Virtual power plant value evaluation method and system based on time sequence production simulation

Abstract

The invention provides a virtual power plant value evaluation method and system based on time sequence production simulation, and the method comprises the steps: dividing a virtual power plant according to the operation characteristics, and constructing the operation characteristic models of different types of virtual power plants; constructing a source network load storage collaborative operation model, dividing a production simulation time period into a plurality of sub time periods, and solving a simulation optimization result of each sub time period; and constructing a virtual power plant value evaluation model based on a simulation optimization result, constructing an economic, environmental protection and safety evaluation index solving method of the virtual power plant, evaluating the contribution effect of the virtual power plant and various resources on the economic, environmental protection and safety of the power system, and evaluating the income condition of a virtual power plant operator. Based on the method, the invention further provides a value evaluation system, the virtual power plant operation characteristic model is constructed and integrated into the power system production simulation model to generate operation data which can be used for value evaluation, and the economical efficiency, the safety and the environmental protection property of the virtual power plant are comprehensively evaluated.

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 structure is undergoing profound changes, and the installed capacity of renewable energy such as wind power and photovoltaics in the power system has increased rapidly. However, wind and solar energy are intermittent, volatile and random, which leads to new challenges in the balance of power supply and demand. The traditional power system lacks sufficient flexible resources to cope with the large-scale access of renewable energy, and the problem of insufficient regulation capacity has become increasingly prominent; the system flexibility shortage has increased rapidly, seriously affecting the safe and stable operation and efficient dispatch of the power system. Virtual power plants, as a new type of power resource integration and dispatching method, are booming in the power system. Virtual power plants aggregate distributed energy, controllable loads and energy storage devices to form comprehensive power resources with considerable regulation capabilities, thereby achieving flexible support for the power system. Grid companies can use the regulation characteristics of virtual power plants to balance supply and demand, alleviate peak-valley differences, improve grid operation efficiency, and thus reduce the occurrence of abnormal operation events. However, the construction and operation of virtual power plants require a lot of capital and technical support. In order to encourage virtual power plant operators to invest in the construction of efficient and responsive virtual power plant resources and actively participate in grid regulation, grid companies need to give virtual power plant operators certain benefits based on virtual power plant regulation and supply contribution during the dispatching process. This compensation mechanism not only makes up for the operator's operating and investment costs, but also brings additional benefits and increases their participation enthusiasm. The user-side resources are efficiently utilized and earn profits, so the power grid company reduces electricity costs and improves power supply reliability, achieving a win-win situation for the power grid and users.

[0003] Under the current development, virtual power plant operators have the need to evaluate whether to build a virtual power plant economically or what type of virtual power plant to build with higher benefits. Grid companies also need to evaluate the impact of the access of virtual power plants on the operation of the power system. A scientific value assessment method is urgently needed to meet the needs of both. However, there is currently a lack of a tool on the market that can effectively analyze the value of virtual power plants, which limits its widespread application and promotion. Existing methods mainly rely on the construction of demonstration projects to evaluate the performance and value of virtual power plants through actual operation. However, this method has many limitations such as high investment, long cycle, delayed results, and single reflection effect. 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] In order to solve the above technical problems, the present invention proposes a virtual power plant value assessment method and system based on time-series production simulation, which 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.

[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 operation characteristics, and the operation characteristic models of power supply type virtual power plants, energy storage type virtual power plants and load type virtual power plants are constructed respectively;

[0008] A source-grid-load-storage collaborative operation model is constructed, and the production simulation time period is divided into multiple sub-time periods, and the production simulation optimization results of each sub-time period are solved respectively; the process of model construction is as follows: the objective function of the source-grid-load-storage collaborative 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 overall power system are considered to construct the constraint conditions of the source-grid-load-storage collaborative operation model;

[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 power systems 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 power systems; 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 power systems.

[0010] A virtual power plant value assessment system based on time-series production simulation, comprising 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: constructing the objective function of the source-grid-load-storage coordinated operation model with the goal of economic optimization, considering the resource endowment of each node and the overall source and storage increase target of the power system, and constructing 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, 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; constructs 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; constructs a virtual power plant safety value evaluation index 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 content 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 virtual power plant value assessment method and system based on time-series production simulation, the method comprising 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, respectively constructing 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; constructing a source-grid-load-storage coordinated operation model, and dividing the production simulation time period into multiple sub-time periods, respectively solving the production simulation optimization results of each sub-time period; the model construction process 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 additional source and storage targets of the power system as a whole, construct the constraints 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 a new power system.

[0017] Through the value assessment of virtual power plants, the present invention can fully understand their contribution to the power system and quantify their effects in terms of supply and demand balance, regulation capacity and environmental impact. This assessment provides a scientific basis for the planning, construction and operation of virtual power plants, helps to optimize resource allocation, improve power grid operation efficiency, guide virtual power plant operators to scientifically invest and allocate 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 The virtual power plant operation characteristic classification and operation model construction process proposed in Example 1 of the present invention;

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

[0021] Figure 4 This is a flow chart of the solution 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 a 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] Fig. 9 This is a typical photovoltaic output curve proposed in Example 1 of the present invention;

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

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

[0029] Fig.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] Fig.13 The system simulation result under the air conditioning control proposed in Example 1 of the present invention;

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

[0032] Fig.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] Fig.16 The load baseline and actual load size of air conditioning and electric hydrogen production proposed in Example 1 of the present invention;

[0034] Fig.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] Embodiment 1 of the present invention proposes a method for evaluating the value of a virtual power plant based on time-series production simulation, which addresses the problem of the lack of quantitative analysis tools for the mutual assistance between virtual power plants and power grids in existing power systems. 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 evaluation, 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.

[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 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 of 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 supporting data information of 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 then 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 equipment manufacturers were investigated to form a database of basic equipment parameter information such as conventional power source ramp rate, start and 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 on the official websites of China Virtual Power Plant Industry Technology Innovation Alliance, China Household Electrical Appliances Association and 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, light intensity statistical information and wind energy 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] Investigate the simulated regional outdoor temperature changes released by the National Meteorological Information Center and the China National Environmental Monitoring Center to form a local outdoor temperature change curve. Investigate authoritative reports released by the China Hydrogen Energy Alliance, the Hydrogen Energy Professional Committee of the China Renewable Energy Society, etc. to form a hydrogen load curve.

[0047] The process of building a power supply type virtual power plant includes: classifying distributed photovoltaic, distributed wind power and other 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. The maximum output curve can be used to describe their operating characteristics. The calculation method of 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 represents 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 Represents 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 have no 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 output limitation of the energy storage unit during the charging and discharging process, the upper and lower power limits of the energy storage unit are constructed:

[0061] in, For the kth ess Maximum discharge power of distributed energy storage units; For the kth ess The discharge power of a 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 state of charge of the energy storage unit 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 a distributed energy storage unit;

[0064] The charge state 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 such as hydrogen production from electricity and air conditioning that can adjust the electricity demand on the load side and participate in the demand response and ancillary service markets as load regulation entities are classified as load-type virtual power plants. Typical operating characteristic models of air conditioning and hydrogen production from electricity are constructed.

[0067] Construct a single air conditioner model, which describes the relationship between indoor temperature and cooling power of the air conditioner using the energy and power relationship of the 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 satisfy: 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 transfer parameter of the air conditioner unit; γ j is the third state transfer parameter of the air conditioner unit, and its calculation method is:

[0070]

[0071]

[0072]

[0073] Among them, C j represents equivalent heat capacity (kJ / ℃); R j represents equivalent thermal resistance (℃ / kW); η represents 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 transfer parameter of the air conditioner unit; γ j is the third state transfer 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;

[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 with the hydrogen consumption curve as input, and then the power change base value and adjustment space curve of hydrogen production in a typical time period are formed. 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 transfer equation, hydrogen consumption power constraint and hydrogen storage tank state constraint. The state transfer equation describing the SOC state transfer 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 in 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 and unchanging; 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 the 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 the hydrogen storage capacity are specified for the hydrogen storage tank respectively.

[0094] In Step 2, a source-grid-load-storage collaborative operation model is constructed, and the production simulation time period is divided into multiple sub-time periods, and the production simulation optimization results of each sub-time period are solved respectively; the process of model construction is as follows: the objective function of the source-grid-load-storage collaborative 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 overall power system are considered to construct the constraint conditions of the source-grid-load-storage collaborative operation model;

[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 economy and oriented to power and electricity balance. First, an objective function is constructed considering the operating cost, penalty for poor operation and regulation cost of the virtual power plant. 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 A flow chart of a method for simulating the time-sequential production of a large-scale power system including a virtual power plant 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 uc , load shedding and power abandonment penalty p and the virtual power plant regulation cost f VPP The objective function of the source-grid-load-storage collaborative operation model is constructed based on the following formula:

[0097] f 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; r is the standby cost; f uc is the start-up and shutdown cost of the unit; p Penalty for load shedding and power abandonment; 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 of coal consumption; Ω VRE Assemble for new energy units; is the unit cost of upper and lower standby of the unit; Up and down standby power provided 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 factor for power abandonment and load shedding; Q drop , Q shed The amount of power abandoned and load cut; is the allowable value of abandoned power; is the allowable value of load shedding; Ω VPP is a collection of resource aggregates; is the regulation 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 additional source and storage target of the power system as a whole, 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 discharge, negative indicates charge; Represents the active power transmitted in line l at time t; Ω from、 Ω to They represent the set of lines with node i as the first and the last node 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 new energy reserve demand, a reserve constraint is constructed in which the reserve supply is greater than the system reserve demand:

[0106]

[0107] Among them, L + % is the upper limit reserve requirement coefficient for load reservation; L-% is the lower limit reserve requirement coefficient for load reservation; W + % is the upper limit reserve requirement coefficient reserved for the output of wind and solar power units; W - % is the lower limit reserve requirement coefficient reserved for the output of wind and solar units; Ω d ,Ω vre It is a set of nodes connected to loads and a set of new energy units; For unit k VRE The amount of power abandoned 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 constraints are formed:

[0109]

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

[0111] Considering the upper and lower limits of the output when the thermal power unit provides backup demand, the output constraint of the thermal power unit is 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 power increase and decrease limit of thermal power units per unit time, a ramp constraint is constructed so that the power change of thermal power units per unit time is less than the ramp upper 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 running 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-stop process of the thermal power unit, a start-stop constraint is constructed in which the start-up and shutdown time of the thermal power unit 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 unit operation characteristic model, the characteristic constraints of new energy equipment are constructed:

[0121]

[0122] in, It 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; k ess The maximum and minimum nuclear power status 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 transfer parameter of the air conditioner; B is the second state transfer parameter of the air conditioner; C is the third state transfer 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; It 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, The gas volume of hydrogen storage tank at time t+1 for hydrogen production by electricity; is the gas volume of hydrogen storage tank at time t for hydrogen production by electricity; is the gas volume of the hydrogen storage tank at the last solution time of hydrogen production by electricity; 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; It is the total amount of gas output by the gas storage device during period t.

[0134] Solve the above production simulation model by rolling optimization solution. Figure 4This is a solution flow chart of 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 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 to evaluate the environmental evaluation of the virtual power plant and a certain type of resources on the power system; 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 of the above steps, a value assessment system is constructed to evaluate the application effect of virtual power plants participating in the interactive regulation of the power grid. Based on the operation and control characteristics of virtual power plants, a method for solving the evaluation indicators of economic efficiency, safety and environmental protection of virtual power plants is constructed to evaluate the contribution of a certain type of resources to the economy and environmental protection 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 economy 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 sources, 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 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 life 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 facilities investment costs due to the construction of a certain resource of a virtual power plant:

[0145] Among them, I VPP,spi Save on power costs for some kind of virtual power plant resource; The unit investment cost of the traditional power source per unit power capacity; is the unit investment cost of the supporting equipment of the power grid 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 the traditional unit of a certain resource;

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

[0147] Among them, I VPP,sp i ,t To save investment costs on conventional power sources and grid supporting equipment for virtual power plants after leveling to 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 load 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 load-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] Among them, ΔQ VPP,t is the power generation (adjusted power) 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 electricity 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 peak shaving response time; Δt psU is the response time of valley filling; p cap is the price for frequency regulation capacity compensation; ΔL fm,t is the frequency modulation capacity; p mil Compensation price for frequency modulation mileage; is the frequency modulation mileage in period t, It is the normalized mean of comprehensive performance indicators.

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

[0155] The operating income of load-based 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-based virtual power plant; R total,t is the total operating income of the virtual power plant during time period t; Adjust the power consumption of a power supply type virtual power plant or an energy storage type virtual power plant within a 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 electricity can be described based on the power generation revenue in time period t, so as to evaluate the contribution revenue of a certain power generation and energy storage virtual power plant in any time period and construct its power generation revenue model:

[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; It 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 power supply type virtual power plant resources or energy storage type virtual power plant resources; It is the total grid-connected electricity of the power supply type virtual power plant or the energy storage type virtual power plant in the time period t.

[0160] For load-type virtual power plants, their contribution to the regulation of the power system can be described based on the regulation revenue contribution in the time period t, so as to evaluate the economic effect of a certain type of load resources in any time period, and 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. When the calculation result is negative, it is taken as 0. The calculation method of this indicator is as follows:

[0162] in, The electricity charge for the load-type virtual power plant during time period t; is the theoretical revenue generated by 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 mid- to long-term contract electricity price of all user-side entities in the current month during time period t can be calculated by weighted average of the proportion of mid- to 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 saving cost and operating cost of a certain resource of a virtual power plant in a certain period of time. Combined with the operating cost of the virtual power plant in this period of time, the cost recovery of the virtual power plant in any period of time during its life cycle can be obtained, and the total 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 in time period t; eco,t is the economic evaluation index of a resource in a virtual power plant within a period of time t; C VPP,op,tis the sum of the operation and regulation costs of a resource of 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 protection value evaluation index of virtual power plants, including the reduction of power abandonment rate and the reduction of carbon dioxide emissions. The specific calculation method of power abandonment rate and carbon dioxide emissions index of 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 generator in time period t; is the system carbon dioxide emissions in 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.

[0172] For power supply type virtual power plants and energy storage type 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 units:

[0173]

[0174] in, Add the amount of electricity generated by wind and solar power units to a certain resource; For a certain resource, it 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 the role of replacing thermal power units and absorbing 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, compared with the wind and solar units that are not considered, the power reduction; 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 power 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, including peak shaving and valley filling effects, load shedding reduction, and emergency control capabilities. The evaluation process is as follows: During the production simulation process, time periods with poor operating indicators are identified first, and virtual power plant adjustments are introduced for these time periods to further evaluate the operating optimization effects of various resources in the virtual power plant to quantify their 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 the regulation capacity of conventional power supply is insufficient and the fluctuation rate is large is selected to evaluate the value of virtual power plant peak shaving and valley leveling. The contribution degree of peak shaving and valley leveling of each resource is quantified as follows:

[0186] in, The contribution index of peak shaving and valley leveling for a resource; Δλ load,t is the reduction in volatility after considering the virtual power plant regulation; 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 abandoned by the virtual power plant. The contribution of each resource to reducing the amount of power abandoned is quantified as follows:

[0188] in, The load shedding reduction contribution index for a resource. To consider the reduction in load shedding 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 assessment. After the moment is selected, a load change ΔL is introduced for the moment, which is allocated to each virtual power plant after being decomposed by the dispatch instruction. The response capability of each virtual power plant to the load change is evaluated as follows: ΔP VPP,t % = ΔP VPP,t / ΔL VPP ; (70)

[0190] Where ΔP VPP,t % is the output response index of a resource of 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 implementation process of the present invention, a simplified power grid system in a certain place is taken as an example to verify the effect of the present method. The starting point 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 57.9GW thermal power plants; 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 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 hydrogen production load; the maximum value of external electricity that the system can purchase from the external system at each moment is set not to exceed 20% of the current moment's load.

[0192] By investigating the typical load change curve in the area and combining the change curve with the maximum load of the system, the load change curve of the system can be formed. 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 various equipment 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 node thermal power units

[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, we can form a table of operating parameters of each device of this coefficient. 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 location and parameters of some wind and solar units in the system

[0201]

[0202] Table 4 System energy storage unit polymer 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] Installing nodes 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; Fig. 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. Fig.10 The air conditioning load baseline proposed in Example 1 of the present invention; Fig.11 This is the load baseline for hydrogen production from electricity proposed in Example 1 of the present invention.

[0210] Based on the variation curves 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-Figure 15As shown, we can see that when the wind and solar outputs are large, the output of 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 output of thermal power units is higher and they undertake the main energy supply of the system; the energy storage unit stores energy when the output of the photovoltaic unit is higher and releases the energy when the output of the photovoltaic unit is lower.

[0211] When the air conditioning and hydrogen production are not regulated, the operating cost of the system is 1.226 billion yuan; the power of wind and solar units accounts for about 38.5% of the total power of the system. At this time, the system has 7.13% wind abandonment and 11.1% solar abandonment, and 1.69% load shedding; the carbon emissions of the whole system are 2.97×10 5 t of carbon dioxide, Fig.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%, and the system power abandonment and load shedding phenomenon was alleviated. The wind abandonment rate was reduced to 5.8%, the solar abandonment rate was 11.1% and remained unchanged, and the load shedding rate was reduced to 1.32%; the carbon emissions of the whole 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 Fig.13 As shown, Fig.13 This is the system simulation result under the air conditioning 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 proportion of electricity from wind and solar units increased to 39%, the system wind abandonment rate was reduced to 4.12%, the solar abandonment rate was reduced to 6.1%, and the load shedding rate was reduced to 0.49%; the carbon emissions of the whole 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 Fig.14 As shown; Fig.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 proportion of electricity generated by 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, down 1%; Fig.15This is the system simulation result considering air conditioning and electric hydrogen production proposed in Example 1 of the present invention. The operating baseline of each resource and the operating curve after regulation are as follows: Fig.16 As shown, Fig.16 The load baseline and actual load size of the air conditioning and electric hydrogen production proposed in Example 1 of the present invention.

[0215] The total unit investment cost of the air-conditioning virtual power plant is set at 300,000 yuan / MW, with a lifespan of 10 years; the total unit investment cost of the hydrogen production equipment and the corresponding virtual power plant supporting facilities is 4 million yuan / MW, with a lifespan of 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 allocation of investment, the income of the air-conditioning unit regulated electricity is 226.96 yuan / MWh, and the average daily income of the air-conditioning unit capacity is 657.13 yuan / MW · The payback period for participating in the virtual power plant regulation is 393.18 days; the income of hydrogen production per unit of regulated electricity is 181.13 yuan / MWh, and the average daily income per unit capacity is 677.79 yuan / MW. · days, and the payback period just by participating in virtual power plant regulation 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, and 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 builds 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 in the geographical location of the evaluated system to form the basic information of the operation and dispatch of the virtual power plant; constructing a typical resource operation characteristic model of a power supply type virtual power plant; constructing a typical resource operation characteristic model of an energy storage type virtual power plant; and constructing a typical resource operation characteristic model of a 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, considers 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 including: 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] Embodiment 1 of the present invention proposes a method for evaluating the value of a virtual power plant based on time-series production simulation. By constructing an operating characteristic model of a virtual power plant and integrating it into a power system production simulation model to generate operating data that can be used for value evaluation, the economic, 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] Embodiment 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 revenue 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 evaluation of the return on investment of various resources, provides a virtual power plant value quantification tool for the power grid and users, and helps to improve the flexibility of the power system and build a new power system.

[0219] Embodiment 1 of the present invention proposes a virtual power plant value assessment method based on time-series production simulation. Through the value assessment of the virtual power plant, its contribution to the power system can be fully understood, and its effects in terms of supply and demand balance, regulation capacity, and environmental impact can be quantified. This assessment provides a scientific basis for the planning, construction, and operation of virtual power plants, helps optimize resource allocation, improve power grid operation efficiency, guide virtual power plant operators to scientifically invest and allocate 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. Fig.17 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 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;

[0223] 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: construct the objective function of the source-grid-load-storage coordinated operation model with the goal of economic optimization, consider the resource endowment of each node and the overall source and storage increase target of the power system, and construct the constraint conditions of the source-grid-load-storage coordinated operation model;

[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; to construct 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.

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

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

[0227]

[0228] in, Indicates wind turbine k W The maximum power output at time t; V t represents 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 Represents 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 operating 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 limits of the energy storage unit are constructed as follows:

[0240] in, For the kth ess Maximum discharge power of distributed energy storage units; For the kth ess The discharge power of a 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 state of charge of the energy storage unit 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 a distributed energy storage unit;

[0243] The charge state 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 the operating 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 transfer parameter of the air conditioner unit; γ j is the third state transfer 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 transfer equation of air conditioner SOC state transfer 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 transfer parameter of the air conditioner unit; γ j is the third state transfer 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 in 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 the 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 the hydrogen storage capacity are specified for the hydrogen storage tank 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] f 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; r is the standby cost; f uc is the start-up and shutdown cost of the unit; p Penalty for load shedding and power abandonment; 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 of coal consumption; Ω VRE Assemble for new energy units; is the unit cost of upper and lower standby of the unit; Up and down standby power provided 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 factor for power abandonment and load shedding; Q drop , Q shedThe amount of power abandoned and load cut; is the allowable value of abandoned power; is the allowable value of load shedding; Ω VPP is a collection of resource aggregates; is the regulation 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; r is the standby cost; f uc is the start-up and shutdown cost of the unit; p Penalty for load shedding and power abandonment; 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 of coal consumption; Ω VRE Assemble for new energy units; is the unit cost of upper and lower standby of the unit; Up and down standby power provided 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 factor for power abandonment and load shedding; Q drop , Q shed The amount of power abandoned and load cut; is the allowable value of abandoned power; is the allowable value of load shedding; Ω VPP is a collection of resource aggregates; is the regulation 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 additional source and storage target of the power system as a whole, 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 with node i as the first and the last node 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 new energy reserve demand, a reserve constraint is constructed in which the reserve supply is greater than the system reserve demand:

[0286]

[0287] Among them, L + % is the upper limit reserve requirement coefficient reserved for load; L - % is the lower limit reserve requirement coefficient reserved for load; W + % is the upper limit reserve requirement coefficient reserved for the output of wind and solar power units; W - % is the lower limit reserve requirement coefficient reserved for the output of wind and solar units; Ω d ,Ω vre It is a set of nodes connected to loads and a set of new energy units; For unit k VRE The amount of power abandoned 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 constraints are formed:

[0289]

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

[0291] Considering the upper and lower limits of the output when the thermal power unit provides backup demand, the output constraint of the thermal power unit is 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 power increase and decrease limit of thermal power units per unit time, a ramp constraint is constructed so that the power change of thermal power units per unit time is less than the ramp upper 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 running time; It is the downward power change of the thermal power unit under the running time.

[0297] Construct the start-stop constraint that the startup and shutdown time of the 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] Construct 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; k ess The maximum and minimum nuclear power status 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] Construct air conditioning equipment characteristic constraints:

[0309]

[0310]

[0311]

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

[0313] Constructing the characteristic constraints of electric hydrogen production equipment:

[0314]

[0315] in, The gas volume of hydrogen storage tank at time t+1 for hydrogen production by electricity; is the gas volume of hydrogen storage tank at time t for hydrogen production by electricity; is the gas volume of the hydrogen storage tank at the last solution time of hydrogen production by electricity; 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; It is the total amount of gas output by the gas storage device 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 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 facilities investment costs due to the construction of a certain resource of a virtual power plant:

[0322] Among them, I VPP,spi Save on power costs for some kind of virtual power plant resource; The unit investment cost of the traditional power source per unit power capacity; is the unit investment cost of the supporting equipment of the power grid 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 the traditional unit of a certain resource;

[0323]

[0324] in, To save investment costs on conventional power sources and grid supporting equipment for virtual power plants that are leveled 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 load-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] Among them, 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 electricity 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 peak shaving response time; Δt psU is the response time of valley filling; p cap is the price for frequency regulation capacity compensation; 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 energy storage virtual power plant are:

[0332] The operating income of load-based 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-based virtual power plant; R total,t is the total operating income of the virtual power plant during time period t; Adjust the power consumption of a power supply type virtual power plant or an energy storage type virtual power plant within a time period t; Regulate power for load-based 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 power supply type virtual power plant resources or energy storage type virtual power plant resources; It is the total grid-connected electricity of the power supply type virtual power plant or the energy storage type virtual power plant in the time period t.

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

[0338] The regulation income of load-based 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; The contribution of load-type virtual power plant resources to regulation revenue in time period t;

[0340]

[0341] in, The electricity charge for the load-type virtual power plant during time period t; 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 mid- to long-term contract electricity price of all user-side entities in the current month during time period t; The real-time unified settlement point electricity price of 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 in time period t; eco,t is the economic evaluation index of a resource in a virtual power plant within a period of time 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 the environmental performance evaluation index of a virtual power plant based on the production simulation optimization results includes:

[0349] The calculation method for the power system's power abandonment 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 generator in time period t; is the system carbon dioxide emissions in 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.

[0353] For power supply type virtual power plants and energy storage type 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 units:

[0354]

[0355] in, Add the amount of electricity generated by wind and solar power units to a certain 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 the role of replacing thermal power units and absorbing 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, compared with the wind and solar units that are not considered, the power reduction; They 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;

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

[0361]

[0362]

[0363] in, Contribute to the power 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 and quantifying the contribution to the improvement of the safety evaluation index includes:

[0365] The fluctuation rate is used to describe the peak-valley changes of 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 in period t;

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

[0369] in, The contribution index of peak shaving and valley leveling for a resource; Δλ load,t is the reduction in volatility after considering the virtual power plant regulation; ΔQ VPP,t They 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 reduction contribution index for a resource. To consider the reduction in load shedding 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] Embodiment 2 of the present invention proposes a virtual power plant value assessment system based on time-series production simulation. It 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. It further comprehensively assesses 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.

[0375] Embodiment 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 revenue 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 evaluation of the return on investment of various resources, provides a virtual power plant value quantification tool for the power grid and users, and helps to improve the flexibility of the power system and build a new power system.

[0376] Embodiment 2 of the present invention proposes a virtual power plant value assessment system based on time-series production simulation. Through the value assessment of the virtual power plant, its contribution to the power system can be fully understood, and its effects in terms of supply and demand balance, regulation capacity, and environmental impact can be quantified. This assessment provides a scientific basis for the planning, construction, and operation of virtual power plants, helps optimize resource allocation, improve power grid operation efficiency, guide virtual power plant operators to scientifically invest and allocate virtual power plant resources, and promote the safe, stable, and green development of the power system.

[0377] For the description of the relevant parts of a virtual power plant value assessment system based on time-series production simulation provided in Example 2 of the present application, please refer to the detailed description of the corresponding parts of a 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 describes the specific implementation of the present invention in conjunction with the accompanying drawings, it is not intended to limit the scope of protection of the present invention. For those skilled in the art, other different forms of modifications or deformations can be made on the basis of the above description. It is not necessary and impossible to list all the implementation methods here. On the basis of the technical solution of the present invention, various modifications or deformations that can be made by those skilled in the art without creative work 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 operation characteristics, and the operation characteristic models of power supply type virtual power plants, energy storage type virtual power plants and load type virtual power plants are constructed respectively; A source-grid-load-storage collaborative operation model is constructed, and the production simulation time period is divided into multiple sub-time periods, and the production simulation optimization results of each sub-time period are solved respectively; the process of model construction is as follows: the objective function of the source-grid-load-storage collaborative 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 overall power system are considered to construct the constraint conditions of the source-grid-load-storage collaborative operation model; 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 power systems 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 power systems; 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 power systems.

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 operating characteristic model of a power-based virtual power plant includes: The typical output curve of a distributed wind turbine as a power source virtual power plant is: in, Indicates wind turbine k W The maximum power output at time t; V t represents 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 source virtual power plant is: in, Indicates photovoltaic unit k P The maximum power output at time t; R t Represents 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 is characterized in that: The process of building an operating 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 limits of the energy storage unit are constructed as follows: in, For the kth ess Maximum discharge power of distributed energy storage units; For the kth ess The discharge power of a 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 state of charge of the energy storage unit 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 a distributed energy storage unit; The charge state 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. A virtual power plant value assessment method based on time-series production simulation according to claim 1, characterized in that: The process of building the operating 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 transfer parameter of the air conditioner unit; γ j is the third state transfer 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 transfer equation of air conditioner SOC state transfer 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 transfer parameter of the air conditioner 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) Among them, 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; 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 in 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 the 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 the hydrogen storage capacity are specified for the hydrogen storage tank respectively.

5. The method for evaluating the value of a virtual power plant based on time-series production simulation according to claim 1 is characterized in that: 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 (3) Among them, f c is the coal consumption and carbon emission cost of coal-fired units; r is the standby cost; f uc is the start-up and shutdown cost of the unit; p Penalty for load shedding and power abandonment; 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 of coal consumption; Ω VRE Assemble for new energy units; is the unit cost of upper and lower standby of the unit; Up and down standby power provided 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 factor for power abandonment and load shedding; Q drop , Q shed The amount of power abandoned and load cut; is the allowable value of abandoned power; is the allowable value of load shedding; Ω VPP is a collection of resource aggregates; is the regulation cost per unit power change of the resource aggregate; is the power change of the resource aggregate at node i at time t.

6. A virtual power plant value assessment method based on time-series production simulation according to claim 5, characterized in that: Considering the resource endowment of each node and the additional source and storage target of the power system as a whole, 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 with node i as the first and the last node 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 new energy reserve demand, a reserve constraint is constructed in which the reserve supply is greater than the system reserve demand: Among them, L + % is the upper limit reserve requirement coefficient for load reservation; L-% is the lower limit reserve requirement coefficient for load reservation; W + % is the upper limit reserve requirement coefficient reserved for the output of wind and solar power units; W - % is the lower limit reserve requirement coefficient reserved for the output of wind and solar units; Ω d ,Ω vre It is a set of nodes connected to loads and a set of new energy units; For unit k VRE The amount of power abandoned 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 constraints are formed: in, is the maximum transmission capacity of line l; Considering the upper and lower limits of the output when the thermal power unit provides backup demand, the output constraint of the thermal power unit is 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; They are the maximum output and minimum output of thermal power units respectively; Considering the power increase and decrease limit of thermal power units per unit time, a ramp constraint is constructed so that the power change of thermal power units per unit time is less than the ramp upper 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 running time; is the downward power change of the thermal power unit under the running time; Construct the start-stop constraint that the startup and shutdown time of the 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; Construct 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; k ess The maximum and minimum nuclear power status 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; Construct air conditioning equipment characteristic constraints: in, is the equivalent SOC of the air conditioner at time t; A is the first state transfer parameter of the air conditioner; B is the second state transfer parameter of the air conditioner; C is the third state transfer 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 boundary of the air conditioner; Constructing the characteristic constraints of electric hydrogen production equipment: in, The gas volume of hydrogen storage tank at time t+1 for hydrogen production by electricity; is the gas volume of hydrogen storage tank at time t for hydrogen production by electricity; is the gas volume of the hydrogen storage tank at the last solution time of hydrogen production by electricity; 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; It is the total amount of gas output by the gas storage device during period t.

7. A method for evaluating the value of a virtual power plant based on time-series production simulation according to claim 6, 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 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 facilities 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 the traditional power source per unit power capacity; S is the unit investment cost of the supporting equipment of the power grid per unit power capacity; 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 the traditional unit of a certain resource; in, To save the investment cost of conventional power source and grid supporting equipment for the virtual power plant 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 load-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) Among them, 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 electricity 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 peak shaving response time; Δt psU is the response time of valley filling; p cap is the price for frequency regulation capacity compensation; 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; The operating benefits of a power supply virtual power plant or energy storage virtual power plant are: The operating income of load-based 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-based virtual power plant; R total,t is the total operating income of the virtual power plant during time period t; Adjust the power consumption of a power supply type virtual power plant or an energy storage type virtual power plant within a time period t; Regulate power for load-based 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 power supply type virtual power plant resources or energy storage type virtual power plant resources; is the total grid-connected power 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 of building a virtual power plant include: The regulation income of load-based 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; The contribution of load-type virtual power plant resources to regulation revenue in time period t; in, The electricity charge for the load-type virtual power plant during time period t; 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 mid- to long-term contract electricity price of all user-side entities in the current month during time period t; The real-time unified settlement point electricity price of the user-side entity in time period t; The overall economic evaluation index of virtual power plant resources is: Among them, IB eco,t is the economic benefit generated by the virtual power plant to the operator in time period t; EI eco,t is the economic evaluation index of a resource in a virtual power plant within a period of time 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.

8. A virtual power plant value assessment method based on time-series production simulation according to claim 7, characterized in that: The method for solving the environmental protection evaluation index of a virtual power plant based on the production simulation optimization results includes: The calculation method for the power system's power abandonment 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 the time period t; P t VRE is the actual output of the wind and solar generator in time period t; is the system carbon dioxide emissions in 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 type virtual power plants and energy storage type 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 units: in, Add the amount of electricity generated by wind and solar power units to a certain 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 the role of replacing thermal power units and absorbing 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, compared with the wind and solar units that are not considered, the power reduction; They 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; The contribution of a certain resource to the power system curtailment rate and carbon dioxide emissions is: in, Contribute to the power curtailment rate indicator; Carbon dioxide emission contribution indicator.

9. A method for evaluating the value of a virtual power plant based on time-series production simulation according to claim 8, characterized in that: The specific process of constructing a virtual power plant safety value evaluation index based on the production simulation optimization results and quantifying the contribution to the improvement of the safety evaluation index includes: The fluctuation rate is used to describe the peak-valley changes of 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 in period t; The contribution of each resource to peak shaving and valley leveling is quantified as: in, The contribution index of a resource to smooth the peak and valley; Δλ load,t is the reduction in volatility after considering the virtual power plant regulation; ΔQ VPP,t They 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 reduction contribution index for a resource. To consider the reduction in load shedding 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) Among them, 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.

10. A virtual power plant value assessment system based on time-series production simulation, 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: constructing the objective function of the source-grid-load-storage coordinated operation model with the goal of economic optimization, considering the resource endowment of each node and the overall source and storage increase target of the power system, and constructing 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; to construct 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.

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