameters of different types of power sources;

[0007] Interaction module, including demand side management module. The parameters of demand side management module include residential demand response load, industrial and commercial demand response load and electric vehicle response load. The interaction module conducts short-term interaction and long-term interaction according to the changes in supply and demand. Short-term interaction establishes the balance relationship between source, load and storage based on the real-time load curve. Long-term interaction establishes the balance between power supply and demand on a periodic scale.

[0008] The objective function of the model is the overall cost;

[0009] The constraints enable the power system to meet the total balance and real-time power load balance through capacity planning and operation simulation. The constraints include power balance constraints, quantity balance constraints, power system expansion constraints, upper and lower limit constraints on power output, ramp constraints, wind power and solar power generation constraints, system margin constraints, grid operation reserve constraints, energy storage equipment operation constraints, power supply upper limit constraints, and energy and power development parameter constraints.

[0010] An embodiment of the present application also provides an electronic device, comprising: one or more processors, a memory, and one or more programs; wherein the one or more programs are stored in the memory, and the one or more programs include instructions, and when the instructions are executed by the electronic device, the electronic device executes the second aspect of the embodiment of the present application and any possible design of the second aspect thereof.

[0011] An embodiment of the present application also provides a computer-readable storage medium, which includes a computer program. When the computer program runs on an electronic device, the electronic device executes the second aspect of the embodiment of the present application and any possible design of the second aspect thereof.

[0012] Beneficial effects of the invention: The model of the power system of the invention takes into account the dual carbon background and demand-side management to achieve source-load interaction. It is a model of the power system with a dual carbon background under source-load interaction scenario simulation. It is a high-resolution and safe load response interactive power system model. This model can make the future transformation cost and capacity planning of the power system more in line with reality. The specific comparative analysis is illustrated in the experimental example of the invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] Figure 1 Model framework diagram of the power system.

[0014] Figure 2 Roadmap for emissions reduction in the power sector.

[0015] Figure 3 The cost of power industry transformation.

[0016] Figure 4 Cost of each power supply.

[0017] Figure 5 Investment priorities in different years.

[0018] Figure 6 Power capacity planning for new power systems.

[0019] Figure 7 Total capacity investment under different scenarios.

[0020] Figure 8 Coal-fired power and energy storage capacity.

[0021] Fig. 9 Power generation of each power source.

[0022] Fig.10 Load interaction in different situations: a. baseline scenario, b. no source-load interaction, c. source-load interaction.

[0023] Fig.11 Wind and solar power output and load conditions, a passive-load interaction, b source-load interaction. DETAILED DESCRIPTION

[0024] The embodiments of the present application are described in detail below with reference to the accompanying drawings, examples of which are shown in the accompanying drawings. The present application provides a method, an electronic device, and a computer-readable storage medium to solve the problem that the model of the power system in the dual-carbon background can be highly-resolution and safely load-oriented in response to interaction under the source-load interaction scenario simulation. Among them, the method, the device, and the computer-readable storage medium are based on the same technical concept, and the principles of solving the problem are similar, so the implementation of each subject can refer to each other, and the repetitions will not be repeated.

[0025] like Figure 1 As shown, a power system, the power system model is a high-resolution power system model for load response interaction under the dual-carbon background, including

[0026] Technical modules, including cost parameters;

[0027] Energy module, including energy parameters of different types of power sources;

[0028] Interaction module, including demand side management module. The parameters of demand side management module include residential demand response load, industrial and commercial demand response load and electric vehicle response load. The interaction module conducts short-term interaction and long-term interaction according to the changes in supply and demand. Short-term interaction establishes the balance relationship between source, load and storage based on the real-time load curve. Long-term interaction establishes the balance between power supply and demand on a periodic scale.

[0029] The objective function of the model is the overall cost;

[0030] The constraints enable the power system to meet the total balance and real-time power load balance through capacity planning and operation simulation. The constraints include quantity balance constraints, power balance constraints, power system expansion constraints, upper and lower limit constraints on power output, climbing constraints, wind power and solar power generation constraints, system redundancy constraints, grid operation reserve constraints, energy storage equipment operation constraints, upper limit constraints on the four power sources of water, light, water and gas, and energy and power development parameter constraints.

[0031] Among them, the cost parameters of the technical module include investment cost Indicates operation and maintenance costs Indicates fuel cost Represents management cost Represents the cost of carbon emissions Indicates CCS cost Indicates the residual value of the equipment Indicates system cost Indicates the start-stop cost

[0032] Energy parameters include energy type, energy price, carbon emission factor, pollutant emission factor, total carbon emission constraint, and clean energy ratio. Energy parameters correspond to relevant parameters in constraint conditions. Multiple parameters in constraint conditions correspond to the above-mentioned specific parameters. Each energy parameter is described in detail in the following constraint conditions.

[0033] The present invention fully considers the economic, safety and low-carbon requirements of the power system. The objective function and constraints of the model are set as follows:

[0034] Among them, the objective function is expressed as:

[0035]

[0036] in:

[0037] represents the investment cost in year t, represents the operation and maintenance cost in the tth year, represents the fuel cost in year t, represents the management cost in the tth year, represents the carbon emission cost in year t, represents the CCS cost in year t, represents the residual value of the equipment in year t, represents the system cost in year t, represents the start-up and shutdown cost in the tth year, r represents the discount rate, t represents the tth year, the current time is the tth year, such as t=2020, which means the current time is 2020, t-1 represents the current time is 2019, and T represents the planning period, for example 2020-2060.

[0038] In some specific examples, the above costs are described in detail:

[0039] Investment cost express:

[0040]

[0041] Among them, i represents the i-th type of generator set, including hydropower generator set, wind power generator set, photovoltaic generator set, nuclear power generator set, coal power generator set, gas power generator set, and biomass power generator set; j represents the j-th type of energy storage; K represents the energy storage capacity type, including physical energy storage and chemical energy storage; k represents the physical energy storage capacity or the chemical energy storage capacity; M represents the capacity type, including thermal power capacity and clean energy capacity; m represents the thermal power capacity or the clean energy capacity; represents the newly added installed capacity of power generation units of type i in year t, represents the newly added energy storage capacity of the jth type of energy storage in year t, represents the newly added power source of the i-th type of generator set in year t, It represents the unit capacity cost of newly added energy storage of the j-th type of energy storage in year t.

[0042] Operation and maintenance costs express:

[0043]

[0044] in, represents the existing power capacity of the i-th type of generator set in year t, represents the power generation of the i-th type of generator in the t-th year, represents the current storage capacity of the jth type of energy storage in year t, represents the energy storage capacity of the jth type of energy storage in the tth year, represents the fixed capacity unit cost of the i-th type of generator in the t-th year, represents the variable capacity unit cost of the i-th type of generator in the t-th year, represents the fixed energy storage unit cost of the j-th type of energy storage in year t, represents the unit cost of variable energy storage of the j-th type of energy storage in year t.

[0045] Fuel costs express:

[0046]

[0047] Among them, h represents the hth hour, represents the fuel cost per unit of electricity of the ith type of generator in the tth year, represents the power generation of the i-th type of generator set in the h-th hour of the t-th year;

[0048] Management costs express:

[0049]

[0050] in, represents the resident response in the h-th hour unit response cost in the t-th year, represents the electric vehicle response in the h-th hour unit response cost in the t-th year, represents the industrial and commercial response in the h-th hour unit response cost in the t-th year, represents the number of residents responding at the hth hour in the tth year, represents the response volume of electric vehicles at the hth hour in the tth year, Represents the industrial and commercial response volume at the hth hour in the tth year.

[0051] Carbon emissions costs express:

[0052]

[0053] in, represents the carbon price of the i-th type of generator in the t-th year, represents the carbon absorption rate of the i-th type of generator in the t-th year, represents the carbon emission coefficient of the i-th type of generator set in the t-th year, It represents the power generation of the i-th type of generator set in the h-th hour of the t-th year.

[0054] Equipment residual value express:

[0055]

[0056] in, It represents the residual value coefficient of unit power capacity of the i-th type of generator set in the t-th year, represents the power capacity of the i-th type of generator set retired in year t, It represents the residual value coefficient of unit energy storage capacity of the i-th type of generator set in the t-th year, represents the retired energy storage capacity of the j-th type of energy storage in year t.

[0057] CCS costs express:

[0058]

[0059] in, represents the CCS investment cost per unit capacity of the i-th type of generating unit in the t-th year, fm i,t represents the fixed operation and maintenance cost of the i-th type of generator set in the t-th year, μ i,t represents the CCS installation ratio of the i-th type of generating unit in the t-th year, represents the variable operation and maintenance cost of the i-th type of generator set in the t-th year, represents the power generation of the i-th type of generator set with CCS installed capacity at the h-th hour in the t-th year, It represents the transportation cost of unit carbon emission of the i-th type of generator set in the t-th year, such as the distance of carbon emission transportation cost is 250km.

[0060] System Cost express:

[0061]

[0062] Among them, h,t It represents the system risk coefficient at the hth hour in the tth year. This coefficient is related to the peak and valley electricity price. The higher the electricity price, the greater the system risk coefficient. In a perfectly competitive market, the price is equal to the marginal cost, and the cost of power generation remains basically the same. The biggest difference is the system cost. Therefore, the system cost coefficient is positively correlated with the electricity price. h,t Indicates that the hth hour in the tth year is a variable load.

[0063] Start-Stop Cost express:

[0064]

[0065] in, represents the cost of shutting down the power capacity of the i-th type of generator at the h-th hour in the t-th year, It represents the cost of opening the power supply capacity of the i-th type of generator set at the h-th hour in the t-th year, U i,h-1,t represents the on / off status of the i-th type of generator set at the h-1 hour in the t-th year, U i,h,t represents the on / off status of the i-th type of generator set at the h-th hour in the t-th year, U i,h-1,t , U i,h,t It is a 0 or 1 variable, 0 is for closing and 1 is for opening.

[0066] The interactive module of the present invention performs short-term interaction and long-term interaction according to the changes in supply and demand. The short-term interaction is limited by power balance constraints, and the long-term interaction is limited by power balance constraints, so that the power system meets the balance of total volume and real-time power load balance.

[0067] The power balance constraint is expressed as:

[0068]

[0069]

[0070]

[0071]

[0072] in:

[0073] M represents the total capacity, M ES Represents energy storage capacity, M RE Indicates the capacity of new energy units;

[0074] It represents the energy storage discharge of the i-th type of generator set at the h-th hour in the t-th year, It represents the energy storage charging of the i-th type of generator set at the h-th hour in the t-th year;

[0075] It represents the non-renewable energy generation of the i-th type of generator set in the h-th hour of the t-th year, It represents the renewable energy power generation of the i-th type of generator set at the h-th hour in the t-th year;

[0076] It represents the amount of wind and solar power abandoned by the i-th type of generator in the h-th hour of the t-th year;

[0077] represents the load after the change at the hth hour in the tth year, represents the original load at the hth hour in the tth year, P t,h represents the base load at the hth hour in the tth year, ΔP t,h represents the h-th hour variable load in the t-th year;

[0078] represents the response of electric vehicles at the hth hour in the tth year, Indicates the industrial and commercial response at the hth hour in the tth year, It represents the residents’ response at hour h in year t;

[0079] It represents the output coefficient of non-renewable energy unit of the i-th type of generator set at the h-th hour in the t-th year, It represents the new energy output coefficient of the i-th type of generator set in the h-th hour of the t-th year, It represents the non-renewable energy capacity of the i-th type of generator set at the h-th hour in the t-th year, Indicates the capacity of new energy power source.

[0080] The power balance constraint indicates that the power constraint setting is made according to the power-saving behavior of the demand response. The coefficients obtained by the present invention based on large-scale experiments are embedded into the power balance constraint model as main parameters.

[0081]

[0082]

[0083]

[0084] in:

[0085] H it represents the power generation of the ith type of general power generation unit (hydropower, nuclear power, coal power, gas power, biomass power) in the tth year, H it,ESG represents the energy storage power generation of the i-th type of generator set in the t-th year, H it,ESS represents the renewable energy power generation of the i-th type of generator set in the t-th year, represents the total amount of wind and solar power abandoned in year t;

[0086] represents the amount of electricity changed in year t, represents the original amount of electricity in year t, ΔH t represents the change in electricity in the tth year;

[0087] ξ represents the upper limit coefficient of the demand response of the power grid, and Ψ represents the response cost of the power grid.

[0088] Power system expansion constraint representation: The present invention determines the existing installed capacity of power sources based on the level year additions and retirements within the study period to represent the power system expansion constraint model.

[0089]

[0090]

[0091] in:

[0092] represents the existing capacity of the generator set of the i-th type of the m-th capacity type in the t-th year, It represents the newly added capacity of the generating unit of the i-th type of the m-th capacity type in the t-th year, It represents the capacity of the generator set of the i-th type of the m-th capacity type retired in the t-th year;

[0093] represents the current storage capacity of the jth type of energy storage in year t, represents the newly added energy storage capacity of the jth type of energy storage in year t, represents the retired energy storage capacity of the jth type of energy storage in year t, represents the retired energy storage capacity of the jth type of energy storage in year t;

[0094] t m,i represents the operating life of the power supply of the i-th type of generator set with the m-th capacity type, t j Represents the operating life of the jth type of energy storage.

[0095] Equations (5) and (6) indicate that the existing capacity is equal to the capacity of the previous period plus the newly added capacity minus the retired capacity.

[0096] The upper and lower limits of the power output are expressed as:

[0097]

[0098] in:

[0099] It represents the minimum output coefficient of the power supply of the i-th type of generator set at the h-th hour in the t-th year,

[0100] It represents the maximum output coefficient of the power supply of the i-th type of generator set at the h-th hour in the t-th year,

[0101] represents the actual output capacity of the i-th type of generator set at the h-th hour in the t-th year,

[0102] It represents the actual online capacity of the i-th type of generator set at the h-th hour in the t-th year.

[0103] The climbing constraint means:

[0104]

[0105]

[0106] in:

[0107] represents the actual output capacity of the i-th type of generator set at the h-th hour in the t-th year, represents the actual online capacity of the i-th type of generator set at the h-th hour in the t-th year, represents the capacity of the i-th type of generating unit shut down at hour h+1 in year t;

[0108] represents the capacity of the i-th type of generator set turned on at the h-th hour in the t-th year, represents the capacity of the i-th type of generating unit shut down at the h-th hour in the t-th year;

[0109] ru i,t represents the maximum upward ramp rate of the i-th type of generator set in the t-th year, du i,t represents the maximum downward ramp rate of the i-th type of generator set in the t-th year;

[0110] The maximum limit of the power supply output capacity should satisfy the following inequality:

[0111]

[0112] in: It represents the maximum output coefficient of the power supply of the i-th type of generator set at the h-th hour in the t-th year.

[0113] The constraints for wind and solar power generation are expressed as:

[0114]

[0115]

[0116] in:

[0117] represents the wind power capacity factor at the hth hour in the tth year, represents the solar capacity factor at hour h in year t;

[0118] represents the wind power generation at the hth hour in the tth year, represents the solar power generation at the hth hour in the tth year; represents the installed capacity of wind power in year t, represents the solar power installed capacity in year t;

[0119] represents the newly added wind power installed capacity, represents the existing wind power installed capacity, represents the potential installed wind power capacity;

[0120] represents the newly added solar power installed capacity in year t, represents the existing solar power installed capacity in year t, represents the potential solar installed capacity in year t.

[0121] System margin constraint means: the system margin constraint of the present invention is made on the basis of ensuring that there is sufficient installed capacity to meet the peak load requirement.

[0122]

[0123] in:

[0124] represents the capacity confidence coefficient of coal-fired power supply in year t, represents the capacity confidence factor of wind power in year t, represents the capacity confidence factor of the solar power source in year t, represents the capacity confidence factor of nuclear power in year t, represents the capacity confidence factor of the hydropower source in year t, represents the capacity confidence factor of the gas power supply in year t, represents the capacity confidence factor of biomass power source in year t;

[0125] represents the total installed capacity of coal-fired power in year t, represents the total installed capacity of wind power in year t, represents the total installed capacity of solar energy in year t, represents the total installed capacity of nuclear power in year t, represents the total installed capacity of hydropower in year t, represents the total installed capacity of gas-fired power generation in year t, represents the total installed capacity of biomass in year t;

[0126] represents the peak load in year t;

[0127] Due to the existence of prediction errors, unexpected situations, etc., the power grid has operation reserve requirements, and the system margin constraint of the present invention is also defined as follows:

[0128]

[0129]

[0130] It represents the maximum output coefficient of the power supply of the i-th type of generator set at the h-th hour in the t-th year, represents the actual online capacity of the i-th type of generator in year t, It represents the actual output capacity of the i-th type of generator set at the h-th hour in the t-th year;

[0131] cg i,h,t The upper limit coefficient of the energy storage output of the i-th type of generator set at the h-th hour in the t-th year, represents the capacity of the i-th type of generating unit in year t, It represents the capacity of energy storage occupied by the i-th type of generator set at the h-th hour in the t-th year;

[0132] represents the wind power capacity factor at the hth hour in the tth year, represents the installed capacity of wind power in year t, represents the wind power generation at the hth hour in the tth year, represents the solar capacity factor at hour h in year t, represents the solar power installed capacity in year t, represents the solar power generation at the hth hour in the tth year;

[0133] represents the demand-side management volume at the hth hour in the tth year, It represents the minimum demand-side management quantity at the hth hour in the tth year;

[0134] P represents the prediction error coefficient of the base load of the i-th type of generator set at the h-th hour in the t-th year, t,h represents the base load at the hth hour in the tth year, represents the prediction error coefficient of wind power output at the hth hour in the tth year, It represents the prediction error coefficient of solar power generation at the hth hour in the tth year.

[0135] The operating constraints of energy storage equipment are expressed as:

[0136]

[0137] in:

[0138] SC t,i,h represents the energy storage situation of the energy storage device of the jth type at the hth hour in the tth year, and SCO represents the initial energy storage situation;

[0139] They represent the j-th type of energy storage charging efficiency, the j-th type of energy storage discharging efficiency and the j-th type of energy storage energy loss rate respectively;

[0140] represents the jth type of energy storage charging, represents the jth type of energy storage discharge;

[0141] They represent the upper and lower limit coefficients of the j-th type of energy storage respectively;

[0142] A t G represents the energy storage capacity in year t, Γ t,j Indicates the number of charge and discharge restrictions for the j-th type of energy storage in the t-th year.

[0143] The power upper limit constraint is expressed as:

[0144]

[0145] in:

[0146] represents the installed capacity of wind power in year t, represents the solar power installed capacity in year t, represents the installed nuclear capacity in year t, represents the installed capacity of hydropower in year t, represents the installed capacity of gas-fired power generation in year t;

[0147] represents the upper limit of wind power installed capacity in year t, represents the upper limit of solar energy installed capacity in year t, represents the upper limit of nuclear power installed capacity in year t, represents the upper limit of hydropower installed capacity in year t, Represents the upper limit of gas-fired power generation capacity in year t.

[0148] Energy and power development parameter constraint representation: The present invention selects the total carbon emissions, the proportion of non-fossil energy power, the wind and solar power abandonment rate and different power output states as energy and power development parameters for constraint model representation, wherein the total carbon emissions is a carbon emission threshold set to achieve the dual carbon goals of the power industry, and this threshold limits the future development direction of new power systems to be clean and low-carbon. The proportion of non-fossil energy is a ratio set to constrain coal-fired power investment and encourage the development of clean energy. The wind and solar power abandonment rate is to prevent the waste of a large amount of clean energy and avoid over-investment.

[0149]

[0150]

[0151]

[0152] represents the carbon absorption rate of the i-th type of generator in the t-th year, represents the carbon emission coefficient of the i-th type of generator set in the t-th year, represents the power generation of the i-th type of generator in the t-th year, TA co represents the total amount of carbon emissions;

[0153] represents the proportion of electricity from non-fossil energy sources;

[0154] represents the wind and solar curtailment rate of the i-th type of generator set at the h-th hour in the t-th year, Indicates the different power output states of the i-th type of generator set at the h-th hour in the t-th year. Linearization of the power system model:

[0155] In the power system model, nonlinear models exist in large numbers in the objective function and constraints. In order to find the global optimal solution, they are converted into linear models.

[0156] (1) Linearization of the problem of multiplying capacity and output coefficient.

[0157] There are two variables multiplied in the model, which need to be linearized. The present invention replaces the product of power supply capacity and output coefficient with the change of capacity, as shown below.

[0158]

[0159] represents the actual capacity at time h, and adds corresponding constraints

[0160]

[0161] Represents the online capacity at time h.

[0162] Through the above processing, an equality is transformed into an equality and an inequality constraint.

[0163] (2) Linearization of the problem of multiplying 0 and 1 variables

[0164] The present invention uses the capacity change per unit time to replace the product of 0 and 1:

[0165]

[0166] In the above formula, Indicates the capacity turned on and off at h.

[0167] At the same time, start and stop capacity constraints are added, as shown below.

[0168]

[0169]

[0170]

[0171] Indicates the capacity of the unused units in the initial stage, GT i,t Indicates the minimum shutdown time.

[0172]

[0173]

[0174]

[0175] Indicates the capacity of the start-up unit in the initial stage, KT it Indicates the minimum on time.

[0176] (3) Linearization of the start-up and shutdown problems of multiple units, while simplifying the unit combination processing problem. In the operation simulation, the start-up and shutdown of each coal-fired power unit will correspond to the corresponding state, including online state, open and close. The more units, the more 0 and 1 variables are needed to characterize them. This not only brings trouble to the solution, but also makes the original model a nonlinear model. Since the focus of the present invention is not to study the shutdown of each unit, but to focus on the current and future development path of new power systems. The present invention uses a continuous variable modeling method to replace the original decision variables (0, 1 variables). The details are as follows.

[0177]

[0178] represents the actual online capacity at time h. i,t Indicates a generator set of type i. Indicates online and offline, 0 means offline, 1 means online. Indicates the cumulative start-up capacity and cumulative shutdown capacity at time h. Indicates on and off respectively. 1 means it is turned on at h. A value of 1 indicates that the transition from h-1 to h is closed. Indicates the capacity turned on and off at h. It represents the online capacity after processing at time h.

[0179] After processing, the traditional mixed integer programming model is transformed into a linear programming model. At the same time, the method of converting discrete variables into continuous processing also significantly reduces the number of decision variables, especially in the 8760-hour operation model, this method has obvious advantages. The present invention uses the CPLEX solver in the GAMS software to solve the problem during the solution process.

[0180] The present invention constructs a power system source-grid-load-storage planning model while considering operation simulation and capacity planning. The model framework is as follows Figure 1As shown. The model involves technical modules, energy modules and interactive modules. The technical module mainly includes parameters of source, load and storage. Technical economy is the principle that determines the measures taken and technology developed by the new power system. The technical module involves 9 aspects of cost, namely investment cost, operation and maintenance cost, fuel cost, management cost, emission cost, equipment residue, system cost, start-up and shutdown cost and CCS cost. Various costs will consider the nonlinear changes of technical and economic costs. The energy module includes energy parameters of different types of power sources, namely energy prices, emission factors, etc. The interactive module includes real-time interaction between source, network, load and storage, among which the demand side is the focus of model characterization. Technical economy, energy conservation and emission reduction policies and micro parameters are considered in parameter preparation. The low carbon of the new power system is characterized by cost and constraints, mainly involving the power supply side. This model handles low-carbon goals by internalizing the external costs of the objective function and by means of binding indicators. The objective function is the overall cost, and the constraints are the total amount constraint to achieve carbon neutrality, energy intensity constraint, carbon intensity constraint and new energy constraint. Low carbon is a constraint for the new power system to take measures and develop technology. The safety of the new power system is characterized by system balance, involving four aspects: source, network, load and storage. Safety is the premise for the new power system to take measures and develop technology. The safety of the new power system is characterized by constraints, mainly involving the capacity planning and operation simulation of the new power system to meet the balance of the total amount and the real-time power load balance. The power balance and power balance constraints are the constraints of long-term power balance and short-term load balance. The source (power supply end), load (user side), and storage (energy storage) interaction modules are mainly based on the changes in supply and demand, involving short-term interaction and long-term interaction. Short-term interaction is to establish a balance relationship between source, network, load and storage based on the real-time load curve. The source and load are the main balance relationship, and the network and storage are the regulators to cooperate with this balance. The long-term interaction relationship is mainly to establish a balance between power supply and demand on a yearly scale. At the same time, in order to avoid the occurrence of emergencies, such as insufficient wind and light processing, dry season, load forecasting errors, and uncertainty in demand-side management, the model will take into account the operating capacity and reserve capacity of various power sources in the future. Under different scenario simulations, the model will output relevant results, mainly including the installed capacity of each power source, various cost situations, demand-side management strategies, peak shaving and valley filling situations, carbon emission reduction paths, CCU layout planning, source-load interaction, and the proportion of clean energy.

[0181] Experimental example: The cost of the new power system transformation towards carbon neutrality in the present invention: The source-load interaction scenario presents a slow-first-faster emission reduction path, avoiding the technical challenges of rapid decarbonization. Figure 2As shown in the figure, three different power system development paths are presented, namely the baseline scenario, non-passive load interaction, and source-load interaction. Under the baseline scenario, carbon emissions in the power industry grow rapidly, and carbon emissions in 2060 increase by 1.9-2.3 times. Under the non-passive load interaction scenario, the power industry achieves the carbon neutrality goal, showing a development model of first fast and then slow. This model has a large withdrawal force on coal-fired power in the early stage, and the speed of energy transformation is relatively fast. The corresponding carbon emission cost will also be reduced, so that more funds will flow to new energy investment. Under the source-load interaction scenario, the first slow and then fast emission reduction model has weak constraints on coal-fired power in the early stage, and there is enough room for the development of other power sources, avoiding "preemptive" and "campaign-style" carbon reduction. At the same time, demand-side management can coordinate the consumption of new energy and the output of other units, avoiding the stabilization cost of the energy system (energy storage investment, stable power source investment, etc.), which is conducive to choosing the appropriate technical and economic time to make investment decisions. In the early stage, compared with the passive load interaction, CCS investment in the source load interaction scenario will be postponed to after 2050, while the passive load interaction will be invested after 2035. This is one of the reasons why the emission reduction in the early source load interaction scenario is slower than that in the passive load interaction scenario. On the other hand, demand-side management slows down the retirement of coal-fired power and coordinates the output of power sources. Among them, power-saving behavior delays the investment in high-cost power sources and gives full play to the foundation role of coal-fired power. In the medium term, demand-side management can help absorb more new energy, coordinate the output of other units, and slow down the investment in gas-fired power. In the later stage, demand-side management can replace more coal-fired power and energy storage, support the output of new energy, and quickly achieve carbon neutrality with the role of CCS.

[0182] Under the dual carbon goals, demand-side management significantly reduces the cost of power system transformation. Under the baseline scenario, the discounted electricity supply cost for the entire planning period is 57.2 trillion yuan by 2020, of which fuel costs are the highest (47%), followed by operation and maintenance costs (29%) and investment costs (22%). In the case of no passive-load interaction, the total cost of power transformation is 67.1 trillion yuan, of which investment costs are the highest (38%), followed by operation and maintenance costs (30%) and fuel costs (27%). In the case of source-load interaction, the total cost of power transformation is 61.3 trillion yuan, of which investment costs are the highest (37%), followed by operation and maintenance costs (31%) and fuel costs (28%). From a cost comparison, the cost of the new power system development path under the source-load interaction scenario is lower than that of the development path without passive-load interaction, a reduction of 8.7%, a reduction of 5.8 trillion yuan, equivalent to 1.4 times the total investment in the EU's power system plan for 2020-2030. Shu Yinbiao et al. believe that compared with the mode of slow first and fast later, the uniform emission reduction and the mode of fast first and slow later will increase the electricity cost. The present invention also has a similar conclusion that the development mode of slow first and fast later presented under demand-side management is more economical. Figure 3As shown in the figure, compared with the baseline scenario, carbon neutrality will increase costs by 17.38%, of which investment costs and operation and maintenance costs will increase by 28.12%. Compared with the scenario without source-load interaction, demand-side management will significantly reduce investment costs (-4.68%). Under the source-load interaction scenario, the cost of electricity supply will rise in the near and medium term, and will first enter a plateau period in the medium and long term and then gradually decline. Figure 4 As shown in the figure, compared with the baseline scenario, from the perspective of the cost of each power source, in 2025, hydropower, nuclear power and coal power are the main costs. In 2030, the investment cost of wind power is the highest. Compared with the passive load interaction, the cost of nuclear power and hydropower is relatively small in the source load interaction scenario. In 2035, in the passive load interaction scenario, the cost of wind power and distributed photovoltaics accounts for a large proportion, while in the source load interaction scenario, the cost of nuclear power and coal power accounts for a large proportion, and the total cost is reduced by 18% compared with the passive load interaction. In 2040, the cost of wind power accounts for more than 80%. In 2045, the cost of centralized photovoltaics increases, and the cost of wind power is still large. In 2050, the cost of distributed photovoltaics is high, and the cost of coal power is also high at this time. This is because on the one hand, there are many natural retirements of coal power, and investment in coal power is needed to ensure power stability. On the other hand, the CCS cost of coal power is high. In 2055, in the case of no load-passive interaction, the costs of onshore wind power, offshore wind power and distributed photovoltaic power are relatively high, while in the case of source-load interaction, only onshore wind power and distributed photovoltaic power are relatively high. In 2060, the costs of gas power and biomass power are relatively high, which is due to the influence of two parts of costs: 1. Investment cost, 2. CCS investment cost. New energy costs and energy storage costs still account for the majority. The investment in energy storage is much higher than in other years. The role of demand-side management is becoming increasingly important in reducing the cost of power system transformation, which was reduced by 45% in 2060.

[0183] In general, if Figure 5 As shown in the figure, compared with the non-passive load interaction scenario, the investment in nuclear power and wind power in the source-load interaction scenario will have time differences. After 2035, the cost difference between energy storage and CCS is obvious. The non-passive load interaction scenario starts to invest in CCS in 2035, while the source-load interaction scenario starts to invest in CCS in 2050. Demand-side management delays CCS investment and makes funds flow to more effective capacity investment. Demand-side management can also replace the regulatory role of energy storage to a certain extent. In 2060, compared with the non-passive load interaction scenario, the energy storage cost on the demand side will drop by 10.7%.

[0184] The present invention is a new type of power system capacity planning for carbon neutrality: demand-side management reduces installed capacity requirements. Figure 6As shown in the figure, compared with the baseline scenario, the installed capacity under the scenarios of no passive-load interaction and source-load interaction is significantly larger. In 2030, wind power and coal-fired power installed capacity are the main power sources. In 2040, wind power installed capacity is much higher than other power sources, the capacity investment of nuclear power and hydropower reaches saturation, and the installed capacity of photovoltaic power becomes the third largest power source after hydropower. In 2050, the installed capacity of wind power and photovoltaic power becomes the main power source, taking on the main power generation of the power system. Compared with the scenario of no passive-load interaction, the coal-fired power, photovoltaic power and energy storage under the scenario of source-load interaction all decline, by 28%, 6% and 34% respectively. In 2060, the installed capacity of wind power and photovoltaic power is much higher than other power sources, and the energy storage capacity becomes the power source second only to them. Stable power sources such as coal-fired power, gas-fired power, biomass and hydropower play a regulating and auxiliary role. Compared with the scenario of no passive-load interaction, the total installed capacity under the scenario of source-load interaction decreases by 13%.

[0185] Carbon neutrality increases total capacity investment, while demand-side management reduces total capacity investment. Figure 7 As shown, compared with the baseline scenario, in the carbon neutral scenario, the passive load interaction scenario will increase capacity by 110%, of which the total capacity of coal power will decrease by -31%, and onshore wind power will increase by 59%. The total capacity of wind and light will increase by 113%, and energy storage will increase by 21% accordingly. In order to make up for the vacancy of coal power, the total capacity of biomass and gas power will increase by 2% and 5% respectively. Compared with the passive load interaction scenario, the distributed photovoltaic, offshore wind power and onshore wind power will decrease by 5%, 3% and 3% respectively in the source load interaction scenario. Concentrated photovoltaic will increase by 2%, which is because demand-side management plays an important role in coordinating photovoltaic output and stabilizing the balance of large power grids.

[0186] Demand-side management reduces the demand for coal-fired power and energy storage. Figure 8 As shown in the figure, compared with the scenario of no passive load interaction, there are obvious differences in the coal-fired power exit path and energy storage capacity investment. Before 2045, the coal-fired power installed capacity under the source-load interaction scenario is higher than that under the scenario of no passive load interaction. This shows that the demand-side management of power system capacity investment can moderately slow down the exit of coal-fired power, give full play to the supply and regulation role of coal-fired power, reasonably plan other power sources, and avoid rapid decarbonization and increase the cost of transformation. After 2045, the coal-fired power installed capacity is less than that under the scenario of no passive load interaction, and it has decreased by 38% in 2060. At this moment, demand-side management plays a key role in replacing the flexible peak-shaving of coal-fired power. In terms of energy storage, in 2060, the energy storage capacity under the scenario of source-load interaction is lower than that under the scenario of no passive load interaction, which is 23% lower. This shows that a high proportion of clean energy requires energy storage to cooperate, and more reasonable demand-side management can replace the role of energy storage and play a huge role.

[0187] Demand side management will change the output of each power source. Fig. 9As shown in the figure, except for the baseline scenario, clean electricity gradually occupies a dominant position in all other scenarios. In 2030, coal-fired power, wind power, nuclear power and hydropower are the main power sources in the scenarios of passive load interaction and source load interaction. In 2040 and 2050, wind power gradually replaces coal-fired power, and photovoltaic power generation also gradually increases. In 2060, the proportion of new energy installed capacity will increase significantly, and wind power and solar power generation will become the main sources of electricity. This conclusion is consistent with Zhuo et al. Clean power such as hydropower and nuclear power has reached saturation in advance due to restrictions. Unlike Chen et al., the proportion of nuclear power in the present invention accounts for 14%, because they have constrained nuclear power, resulting in a pessimistic view of nuclear power in the future. At the same time, although the installed capacity of coal-fired power will remain at 300 million kilowatts to 500 million kilowatts, the total amount of coal-fired power generation is not high, and coal-fired power plays a greater role in coordinating the output of new energy and peak regulation. In Shu Yinbiao's research, the proportion of nuclear power is relatively large, and the results of the present invention are slightly lower than its conclusion. However, the status of coal power in its research is not particularly high. At the same time, solar power generation is relatively common. The results of this invention are different from them. Due to the intermittent characteristics of solar power generation, it brings great instability to the power grid. The obvious "duck curve" makes it difficult to adjust other units frequently and quickly, which requires a large amount of energy storage and more flexible thermal power units. Under cost constraints, biomass, nuclear power and hydropower will replace part of solar power generation. In addition, wind power has more advantages than solar power. It can generate electricity at any time. Although it is more volatile, it can be better coordinated and cooperated with other units, thereby greatly reducing the cost of energy storage and system costs.

[0188] The present invention simulates the interactive operation of the new power system with carbon neutrality goal: demand-side management increases the consumption of new energy and reduces the difficulty of load regulation. Fig.10As shown in the figure, in the baseline scenario, coal-fired power, hydropower and nuclear power play a major role. Compared with the baseline scenario, wind power is the main power supply in the cases of no passive load interaction and source load interaction, followed by photovoltaic power, nuclear power and hydropower. The role of gas power, biomass and coal power in the peak load regulation process is prominent, which shows that retaining a certain amount of thermal power is beneficial to the economy and stability of the power grid. The volatility and intermittency of new energy are very obvious, and the output of other units is also frequently adjusted to adapt to the output of new energy. At the same time, in order to stabilize the power grid, some periods of time also need to adapt to the load of the power grid by shielding photovoltaic panels and adjusting the size of wind power, or by absorbing excess electricity through hydrogen production, wind abandonment and light abandonment. Wind power has become the main power source. Compared with photovoltaic power, wind power is volatile, but it can output at all times. If photovoltaic power is invested on a large scale, its intermittent impact on the power grid will be greater, and the corresponding "duck curve" will be too big. When photovoltaic power is connected to the grid, other power sources need to be reduced sharply, and other power sources need to output quickly at night. Frequent start-stop and ramp-up put higher demands on thermal power and energy storage. Compared with the scenario without source-load interaction, demand-side management in the source-load interaction scenario plays the role of "peak shaving and valley filling" and saving electricity, which greatly reduces the investment in spare capacity and the cost of maintaining peak load. At the same time, demand-side management also reduces the number of adjustments of other power sources in maintaining load balance, reducing the difficulty of stabilizing the balance of the power grid system.

[0189] Depend on Fig.11 It can be seen that compared with passive load interaction, demand-side management adjusts the load curve to make it more adaptable to wind and solar output, reducing system risks and costs caused by intermittent and unstable wind and solar output. It is obvious that in many moments, in order to absorb wind and solar power, the load curve will change from its original shape to a scenario that conforms to energy output, realizing the interaction between source, grid, load and storage.

[0190] Based on the above embodiments, an embodiment of the present application also provides an electronic device, which includes: one or more processors, a memory, and one or more programs; wherein the one or more programs are stored in the memory, and the one or more programs include instructions, and when the instructions are executed by the electronic device, the electronic device executes the method provided in the above embodiments.

[0191] Based on the above embodiments, the embodiments of the present application further provide a computer storage medium, in which a computer program is stored. When the computer program is executed by a computer, the computer executes the method provided in the above embodiments.

[0192] The storage medium may be any available medium that can be accessed by a computer. For example, but not limited to, a computer-readable medium may include RAM, ROM, EEPROM, CD-ROM or other optical disk storage, magnetic disk storage medium or other magnetic storage device, or any other medium that can be used to carry or store the desired program code in the form of instructions or data structures and can be accessed by a computer.

[0193] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0194] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0195] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0196] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0197] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is also intended to include these modifications and variations.

Claims

1. A power system, characterized in that: The power system model includes Technical modules, including cost parameters; Energy module, including energy parameters of different types of power sources; Interaction module, including demand side management module. The parameters of demand side management module include residential demand response load, industrial and commercial demand response load and electric vehicle response load. The interaction module conducts short-term interaction and long-term interaction according to the changes in supply and demand. Short-term interaction establishes the balance relationship between source, load and storage based on the real-time load curve. Long-term interaction establishes the balance between power supply and demand on a periodic scale. The objective function of the model is the overall cost; The constraints enable the power system to meet the total balance and real-time power load balance through capacity planning and operation simulation. The constraints include power balance constraints, quantity balance constraints, power system expansion constraints, upper and lower limit constraints on power output, ramp constraints, wind power and solar power generation constraints, system margin constraints, grid operation reserve constraints, energy storage equipment operation constraints, power supply upper limit constraints, and energy and power development parameter constraints.

2. The power system according to claim 1, characterized in that: The objective function is expressed as: in: represents the investment cost in year t, represents the operation and maintenance cost in the tth year, represents the fuel cost in year t, represents the management cost in the tth year, represents the carbon emission cost in year t, represents the CCS cost in year t, represents the residual value of the equipment in year t, represents the system cost in year t, represents the start-up and shutdown cost in the tth year, r represents the discount rate, t represents the tth year, and T represents the planning period.

3. The power system according to claim 2, characterized in that: Investment cost express: Among them, i represents the i-th type of generator set, j represents the j-th type of energy storage, k represents physical energy storage and chemical energy storage, K represents the energy storage capacity type, including physical energy storage and chemical energy storage, m represents thermal power capacity or clean energy capacity, M represents the capacity type, including thermal power capacity and clean energy capacity, represents the newly added installed capacity of power generation units of type i in year t, represents the newly added energy storage capacity of the jth type of energy storage in year t, represents the newly added power source of the i-th type of generator set in year t, represents the unit capacity cost of newly added energy storage of the jth type of energy storage in year t; Operation and maintenance costs express: in, represents the existing power capacity of the i-th type of generator set in year t, represents the power generation of the i-th type of generator in the t-th year, represents the current storage capacity of the jth type of energy storage in year t, represents the energy storage capacity of the jth type of energy storage in the tth year, represents the fixed capacity unit cost of the i-th type of generator in the t-th year, represents the variable capacity unit cost of the i-th type of generator in the t-th year, represents the fixed energy storage unit cost of the j-th type of energy storage in year t, represents the unit cost of variable energy storage of the jth type of energy storage in year t; Fuel costs express: Among them, h represents the hth hour, represents the fuel cost per unit of electricity of the ith type of generator in the tth year, represents the power generation of the i-th type of generator set in the h-th hour of the t-th year; in, represents the resident response in the h-th hour unit response cost in the t-th year, represents the electric vehicle response in the h-th hour unit response cost in the t-th year, represents the industrial and commercial response in the h-th hour unit response cost in the t-th year, represents the number of residents responding at the hth hour in the tth year, represents the response volume of electric vehicles at the hth hour in the tth year, represents the industrial and commercial response volume at the hth hour in the tth year; Carbon emissions costs express: in, represents the carbon price of the i-th type of generator in the t-th year, represents the carbon absorption rate of the i-th type of generator in the t-th year, represents the carbon emission coefficient of the i-th type of generator set in the t-th year, represents the power generation of the i-th type of generator set in the h-th hour of the t-th year; CCS costs express: in, represents the CCS investment cost per unit capacity of the i-th type of generating unit in the t-th year, fm i,t represents the fixed operation and maintenance cost of the i-th type of generator in the t-th year, μ i,t represents the CCS installation ratio of the i-th type of generating unit in the t-th year, represents the variable operation and maintenance cost of the i-th type of generator set in the t-th year, represents the power generation of the i-th type of generator set with CCS installed capacity at the h-th hour in the t-th year, represents the transportation cost of unit carbon emissions of the i-th type of generator in year t; Equipment residual value express: in, It represents the residual value coefficient of unit power capacity of the i-th type of generator set in the t-th year, represents the power capacity of the i-th type of generator set retired in year t, It represents the residual value coefficient of unit energy storage capacity of the jth type of energy storage in the tth year, represents the retired energy storage capacity of the jth type of energy storage in year t; System Cost express: Among them, h,t represents the system risk coefficient at the hth hour in the tth year. The system cost coefficient is positively correlated with the electricity price. ΔO h,t represents the variable load at the hth hour in the tth year; Start-Stop Cost express: in, represents the cost of shutting down the power capacity of the i-th type of generator at the h-th hour in the t-th year, It represents the cost of opening the power supply capacity of the i-th type of generator set at the h-th hour in the t-th year, U i,h-1,t represents the on / off status of the i-th type of generator set at the h-1 hour in the t-th year, U i,h,t represents the on / off status of the i-th type of generator set at the h-th hour in the t-th year, U i,h-1,t , U i,h,t It is a 0 or 1 variable, 0 is for closing and 1 is for opening.

4. The power system according to claim 1 or 2, characterized in that: The power balance constraint is expressed as: in: M represents the total capacity, M ES Represents energy storage capacity, M RE Indicates the capacity of new energy units; It represents the energy storage discharge of the i-th type of generator set at the h-th hour in the t-th year, It represents the energy storage charging of the i-th type of generator set at the h-th hour in the t-th year; It represents the non-renewable energy generation of the i-th type of generator set in the h-th hour of the t-th year, It represents the renewable energy power generation of the i-th type of generator set at the h-th hour in the t-th year; It represents the amount of wind and solar power abandoned by the i-th type of generator in the h-th hour of the t-th year; represents the load after the change at the hth hour in the tth year, represents the original load at the hth hour in the tth year, P t,h represents the base load at the hth hour in the tth year, ΔP t,h represents the variable load at the hth hour in the tth year; represents the response of electric vehicles at the hth hour in the tth year, Indicates the industrial and commercial response at the hth hour in the tth year, It represents the residents’ response at hour h in year t; It represents the output coefficient of non-renewable energy unit of the i-th type of generator set at the h-th hour in the t-th year, It represents the new energy output coefficient of the i-th type of generator set in the h-th hour of the t-th year, represents the non-renewable energy capacity of the i-th type of generator set in the t-th year, It represents the new energy power capacity of the i-th type of generator set in the t-th year; The power balance constraint is expressed as: in: H it represents the annual power generation of the general power generation unit of the i-th type, H it,ESG represents the energy storage power generation of the i-th type of generator set in the t-th year, H it,ESS represents the renewable energy power generation of the i-th type of generator set in the t-th year, represents the total amount of wind and solar power abandoned in year t; represents the amount of electricity changed in year t, represents the original amount of electricity in year t, ΔH t represents the change in electricity in year t; ξ represents the upper limit coefficient of the demand response of the power grid, and Ψ represents the response cost of the power grid.

5. The power system according to claim 4, characterized in that: The power system expansion constraint is expressed as: in: represents the existing capacity of the generator set of the i-th type of the m-th capacity type in the t-th year, It represents the newly added capacity of the generating unit of the i-th type of the m-th capacity type in the t-th year, It represents the capacity of the generator set of the i-th type of the m-th capacity type retired in the t-th year; represents the current storage capacity of the jth type of energy storage in year t, represents the newly added energy storage capacity of the jth type of energy storage in year t, represents the retired energy storage capacity of the jth type of energy storage in year t, represents the retired energy storage capacity of the jth type of energy storage in year t; t m,i represents the operating life of the power supply of the i-th type of generator set with the m-th capacity type, t j represents the operating life of the jth type of energy storage; The upper and lower limits of the power output are expressed as: in: It represents the minimum output coefficient of the power supply of the i-th type of generator set at the h-th hour in the t-th year, It represents the maximum output coefficient of the power supply of the i-th type of generator set at the h-th hour in the t-th year, represents the actual output capacity of the i-th type of generator set at the h-th hour in the t-th year, It represents the actual online capacity of the i-th type of generator set at the h-th hour in the t-th year.

6. The power system according to claim 5, characterized in that: The climbing constraint means: in: represents the actual output capacity of the i-th type of generator set at the h-th hour in the t-th year, represents the actual online capacity of the i-th type of generator set at the h-th hour in the t-th year, It represents the minimum output coefficient of the power supply of the i-th type of generator set at the h-th hour in the t-th year, represents the capacity of the i-th type of generating unit shut down at the h+1 hour in the t-th year; represents the capacity of the i-th type of generator set turned on at the h-th hour in the t-th year, represents the capacity of the i-th type of generating unit shut down at the h-th hour in the t-th year; ru i,t represents the maximum upward ramp rate of the i-th type of generator set in the t-th year, du i,t represents the maximum downward ramp rate of the i-th type of generator set in the t-th year; The maximum limit of the power supply output capacity should satisfy the following inequality: in: It represents the maximum output coefficient of the power supply of the i-th type of generator set at the h-th hour in the t-th year.

7. The power system according to claim 6, characterized in that: The constraints for wind and solar power generation are expressed as: in: represents the wind power capacity factor at the hth hour in the tth year, represents the solar capacity factor at hour h in year t; represents the wind power generation at the hth hour in the tth year, represents the solar power generation at the hth hour in the tth year; represents the installed capacity of wind power in year t, represents the solar power installed capacity in year t; represents the newly added wind power installed capacity in year t, represents the existing wind power installed capacity in year t, represents the potential installed wind power capacity in year t; represents the newly added solar power installed capacity in year t, represents the existing solar power installed capacity in year t, represents the potential solar installed capacity in year t.

8. The power system according to claim 7, characterized in that: The system margin constraint is expressed as: in: represents the capacity confidence coefficient of coal-fired power supply in year t, represents the capacity confidence factor of wind power in year t, represents the capacity confidence factor of the solar power source in year t, represents the capacity confidence factor of nuclear power in year t, represents the capacity confidence factor of the hydropower source in year t, represents the capacity confidence factor of the gas power supply in year t, represents the capacity confidence factor of biomass power source in year t; represents the total installed capacity of coal-fired power in year t, represents the total installed capacity of wind power in year t, represents the total installed capacity of solar energy in year t, represents the total installed capacity of nuclear power in year t, represents the total installed capacity of hydropower in year t, represents the total installed capacity of gas-fired power generation in year t, represents the total installed capacity of biomass in year t; P t max represents the peak load in year t; It represents the maximum output coefficient of the power supply of the i-th type of generator set at the h-th hour in the t-th year, represents the actual online capacity of the i-th type of generator in year t, It represents the actual output capacity of the i-th type of generator set at the h-th hour in the t-th year; cg i,h,t The upper limit coefficient of the energy storage output of the i-th type of generator set in the h-th hour of the t-th year, represents the capacity of the i-th type of generating unit in year t, It represents the capacity of energy storage occupied by the i-th type of generator set at the h-th hour in the t-th year; represents the wind power capacity factor at the hth hour in the tth year, represents the installed capacity of wind power in year t, represents the wind power generation at the hth hour in the tth year, represents the solar capacity factor at hour h in year t, represents the solar power installed capacity in year t, represents the solar power generation at the hth hour in the tth year; represents the demand-side management volume at the hth hour in the tth year, It represents the minimum demand-side management quantity at the hth hour in the tth year; P represents the prediction error coefficient of the base load of the i-th type of generator set at the h-th hour in the t-th year, t,h represents the base load at the hth hour in the tth year, represents the prediction error coefficient of wind power output at the hth hour in the tth year, It represents the prediction error coefficient of solar power generation at the hth hour in the tth year.

9. The power system according to claim 8, characterized in that: The operating constraints of energy storage equipment are expressed as: in: SC t,i,h represents the energy storage situation of the energy storage device of the jth type at the hth hour in the tth year, and SCO represents the initial energy storage situation; They represent the j-th type of energy storage charging efficiency, the j-th type of energy storage discharging efficiency and the j-th type of energy storage energy loss rate respectively; represents the jth type of energy storage charging, represents the jth type of energy storage discharge; They represent the upper and lower limit coefficients of the j-th type of energy storage respectively; represents the energy storage capacity in year t, Γ t,j Indicates the number of charge and discharge restrictions for the j-th type of energy storage in the t-th year; The power upper limit constraint is expressed as: in: represents the installed capacity of wind power in year t, represents the solar power installed capacity in year t, represents the installed nuclear capacity in year t, represents the installed capacity of hydropower in year t, represents the installed capacity of gas-fired power generation in year t; represents the upper limit of wind power installed capacity in year t, represents the upper limit of solar energy installed capacity in year t, represents the upper limit of nuclear power installed capacity in year t, represents the upper limit of hydropower installed capacity in year t, Represents the upper limit of gas-fired power generation capacity in year t.

10. The power system according to claim 9, characterized in that: Energy and power development parameter constraints are expressed as: represents the carbon absorption rate of the i-th type of generator in the t-th year, represents the carbon emission coefficient of the i-th type of generator set in the t-th year, represents the power generation of the i-th type of generator in the t-th year, TA co represents the total amount of carbon emissions; represents the proportion of electricity from non-fossil energy sources; represents the wind and solar curtailment rate of the i-th type of generator set at the h-th hour in the t-th year, Represents the different power output states of the i-th type of generator set at the h-th hour in the t-th year.

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