A virtual power plant value assessment method and system based on time-series production simulation

Abstract

The present invention proposes a method and system for evaluating the value of a virtual power plant based on time-series production simulation. The method includes: dividing virtual power plants according to their operating characteristics and constructing operating characteristic models of different types of virtual power plants; constructing a source-grid-load-storage collaborative operation model, and dividing the production simulation time period into multiple sub-time periods, solving the simulation optimization results of each sub-time period; constructing a virtual power plant value evaluation model based on the simulation optimization results, constructing a method for solving the economic, environmental and safety evaluation indicators of the virtual power plant, evaluating the contribution of the virtual power plant and various resources to the economic, environmental and safety of the power system, and evaluating the benefits to the virtual power plant operator. Based on this method, a value evaluation system is also proposed. The present invention constructs an operating characteristic model of a virtual power plant and integrates it into the power system production simulation model to generate operating data that can be used for value evaluation, thereby comprehensively evaluating the economic, safety and environmental protection of the virtual power plant.

Description

Technical Field

[0001] The present invention belongs to the technical field of novel power system operation and dispatching, and in particular relates to a virtual power plant value assessment method and system based on time-series production simulation. Background Art

[0002] The global energy mix is ​​undergoing profound transformation, with the installed capacity of renewable energy sources such as wind power and photovoltaics rapidly increasing in power systems. However, the intermittent, volatile, and random nature of wind and solar power poses new challenges to balancing electricity supply and demand. Traditional power systems lack sufficient flexibility to accommodate the large-scale integration of renewable energy, leading to an increasing lack of regulation capacity. This rapidly increasing system flexibility deficit is seriously impacting the safe, stable operation and efficient dispatch of power systems. Virtual power plants, as a new form of power resource integration and dispatch, are flourishing in power systems. By aggregating distributed energy resources, controllable loads, and energy storage devices, they form a comprehensive power resource with considerable regulation capabilities, providing flexible support for the power system. Grid companies can leverage the regulation capabilities of virtual power plants to balance supply and demand, mitigate peak-to-valley fluctuations, improve grid efficiency, and reduce the occurrence of abnormal operations. However, the construction and operation of virtual power plants require significant financial and technical support. To incentivize virtual power plant operators to invest in efficient and responsive virtual power plant resources and actively participate in grid regulation, grid companies should provide them with a certain amount of benefits during dispatch based on their regulation and supply contributions. This compensation mechanism not only offsets operators' operating and investment costs but also generates additional revenue, boosting their participation. By efficiently utilizing user-side resources and generating revenue, the grid company reduces electricity costs and improves power supply reliability, achieving a win-win situation for both the grid and users.

[0003] Under current developments, virtual power plant operators face the need to assess the economic benefits of building a virtual power plant and the type of virtual power plant that is most cost-effective. Grid companies also need to assess the impact of virtual power plant access on power system operations, necessitating a scientific value assessment method that meets both needs. However, the market currently lacks a tool that can effectively analyze the value of virtual power plants, limiting their widespread application and promotion. Existing methods primarily rely on constructing demonstration projects and evaluating the performance and value of virtual power plants through actual operation. However, this approach has numerous limitations, including high investment, long cycles, delayed results, and a single reflection of the effects. Therefore, there is an urgent need for an efficient and accurate value assessment method that can simulate the operating characteristics of virtual power plants in a virtual environment, quantify their contribution to the power system, and provide a scientific reference for decision makers and operators. Summary of the Invention

[0004] To address the above technical issues, this paper proposes a virtual power plant value assessment method and system based on time-series production simulation. This method can simulate the operating characteristics of a virtual power plant in a virtual environment, quantify its contribution to the power system, and provide a scientific reference for decision makers and operators.

[0005] To achieve the above object, the present invention adopts the following technical solutions:

[0006] A virtual power plant value assessment method based on time-series production simulation includes the following steps:

[0007] Virtual power plants are divided into power supply type virtual power plants, energy storage type virtual power plants and load type virtual power plants according to their operating characteristics, and their operating characteristic models are constructed respectively.

[0008] A coordinated operation model of power generation, grid, load and storage is constructed, and the production simulation time period is divided into multiple sub-time periods. The production simulation optimization results of each sub-time period are solved separately. The model construction process is as follows: the objective function of the coordinated operation model of power generation, grid, load and storage is constructed with the goal of economic optimization. The resource endowment of each node and the additional power generation and storage target of the entire power system are considered, and the constraint conditions of the coordinated operation model of power generation, grid, load and storage are established.

[0009] Based on the production simulation optimization results, a virtual power plant value assessment model is constructed, and a method for solving the economic evaluation indicators of virtual power plants is constructed, so as to evaluate the economic evaluation of virtual power plants and a certain type of resources on the power system and virtual power plant operators; a method for solving the environmental evaluation indicators of virtual power plants is constructed, so as to evaluate the environmental evaluation of virtual power plants and a certain type of resources on the power system; and a virtual power plant safety value evaluation index is constructed to quantify the contribution to the improvement of the safety evaluation indicators of virtual power plants and a certain type of resources on the power system.

[0010] A virtual power plant value assessment system based on time-series production simulation includes a first building module, a second building module and an assessment module;

[0011] The first construction module is used to divide the virtual power plant into power supply type virtual power plant, energy storage type virtual power plant and load type virtual power plant according to the operation characteristics, and respectively construct the operation characteristic models of the power supply type virtual power plant, the energy storage type virtual power plant and the load type virtual power plant;

[0012] The second construction module is used to construct a source-grid-load-storage coordinated operation model, and divide the production simulation time period into multiple sub-time periods, and solve the production simulation optimization results of each sub-time period respectively. The model construction process is as follows: the objective function of the source-grid-load-storage coordinated operation model is constructed with the goal of economic optimization, and the resource endowment of each node and the additional source and storage target of the power system as a whole are considered to construct the constraint conditions of the source-grid-load-storage coordinated operation model.

[0013] The evaluation module constructs a virtual power plant value evaluation model based on the production simulation optimization results, constructs a method for solving the economic evaluation indicators of the virtual power plant, thereby evaluating the economic evaluation of the virtual power plant and a certain type of resources on the power system and the virtual power plant operator; constructs a method for solving the environmental evaluation indicators of the virtual power plant, thereby evaluating the environmental evaluation of the virtual power plant and a certain type of resources on the power system; constructs a virtual power plant safety value evaluation index, which is used to quantify the contribution to the improvement of the safety evaluation indicators of the virtual power plant and a certain type of resources on the power system.

[0014] The effects provided in the summary of the invention are only the effects of the embodiments, not all the effects of the invention. One of the above technical solutions has the following advantages or beneficial effects:

[0015] The present invention proposes a method and system for evaluating the value of a virtual power plant based on time-series production simulation. The method comprises the following steps: dividing the virtual power plant into power supply type virtual power plant, energy storage type virtual power plant and load type virtual power plant according to the operation characteristics, and constructing the operation characteristic models of the power supply type virtual power plant, energy storage type virtual power plant and load type virtual power plant respectively; constructing a source-grid-load-storage coordinated operation model, and dividing the production simulation time period into multiple sub-time periods, and solving the production simulation optimization results of each sub-time period respectively; the process of model construction is: constructing the objective function of the source-grid-load-storage coordinated operation model with the goal of economic optimization, taking into account the resources of each node Endowment and the new source and storage targets of the power system as a whole, construct the constraint conditions of the source-grid-load-storage collaborative operation model; construct a virtual power plant value assessment model based on the production simulation optimization results, and construct a method for solving the economic evaluation index of the virtual power plant, so as to evaluate the economic evaluation of the virtual power plant and a certain type of resources on the power system and the virtual power plant operator; construct a method for solving the environmental evaluation index of the virtual power plant, so as to evaluate the environmental evaluation of the virtual power plant and a certain type of resources on the power system; construct a virtual power plant safety value evaluation index, which is used to quantify the contribution to the improvement of the safety evaluation index of the virtual power plant and a certain type of resource power system. Based on a virtual power plant value assessment method based on time-series production simulation, a virtual power plant value assessment system based on time-series production simulation is also proposed. The present invention constructs a virtual power plant operation characteristic model and integrates it into the power system production simulation model to generate operation data that can be used for value assessment, further comprehensively evaluates the economy, safety and environmental protection of the virtual power plant, and provides a scientific basis for the planning and operation of the virtual power plant.

[0016] The present invention can comprehensively reflect the operating characteristics, regulatory contributions and benefits of virtual power plants, and provide a scientific basis for the optimal scheduling, resource allocation and investment planning of virtual power plants. The present invention supports the evaluation of the return on investment of various resources, and provides virtual power plant value quantification tools for power grids and users, thereby helping to improve the flexibility of the power system and build new power systems.

[0017] This method, through the valuation of virtual power plants, can comprehensively understand their contributions to the power system and quantify their effects on supply and demand balance, regulatory capacity, and environmental impact. This assessment provides a scientific basis for the planning, construction, and operation of virtual power plants, helping to optimize resource allocation, improve grid operating efficiency, guide virtual power plant operators in the rational investment and allocation of virtual power plant resources, and promote the safe, stable, and green development of the power system. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 This is an overall flow chart of a virtual power plant value assessment method based on time-series production simulation proposed in Example 1 of the present invention;

[0019] Figure 2 This is the process of dividing the operation characteristics of the virtual power plant and building the operation model proposed in Example 1 of the present invention;

[0020] Figure 3 This is a flow chart of a method for simulating time-sequential production of a large-scale power system including a virtual power plant, as proposed in Example 1 of the present invention;

[0021] Figure 4 This is a flowchart of the rolling optimization method proposed in Example 1 of the present invention;

[0022] Figure 5 A flowchart for constructing a virtual power plant value assessment model proposed in Example 1 of the present invention;

[0023] Figure 6 This is the system topology diagram proposed in Example 1 of the present invention;

[0024] Figure 7 This is the load curve proposed in Example 1 of the present invention;

[0025] Figure 8 This is a typical wind power output curve proposed in Example 1 of the present invention;

[0026] Figure 9 This is a typical photovoltaic output curve proposed in Example 1 of the present invention;

[0027] Figure 10 The air conditioning load baseline proposed in Example 1 of the present invention;

[0028] Figure 11 The load baseline for hydrogen production by electricity proposed in Example 1 of the present invention;

[0029] Figure 12 This is the system simulation result when the air conditioning and electric hydrogen production loads are not involved in the regulation proposed in Example 1 of the present invention;

[0030] Figure 13 This is the system simulation result under the air conditioning control proposed in Example 1 of the present invention;

[0031] Figure 14 This is the system simulation result under the regulation of electric hydrogen production proposed in Example 1 of the present invention;

[0032] Figure 15 This is the simulation result of the system considering air conditioning and electric hydrogen production proposed in Example 1 of the present invention;

[0033] Figure 16 The load baseline and actual load size of air conditioning and electric hydrogen production proposed in Example 1 of the present invention;

[0034] Figure 17 This is a schematic diagram of a virtual power plant value assessment system based on time-series production simulation proposed in Example 2 of the present invention. DETAILED DESCRIPTION

[0035] Example 1

[0036] Example 1 of the present invention proposes a virtual power plant value assessment method based on time-series production simulation. This method addresses the problem of a lack of quantitative analysis tools for the interaction between virtual power plants and power grids in existing power systems. By constructing a virtual power plant operating characteristic model and integrating it into a power system production simulation model to generate operational data that can be used for value assessment, the method further comprehensively evaluates the economic, safety, and environmental performance of virtual power plants, providing a scientific basis for their planning and operation.

[0037] Figure 1 This is an overall flow chart of a virtual power plant value assessment method based on time-series production simulation proposed in Example 1 of the present invention;

[0038] In Step 1, virtual power plants are divided into power supply type virtual power plants, energy storage type virtual power plants and load type virtual power plants according to their operating characteristics, and the operating characteristic models of power supply type virtual power plants, energy storage type virtual power plants and load type virtual power plants are constructed respectively. Figure 2 This is a flowchart for the virtual power plant operation characteristic division and operation model construction proposed in Example 1 of the present invention.

[0039] In this step, based on the specific physical characteristics and resource properties of virtual power plants, existing virtual power plants are divided into power supply type, energy storage type and load type according to their operating characteristics.

[0040] The survey data includes basic system operation information, basic equipment parameter information of the virtual power plant and data supporting its actual operation mode.

[0041] The basic operating information of the system includes the load changes of the existing system and the operating parameters of conventional power sources. First, the typical power load change curve published by the power department and the maximum power load of the system are investigated, and the load change curve of the simulated system is fitted.

[0042] Research the China National Statistical Yearbook or the power energy report released by the China Electricity Council to form the installed capacity, line voltage level and length of the conventional power supply of the simulated system.

[0043] The model information of typical conventional power sources (such as coal-fired power units and gas-fired power units) and typical lines published on the official websites of the research equipment manufacturers was used to form a database of basic equipment parameter information such as conventional power source ramp rate, start-stop time, operating cost, coal consumption rate, and line unit resistance and reactance.

[0044] Investigate the equipment parameters of common virtual power plant resources published by the China Virtual Power Plant Industry Technology Innovation Alliance, the China Household Electrical Appliances Association, and the official websites of equipment manufacturers, and form basic parameter information of various virtual power plant resources such as air conditioning thermal resistance, heat capacity, refrigeration power, electrolysis power for hydrogen production, hydrogen production power, typical installed capacity of distributed photovoltaics, and form a basic information database of virtual power plants.

[0045] Investigate the wind speed and light intensity statistical information and wind and solar energy assessment reports released by the National Meteorological Information Center and the National Renewable Energy Center, and form a wind speed and light intensity change curve for the area where the simulation system is located.

[0046] Research the simulated regional outdoor temperature changes published by the National Meteorological Information Center and the China National Environmental Monitoring Center to develop a local outdoor temperature change curve. Research authoritative reports published by the China Hydrogen Energy Alliance and the Hydrogen Energy Professional Committee of the China Renewable Energy Society to develop a hydrogen load curve.

[0047] The process of building a power supply-type virtual power plant includes: classifying distributed photovoltaic and distributed wind power virtual power plants that have power generation capabilities and participate in power market transactions and bidding as power generation entities as power supply-type virtual power plants, and building their typical operating characteristic models. Both can use maximum output curves to describe their operating characteristics. The calculation method for the typical output curve of distributed wind turbines is as follows:

[0048]

[0049] in, Indicates wind turbine k W The maximum power output at time t; V t Indicates the actual wind speed at time t; Vin Indicates the cut-in wind speed; V out Indicates the cut-out wind speed; V N Indicates rated wind speed; k is the wind turbine W Rated power.

[0050] Based on the typical output curve of wind turbines, the actual output constraint of distributed wind turbines is:

[0051]

[0052] in, Indicates wind turbine k W The actual output at time t.

[0053] The typical output curve of a distributed photovoltaic unit as a power supply virtual power plant is:

[0054]

[0055] in, Indicates photovoltaic unit k P The maximum power output at time t; R t Indicates the light intensity at time t, R N Indicates the rated light intensity of the photovoltaic unit; Indicates photovoltaic unit k P Rated power.

[0056] Based on the typical output curve of photovoltaic units, the actual output constraint of distributed photovoltaic units is obtained as follows:

[0057]

[0058] in, Indicates photovoltaic unit k P The actual output at time t.

[0059] Distributed energy storage units and other virtual power plants that have the ability to transfer energy over time and do not have a basic operating baseline are classified as energy storage virtual power plants. Construct a typical operating characteristic model of distributed energy storage:

[0060] Considering the active power output limitation of the energy storage unit during the charging and discharging process, the upper and lower power constraints of the energy storage unit are constructed:

[0061] in, For the kth ess Maximum discharge power of a distributed energy storage unit; For the kth ess The discharge power of the distributed energy storage unit at time t;

[0062] Considering the energy storage capacity limitation during normal operation of the energy storage unit, the upper and lower limit constraints of the energy storage unit's state of charge are constructed as follows:

[0063] in, For the kth ess The state of charge of the distributed energy storage unit at time t, For the kth ess The maximum nuclear power state of the distributed energy storage unit; For the kth ess The minimum nuclear power state of the distributed energy storage unit;

[0064] The state of charge constraint formula of the distributed energy storage unit is:

[0065] in, For the kth ess The state of charge of the distributed energy storage unit at time t-1; For the kth ess The state of charge of the distributed energy storage unit at time t-1; For the kth ess The discharge power of a distributed energy storage unit at time t-1; Δt is the time step.

[0066] Virtual power plants (VPPs) such as hydrogen production and air conditioning that can regulate load-side electricity demand and participate in demand response and ancillary service markets as load regulation entities are classified as load-type VPPs. Typical operating characteristic models for air conditioning and hydrogen production are constructed.

[0067] Construct a single air conditioner model. This model describes the relationship between indoor temperature and cooling power of the air conditioner using the energy and power relationship of a traditional energy storage system:

[0068] in, is the equivalent SOC size of the jth air conditioner at time t; ΔP j (t) is the adjustable power value of the air conditioner, which must meet the following requirements: P jmin -P jbaseline (t)≤ΔP j (t)≤P jmax -P jbaseline (t); (9)

[0069] Among them, P jbaseline (t) is the power baseline of the jth air conditioner at time t; P jmax is the upper limit of the operating power of the jth air conditioner; P jmin is the lower limit of the operating power of the jth air conditioner; ΔP j (t) is the adjustment power of the jth air conditioner at time t; α jis the first state transfer parameter of the air-conditioning unit; β j is the second state transition parameter of the air-conditioning unit; γ j is the third state transition parameter of the air conditioner unit, which is calculated as follows:

[0070]

[0071]

[0072]

[0073] Among them, C j represents equivalent heat capacity (kJ / ℃); R j represents the equivalent thermal resistance (℃ / kW); η represents the refrigeration energy efficiency coefficient; T jmin Represents the minimum indoor temperature to meet user comfort; T jmax Represents the maximum indoor temperature to meet user comfort; T jset is the set temperature of the jth air conditioner.

[0074] An aggregate model of air conditioners is constructed by normalizing the SOC of individual air conditioners. The constructed aggregate model of air conditioners includes state transfer equations, upper and lower power boundaries, and SOC state constraints.

[0075] The state transition equation describing the air conditioner SOC state transition is constructed as:

[0076] soc AC (t+1)=Asoc AC (t)+BΔP(t)+C; (11)

[0077] Among them, A is the first state transfer coefficient of the air conditioner; B is the second state transfer coefficient of the air conditioner; C is the third state transfer coefficient of the air conditioner; is the equivalent SOC size of the jth air conditioner at time t+1; ΔP(t) is the adjustment power of the jth air conditioner at time t;

[0078]

[0079] Among them, α j is the first state transfer parameter of the air-conditioning unit; β j is the second state transition parameter of the air-conditioning unit; γ j is the third state transition parameter of the air-conditioning unit; n is the total number of air-conditioning aggregates.

[0080] Construct upper and lower power constraints that describe the power output limits of the air conditioner:

[0081] P Smin ≤ΔP S(t)+P Sbaseline ≤P Smax ; (13)

[0082] Where ΔP S (t) is the output of the air conditioning aggregate at time t; P Sbaseline is the power baseline of the air conditioning aggregate; P Smax is the upper power limit; P Smin is the lower power boundary;

[0083]

[0084] Among them, P jbaseline (t) is the power baseline of the jth air conditioner at time t; P jmax is the upper limit of the operating power of the jth air conditioner; P jmin is the lower limit of the operating power of the jth air conditioner unit;

[0085] Construct the SOC state constraints that describe the upper and lower boundaries of the air conditioner equivalent SOC: 0≤soc AC (t)≤1;(15).

[0086] The external characteristic model of hydrogen production is constructed using the hydrogen consumption curve as input, thereby forming the power variation base value and adjustment space curve of hydrogen production in a typical time period. The equivalent SOC is used to describe the hydrogen storage capacity of the hydrogen storage tank of the hydrogen production equipment. The constructed model includes the state transition equation, hydrogen consumption power constraint, and hydrogen storage tank state constraint. The state transition equation describing the SOC state transition of hydrogen production is constructed:

[0087]

[0088] Among them, SOC HST (t) is the current SOC of hydrogen produced by electricity at time t; V HST The maximum volume of hydrogen that can be stored in a hydrogen storage tank at rated pressure and temperature; is the gas storage capacity of the gas storage equipment during period t; is the total amount of gas output by the gas storage device during period t; SCR is the self-consumption rate of the hydrogen storage tank; η H2,in is the utilization rate of hydrogen entering the gas tank; is the utilization rate of hydrogen output tank; α e,t is the electrical efficiency, is the efficiency of hydrogen production by electrolysis; Q HHV is the calorific value of hydrogen, which is 1.43×10 8 Fixed; is the operating power of the electric hydrogen production equipment at time t.

[0089] Construct a hydrogen power constraint that describes the power limit when hydrogen production is actually running:

[0090]

[0091] in, Maximum hydrogen power;

[0092] Construct a hydrogen storage tank state constraint that describes the capacity limit of the hydrogen storage tank:

[0093] in, The upper and lower limits of hydrogen storage capacity are specified for hydrogen storage tanks respectively.

[0094] In Step 2, a source-grid-load-storage coordinated operation model is constructed, and the production simulation time period is divided into multiple sub-time periods. The production simulation optimization results of each sub-time period are solved separately. The model construction process is as follows: the objective function of the source-grid-load-storage coordinated operation model is constructed with the goal of economic optimization. Considering the resource endowment of each node and the additional source and storage target of the power system as a whole, the constraint conditions of the source-grid-load-storage coordinated operation model are constructed.

[0095] In this step, the operating characteristic model constructed in the above step is used to construct a time-series production simulation model of a large-scale power system containing a virtual power plant with optimal economic efficiency and oriented towards power and electricity balance. First, an objective function is constructed considering operating costs, penalties for poor operation and adjustment costs of virtual power plants. Then, constraints are constructed considering the main components of the power system such as source, network, load, storage and the physical characteristics of the virtual power plant. Finally, a time-series production simulation solution method based on daily rolling solution is used to solve the constructed production simulation model, thereby obtaining a virtual power plant that can be used for value assessment. Figure 3 This is a flow chart of a method for simulating time-sequential production of a large-scale power system including a virtual power plant, as proposed in Example 1 of the present invention;

[0096] With the goal of economic optimization, a comprehensive consideration of coal consumption and carbon emission costs of coal-fired units is constructed. c , standby cost f r , unit start-up and shutdown costs f uc , load shedding and power curtailment penalties p and the virtual power plant regulation cost f VPP The objective function of the source-grid-load-storage coordinated operation model is constructed based on the following formula:

[0097] minF op =f c +f r +f uc +f p +f VPP ; (16)

[0098] Among them, fc is the coal consumption and carbon emission cost of coal-fired units; f r is the standby cost; f uc is the unit start-up and shutdown cost; f p Penalty for load shedding and power abandonment; f VPP Adjusting costs for virtual power plants; minF op is the objective function of the source-grid-load-storage collaborative operation model;

[0099]

[0100]

[0101] in, is the coal consumption function of the coal-fired unit; For the kth G The output of the thermal power unit at time t; c coal is the unit coal consumption cost; is the carbon dioxide emission cost per unit coal consumption; Ω VRE For the new energy unit collection; The unit cost of upper and lower standby of the unit; Provide upper and lower standby power for thermal power units; It is the startup and shutdown state variable of the thermal power unit; is the cost of starting and stopping the unit; c drop 、c shed is the penalty coefficient for power curtailment and load shedding; Q drop , Q shed The amount of power abandoned and load cut; is the allowable value of abandoned electricity; is the allowable value of load shedding; Ω VPP is a collection of resource aggregates; is the adjustment cost per unit power change of the resource aggregate; is the power change of the resource aggregate at node i at time t.

[0102] Taking into account the resource endowment of each node and the overall source and storage increase target of the power system, the constraints of the source-grid-load-storage coordinated operation model are constructed: considering that the total power injection of the node is equal to the load, the power balance constraint is constructed:

[0103]

[0104] in, The kth node of node i is VRE The output of each VRE unit at time t; The kth node of node i is G The output of each thermal power unit at time t; The kth node of node i isess The output of each energy storage unit at time t, positive indicates discharging, negative indicates charging; represents the active power transmitted in line l at time t; Ω from、 Ω to They represent the set of lines starting and ending at node i respectively; is the active power of the load at node i at time t; is the load shedding size of node i at time t.

[0105] Considering the load reserve demand and the new energy reserve demand, a reserve constraint is constructed so that the reserve supply is greater than the system reserve demand:

[0106]

[0107] Among them, L + % is the upper limit reserve requirement coefficient of load reservation; L-% is the lower limit reserve requirement coefficient of load reservation; W + % is the upper limit reserve requirement coefficient reserved for wind and solar power unit output; W - % is the lower limit reserve requirement coefficient reserved for wind and solar power unit output; Ω d ,Ω vre It is the node set for load access and the new energy unit set; k for unit VRE The amount of power wasted at time t; For the kth VRE The output of each new energy unit at time t; here, the backup needs come from load fluctuations and wind and solar power output forecast errors, and the backup supply is only provided by thermal power units.

[0108] Considering that the line transmission power is less than the maximum transmission capacity of the line, the line operation constraint is formed:

[0109]

[0110] in, is the maximum transmission capacity of line l.

[0111] Considering the upper and lower limits of thermal power generation units' output when providing backup demand, the output constraints of thermal power generation units are constructed:

[0112]

[0113] in, is the operating state variable of the kth thermal power unit at time t, the operating state is 1 and the shutdown state is 0; They are the maximum output and minimum output of thermal power units respectively.

[0114] Considering the limitation of power rise and fall of thermal power units per unit time, a ramp constraint is constructed to ensure that the power change of thermal power units per unit time is less than the ramp limit:

[0115]

[0116] in, For the kth G The output of the thermal power unit at time t-1; is the upward power change of the thermal power unit under the operating time; It is the downward power change of the thermal power unit under the running time.

[0117] Considering the start-stop state changes and start-stop time restrictions during the start-up and shutdown process of thermal power units, a start-stop constraint is constructed to ensure that the start-up and shutdown time of the thermal power units is greater than the minimum start-up and shutdown time:

[0118]

[0119] in, Thermal power unit startup and shutdown status variables; k for coal-fired units G Minimum startup and shutdown duration.

[0120] Considering the above construction of the distributed wind and solar power unit operation characteristic model, the characteristic constraints of new energy equipment are constructed:

[0121]

[0122] in, is the maximum output of the new energy unit at time t.

[0123] Considering the above construction of distributed energy storage operation characteristic model, the characteristic constraints of energy storage equipment are constructed:

[0124]

[0125]

[0126]

[0127] in, is the discharge power of the energy storage unit at time t; is the maximum discharge power of the energy storage unit; Ω ess It is a collection of energy storage units; Indicates the kth ess The state of charge of the energy storage unit at time t; kth ess The maximum and minimum nuclear power states of each energy storage unit; For the kth essRated energy storage capacity of each energy storage unit; is the discharge power of the energy storage unit at time t-1.

[0128] Considering the above construction of the air conditioning operation characteristic model, the characteristic constraints of the air conditioning equipment are constructed:

[0129]

[0130] is the equivalent SOC of the air conditioner at time t; A is the first state transition parameter of the air conditioner; B is the second state transition parameter of the air conditioner; C is the third state transition parameter of the air conditioner; The regulating power of the air conditioner; is the baseline power of the air conditioner; is the upper power limit of the air conditioner; is the lower power limit of the air conditioner.

[0131] Considering the above construction of the electric hydrogen production operation characteristic model, the characteristic constraints of the electric hydrogen production equipment are constructed:

[0132]

[0133] in, is the gas volume of the hydrogen storage tank at time t+1 for hydrogen production by electricity; is the gas volume of the hydrogen storage tank at time t for hydrogen production by electricity; is the gas volume of the hydrogen storage tank at the last moment of hydrogen production; Δt is the simulation step length; Q HHV is the calorific value of hydrogen; is the efficiency of hydrogen production by electrolysis; α e,t is electrical efficiency; is the total amount of gas output by the gas storage device during period t; SCR is the self-consumption rate of the hydrogen storage tank; η H2,in is the utilization rate of hydrogen entering the gas tank; is the utilization rate of hydrogen output tank; V HST The maximum volume of hydrogen that can be stored in a hydrogen storage tank at rated pressure and temperature; The upper and lower limits of hydrogen storage capacity are specified for hydrogen storage tanks respectively; is the efficiency of hydrogen production by electrolysis; is the output power of the hydrogen production system; is the total amount of gas output by the gas storage equipment during period t.

[0134] Solve the above production simulation model in a rolling optimization solution mode. Figure 4This is a flowchart for solving the rolling optimization method proposed in Example 1 of the present invention; the specific steps include: first, dividing the complete production simulation time period into N sub-time periods, and then solving the production simulation optimization results of each sub-time period respectively; the solution results of the previous sub-time period, such as the SOC of each device, the status of the thermal power unit and other data, will be used as the input data of the next time period, and the solution will be iteratively solved until the production simulation process of all sub-time periods is solved.

[0135] In Step 3, a virtual power plant value assessment model is constructed based on the production simulation optimization results, and a method for solving the economic evaluation indicators of the virtual power plant is constructed, so as to evaluate the economic evaluation of the virtual power plant and a certain type of resources on the power system and the virtual power plant operator; a method for solving the environmental evaluation indicators of the virtual power plant is constructed, so as to evaluate the environmental evaluation of the virtual power plant and a certain type of resources on the power system; and a virtual power plant safety value evaluation index is constructed to quantify the contribution to the improvement of the safety evaluation indicators of the virtual power plant and a certain type of resources on the power system.

[0136] Based on the simulation results from the above steps, a value assessment system is constructed to evaluate the effectiveness of virtual power plants in participating in grid interaction regulation. Based on the operational and control characteristics of virtual power plants, a method for solving virtual power plant economic, safety, and environmental evaluation indicators is constructed to evaluate the contribution of a specific resource type to the economic and environmental performance of the power system. Figure 5 A flowchart for constructing a virtual power plant value assessment model proposed in Example 1 of the present invention;

[0137] Construct economic evaluation indicators, which include operating benefits and investment benefits. The former is manifested as the optimization of the operating economic level of the power system, and is described by the power generation and regulation benefits of resources in the power market. The latter is reflected in the allocation and recovery of the investment cost in the construction of the virtual power plant during its life cycle.

[0138] Construct a virtual power plant investment cost model. The investment cost of a virtual power plant includes hardware investment and software investment. The investment cost of designing distributed power supply, energy storage, control center and supporting control settings is as follows:

[0139] I total =I si +I hi ; (41)

[0140] Among them, I total is the total investment of the virtual power plant; I si For software investment, I hi Invest in hardware.

[0141] In normal use, the average investment cost over a period of time is often used to describe:

[0142]

[0143] in, is the investment cost of the virtual power plant leveled to time period t, r is the annual discount rate, m is the maximum lifespan of the virtual power plant that can participate in grid regulation, and T is one year with the same dimension as t.

[0144] Construct a model to represent the savings in system power supply and supporting grid infrastructure investment costs due to the construction of a certain resource of a virtual power plant:

[0145] Among them, I VPP,spi Save power costs for some kind of virtual power plant resource; The unit investment cost of traditional power supply per unit power capacity; is the unit investment cost of the grid supporting equipment per unit power capacity; S VPP is the total capacity of the virtual power plant of a certain resource; γ VPP is the capacity conversion factor between the virtual power plant and traditional units of a certain resource;

[0146] The investment cost is converted into a levelized value:

[0147] Among them, I VPP,sp i ,t To levelize the virtual power plant to save investment costs on conventional power sources and grid supporting equipment after time period t.

[0148] The total operating income of the virtual power plant is the income from its participation in the electricity market regulation, including electricity sales income, peak regulation income and frequency regulation income, as follows: The total operating income of the virtual power plant is: R total,t =R e,t +R ps,t +R fm,t ;(45)

[0149] Among them, R total,t is the total operating income of the virtual power plant during time period t; R ps,t is the total peak-shaving revenue of the virtual power plant in time period t; R fm,t is the total frequency regulation revenue of the virtual power plant in time period t;

[0150] R e,t =ΔQ VPP,t ×C t ; (46)

[0151] R ps,t =p psD ΔQ psD,t Δt psD +p psU ΔQ psU,t Δt psU; (47)

[0152]

[0153] Where ΔQ VPP,t is the power generation (regulated power) of the virtual power plant during the time period t; C t is the on-grid electricity price at the current moment; p psD is the incentive price for peak shaving; p psU is the incentive electricity price for valley filling; ΔQ psD,t is the response capacity of peak clipping; ΔQ psU,t is the response capacity of valley filling; Δt psD is the response time of peak clipping; Δt psU is the response time of valley filling; p cap is the frequency regulation capacity compensation price; ΔL fm,t is the frequency modulation capacity; p mil Compensation price for frequency modulation mileage; is the frequency modulation mileage in period t, is the normalized mean of the comprehensive performance index.

[0154] The operating benefits of a power supply virtual power plant or an energy storage virtual power plant are:

[0155] The operating income of load-type virtual power plants is:

[0156] in, The total power generation revenue of the power supply type virtual power plant or the energy storage type virtual power plant; is the total power generation revenue of the load-type virtual power plant; R total,t is the total operating income of the virtual power plant during time period t; Regulate the amount of electricity for a power supply virtual power plant or an energy storage virtual power plant during time period t; Regulate electricity for load-type virtual power plants.

[0157] For power generation and energy storage virtual power plants, their contribution to the power system's electricity volume can be described based on the power generation revenue within time period t. This allows the contribution revenue of a power generation or energy storage virtual power plant in any time period to be evaluated, and its power generation revenue model to be constructed:

[0158] in, is the power generation income of the power supply type virtual power plant resource or the energy storage type virtual power plant resource in time period t; is the proportion of aggregated resource income; is the contribution of power generation revenue of power supply type virtual power plant resources or energy storage type virtual power plant resources in time period t; the calculation formula is:

[0159] in, is the amount of electricity that is online in the time period t for the power supply type virtual power plant resource or the energy storage type virtual power plant resource; is the total on-grid electricity of the power supply type virtual power plant or energy storage type virtual power plant in time period t.

[0160] For load-type virtual power plants, their contribution to power system regulation can be described based on the regulation revenue contribution within time period t, thereby evaluating the economic effect of a certain type of load resource within any time period. The specific model is constructed as follows:

[0161] in, is the regulation income of load-type virtual power plant resources in time period t; is the proportion of aggregated resource income; It is the regulation benefit contribution of load-type virtual power plant resources in time period t. If the calculation result is negative, it is taken as 0. The calculation method of this indicator is as follows:

[0162] in, The electricity price of the load-type virtual power plant during the t period; is the theoretical revenue generated by the load-type virtual power plant resources in time period t. If the calculation result is negative, it is taken as 0. The calculation method of this formula is as follows:

[0163] in, is the actual power consumption of the load resource in time period t; The power consumption of the baseline load of the load resource in time period t; The monthly medium- and long-term contract electricity price of all user-side entities in time period t can be calculated by taking the weighted average of the proportion of medium- and long-term contract electricity in the corresponding period; It is the real-time unified market settlement point electricity price for the user-side entity during time period t.

[0164] The above forms the levelized investment cost, levelized power savings cost, and operating cost of a certain resource of a virtual power plant in a certain time period. Combined with the operating cost of the virtual power plant in this time period, the cost recovery of the virtual power plant in any time period during its life cycle can be obtained, and the overall economic evaluation index of the virtual power plant resources can be further obtained:

[0165]

[0166] Among them, IB eco,t EI is the economic benefit generated by the virtual power plant to the operator during the time period t; eco,t is the economic evaluation index of a resource in the virtual power plant within the time period t; C VPP,op,tis the sum of the operation and regulation costs of a resource in the virtual power plant during this period; further calculation shows that the investment payback period of the virtual power plant is:

[0167] PBP VPP =I total / (R VPP,t -C VPP,op,t )| t＝1 ; (20)

[0168] Among them, PBP VPP is the investment payback period; its unit is consistent with the dimension of t.

[0169] Construct the environmental value evaluation index of the virtual power plant, including the reduction of power curtailment rate and carbon dioxide emissions. The specific calculation method of the power curtailment rate and carbon dioxide emissions index of the power system is as follows:

[0170]

[0171] Among them, P s,VRE,t % is the power abandonment rate in time period t; is the maximum output of the wind turbine in time period t; is the actual output of the wind and solar power generator in time period t; is the system carbon dioxide emissions during time period t; is the carbon dioxide emission coefficient per unit coal consumption; is the output of the thermal power unit during time period t.

[0172] For power supply virtual power plants and energy storage virtual power plants, construct a representation of the contribution of a certain resource to reducing the output of thermal power units and increasing the output of wind and solar power units:

[0173]

[0174] in, Adding wind and solar power to a resource; A resource is used to reduce the power consumption of thermal power units.

[0175] For load-type virtual power plants, the following characterizations are constructed to characterize their role in replacing thermal power units and accommodating wind and solar power units:

[0176]

[0177]

[0178] in, To consider the increase in power generation of wind and solar units under the regulation of load-type virtual power plants relative to those not considered; To consider the load-type virtual power plant regulation effect and reduce the amount of thermal power generated by wind and solar units compared to those not considered; are the regulated power of a certain resource in time period t and the regulated power of a full-load virtual power plant in time period t; in summary, the contribution of a certain resource to the power curtailment rate and carbon dioxide emissions of the power system is:

[0179]

[0180]

[0181] in, Contribute to the curtailment rate indicator; Carbon dioxide emission contribution indicator.

[0182] Based on the production simulation optimization results, a virtual power plant safety value evaluation index is constructed to quantify the contribution to the improvement of safety evaluation indicators, specifically including the peak shaving and valley filling effect, the reduction in load shedding, and the emergency control capability. The evaluation process is as follows: During the production simulation process, priority is given to identifying time periods with poor operating indicators, introducing virtual power plant adjustments for these time periods, and further evaluating the operating optimization effects of various resources in the virtual power plant to quantify its contribution to the improvement of overall operating indicators, as follows: The changes in volatility are used to describe the peak and valley changes in the power system. The calculation method of volatility is as follows:

[0183] λ load,t =(L t -L t-1 ) / L t-1 ; (26)

[0184] Among them, λ load,t is the volatility of period t, L t is the load size during period t.

[0185] The time period when conventional power supply regulation capacity is insufficient and fluctuation rate is large is selected to evaluate the value of virtual power plant peak shaving and valley smoothing. The contribution degree of each resource to peak shaving and valley smoothing is quantified as follows:

[0186] in, Δλ is the contribution index of a resource to peak shaving and valley leveling; load,t is the reduction in volatility after considering the virtual power plant adjustment; ΔQ VPP,t They are respectively the power changes of a certain resource after considering the regulation of the virtual power plant.

[0187] The time period with large system load shedding is selected to evaluate the value of reducing the amount of power curtailment in the virtual power plant. The contribution of each resource to reducing the amount of power curtailment is quantified as follows:

[0188] in, The load shedding contribution index for a resource. This is to take into account the reduction in load shedding power after virtual power plant regulation.

[0189] The moment when the flexibility of the conventional units in the system is insufficient is selected for emergency control value evaluation. After the moment is selected, a load change ΔL is introduced for that moment. After the dispatch instruction is decomposed, it is allocated to each virtual power plant to evaluate the response capability of each virtual power plant to the load change. Specifically, ΔP VPP,t % = ΔP VPP,t / ΔL VPP ; (70)

[0190] Where ΔP VPP,t % is the output response index of a resource in the virtual power plant, which is used to quantify the emergency control capability of each resource; ΔP VPP,t The actual response output of a resource; ΔL VPP It is the scheduling instruction of a certain aggregate resource after the scheduling instruction is decomposed.

[0191] In order to fully illustrate the process of the present invention, a simplified power grid system in a certain place is used as an example to verify the effect of the present method. The starting system topology diagram is shown in FIG. Figure 6 , Figure 6 This is the system topology diagram proposed in Example 1 of the present invention; the system has a total of 90 nodes, 200 lines and a 57.9GW thermal power plant; 30.225GW distributed wind power, 65.34GW distributed photovoltaic, and a total of 2GW distributed energy storage units, and includes two voltage levels of 500kV and 100kV. We aggregate the distributed wind, photovoltaic and energy storage units to the nearest 500kV node to form a distributed power supply type and energy storage type virtual power plant aggregate; the initial maximum load is 84.8GW, and the system is equipped with a total of 25.14GW of load-type virtual power plants, including 9.58GW of air-conditioning load and 15.56GW of electric hydrogen production load; it is set that the maximum value of external electricity that the system can purchase from the external system at each moment does not exceed 20% of the current load.

[0192] Investigate the typical load change curve of the region, combine the change curve with the maximum load of the system, and form the load change curve of the system. Figure 7 This is the load curve proposed in Example 1 of the present invention.

[0193] Investigate the installation model data of various conventional power sources and lines of the system, and form a table of operating parameters of each device that can form this coefficient. The parameters of some thermal power units and lines of the system are shown in Table 1-Table 2;

[0194] Table 1 Equipment status and parameters of some thermal power plants at some nodes

[0195]

[0196]

[0197] Table 2 Installation location and parameters of some lines in the system

[0198]

[0199] By investigating the installation data of various distributed power sources, energy storage, lines, and virtual power plants in the system, a table of operating parameters for each device in this coefficient can be formed. The parameters of some wind and solar power aggregates, energy storage unit aggregates, and load-type virtual power plant aggregates in this system are shown in Tables 3 to 6;

[0200] Table 3 Installation positions and parameters of some wind and solar turbines in the system

[0201]

[0202] Table 4 System energy storage unit aggregate installation status and parameters

[0203] ESS Number Node number Pcmax(MW) Pdmax(MW) Emax(MWh) SOCmax SOCmin SOCInit SOCEnd 1 41 1000 1000 6000 0.9 0.1 0.5 0.5 2 36 1000 1000 6000 0.9 0.1 0.5 0.5

[0204] Table 5 Air conditioning load installation and parameters

[0205] Installation Node Rated power (MW) Ramp-up rate (MW / h) Down ramp rate (MW / h) A B C Discrete time 55 1696.59 848.30 848.30 0.415 1.51 0.209 0.00833 31 1915.77 957.89 957.89 0.313 1.20 0.156 0.00833 53 1534.81 767.41 767.41 0.415 1.51 0.209 0.00833 35 1579.58 789.79 789.79 0.313 1.20 0.156 0.00833 51 1397.19 698.60 698.60 0.415 1.51 0.209 0.00833 46 1451.50 725.75 725.75 0.313 1.20 0.156 0.00833

[0206] Table 6 Installation status and parameters of some electric hydrogen production loads

[0207]

[0208] By investigating the typical wind speed and light intensity changes in the area and referring to various wind and solar units, a typical wind and solar output curve of the system can be formed as follows: Figure 8-Figure 9 As shown; Figure 8 This is a typical wind power output curve proposed in Example 1 of the present invention; Figure 9 This is a typical photovoltaic output curve proposed in Example 1 of the present invention.

[0209] Investigate typical outdoor temperature change curves and hydrogen consumption curves, build external characteristic models of air conditioning and electric hydrogen production, and form a typical operating baseline for a typical load-type virtual power plant, such as Figure 10-11 shown. Figure 10 The air conditioning load baseline proposed in Example 1 of the present invention; Figure 11 This is the load baseline for hydrogen production by electricity proposed in Example 1 of the present invention.

[0210] Based on the variation curve formed above, two days of high load and low wind power and two days of high wind power and low load are selected as typical days. The whole system is simulated based on the production simulation model constructed in step 2. The operation curves of the system under different conditions are as follows: Figure 12-15As shown, it can be seen that when the wind and solar outputs are large, the output of the thermal power units is reduced to supplement the system energy supply. At this time, the thermal power units mainly play a regulating role; when the wind and solar outputs are poor, the thermal power units have a higher output and undertake the main energy supply of the system; the energy storage unit stores energy when the photovoltaic unit output is high and releases the energy when the photovoltaic unit output is low.

[0211] When air conditioning and hydrogen production are not regulated, the system's operating cost is 1.226 billion yuan. The electricity generated by wind and solar power units accounts for about 38.5% of the total system electricity. At this time, the system has 7.13% wind curtailment and 11.1% solar curtailment, and 1.69% load shedding. The carbon emissions of the entire system are 2.97×10 5 t of carbon dioxide, Figure 12 This is the system simulation result when the air conditioning and electric hydrogen production loads are not involved in the regulation proposed in Example 1 of the present invention;

[0212] After considering the regulation of air conditioning load, the system operating cost was reduced to 1.17 billion yuan, a decrease of 4.8%. The electricity share of wind and solar units increased to 38.89%. At this time, the system power curtailment and load shedding phenomena were alleviated. The wind curtailment rate was reduced to 5.8%, the solar curtailment rate remained unchanged at 11.1%, and the load shedding rate was reduced to 1.32%. The carbon emissions of the entire system were 2.97×10 5 The carbon dioxide at t has not decreased significantly; the operating curve of the system at this time is as follows Figure 13 As shown, Figure 13 This is the system simulation result under the air conditioning participation control proposed in Example 1 of the present invention.

[0213] After considering the regulation of hydrogen production load, the system operating cost was reduced to 1.091 billion yuan, a decrease of 12.4%. The electricity share of wind and solar units increased to 39%. The system wind curtailment rate was reduced to 4.12%, the solar curtailment rate was reduced to 6.1%, and the load shedding rate was reduced to 0.49%. The carbon emissions of the entire system were reduced to 2.95×10 5 The carbon dioxide in t is reduced by 0.67%. The operating curve of the system at this time is as follows Figure 14 As shown; Figure 14 This is the system simulation result under the regulation of hydrogen production by electricity proposed in Example 1 of the present invention.

[0214] Taking into account the dispatch of various typical flexible loads, the system operating cost was reduced to 1.057 billion yuan, a decrease of 15.99%; the electricity share of wind and solar units increased to 40.1%; the system wind curtailment rate was reduced to 4%, the solar curtailment rate was reduced to 6.1%, and the load shedding rate was reduced to 0.28%; the carbon emissions of the entire system were reduced to 2.94×10 5 t of carbon dioxide, decreased by 1%; Figure 15This is the simulation result of the system considering air conditioning and electric hydrogen production proposed in Example 1 of the present invention. The operating baseline and the operating curve after regulation of various resources are as follows: Figure 16 As shown, Figure 16 The load baseline and actual load size of air conditioning and electric hydrogen production proposed in Example 1 of the present invention.

[0215] Assume that the total unit investment cost of the air-conditioning virtual power plant is RMB 300,000 / MW and the life span is 10 years; the total unit investment cost of the hydrogen production equipment and the corresponding virtual power plant supporting facilities is RMB 4 million / MW and the life span is 15 years; the annual discount rate is 0.05; assuming that the above typical day conforms to the actual average situation, then in the above calculation, considering the investment allocation, the profit of the air-conditioning unit regulated electricity is RMB 226.96 / MWh, and the average daily profit of the air-conditioning unit capacity is RMB 657.13 / MW · The payback period for participating in virtual power plant regulation alone is 393.18 days; the profit per unit of regulated electricity for hydrogen production is 181.13 yuan / MWh, and the average daily profit per unit capacity is 677.79 yuan / MW. · days, and the payback period through participating in virtual power plant regulation alone is 2307.35 days.

[0216] The present invention is applicable to the economic benefit evaluation and mutual benefit value assessment of various types of virtual power plants. It includes three parts: virtual power plant characteristic modeling, power system production simulation and value assessment, aiming to realize the interactive value quantification and return analysis of virtual power plants. The first part is to build an operation characteristic model based on the physical characteristics and operation characteristics of virtual power plant resources. Specifically, it includes: investigating the geographical information such as wind speed, light, outdoor temperature and basic parameters of the virtual power plant at the geographical location of the evaluated system to form the basic information of virtual power plant operation and scheduling; constructing a typical resource operation characteristic model of power supply type virtual power plant; constructing a typical resource operation characteristic model of energy storage type virtual power plant; and constructing a typical resource operation characteristic model of load type virtual power plant. The second part integrates the above-mentioned operation characteristic model into the power system production simulation, constructs a production simulation model with the goal of economic optimization, takes into account the operation cost, bad operation penalty and virtual power plant regulation cost, establishes power balance constraints, backup constraints, line transmission capacity constraints, equipment characteristic constraints and virtual power plant characteristic constraints, and adopts a time-series production simulation method based on daily rolling solution to solve; the third part is based on the power system operation analysis data generated by the production simulation results, and constructs quantitative indicators for virtual power plant value assessment from the perspective of economy, environmental protection, safety and stability. Specifically, it includes: establishing an economic evaluation model to quantitatively evaluate the economic benefits generated by the virtual power plant to the operator and the economic benefits generated by the power grid company; establishing an environmental evaluation model to quantitatively evaluate the contribution of the regulation of various resources of the virtual power plant to environmental protection; establishing a safety and stability evaluation model to quantitatively evaluate the contribution of various resources of the virtual power plant to the safe and stable operation of the power system.

[0217] Example 1 of the present invention proposes a virtual power plant value assessment method based on time-series production simulation. By constructing a virtual power plant operation characteristic model and integrating it into the power system production simulation model to generate operation data that can be used for value assessment, the economy, safety and environmental protection of the virtual power plant are further comprehensively evaluated, providing a scientific basis for the planning and operation of the virtual power plant.

[0218] Example 1 of the present invention proposes a virtual power plant value assessment method based on time-series production simulation, which can comprehensively reflect the operating characteristics, regulation contribution and income of the virtual power plant, and provide a scientific basis for the optimal scheduling, resource allocation and investment planning of the virtual power plant. The present invention supports the investment return evaluation of various resources, provides virtual power plant value quantification tools for power grids and users, and helps improve the flexibility of the power system and build a new power system.

[0219] Example 1 of the present invention proposes a virtual power plant value assessment method based on time-series production simulation. This assessment provides a comprehensive understanding of the virtual power plant's contribution to the power system and quantifies its effectiveness in terms of supply and demand balance, regulatory capacity, and environmental impact. This assessment provides a scientific basis for the planning, construction, and operation of virtual power plants, helping to optimize resource allocation, improve grid operating efficiency, guide virtual power plant operators in scientifically investing and allocating virtual power plant resources, and promote the safe, stable, and green development of the power system.

[0220] Example 2

[0221] Based on the virtual power plant value assessment method based on time-series production simulation proposed in Example 1 of the present invention, Example 2 of the present invention proposes a virtual power plant value assessment system based on time-series production simulation. Figure 17 This is a schematic diagram of a virtual power plant value assessment system based on time-series production simulation proposed in Example 2 of the present invention, the system comprising a first building module, a second building module, and an assessment module;

[0222] The first construction module is used to divide the virtual power plant into power supply type virtual power plant, energy storage type virtual power plant and load type virtual power plant according to its operating characteristics, and construct the operating characteristic models of the power supply type virtual power plant, energy storage type virtual power plant and load type virtual power plant respectively;

[0223] The second construction module is used to build a source-grid-load-storage coordinated operation model, divide the production simulation time period into multiple sub-time periods, and solve the production simulation optimization results for each sub-time period. The model construction process is as follows: the objective function of the source-grid-load-storage coordinated operation model is constructed with the goal of economic optimization, and the constraints of the source-grid-load-storage coordinated operation model are constructed by considering the resource endowment of each node and the overall source and storage addition target of the power system.

[0224] The evaluation module is used to construct a virtual power plant value evaluation model based on the production simulation optimization results, and to construct a method for solving the economic evaluation indicators of the virtual power plant, so as to evaluate the economic evaluation of the virtual power plant and a certain type of resources on the power system and the virtual power plant operator; to construct a method for solving the environmental evaluation indicators of the virtual power plant, so as to evaluate the environmental evaluation of the virtual power plant and a certain type of resources on the power system; and to construct a virtual power plant safety value evaluation indicator to quantify the contribution to the improvement of the safety evaluation indicators of the virtual power plant and a certain type of resources on the power system.

[0225] During the execution of the first building module, the process of building the operating characteristic model of the power supply type virtual power plant includes:

[0226] The typical output curve of a distributed wind turbine as a power-type virtual power plant is:

[0227]

[0228] in, Indicates wind turbine k W The maximum power output at time t; V t Indicates the actual wind speed at time t; V in Indicates the cut-in wind speed; V out Indicates the cut-out wind speed; V N Indicates rated wind speed; k is the wind turbine W Rated power.

[0229] Based on the typical output curve of wind turbines, the actual output constraint of distributed wind turbines is:

[0230]

[0231] in, Indicates wind turbine k W The actual output at time t;

[0232] The typical output curve of a distributed photovoltaic unit as a power supply virtual power plant is:

[0233]

[0234] in, Indicates photovoltaic unit k P The maximum power output at time t; R t Indicates the light intensity at time t, R N Indicates the rated light intensity of the photovoltaic unit; Indicates photovoltaic unit k P Rated power.

[0235] Based on the typical output curve of photovoltaic units, the actual output constraint of distributed photovoltaic units is obtained as follows:

[0236]

[0237] in, Indicates photovoltaic unit k P The actual output at time t.

[0238] The process of building an operational characteristic model of an energy storage virtual power plant includes:

[0239] Considering the active output limitation of the energy storage unit during the charging and discharging process, the upper and lower power constraints of the energy storage unit are constructed as follows:

[0240] in, For the kth ess Maximum discharge power of a distributed energy storage unit; For the kth ess The discharge power of the distributed energy storage unit at time t;

[0241] Considering the energy storage capacity limitation during normal operation of the energy storage unit, the upper and lower limit constraints of the energy storage unit's state of charge are constructed as follows:

[0242] in, For the kth ess The state of charge of the distributed energy storage unit at time t, For the kth ess The maximum nuclear power state of the distributed energy storage unit; For the kth ess The minimum nuclear power state of the distributed energy storage unit;

[0243] The state of charge constraint formula of the energy storage unit is:

[0244] in, For the kth ess The state of charge of the distributed energy storage unit at time t-1; For the kth ess The state of charge of the distributed energy storage unit at time t-1; For the kth ess The discharge power of a distributed energy storage unit at time t-1; Δt is the time step.

[0245] The process of building an operational characteristic model of a load-based virtual power plant includes:

[0246] The air conditioning unit model as a load-type virtual power plant is expressed as:

[0247]

[0248] in, is the equivalent SOC size of the jth air conditioner at time t; ΔP j (t) is the adjustable power value of the air conditioner;

[0249] P jmin -P jbaseline (t)≤ΔP j (t)≤P jmax -P jbaseline (t); (9)

[0250] Among them, P jbaseline (t) is the power baseline of the jth air conditioner at time t; P jmax is the upper limit of the operating power of the jth air conditioner; P jmin is the lower limit of the operating power of the jth air conditioner; ΔP j (t) is the adjustment power of the jth air conditioner at time t; α j is the first state transfer parameter of the air-conditioning unit; β j is the second state transition parameter of the air-conditioning unit; γ j is the third state transition parameter of the air-conditioning unit;

[0251]

[0252] Among them, C j Represents equivalent heat capacity; R j represents the equivalent thermal resistance; η represents the refrigeration energy efficiency coefficient; T jmin Represents the minimum indoor temperature to meet user comfort; T jmax Represents the maximum indoor temperature to meet user comfort; T jset is the set temperature of the jth air conditioner.

[0253] The state transition equation of the air conditioner SOC state transition is:

[0254] soc AC (t+1)=Asoc AC (t)+BΔP(t)+C; (11)

[0255] Among them, A is the first state transfer coefficient of the air conditioner; B is the second state transfer coefficient of the air conditioner; C is the third state transfer coefficient of the air conditioner; is the equivalent SOC size of the jth air conditioner at time t+1; ΔP(t) is the adjustment power of the jth air conditioner at time t;

[0256]

[0257] Among them, α j is the first state transfer parameter of the air-conditioning unit; βj is the second state transition parameter of the air-conditioning unit; γ j is the third state transition parameter of the air-conditioning unit; n is the total number of air-conditioning aggregates.

[0258] Construct upper and lower power constraints that describe the power output limits of the air conditioner:

[0259] P Smin ≤ΔP S (t)+P Sbaseline ≤P Smax ; (13)

[0260] Where ΔP S (t) is the output of the air conditioning aggregate at time t; P Sbaseline is the power baseline of the air conditioning aggregate; P Smax is the upper power limit; P Smin is the lower power boundary;

[0261]

[0262] Among them, P jbaseline (t) is the power baseline of the jth air conditioner at time t; P jmax is the upper limit of the operating power of the jth air conditioner; P jmin is the lower limit of the operating power of the jth air conditioner unit.

[0263] Construct the SOC state constraints that describe the upper and lower boundaries of the air conditioner equivalent SOC: 0≤soc AC (t)≤1;(15)

[0264] Construct the state transition equation to describe the SOC state transition of hydrogen production:

[0265]

[0266] Among them, SOC HST (t) is the current SOC of hydrogen produced by electricity at time t; V HST The maximum volume of hydrogen that can be stored in a hydrogen storage tank at rated pressure and temperature; is the gas storage capacity of the gas storage equipment during period t; is the total amount of gas output by the gas storage device during period t; SCR is the self-consumption rate of the hydrogen storage tank; η H2,in is the utilization rate of hydrogen entering the gas tank; is the utilization rate of hydrogen output tank; α e,t is the electrical efficiency, is the efficiency of hydrogen production by electrolysis; Q HHV is the calorific value of hydrogen; is the operating power of the electric hydrogen production equipment at time t.

[0267] Construct a hydrogen power constraint that describes the power limit when hydrogen production is actually running:

[0268]

[0269] in, Maximum hydrogen power;

[0270] Construct a hydrogen storage tank state constraint that describes the capacity limit of the hydrogen storage tank:

[0271] in, The upper and lower limits of hydrogen storage capacity are specified for hydrogen storage tanks respectively.

[0272] The process of constructing the objective function of the source-grid-load-storage collaborative operation model with the goal of economic optimization includes:

[0273] minF op =f c +f r +f uc +f p +f VPP ;(29)

[0274] Among them, f c is the coal consumption and carbon emission cost of coal-fired units; f r is the standby cost; f uc is the unit start-up and shutdown cost; f p Penalty for load shedding and power abandonment; f VPP Adjusting costs for virtual power plants; minF op is the objective function of the source-grid-load-storage collaborative operation model;

[0275]

[0276] in, is the coal consumption function of the coal-fired unit; For the kth G The output of the thermal power unit at time t; c coal is the unit coal consumption cost; is the carbon dioxide emission cost per unit coal consumption; Ω VRE For the new energy unit collection; The unit cost of upper and lower standby of the unit; Provide upper and lower standby power for thermal power units; Startup and shutdown status variables of thermal power units; is the cost of starting and stopping the unit; c drop 、c shed is the penalty coefficient for power curtailment and load shedding; Q drop , Q shedThe amount of power abandoned and load cut; is the allowable value of abandoned electricity; is the allowable value of load shedding; Ω VPP is a collection of resource aggregates; is the adjustment cost per unit power change of the resource aggregate; is the power change of the resource aggregate at node i at time t.

[0277] In the second building block, the process of constructing the objective function of the source-grid-load-storage collaborative operation model with the goal of economic optimization includes: minF op =f c +f r +f uc +f p +f VPP ; (30)

[0278] Among them, f c is the coal consumption and carbon emission cost of coal-fired units; f r is the standby cost; f uc is the unit start-up and shutdown cost; f p Penalty for load shedding and power abandonment; f VPP Adjusting costs for virtual power plants; minF op is the objective function of the source-grid-load-storage collaborative operation model;

[0279]

[0280] in, is the coal consumption function of the coal-fired unit; For the kth G The output of the thermal power unit at time t; c coal is the unit coal consumption cost; is the carbon dioxide emission cost per unit coal consumption; Ω VRE For the new energy unit collection; The unit cost of upper and lower standby of the unit; Provide upper and lower standby power for thermal power units; It is the startup and shutdown state variable of the thermal power unit; is the start-up and shutdown cost of the unit; c drop 、c shed is the penalty coefficient for power curtailment and load shedding; Q drop , Q shed The amount of power abandoned and load cut; is the allowable value of abandoned electricity; is the allowable value of load shedding; Ω VPP is a collection of resource aggregates; is the adjustment cost per unit power change of the resource aggregate; is the power change of the resource aggregate at node i at time t.

[0281] Considering the resource endowment of each node and the overall power system source and storage addition target, the process of constructing the constraint conditions of the source-grid-load-storage coordinated operation model includes:

[0282] Considering that the total node power injection is equal to the load, the power balance constraint is constructed:

[0283]

[0284] in, The kth node of node i is VRE The output of each VRE unit at time t; represents the active power transmitted in line l at time t; Ω from、 Ω to They represent the set of lines starting and ending at node i respectively; is the active power of the load at node i at time t; is the load shedding size of node i at time t;

[0285] Considering the load reserve demand and the new energy reserve demand, a reserve constraint is constructed so that the reserve supply is greater than the system reserve demand:

[0286]

[0287] Among them, L + % is the upper limit reserve requirement coefficient of load reserve; L - % is the lower limit reserve requirement coefficient reserved for load; W + % is the upper limit reserve requirement coefficient reserved for wind and solar power unit output; W - % is the lower limit reserve requirement coefficient reserved for wind and solar power unit output; Ω d ,Ω vre It is the node set for load access and the new energy unit set; k for unit VRE The amount of power wasted at time t; For the kth VRE The output of each new energy unit at time t.

[0288] Considering that the line transmission power is less than the maximum transmission capacity of the line, the line operation constraint is formed:

[0289]

[0290] in, is the maximum transmission capacity of line l.

[0291] Considering the upper and lower limits of thermal power generation units' output when providing backup demand, the output constraints of thermal power generation units are constructed:

[0292]

[0293] in, is the operating state variable of the kth thermal power unit at time t, the operating state is 1 and the shutdown state is 0; They are the maximum output and minimum output of thermal power units respectively.

[0294] Considering the limitation of power rise and fall of thermal power units per unit time, a ramp constraint is constructed to ensure that the power change of thermal power units per unit time is less than the ramp limit:

[0295]

[0296] in, For the kth G The output of the thermal power unit at time t-1; is the upward power change of the thermal power unit under the operating time; It is the downward power change of the thermal power unit under the running time.

[0297] Construct a start-stop constraint that the startup and shutdown time of a thermal power unit is greater than the minimum startup and shutdown time:

[0298]

[0299]

[0300] in, Thermal power unit startup and shutdown status variables; k for coal-fired units G Minimum startup and shutdown duration.

[0301] Constructing new energy equipment characteristic constraints:

[0302] in, is the maximum output of the new energy unit at time t;

[0303] Construct energy storage device characteristic constraints:

[0304]

[0305]

[0306]

[0307] in, is the discharge power of the energy storage unit at time t; is the maximum discharge power of the energy storage unit; Ω ess It is a collection of energy storage units; Indicates the kthess The state of charge of the energy storage unit at time t; kth ess The maximum and minimum nuclear power states of each energy storage unit; For the kth ess Rated energy storage capacity of each energy storage unit; is the discharge power of the energy storage unit at time t-1.

[0308] Constructing characteristic constraints for air conditioning equipment:

[0309]

[0310]

[0311]

[0312] in, is the equivalent SOC of the air conditioner at time t; A is the first state transition parameter of the air conditioner; B is the second state transition parameter of the air conditioner; C is the third state transition parameter of the air conditioner; The regulating power of the air conditioner; is the baseline power of the air conditioner; is the upper power limit of the air conditioner; is the lower power limit of the air conditioner.

[0313] Construct characteristic constraints of electric hydrogen production equipment:

[0314]

[0315] in, is the gas volume of the hydrogen storage tank at time t+1 for hydrogen production by electricity; is the gas volume of the hydrogen storage tank at time t for hydrogen production by electricity; is the gas volume of the hydrogen storage tank at the last moment of hydrogen production; Δt is the simulation step length; Q HHV is the calorific value of hydrogen; is the efficiency of hydrogen production by electrolysis; α e,t is electrical efficiency; is the total amount of gas output by the gas storage device during period t; SCR is the self-consumption rate of the hydrogen storage tank; is the utilization rate of hydrogen entering the gas tank; is the utilization rate of hydrogen output tank; V HST The maximum volume of hydrogen that can be stored in a hydrogen storage tank at rated pressure and temperature; The upper and lower limits of hydrogen storage capacity are specified for hydrogen storage tanks respectively; is the efficiency of hydrogen production by electrolysis; is the output power of the hydrogen production system; is the total amount of gas output by the gas storage equipment during period t.

[0316] In the evaluation module, the process of constructing a virtual power plant economic value evaluation model based on the production simulation optimization results includes:

[0317] Constructing a virtual power plant investment cost model; I total =I si +I hi ;(41)

[0318] Among them, I total is the total investment of the virtual power plant; I si For software investment, I hi Invest in hardware;

[0319]

[0320] in, is the investment cost of the virtual power plant levelized to the time period t, r is the annual discount rate, m is the maximum life span of the virtual power plant that can participate in grid regulation, and T is one year with the same dimension as t;

[0321] Construct a model to represent the savings in system power supply and supporting grid infrastructure investment costs due to the construction of a certain resource of a virtual power plant:

[0322] Among them, I VPP,spi Save power costs for some kind of virtual power plant resource; The unit investment cost of traditional power supply per unit power capacity; is the unit investment cost of the grid supporting equipment per unit power capacity; S VPP is the total capacity of the virtual power plant of a certain resource; γ VPP is the capacity conversion factor between the virtual power plant and traditional units of a certain resource;

[0323]

[0324] in, To save investment costs on conventional power sources and grid supporting equipment for virtual power plants after leveling to time period t.

[0325] The total operating income of the virtual power plant is: R total,t =R e,t +R ps,t +R fm,t ;(45)

[0326] Among them, R total,t is the total operating income of the virtual power plant during time period t; R ps,t is the total peak-shaving revenue of the virtual power plant in time period t; R fm,t is the total frequency regulation revenue of the virtual power plant in time period t;

[0327] R e,t =ΔQ VPP,t ×C t ; (46)

[0328] R ps,t =p psD ΔQ psD,t Δt psD +p psU ΔQ psU,t Δt psU ; (47)

[0329]

[0330] Where ΔQ VPP,t is the power generation of the virtual power plant in time period t; C t is the on-grid electricity price at the current moment; p psD is the incentive price for peak shaving; p psU is the incentive electricity price for valley filling; ΔQ psD,t is the response capacity of peak clipping; ΔQ psU,t is the response capacity of valley filling; Δt psD is the response time of peak clipping; Δt psU is the response time of valley filling; p cap is the frequency regulation capacity compensation price; ΔL fm,t is the frequency modulation capacity; p mil Compensation price for frequency modulation mileage; is the frequency modulation mileage in period t, is the normalized mean of the comprehensive performance index;

[0331] The operating benefits of a power supply virtual power plant or an energy storage virtual power plant are:

[0332] The operating income of load-type virtual power plants is:

[0333] in, The total power generation revenue of the power supply type virtual power plant or the energy storage type virtual power plant; is the total power generation revenue of the load-type virtual power plant; R total,t is the total operating income of the virtual power plant during time period t; Regulate the amount of electricity for a power supply virtual power plant or an energy storage virtual power plant during time period t; Regulate power for load-type virtual power plants;

[0334]

[0335] in, is the power generation income of the power supply type virtual power plant resource or the energy storage type virtual power plant resource in time period t; is the proportion of aggregated resource income; The contribution of power generation revenue of power supply type virtual power plant resources or energy storage type virtual power plant resources in time period t;

[0336] in, is the amount of electricity that is online in the time period t for the power supply type virtual power plant resource or the energy storage type virtual power plant resource; is the total on-grid electricity of the power supply type virtual power plant or energy storage type virtual power plant in time period t.

[0337] The methods for solving the economic evaluation indicators for building a virtual power plant include:

[0338] The regulation income of load-type virtual power plants is:

[0339] in, is the regulation income of load-type virtual power plant resources in time period t; is the proportion of aggregated resource income; is the regulation benefit contribution of load-type virtual power plant resources in time period t;

[0340]

[0341] in, The electricity price of the load-type virtual power plant during the t period; is the theoretical revenue generated by load-type virtual power plant resources in time period t;

[0342] in, is the actual power consumption of the load resource in time period t; The power consumption of the baseline load of the load resource in time period t; The monthly medium- and long-term contract electricity price for all user-side entities in time period t; The real-time market unified settlement point electricity price for the user-side entity in time period t;

[0343] The overall economic evaluation index of virtual power plant resources is:

[0344]

[0345]

[0346] Among them, IB eco,t EI is the economic benefit generated by the virtual power plant to the operator during the time period t; eco,t is the economic evaluation index of a resource in the virtual power plant within the time period t; C VPP,op,t PBP is the sum of the operation and regulation costs of a resource in the virtual power plant during this period;VPP =I total / (R VPP,t -C VPP,op,t )| t＝1 ; (34)

[0347] Among them, PBP VPP The investment payback period;

[0348] The process of constructing a method for solving environmental performance evaluation indicators of a virtual power plant based on the production simulation optimization results includes:

[0349] The calculation method for the power system's curtailment rate and carbon dioxide emissions indicators is as follows:

[0350]

[0351]

[0352] Among them, P s,VRE,t % is the power abandonment rate in time period t; is the maximum output of the wind turbine in time period t; is the actual output of the wind and solar power generator in time period t; is the system carbon dioxide emissions during time period t; is the carbon dioxide emission coefficient per unit coal consumption; is the output of the thermal power unit during time period t.

[0353] For power supply virtual power plants and energy storage virtual power plants, construct a representation of the contribution of a certain resource to reducing the output of thermal power units and increasing the output of wind and solar power units:

[0354]

[0355] in, Adding wind and solar power to a resource; For a certain resource, it is to reduce the power consumption of thermal power units;

[0356] For load-type virtual power plants, the following characterizations are constructed to characterize their role in replacing thermal power units and accommodating wind and solar power units:

[0357]

[0358]

[0359] in, To consider the increase in power generation of wind and solar units under the regulation of load-type virtual power plants relative to those not considered; To consider the load-type virtual power plant regulation effect and reduce the amount of thermal power generated by wind and solar units compared to those not considered; They are the regulated power of a resource in time period t and the regulated power of a full-load virtual power plant in time period t;

[0360] The contribution of a certain resource to the power system curtailment rate and carbon dioxide emissions is as follows:

[0361]

[0362]

[0363] in, Contribute to the curtailment rate indicator; Carbon dioxide emission contribution indicator.

[0364] The specific process of constructing a virtual power plant safety value evaluation index based on the production simulation optimization results to quantify the contribution to the improvement of the safety evaluation index includes:

[0365] The fluctuation rate is used to describe the peak-valley changes in the power system. The fluctuation rate is calculated as follows:

[0366] λ load,t =(L t -L t-1 ) / L t-1 ; (40)

[0367] Among them, λ load,t is the volatility of period t, L t is the load size during period t;

[0368] The contribution of each resource to peak shaving and valley smoothing is quantified as follows:

[0369] in, Δλ is the contribution index of a resource to peak shaving and valley leveling; load,t is the reduction in volatility after considering the virtual power plant adjustment; ΔQ VPP,t are the power changes of a certain resource after considering the regulation of the virtual power plant;

[0370] The contribution of each resource to reducing curtailed electricity is quantified as follows:

[0371] in, The load shedding contribution index for a resource. To consider the reduction in load shedding power after virtual power plant regulation;

[0372] The response capability of each virtual power plant to load changes is as follows: ΔP VPP,t % = ΔP VPP,t / ΔL VPP ; (70)

[0373] Where ΔP VPP,t % is the output response index of a resource in the virtual power plant; ΔP VPP,t The actual response output of a resource; ΔL VPP It is the scheduling instruction of a certain aggregate resource after the scheduling instruction is decomposed.

[0374] Example 2 of the present invention proposes a virtual power plant value assessment system based on time-series production simulation. By constructing a virtual power plant operation characteristic model and integrating it into the power system production simulation model to generate operation data that can be used for value assessment, the economy, safety and environmental protection of the virtual power plant are further comprehensively evaluated, providing a scientific basis for the planning and operation of the virtual power plant.

[0375] Example 2 of the present invention proposes a virtual power plant value assessment system based on time-series production simulation, which can comprehensively reflect the operating characteristics, regulation contribution and income of the virtual power plant, and provide a scientific basis for the optimal scheduling, resource allocation and investment planning of the virtual power plant. The present invention supports the investment return evaluation of various resources, provides virtual power plant value quantification tools for power grids and users, and helps improve the flexibility of the power system and build new power systems.

[0376] Example 2 of the present invention proposes a virtual power plant value assessment system based on time-series production simulation. By evaluating the value of a virtual power plant, we can fully understand its contribution to the power system and quantify its effects on supply and demand balance, regulatory capacity, and environmental impact. This assessment provides a scientific basis for the planning, construction, and operation of virtual power plants, helping to optimize resource allocation, improve grid operating efficiency, guide virtual power plant operators in scientifically investing and allocating virtual power plant resources, and promote the safe, stable, and green development of the power system.

[0377] The description of the relevant parts of the virtual power plant value assessment system based on time-series production simulation provided in Example 2 of the present application can be found in the detailed description of the corresponding parts of the virtual power plant value assessment method based on time-series production simulation provided in Example 1 of the present application, and will not be repeated here.

[0378] Although the above description is of specific embodiments of the present invention in conjunction with the accompanying drawings, it does not limit the scope of protection of the present invention. For those skilled in the art, other different forms of modifications or variations can be made based on the above description. It is not necessary and impossible to list all embodiments here. Based on the technical solution of the present invention, various modifications or variations that can be made by those skilled in the art without expending creative effort are still within the scope of protection of the present invention.

Claims

1. A virtual power plant value assessment method based on time-series production simulation, characterized in that: The following steps are involved: Virtual power plants are divided into power supply type virtual power plants, energy storage type virtual power plants and load type virtual power plants according to their operating characteristics, and their operating characteristic models are constructed respectively. A coordinated operation model of power generation, grid, load and storage is constructed, and the production simulation time period is divided into multiple sub-time periods. The production simulation optimization results of each sub-time period are solved separately. The model construction process is as follows: the objective function of the coordinated operation model of power generation, grid, load and storage is constructed with the goal of economic optimization. The resource endowment of each node and the additional power generation and storage target of the entire power system are considered, and the constraint conditions of the coordinated operation model of power generation, grid, load and storage are established. The process of constructing the objective function of the source-grid-load-storage collaborative operation model with the goal of economic optimization includes: minF op =f c +f r +f uc +f p +f VPP (1) Among them, f c is the coal consumption and carbon emission cost of coal-fired units; f r is the standby cost; f uc is the unit start-up and shutdown cost; f p Penalty for load shedding and power abandonment; f VPP Adjusting costs for virtual power plants; minF op is the objective function of the source-grid-load-storage collaborative operation model; in, is the coal consumption function of the coal-fired unit; For the kth G The output of the thermal power unit at time t; c coal is the unit coal consumption cost; is the carbon dioxide emission cost per unit coal consumption; Ω VRE For the new energy unit collection; The unit cost of upper and lower standby of the unit; Provide upper and lower standby power for thermal power units; It is the startup and shutdown state variable of the thermal power unit; is the start-up and shutdown cost of the unit; c drop 、c shed is the penalty coefficient for power curtailment and load shedding; Q drop , Q shed The amount of power abandoned and load cut; is the allowable value of abandoned electricity; is the allowable value of load shedding; Ω VPP is a collection of resource aggregates; is the adjustment cost per unit power change of the resource aggregate; is the power change of the resource aggregate at node i at time t; Based on the production simulation optimization results, a virtual power plant value assessment model is constructed, and a method for solving the economic evaluation indicators of virtual power plants is constructed, so as to evaluate the economic evaluation of virtual power plants and a certain type of resources on the power system and virtual power plant operators; a method for solving the environmental evaluation indicators of virtual power plants is constructed, so as to evaluate the environmental evaluation of virtual power plants and a certain type of resources on the power system; and a virtual power plant safety value evaluation index is constructed to quantify the contribution to the improvement of the safety evaluation indicators of virtual power plants and a certain type of resources on the power system.

2. A virtual power plant value assessment method based on time-series production simulation according to claim 1, characterized in that: The process of building an operational characteristic model of a power-based virtual power plant includes: The typical output curve of a distributed wind turbine as a power-type virtual power plant is: in, Indicates wind turbine k W The maximum power output at time t; V t Indicates the actual wind speed at time t; V in Indicates the cut-in wind speed; V out Indicates the cut-out wind speed; V N Indicates rated wind speed; k is the wind turbine W Rated power; Based on the typical output curve of wind turbines, the actual output constraint of distributed wind turbines is: in, Indicates wind turbine k W The actual output at time t; The typical output curve of a distributed photovoltaic unit as a power supply virtual power plant is: in, Indicates photovoltaic unit k P The maximum power output at time t; R t Indicates the light intensity at time t, R N Indicates the rated light intensity of the photovoltaic unit; Indicates photovoltaic unit k P Rated power; Based on the typical output curve of photovoltaic units, the actual output constraint of distributed photovoltaic units is obtained as follows: in, Indicates photovoltaic unit k P The actual output at time t.

3. The method for evaluating the value of a virtual power plant based on time-series production simulation according to claim 1, characterized in that: The process of building an operational characteristic model of an energy storage virtual power plant includes: Considering the active output limitation of the energy storage unit during the charging and discharging process, the upper and lower power constraints of the energy storage unit are constructed as follows: in, For the kth ess Maximum discharge power of a distributed energy storage unit; For the kth ess The discharge power of the distributed energy storage unit at time t; Considering the energy storage capacity limitation during normal operation of the energy storage unit, the upper and lower limit constraints of the energy storage unit's state of charge are constructed as follows: in, For the kth ess The state of charge of the distributed energy storage unit at time t, For the kth ess The maximum nuclear power state of the distributed energy storage unit; For the kth ess The minimum nuclear power state of the distributed energy storage unit; The state of charge constraint formula of the energy storage unit is: in, For the kth ess The state of charge of the distributed energy storage unit at time t-1; For the kth ess The state of charge of the distributed energy storage unit at time t-1; For the kth ess The discharge power of a distributed energy storage unit at time t-1; Δt is the time step.

4. The method for evaluating the value of a virtual power plant based on time-series production simulation according to claim 1, characterized in that: The process of building an operational characteristic model of a load-based virtual power plant includes: The air conditioning unit model as a load-type virtual power plant is expressed as: in, is the equivalent SOC size of the jth air conditioner at time t; ΔP j (t) is the adjustable power value of the air conditioner; P jmin -P jbaseline (t)≤ΔP j (t)≤P jmax -P jbaseline (t); (9) Among them, P jbaseline (t) is the power baseline of the jth air conditioner at time t; P jmax is the upper limit of the operating power of the jth air conditioner; P jmin is the lower limit of the operating power of the jth air conditioner; ΔP j (t) is the adjustment power of the jth air conditioner at time t; α j is the first state transfer parameter of the air-conditioning unit; β j is the second state transition parameter of the air-conditioning unit; γ j is the third state transition parameter of the air-conditioning unit; Among them, C j Represents equivalent heat capacity; R j represents the equivalent thermal resistance; η represents the refrigeration energy efficiency coefficient; T jmin Represents the minimum indoor temperature to meet user comfort; T jmax Represents the maximum indoor temperature to meet user comfort; T jset is the set temperature of the jth air conditioner; The state transition equation of the air conditioner SOC state transition is: soc AC (t+1)=Asoc AC (t)+BΔP(t)+C; (11) Among them, A is the first state transfer coefficient of the air conditioner; B is the second state transfer coefficient of the air conditioner; C is the third state transfer coefficient of the air conditioner; is the equivalent SOC size of the jth air conditioner at time t+1; ΔP(t) is the adjustment power of the jth air conditioner at time t; Among them, α j is the first state transfer parameter of the air-conditioning unit; β j is the second state transition parameter of the air-conditioning unit; γ j is the third state transition parameter of the air conditioner unit; n is the total number of air conditioner aggregates; Construct upper and lower power constraints that describe the power output limits of the air conditioner: P Smin ≤ΔP S (t)+P Sbaseline ≤P Smax ; (13) Where ΔP S (t) is the output of the air conditioning aggregate at time t; P Sbaseline is the power baseline of the air conditioning aggregate; P Smax is the upper power limit; P Smin is the lower power boundary; Among them, P jbaseline (t) is the power baseline of the jth air conditioner at time t; P jmax is the upper limit of the operating power of the jth air conditioner; P jmin is the lower limit of the operating power of the jth air conditioner unit; Construct the SOC state constraints that describe the upper and lower boundaries of the air conditioner equivalent SOC: 0≤soc AC (t)≤1; (15) Construct the state transition equation to describe the SOC state transition of hydrogen production: Among them, SOC HST (t) is the current SOC of hydrogen produced by electricity at time t; V HST The maximum volume of hydrogen that can be stored in a hydrogen storage tank at rated pressure and temperature; is the gas storage capacity of the gas storage equipment during period t; is the total amount of gas output by the gas storage device during period t; SCR is the self-consumption rate of the hydrogen storage tank; is the utilization rate of hydrogen entering the gas tank; is the utilization rate of hydrogen output tank; α e,t is the electrical efficiency, is the efficiency of hydrogen production by electrolysis; Q HHV is the calorific value of hydrogen; is the operating power of the electric hydrogen production equipment at time t; Construct a hydrogen power constraint that describes the power limit when hydrogen production is actually running: in, Maximum hydrogen power; Construct a hydrogen storage tank state constraint that describes the capacity limit of the hydrogen storage tank: in, The upper and lower limits of hydrogen storage capacity are specified for hydrogen storage tanks respectively.

5. The method for evaluating the value of a virtual power plant based on time-series production simulation according to claim 1, characterized in that: Considering the resource endowment of each node and the overall power system source and storage addition target, the process of constructing the constraint conditions of the source-grid-load-storage coordinated operation model includes: Considering that the total node power injection is equal to the load, the power balance constraint is constructed: in, The kth node of node i is VRE The output of each VRE unit at time t; represents the active power transmitted in line l at time t; Ω from、 Ω to They represent the set of lines starting and ending at node i respectively; is the active power of the load at node i at time t; is the load shedding size of node i at time t; Considering the load reserve demand and the new energy reserve demand, a reserve constraint is constructed so that the reserve supply is greater than the system reserve demand: Among them, L + % is the upper limit reserve requirement coefficient of load reserve; L - % is the lower limit reserve requirement coefficient reserved for load; W + % is the upper limit reserve requirement coefficient reserved for wind and solar power unit output; W - % is the lower limit reserve requirement coefficient reserved for wind and solar power unit output; Ω d ,Ω vre It is the node set for load access and the new energy unit set; k for unit VRE The amount of power wasted at time t; For the kth VRE The output of each new energy unit at time t; Considering that the line transmission power is less than the maximum transmission capacity of the line, the line operation constraint is formed: in, is the maximum transmission capacity of line l; Considering the upper and lower limits of thermal power generation units' output when providing backup demand, the output constraints of thermal power generation units are constructed: in, is the operating state variable of the kth thermal power unit at time t, the operating state is 1 and the shutdown state is 0; are the maximum output and minimum output of thermal power units respectively; Considering the limitation of power rise and fall of thermal power units per unit time, a ramp constraint is constructed to ensure that the power change of thermal power units per unit time is less than the ramp limit: in, For the kth G The output of the thermal power unit at time t-1; is the upward power change of the thermal power unit under the operating time; is the downward power change of the thermal power unit under the running time; Construct a start-stop constraint that the startup and shutdown time of a thermal power unit is greater than the minimum startup and shutdown time: in, Thermal power unit startup and shutdown status variables; k for coal-fired units G Minimum startup and shutdown duration; Constructing new energy equipment characteristic constraints: in, is the maximum output of the new energy unit at time t; Construct energy storage device characteristic constraints: in, is the discharge power of the energy storage unit at time t; is the maximum discharge power of the energy storage unit; Ω ess It is a collection of energy storage units; Indicates the kth ess The state of charge of the energy storage unit at time t; kth ess The maximum and minimum nuclear power states of each energy storage unit; For the kth ess Rated energy storage capacity of each energy storage unit; is the discharge power of the energy storage unit at time t-1; Constructing characteristic constraints for air conditioning equipment: in, is the equivalent SOC of the air conditioner at time t; A is the first state transition parameter of the air conditioner; B is the second state transition parameter of the air conditioner; C is the third state transition parameter of the air conditioner; The regulating power of the air conditioner; is the baseline power of the air conditioner; is the upper power limit of the air conditioner; is the lower power limit of the air conditioner; Construct characteristic constraints of electric hydrogen production equipment: in, is the gas volume of the hydrogen storage tank at time t+1 for hydrogen production by electricity; is the gas volume of the hydrogen storage tank at time t for hydrogen production by electricity; is the gas volume of the hydrogen storage tank at the last moment of hydrogen production; Δt is the simulation step length; Q HHV is the calorific value of hydrogen; is the efficiency of hydrogen production by electrolysis; α e,t is electrical efficiency; is the total amount of gas output by the gas storage device during period t; SCR is the self-consumption rate of the hydrogen storage tank; is the utilization rate of hydrogen entering the gas tank; is the utilization rate of hydrogen output tank; V HST The maximum volume of hydrogen that can be stored in a hydrogen storage tank at rated pressure and temperature; The upper and lower limits of hydrogen storage capacity are specified for hydrogen storage tanks respectively; is the efficiency of hydrogen production by electrolysis; is the output power of the hydrogen production system; is the total amount of gas output by the gas storage equipment during period t.

6. The method for evaluating the value of a virtual power plant based on time-series production simulation according to claim 5, characterized in that: The process of constructing a virtual power plant economic value assessment model based on the production simulation optimization results includes: Constructing a virtual power plant investment cost model; I total =I si +I hi ; (41) Among them, I total is the total investment of the virtual power plant; I si For software investment, I hi Invest in hardware; in, is the investment cost of the virtual power plant levelized to the time period t, r is the annual discount rate, m is the maximum life span of the virtual power plant that can participate in grid regulation, and T is one year with the same dimension as t; Construct a model to represent the savings in system power supply and supporting grid infrastructure investment costs due to the construction of a certain resource of a virtual power plant: Among them, I VPP,spi Save power costs for some kind of virtual power plant resource; The unit investment cost of traditional power supply per unit power capacity; is the unit investment cost of the grid supporting equipment per unit power capacity; S VPP is the total capacity of the virtual power plant of a certain resource; γ VPP is the capacity conversion factor between the virtual power plant and traditional units of a certain resource; in, To save investment costs on conventional power sources and grid supporting equipment for virtual power plants after leveling to time period t; The total operating income of the virtual power plant is: R total,t =R e,t +R ps,t +R fm,t ;(45) Among them, R total,t is the total operating income of the virtual power plant during time period t; R ps,t is the total peak-shaving revenue of the virtual power plant in time period t; R fm,t is the total frequency regulation revenue of the virtual power plant in time period t; R e,t =ΔQ VPP,t ×C t ; (46) R ps,t =p psD ΔQ psD,t Δt psD +p psU ΔQ psU,t Δt psU ; (47) Where ΔQ VPP,t is the power generation of the virtual power plant in time period t; C t is the on-grid electricity price at the current moment; p psD is the incentive price for peak shaving; p psU is the incentive electricity price for valley filling; ΔQ psD,t is the response capacity of peak clipping; ΔQ psU,t is the response capacity of valley filling; Δt psD is the response time of peak clipping; Δt psU is the response time of valley filling; p cap is the frequency regulation capacity compensation price; ΔL fm,t is the frequency modulation capacity; p mil Compensation price for frequency modulation mileage; is the frequency modulation mileage in period t, is the normalized mean of comprehensive performance indicators; The operating benefits of a power supply virtual power plant or an energy storage virtual power plant are: The operating income of load-type virtual power plants is: in, The total power generation revenue of the power supply type virtual power plant or the energy storage type virtual power plant; is the total power generation revenue of the load-type virtual power plant; R total,t is the total operating income of the virtual power plant during time period t; Regulate the amount of electricity for a power supply virtual power plant or an energy storage virtual power plant during time period t; Regulate power for load-type virtual power plants; in, is the power generation income of the power supply type virtual power plant resource or the energy storage type virtual power plant resource in time period t; is the proportion of aggregated resource income; The contribution of power generation revenue of power supply type virtual power plant resources or energy storage type virtual power plant resources in time period t; in, is the amount of electricity that is online in the time period t for the power supply type virtual power plant resource or the energy storage type virtual power plant resource; is the total on-grid electricity of the power supply type virtual power plant or energy storage type virtual power plant in time period t; The methods for solving the economic evaluation indicators for building a virtual power plant include: The regulation income of load-type virtual power plants is: in, is the regulation income of load-type virtual power plant resources in time period t; is the proportion of aggregated resource income; is the regulation benefit contribution of load-type virtual power plant resources in time period t; in, The electricity price of the load-type virtual power plant during the t period; is the theoretical revenue generated by load-type virtual power plant resources in time period t; in, is the actual power consumption of the load resource in time period t; The power consumption of the baseline load of the load resource in time period t; The monthly medium- and long-term contract electricity price for all user-side entities in time period t; The real-time market unified settlement point electricity price for the user-side entity in time period t; The overall economic evaluation index of virtual power plant resources is: Among them, IB eco,t EI is the economic benefit generated by the virtual power plant to the operator during the time period t; eco,t is the economic evaluation index of a resource in the virtual power plant within the time period t; C VPP,op,t It is the sum of the operation and regulation costs of a resource of the virtual power plant during this period; PBP VPP =I total / (R VPP,t -C VPP,op,t )| t＝1 ; (7) Among them, PBP VPP The investment payback period.

7. A virtual power plant value assessment method based on time series production simulation according to claim 6, characterized in that: The method for solving the environmental performance evaluation index of a virtual power plant based on the production simulation optimization results includes: The calculation method for the power system's curtailment rate and carbon dioxide emissions indicators is as follows: Among them, P s,VRE,t % is the power abandonment rate in time period t; is the maximum output of the wind turbine in time period t; is the actual output of the wind and solar power generator in time period t; is the system carbon dioxide emissions during time period t; is the carbon dioxide emission coefficient per unit coal consumption; is the output of the thermal power unit in time period t; For power supply virtual power plants and energy storage virtual power plants, construct a representation of the contribution of a certain resource to reducing the output of thermal power units and increasing the output of wind and solar power units: in, Adding wind and solar power to a resource; For a certain resource, it is to reduce the power consumption of thermal power units; For load-type virtual power plants, the following characterizations are constructed to characterize their role in replacing thermal power units and accommodating wind and solar power units: in, To consider the increase in power generation of wind and solar units under the regulation of load-type virtual power plants relative to those not considered; To consider the load-type virtual power plant regulation effect and reduce the amount of thermal power generated by wind and solar units compared to those not considered; They are the regulated power of a resource in time period t and the regulated power of a full-load virtual power plant in time period t; The contribution of a certain resource to the power system curtailment rate and carbon dioxide emissions is as follows: in, Contribute to the curtailment rate indicator; Carbon dioxide emission contribution indicator.

8. The method for evaluating the value of a virtual power plant based on time-series production simulation according to claim 7 is characterized in that: The specific process of constructing a virtual power plant safety value evaluation index based on the production simulation optimization results to quantify the contribution to the improvement of the safety evaluation index includes: The fluctuation rate is used to describe the peak-valley changes in the power system. The fluctuation rate is calculated as follows: λ load,t =(L t -L t-1 ) / L t-1 ; (13) Among them, λ load,t is the volatility of period t, L t is the load size during period t; The contribution of each resource to peak shaving and valley smoothing is quantified as follows: in, Δλ is the contribution index of a resource to peak shaving and valley leveling; load,t is the reduction in volatility after considering the virtual power plant adjustment; ΔQ VPP,t are the power changes of a certain resource after considering the regulation of the virtual power plant; The contribution of each resource to reducing curtailed electricity is quantified as follows: in, The load shedding contribution index for a resource. To consider the reduction in load shedding power after virtual power plant regulation; The response capabilities of each virtual power plant to load changes are as follows: ΔP VPP,t %=ΔP VPP,t / ΔL VPP ;(70) Where ΔP VPP,t % is the output response index of a resource in the virtual power plant; ΔP VPP,t The actual response output of a resource; ΔL VPP It is the scheduling instruction of a certain aggregate resource after the scheduling instruction is decomposed.

9. A virtual power plant value assessment system based on time-series production simulation, used to execute a virtual power plant value assessment method based on time-series production simulation according to any one of claims 1 to 8, characterized in that: comprising a first building module, a second building module and an evaluation module; The first construction module is used to divide the virtual power plant into power supply type virtual power plant, energy storage type virtual power plant and load type virtual power plant according to the operation characteristics, and respectively construct the operation characteristic models of the power supply type virtual power plant, the energy storage type virtual power plant and the load type virtual power plant; The second construction module is used to construct a source-grid-load-storage coordinated operation model, and divide the production simulation time period into multiple sub-time periods, and solve the production simulation optimization results of each sub-time period respectively. The model construction process is as follows: the objective function of the source-grid-load-storage coordinated operation model is constructed with the goal of economic optimization, and the resource endowment of each node and the additional source and storage target of the power system as a whole are considered to construct the constraint conditions of the source-grid-load-storage coordinated operation model. The evaluation module is used to construct a virtual power plant value evaluation model based on the production simulation optimization results, and to construct a method for solving the economic evaluation indicators of the virtual power plant, so as to evaluate the economic evaluation of the virtual power plant and a certain type of resources on the power system and the virtual power plant operator; to construct a method for solving the environmental evaluation indicators of the virtual power plant, so as to evaluate the environmental evaluation of the virtual power plant and a certain type of resources on the power system; and to construct a virtual power plant safety value evaluation indicator, which is used to quantify the contribution to the improvement of the safety evaluation indicators of the virtual power plant and a certain type of resources on the power system.

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