A method and system for optimal scheduling of a multi-energy complementary virtual power plant

Abstract

The application discloses a kind of optimization scheduling methods of multi-energy complementary virtual power plant, which comprises the following steps: establishing the objective function of multi-energy complementary virtual power plant optimization scheduling model, the objective function includes two optimization goals of economic optimization and carbon emission optimization;Establish the constraint condition of multi-energy complementary virtual power plant optimization scheduling model, and the constraint condition includes power supply system balance, gas supply system constraint, heat supply system constraint, heat supply network constraint and cooling system constraint;Based on the objective function and constraint condition, a two-stage robust optimization model of multi-energy complementary virtual power plant is constructed, and two types of adjustment resources, distributed energy storage and demand response load, are introduced into the two-stage robust optimization model;For the two-stage robust optimization model of multi-energy complementary virtual power plant, the column and constraint generation algorithm is used to decompose the original problem and iteratively solve, to obtain the optimization scheduling strategy for multi-energy complementary virtual power plant.The application can realize flexible regulation and control of power grid through optimization scheduling strategy.

Description

Technical Field

[0001] This application relates to the field of power system optimization dispatching technology, and in particular to an optimization dispatching method and system for a multi-energy complementary virtual power plant. Background Technology

[0002] While current multi-energy complementary virtual power plant technology has made progress in aggregating distributed resources and realizing multi-energy interconnection, it still has shortcomings. In traditional methods, the output uncertainty of distributed power sources such as wind and solar power leads to fluctuations in load demand, increasing the complexity and instability of system operation and posing potential risks to the safe and stable operation of power plants. Existing dispatch strategies lack flexibility in dealing with internal system uncertainties, making it difficult to achieve economic and environmentally friendly dispatch goals while ensuring user electricity experience. Therefore, an innovative and optimized dispatch strategy is urgently needed to overcome these technical shortcomings. Summary of the Invention

[0003] To address the shortcomings of existing technologies, this application adopts the following technical solution:

[0004] In a first aspect, this application provides an optimized scheduling method for a multi-energy complementary virtual power plant, characterized by the following steps:

[0005] Establish the objective function of the multi-energy complementary virtual power plant optimization scheduling model. The objective function includes two optimization objectives: economic optimization and carbon emission optimization.

[0006] The constraints for establishing the optimal scheduling model of a multi-energy complementary virtual power plant include power supply system balance, gas supply system constraints, heating system constraints, heating network constraints, and cooling system constraints. Among them, power supply system balance includes at least demand response load constraints and energy storage charging and discharging power constraints.

[0007] Based on the objective function and constraints, a two-stage robust optimization model for a multi-energy complementary virtual power plant is constructed. The two-stage robust optimization model introduces two types of regulation resources: distributed energy storage and demand response load. The outer layer of the two-stage robust optimization model seeks the best decisions for purchasing / selling electricity and charging / discharging under the known worst-case scenario, while the inner layer searches for the worst-case scenario and the corresponding scheduling strategy.

[0008] For a two-stage robust optimization model of a multi-energy complementary virtual power plant, a column and constraint generation algorithm is used to decompose the original problem and iteratively solve it to obtain an optimized scheduling strategy for the multi-energy complementary virtual power plant.

[0009] Based on the above description, this application provides an optimized scheduling method for a multi-energy complementary virtual power plant. Based on the constructed objective function and constraints, it uses distributed energy storage and demand response load as regulation resources, establishes a two-stage robust optimization model, obtains an optimized scheduling strategy for the multi-energy complementary virtual power plant, realizes flexible scheduling of grid power, and improves the utilization efficiency of grid power.

[0010] Furthermore, the demand response load constraint is expressed by the following formula:

[0011]

[0012]

[0013] P DR,t -P DR*,t +P DR1,t -P DR2,t =0;

[0014] P DR1,t ≥0, P DR2,t ≥0;

[0015] In the formula, P DR,t D represents the actual dispatch power of the virtual power plant in response to demand response load during time period t. DR The total electricity demand of the demand response load during the dispatch period. The minimum electricity demand of the demand response load during time period t. This represents the maximum electricity demand of the demand response load during time period t.

[0016] Furthermore, the energy storage charging and discharging power constraint is expressed by the following formula:

[0017]

[0018] In the formula, U S,t This indicates the charging and discharging state of the energy storage device; a value of 0 indicates charging, and a value of 1 indicates discharging; N T For the scheduling period, E S (0) represents the initial capacity of the energy storage device. This represents the maximum remaining capacity of energy storage during scheduling. This represents the minimum remaining capacity of energy storage during scheduling. P is the maximum allowable charge and discharge power of the energy storage device. ch,t P represents the charging power input to the AC side of the energy storage inverter during time period t. dis,t η represents the discharge power output from the AC side of the energy storage inverter during time period t, and η is the charging and discharging efficiency of the energy storage unit.

[0019] Furthermore, the two-stage robust optimization model is expressed by the following formula:

[0020]

[0021] The specific expressions for x and y are as follows:

[0022]

[0023] In the formula, the optimization variable x includes two state variables, where U M,t This represents the power purchase and sale status of the virtual power plant to the main power grid. A value of 1 indicates that the power plant is purchasing electricity from the grid, and a value of 0 indicates that the power plant is selling electricity to the grid. U S,t This represents the charging and discharging state of the energy storage device, with values ​​of 0 and 1 representing charging and discharging respectively. The optimization variable y includes the output levels of various regulating resources within the virtual power plant during time period t. P GT,t Let P be the output power of the micro-turbine at time t. buy,t Let P be the amount of electricity purchased by the virtual power plant from the main power grid at time t. EC,t Let P be the electrical power consumed by EC at time t. sell,t Let P be the amount of electricity sold by the virtual power plant to the main grid at time t. PV,t Let P be the photovoltaic output power at time t. L,t Let P be the electricity demand at time t; ch,t P represents the charging power input to the AC side of the distributed energy storage inverter during time period t. dis,t P represents the discharge power P output from the AC side of the distributed energy storage inverter during time period t. DR,t P represents the actual dispatch power of the virtual power plant in response to demand response load during time period t. DR1,t P DR2,t These are the introduced auxiliary variables, G. grid,t Let G be the natural gas network supply at time t. P2G,t G represents the gas production capacity of the P2G unit during time period t. GT,t G GB,t H represents the gas consumption power of the gas turbine and the gas boiler during time period t, respectively. HB,t H GB,t H represents the output of HB and GB at time t, respectively. L,t H is the magnitude of the heat load at time t. AC,t T is the amount of heat absorbed by AC at time t; g,t and T h,t These are the supply and return water temperatures of the heating pipes, λ PMV It is the predicted mean vote (PMV), which describes users' comfort level with respect to the indoor thermal environment. n,t It is the indoor temperature; Q AC,t Q EC,t These are the cooling capacities of AC and EC at time t, respectively, Q. L,tIt is the total cooling power at time t. These refer to the indoor and outdoor temperatures of the refrigerated building.

[0024] Furthermore, the decomposition of the original problem and the iterative solution using the column and constraint generation algorithm includes the following steps:

[0025] Step S11: Given the initial worst-case scenario With a convergence threshold ε, set the number of iterations k = 1, and initialize the upper and lower bounds U0 = +∞ and L0 = -∞;

[0026] Step S12: Based on the worst-case scenario Solve the main problem to obtain the optimal solution.

[0027] Step S13, at this time Becoming the new Nether will Substituting the solutions into the linearized subproblem yields the optimal solution. and Update the Nether at the same time.

[0028] Step S14: If UB - LB ≤ ε, then the optimal solution is derived and the iteration ends; otherwise, the variable y is increased. k+1 With the corresponding constraints, let k = k + 1, and return to step S12;

[0029] Where LB is the lower bound of the operating cost corresponding to the final scheduling scheme, and ε is the convergence threshold.

[0030] Furthermore, the outer principal problem of the two-stage robust optimization model is expressed by the following formula:

[0031]

[0032] In the formula, k is the current iteration number, and y l It is the solution to the subproblem after one iteration. Let be the value of the uncertain variable u under the worst-case scenario after one iteration, d and h be constant vectors, and D, F, and G be the coefficient matrices of the variables under the corresponding constraints. It means that "for all l that satisfy the condition, it is less than or equal to k".

[0033] Furthermore, the subproblems of the inner layer of the two-stage robust optimization model are represented by the following formula:

[0034]

[0035] In the formula, c is the column vector of coefficients corresponding to the objective function, y is the optimization variable, and c T y is the optimal solution of the objective function.

[0036] Furthermore, in optimizing the scheduling strategy, distributed energy storage is prioritized for peak shaving. If distributed energy storage cannot complete the peak shaving command on its own, the demand response load participates in the peak shaving.

[0037] Secondly, this application also provides an optimized scheduling system for a virtual power plant, wherein the optimized scheduling system adopts any of the above-mentioned optimized scheduling methods. Attached Figure Description

[0038] Figure 1 This is a flowchart illustrating an optimized scheduling method for a multi-energy complementary virtual power plant provided in one embodiment of this application.

[0039] Figure 2 This is a schematic diagram of the constraints in the optimized scheduling method for a multi-energy complementary virtual power plant provided in one embodiment of this application;

[0040] Figure 3 This is a schematic diagram of the structure of a multi-energy complementary virtual power plant in a specific implementation case of this application;

[0041] Figure 4 This is a schematic diagram illustrating the impact of equivalent thermal resistance on cooling buildings in the optimized scheduling method of a multi-energy complementary virtual power plant provided in one embodiment of this application.

[0042] Figure 5 This is a schematic diagram of the demand response load in the optimized scheduling method of a multi-energy complementary virtual power plant provided in one embodiment of this application;

[0043] Figure 6 This is a schematic diagram of a two-stage robust optimization model in the optimized scheduling method for a multi-energy complementary virtual power plant provided in one embodiment of this application.

[0044] Figure 7 This is a schematic diagram illustrating the demand response under the economic optimal objective in the optimized scheduling method of a multi-energy complementary virtual power plant provided in one embodiment of this application.

[0045] Figure 8 The diagram shows the impact of the PMV evaluation index, obtained in the optimized scheduling method of a multi-energy complementary virtual power plant provided in one embodiment of this application, on the heating system.

[0046] Figure 9 This is a flowchart illustrating the decomposition of the original problem and iterative solution steps using a column and constraint generation algorithm in an optimized scheduling method for a multi-energy complementary virtual power plant provided in one embodiment of this application.

[0047] Figure 10 This is a schematic diagram illustrating the cooling power scheduling under the economic optimal objective in the optimization scheduling method for a multi-energy complementary virtual power plant provided in one embodiment of this application.

[0048] Figure 11This is a schematic diagram illustrating the thermal energy dispatch situation under the economic optimal objective in the optimization dispatch method of a multi-energy complementary virtual power plant provided in one embodiment of this application;

[0049] Figure 12 This is a schematic diagram illustrating the power dispatch situation under the economic optimal objective in the optimization dispatch method of a multi-energy complementary virtual power plant provided in one embodiment of this application;

[0050] Figure 13 This is a schematic diagram illustrating the natural gas dispatch situation under the economic optimal objective in the optimization dispatch method of a multi-energy complementary virtual power plant provided in one embodiment of this application. Detailed Implementation

[0051] The present application will be described in detail below with reference to the specific embodiments shown in the accompanying drawings. However, these embodiments do not limit the present application. Any structural, methodological, or functional modifications made by those skilled in the art based on these embodiments are included within the protection scope of the present application.

[0052] To address the shortcomings of existing technologies, this application aims to provide an optimized scheduling method for multi-energy complementary virtual power plants. Based on the constructed objective function and constraints, a two-stage robust optimization model is established using distributed energy storage and demand response load as regulation resources to obtain an optimized scheduling strategy for multi-energy complementary virtual power plants. This enables flexible scheduling of grid power and improves the utilization efficiency of grid power.

[0053] Firstly, such as Figure 1 As shown in the figure, one embodiment of this application provides an optimized scheduling method for a multi-energy complementary virtual power plant, the method comprising the following steps:

[0054] Step S101: Establish the objective function of the multi-energy complementary virtual power plant optimization scheduling model. The objective function includes two optimization objectives: economic optimization and carbon emission optimization.

[0055] Specifically, in the optimal scheduling method for multi-energy complementary virtual power plants according to the embodiments of this application, the economic optimality objective means finding a strategy that maximizes economic benefits while ensuring the normal operation of the multi-energy complementary virtual power plant. For example, when the uncertainties of photovoltaic output and load power are not considered, the objective function of the optimal scheduling model for multi-energy complementary virtual power plants under the economic optimality objective can be expressed as follows:

[0056]

[0057] In the formula, G grid,t Let p be the natural gas network supply at time t. gas P represents the unit energy cost of natural gas. GT,t Let ρ be the output power of the micro-turbine at time t.GT The unit power generation cost of a micro gas turbine; K DR P represents the unit scheduling cost of demand response load. DR,t P represents the actual dispatch power of the virtual power plant in response to demand response load during time period t. DR*,t Let K be the expected power consumption of the virtual power plant in response to the demand response load during time period t. S P represents the converted unit charge / discharge cost. ch,t and P dis,t Let P represent the charging / discharging power of the AC side input / output of the energy storage inverter during time period t, respectively, and η be the charging / discharging efficiency of the energy storage unit. huy,t Let p be the amount of electricity purchased by the virtual power plant from the main grid at time t. buy P represents the electricity purchase price of the virtual power plant from the large power grid unit. sell,t Let p be the amount of electricity sold by the virtual power plant to the main grid at time t. sell The price at which virtual power plants sell electricity to large power grid units.

[0058] Specifically, in the optimized scheduling method of the multi-energy complementary virtual power plant in this application embodiment, the optimal carbon emission target can be achieved by adjusting the emissions of greenhouse gases such as carbon dioxide. For example, when the uncertainties of photovoltaic output and load power are not considered, the objective function of the multi-energy virtual power plant coordinated scheduling model under the optimal carbon emission target can be:

[0059]

[0060] In the formula, G grid,t Let m be the natural gas network supply at time t. gas P represents the carbon emissions generated per unit of natural gas purchased by a virtual power plant from a natural gas network. buy,t Let m be the amount of electricity purchased by the virtual power plant from the main grid at time t. ele This represents the carbon emissions generated per unit of electricity purchased by the virtual power plant from the main grid. Furthermore, as an alternative implementation, the constraints of the multi-energy complementary virtual power plant optimal scheduling model under the carbon emission optimization objective can be the same as those for the economic optimization objective.

[0061] Step S102: Establish the constraints for the multi-energy complementary virtual power plant optimal scheduling model, such as... Figure 2 As shown, the constraints include power supply system balance, gas supply system constraints, heating system constraints, heating network constraints, and cooling system constraints. Among them, power supply system balance includes at least demand response load constraints and energy storage charging and discharging power constraints.

[0062] Specifically, in the optimized scheduling method of the multi-energy complementary virtual power plant in this application embodiment, the setting of constraints ensures the feasibility and safety of the optimized scheduling strategy in actual operation. Firstly, the power supply system balance constraint ensures real-time matching of power supply and demand, avoiding waste or insufficient supply caused by power surplus or shortage. The power supply system balance includes at least demand response load constraints and energy storage charging and discharging power constraints.

[0063] Based on the above explanation, the following will provide further elaboration in conjunction with practical application scenarios:

[0064] like Figure 3 As shown, taking a multi-energy complementary virtual power plant (VAD) as an example, its basic architecture includes a power source that can include renewable energy generation (PV), micro gas turbines (GT), and a large power grid (MG); a gas source consisting of a natural gas network and a power-to-gas (P2G) unit; heating resources including a gas-fired boiler (GB) and a waste heat boiler (HB) connected to a combined heat and power unit; and cooling devices including an absorption chiller (AC) and an electric chiller (EC). This application provides an optimized scheduling method for a multi-energy complementary virtual power plant by introducing flexible adjustment resources, including demand response loads and distributed energy storage, into the VAD operation platform. When responding to electricity market or grid demands, this method achieves coordinated optimization of internal resources based on their adjustability.

[0065] As an optional implementation, demand response load constraints may include:

[0066]

[0067] P DR,t -P DR*,t +P DR1,t -P DR2,t =0 (5);

[0068] P DR1,t ≥0, P DR2,t ≥0 (6);

[0069] Among them, P DR,t D represents the actual dispatch power of the virtual power plant in response to demand response load during time period t. DR The total electricity demand of the demand response load during the dispatch period. The minimum electricity demand of the demand response load during time period t. This represents the maximum electricity demand of the demand response load during time period t.

[0070] As an optional implementation, energy storage charging and discharging power constraints may include:

[0071]

[0072]

[0073] In the formula, U S,t This indicates the charging and discharging state of the energy storage device; a value of 0 indicates charging, and a value of 1 indicates discharging; N T For the scheduling period, E S (0) represents the initial capacity of the energy storage device. This represents the maximum remaining capacity of energy storage during scheduling. This represents the minimum remaining capacity of energy storage during scheduling. P is the maximum allowable charge and discharge power of the energy storage device. ch,t P represents the charging power input to the AC side of the energy storage inverter during time period t. dis,t η represents the discharge power output from the AC side of the energy storage inverter during time period t, and η is the charging and discharging efficiency of the energy storage unit.

[0074] Secondly, the constraints on gas, heating, and cooling systems follow a similar logic to those on the power supply system, ensuring a reasonable distribution and balance of energy among the systems and preventing overloading or waste of energy in any one system. The constraints on the heating network, however, focus more on the stability and efficiency of the heat transmission network, ensuring that heat energy can be efficiently and evenly distributed to all demand points.

[0075] Specifically, the gas supply system constraints can be as follows:

[0076] G grid,t +G P2G,t =G L,t +G GT,t +G GB,t (11);

[0077] Among them, G grid,t Let G be the natural gas network supply at time t. P2G,t G represents the gas production capacity of the P2G unit during time period t. L,t Let G be the natural gas demand in the virtual power plant during time period t. GT,t G GB,t These represent the gas consumption power of the gas turbine and the gas boiler during time period t, respectively.

[0078] Specifically, the thermal inertia in the constraints of the heating system can be described using an autoregressive moving average model, where the building's indoor temperature T... n,t and the return water temperature T of the heating pipes h,t The relationship can be shown as follows:

[0079]

[0080] T n,t =θ1T n,t-1 +φ1T g,t-1 +w1Tw,t-1 (13);

[0081] Among them, T g,t It refers to the water supply temperature of the heating pipes, T. w,t J is the outdoor temperature, J is the thermal inertia of the heating system, and a is the thermal inertia of the heating system. j b j c j θ1, φ1, and w1 are relevant parameters of the thermal inertia of the heating system, which can be obtained through actual measurement.

[0082] Specifically, the dynamic temperature characteristics within the constraints of a cooling system can be described using an equivalent thermal parameter model, as shown below:

[0083]

[0084] in, These are the indoor and outdoor temperatures of the refrigerated building, respectively; R and C are the equivalent thermal resistance and equivalent heat capacity of the refrigerated building, respectively; and Q is the equivalent thermal resistance and equivalent heat capacity of the refrigerated building, respectively. L,t It represents the total cooling power at time t, and e is the base of the natural logarithm. Δt It is a time interval.

[0085] like Figure 4 As shown, different equivalent thermal resistances affect building cooling load. It should be noted that a larger equivalent thermal resistance is associated with a lower cooling load, indicating an increase in the inertial behavior of the cooling system. This enhanced inertial behavior improves the system's ability to store "cold energy" and increases the flexibility of cooling load management. Furthermore, with increasing equivalent thermal resistance, the cooling system is more inclined to further lower the temperature, utilizing inertial cooling for energy storage.

[0086] The constraints on the cooling system for the cooling building can be as follows:

[0087] Q AC,t +Q EC,t =Q L,t (15);

[0088]

[0089] Among them, Q AC,t Q EC,t These are the cooling capacities of AC and EC at time t, respectively. It is the lower limit of indoor temperature in refrigerated buildings. The indoor temperature of the building at time t is the temperature measured in the cold storage. It is the upper limit of indoor temperature for buildings that rely on cooling.

[0090] Furthermore, the constraints for AC and EC can be defined as follows:

[0091] Q AC,t=H AC,t η AC (17);

[0092]

[0093] Q EC,t =P EC,t η EC (19);

[0094]

[0095] Where, η AC η EC These are the energy conversion efficiencies of AC and EC, respectively. These are the upper and lower limits of AC output power, respectively. These are the upper and lower limits of EC output power, respectively, and H AC,t P represents the amount of heat supplied to the absorption chiller at time t. EC,t This represents the amount of electricity consumed by the absorption chiller at time t.

[0096] Specifically, the supply and demand balance within the constraints of the heating network can be summarized as follows:

[0097] H HB,t +H GB,t =H L,t +H AC,t (twenty one);

[0098] Among them, H HB,t H GB,t H represents the output of HB and GB at time t, respectively. L,t H is the magnitude of the heat load at time t. AC,t It is the heat absorbed by AC at time t.

[0099] The relationship between heat load and supply and return water temperatures within the constraints of a heating network can be summarized as follows:

[0100] H L,t =γ(T) g,t +T h,t ) (twenty two);

[0101] The supply and return water temperature constraints in the heating network can be as follows:

[0102]

[0103] Wherein, γ is the coefficient relating heat load and supply / return water temperature. It is the maximum water supply temperature, T h,t It is the return water temperature in the heating network, T g,t It refers to the water supply temperature in the heating network, H. L,tIt is the heat provided to the user at time t.

[0104] The various boiler output constraints in the heating network can be as follows:

[0105]

[0106] H GB,t =G GB,t η GB (26);

[0107]

[0108] Where, η GT η HB η GB These are the GT heat production efficiency, HB waste heat efficiency, and gas boiler heat production efficiency. These are the upper and lower limits of the output thermal power of HB, respectively. These are the upper and lower limits of GB output thermal power, respectively.

[0109] Step S103: Based on the objective function and constraints, construct a two-stage robust optimization model for a multi-energy complementary virtual power plant. The two-stage robust optimization model introduces two types of regulation resources: distributed energy storage and demand response load. The outer layer of the two-stage robust optimization model seeks the best decisions for purchasing / selling electricity and charging / discharging under the known worst-case scenario, while the inner layer searches for the worst-case scenario and the corresponding scheduling strategy.

[0110] Specifically, in the optimization scheduling method of the multi-energy complementary virtual power plant in this application embodiment, based on the objective function and constraints established in the aforementioned steps, step S103 can construct a two-stage robust optimization model for the multi-energy complementary virtual power plant. For example... Figure 5 As shown, the model can include two stages: an outer decision-making stage and an inner search stage. In the outer stage, the model makes optimal decisions such as purchasing / selling electricity and charging / discharging energy storage based on the known worst-case scenarios (e.g., extreme weather, equipment failure) to minimize economic losses and carbon emissions in the worst-case scenario. The inner stage is responsible for searching for the worst-case scenario and its corresponding optimal scheduling strategy to ensure the effectiveness and robustness of the decision.

[0111] Furthermore, distributed energy storage devices can make full use of the time-of-use pricing mechanism. Under the constraint that the energy storage capacity must be equal at the beginning and end of the time, the low-priced electricity at the off-peak time can be transferred to the peak time, effectively reducing the operating cost of the system.

[0112] Furthermore, such as Figure 6As shown, demand response loads can include shiftable loads and reduceable loads. Reduceable loads alleviate grid pressure by temporarily reducing some non-critical loads (such as discontinuous processes in industrial production) during peak grid load periods. Shiftable loads reduce load pressure during peak periods and lower system instability by adjusting the temporal and spatial distribution of loads and transferring loads during periods of relatively low demand. Figure 7 As shown, by shifting loads, demand response loads are strategically transferred to periods with lower electricity prices and lower overall consumption, specifically 6:00 to 12:00, 20:00 to 22:00, 24:00 to 5:00-6:00, and 16:00. This adjustment reduces electricity costs during peak hours and alleviates system operational pressure while maintaining various constraints.

[0113] The two-stage robust optimization model incorporates distributed energy storage and demand response load as regulation resources, enhancing the system's ability to cope with uncertainties in application scenarios and further improving the flexibility and robustness of the multi-energy complementary virtual power plant scheduling strategy.

[0114] Specifically, the two-stage robust optimization model can be expressed by the following formula:

[0115]

[0116] The specific expressions for x and y can be as follows:

[0117]

[0118] In the two-stage robust optimization model expression, c is the coefficient column vector of the two objective functions: economic optimality and carbon emission optimality. K, D, F, G, and I... u Let be the coefficient matrix of the variables under the corresponding constraints, and d and h be constant vectors. The first row in the formula represents the equality constraints in the virtual power plant model, the second row represents the inequality constraints, the third row represents the inequality constraints that include both x and y variables, and the fourth row represents the predicted values ​​for each time period in the deterministic model.

[0119] In the specific expressions for x and y, the optimization variable x contains two state variables, where U M,t This represents the power purchase and sale status of the virtual power plant to the main power grid. A value of 1 indicates that the power plant is purchasing electricity from the grid, and a value of 0 indicates that the power plant is selling electricity to the grid. U S,t The value represents the charging and discharging state of the energy storage device, with values ​​of 0 and 1 representing charging and discharging respectively. The optimization variable y includes the output levels of various regulating resources within the virtual power plant during time period t. FT,t Let P be the output power of the micro-turbine at time t. buy,tLet P be the amount of electricity purchased by the virtual power plant from the main power grid at time t. EC,t Let P be the electrical power consumed by EC at time t. sell,t Let P be the amount of electricity sold by the virtual power plant to the main grid at time t. PV,t Let P be the photovoltaic output power at time t. L,t Let P be the electricity demand at time t; ch,t P represents the charging power input to the AC side of the distributed energy storage inverter during time period t. dis,t P represents the discharge power P output from the AC side of the distributed energy storage inverter during time period t. DR,t P represents the actual dispatch power of the virtual power plant in response to demand response load during time period t. DR1,t P DR2,t These are the introduced auxiliary variables, G. grid,t Let G be the natural gas network supply at time t. P2G,t G represents the gas production capacity of the P2G unit during time period t. GT,t G GB,t H represents the gas consumption power of the gas turbine and the gas boiler during time period t, respectively. HB,t H GB,t H represents the output of HB and GB at time t, respectively. L,t H is the magnitude of the heat load at time t. AC,t T is the amount of heat absorbed by AC at time t; g,t and T h,t These are the supply and return water temperatures of the heating pipes, λ PMV It is the predicted mean vote (PMV), which describes users' comfort level with respect to the indoor thermal environment. n,t It is the indoor temperature; Q AC,t Q EC,t These are the cooling capacities of AC and EC at time t, respectively, Q. L,t It is the total cooling power at time t. This refers to the indoor and outdoor temperatures of the refrigerated building. Furthermore, λ PMV The mathematical equation can be expressed as follows:

[0120] λ PMV =(0.303e -0.036M +0.028){MW-3.05×10 -3 ×[5733-

[0121] 6.99(MW)-P a -0.42[(MW)-58.15]-1.7×10 -5 M(5867-P a )-

[0122] 0.0014M(34-Tn -3.96×10 -8 f c1 [(t c1 +273) 4 -(t r +273) 4 ]-f c1 h c (t c1 -

[0123] T n )}(30);

[0124] In the expression, M is the human body's metabolic rate, W is the corresponding mechanical power, and f c1 h is the ratio of the area of ​​the body covered by clothing to the area not covered. c It is the surface heat transfer coefficient, T n It is the temperature of the air surrounding the human body, t c1 and t r These are the outer surface temperature of the clothing and the average radiant temperature, respectively. Since the most direct sensory experience of thermal comfort is temperature, it is assumed that, in addition to T... n All other parameters are set to constant values. To ensure comfort, λ PMV,t It can vary within a suitable range:

[0125] -σ≤λ PMV,t ≤σ(31);

[0126] like Figure 8 As shown, the PMV index affects heating systems in heated buildings. A smaller PMV constraint range means that users have a higher demand for temperature, requiring more heat input, which indicates a reduced heat storage capacity. Increasing the constraint range can enhance the flexibility of heat load and improve storage capacity, and has a similar effect on the temperature control of cooling systems.

[0127] Step S104: For the two-stage robust optimization model of the multi-energy complementary virtual power plant, the column and constraint generation algorithm is used to decompose the original problem and iteratively solve it to obtain the optimized scheduling strategy for the multi-energy complementary virtual power plant.

[0128] Specifically, in the optimal scheduling method for the multi-energy complementary virtual power plant in step S104, the original problem and iterative solution steps can be decomposed using a column and constraint generation algorithm as follows: Figure 9 As shown, it includes the following steps:

[0129] Step S11: Given the initial worst-case scenario With a convergence threshold ε, set the number of iterations k = 1, and initialize the upper and lower bounds U0 = +∞ and L0 = -∞;

[0130] Step S12: Based on the worst-case scenario Solve the main problem to obtain the optimal solution.

[0131] Step S13: At this time Becoming the new Nether will Substituting the solutions into the linearized subproblem yields the optimal solution. and Update the Nether at the same time.

[0132] Step S14: If UB - LB ≤ ε, then derive the optimal solution and the iteration ends; otherwise, increase the variable y. k+1 With the corresponding constraints, let k = k + 1, and return to step S12;

[0133] Where LB is the lower bound of the operating cost corresponding to the final scheduling scheme, and ε is the convergence threshold.

[0134] Specifically, the outer principal problem of the two-stage robust optimization model can be expressed by the following formula:

[0135]

[0136] In the formula, k is the current iteration number, and y l It is the solution to the subproblem after one iteration. Let be the value of the uncertain variable u under the worst-case scenario after one iteration, d and h be constant vectors, and D, F, and G be the coefficient matrices of the variables under the corresponding constraints. It means that "for all l that satisfy the condition, it is less than or equal to k".

[0137] Specifically, the subproblems of the inner layer of the two-stage robust optimization model can be represented by the following formula:

[0138]

[0139] In the formula, c is the column vector of coefficients corresponding to the objective function, y is the optimization variable, and c T y is the optimal solution of the objective function.

[0140] As an optional implementation, the optimized scheduling strategy prioritizes the scheduling of distributed energy storage for peak shaving. If distributed energy storage cannot complete the peak shaving command on its own, the demand response load participates in the peak shaving.

[0141] Specifically, in the optimized scheduling method of the multi-energy complementary virtual power plant in this application embodiment, distributed energy storage features rapid response and flexible adjustment, such as battery energy storage and supercapacitors. These energy storage devices can release stored electrical energy during peak grid load periods to supplement power supply and reduce pressure on the generation side of the grid; during off-peak grid load periods, they can absorb excess electrical energy for charging, ensuring grid stability. Therefore, in the optimized scheduling strategy, distributed energy storage can be prioritized for peak shaving. When the charging and discharging capacity of distributed energy storage cannot independently meet peak shaving demands, demand response loads can participate in peak shaving tasks as a supplementary means. Demand response loads can include shiftable loads and reduceable loads. Shiftable loads can transfer loads during periods of relatively low demand by adjusting the temporal and spatial distribution of the load, thereby reducing load pressure during peak grid periods; reduceable loads can temporarily reduce some non-critical loads (such as discontinuous processes in industrial production) during peak grid load periods, alleviating grid pressure. The above-mentioned coordinated scheduling mode improves the flexibility of the optimized scheduling strategy and enhances the utilization efficiency of grid power.

[0142] like Figure 10 , Figure 11 , Figure 12 and Figure 13 The diagram illustrates the optimal scheduling strategy of the multi-energy complementary virtual power plant (MEVPP) for various energy sources in an application scenario. During peak electricity consumption periods (8:00-11:00, 20:00-24:00), the gas turbine operates at full load due to limited solar energy output. Despite the high electricity price, electricity still needs to be purchased from the MG (Multi-Energy Complementary Virtual Power Plant). From 12:00 to 15:00, solar energy output reaches its peak, allowing the MEVPP to sell electricity to the MG and reduce costs. To take advantage of the lower electricity price, demand response loads are also shifted during this period. At 17:00, because the total thermal power of HB and GB is insufficient to meet the heat load demand, the air conditioning starts operating.

[0143] Based on the above explanation, the following will provide further elaboration in conjunction with practical application scenarios:

[0144] In this embodiment, a multi-energy complementary virtual power plant in the industrial park is selected. The example data mainly refers to the Haining Jianshan New Area Active Distribution Network Demonstration Project, in which the micro-gas turbine is 18MW and the photovoltaic power generation is 10MW. The fluctuation deviations of load power and photovoltaic output are considered to be 10% and 15% of the predicted values, respectively. The grid-side energy storage device has a capacity of 2000 kWh, and the unit charge-discharge cost is 0.38 RMB / (kWh). The charge-discharge efficiency is 0.95, and the maximum and minimum remaining capacities are 1800 kWh and 400 kWh, respectively. The total electricity demand D of the demand response load during the dispatch cycle is... DR The unit dispatch cost is 2940 kWh, and the unit dispatch cost is K. DR Take 0.32 RMB / (kW·h); use the 2023 Hangzhou residential electricity tiered price as the day-ahead transaction price for power exchange between the distribution network and virtual power plants.

[0145] Step 1: Establish a low-carbon economic optimization scheduling model for multi-energy complementary virtual power plants;

[0146] Step 2: Call the column and constraint generation algorithm to solve the model and obtain the impact of PMV parameters on the heating system and equivalent thermal resistance on the cooling building in the multi-energy virtual power plant.

[0147] Step 3: Calculate the economically optimal and carbon-emission-minimum objective functions respectively, and determine whether the termination condition is met. The criterion used here is that the difference between the upper and lower bounds of the current objective function is not greater than a preset convergence threshold. If this condition is met, stop the iteration process; otherwise, return, update the output status of each operating unit in the column and constraint generation algorithm, determine the optimal solution for different objectives, and output the corresponding optimized scheduling scheme.

[0148] Step 4: By solving the optimization scheduling model of the multi-energy complementary virtual power plant (cooling, heating, and electrical), multiple optimal scheduling schemes under different uncertainty adjustment parameters are obtained. The optimal solution can be selected from multiple schemes according to demand. Taking economic optimality as an example, the scheduling target differences are shown in Table 1 below:

[0149] <![CDATA[Π L =0,P pv =0]]> 30392 0.6913 <![CDATA[Π L =12,P pv =6]]> 31998 0.7087 <![CDATA[Π L =24,P pv =12]]> 32836 0.7195

[0150] Table 1 shows the results of electricity and gas purchases under different uncertainty parameters at the economic optimal point. Table 2 shows the results of electricity and gas purchases under different uncertainty parameters in Table 1.

[0151] <![CDATA[Π L =0,P pv =0]]> 12.27 6.93 1562 <![CDATA[Π L =0,P pv =6]]> 13.48 6.28 1564 <![CDATA[Π L =12,P pv =0]]> 27.05 4.93 1573

[0152] Table 2

[0153] As can be seen from Tables 1 and 2, according to the virtual power plant optimization scheduling method provided in the embodiments of this application, taking economic optimization as an example, the scheduling target difference of the power plant and the results of electricity and gas purchase and sale change with the changes of uncertain adjustment parameters.

[0154] One embodiment of this application also provides an optimized scheduling system for a virtual power plant, which can be applied to any of the optimized scheduling methods described in this application above.

[0155] In summary, the optimal scheduling method for a multi-energy complementary virtual power plant provided in this application, based on the constructed objective function and constraints, uses distributed energy storage and demand response load as regulation resources, establishes a two-stage robust optimization model, obtains an optimal scheduling strategy for the multi-energy complementary virtual power plant, realizes flexible scheduling of grid power, and improves the utilization efficiency of grid power.

[0156] It is understood that the term "exemplary" as used herein means "as an example, illustration, or description." Any embodiment described as "exemplary" is not necessarily preferred or superior to other embodiments and / or does not exclude features in combination with other embodiments. It should be understood that certain features of this application described in the context of a single embodiment for clarity may also be provided in combination in a single embodiment. Conversely, various features of this application described in the context of a single embodiment for clarity may also be provided individually or in any suitable combination or as part of any other described embodiment of this application.

[0157] The above-disclosed embodiments are merely preferred embodiments of this application, but are not intended to limit the scope of this application. Those skilled in the art will understand that any changes, modifications, substitutions, combinations, or simplifications made without departing from the spirit and scope of this application and the appended claims are equivalent substitutions and still fall within the scope of the invention.

[0158] The application fields of the technology of this invention include, but are not limited to, pipeline safety. As long as the essence of the technical solution does not deviate from the scope of the technical solutions of the various embodiments of this invention, it falls within the protection scope of this invention.

Claims

1. An optimized scheduling method for a multi-energy complementary virtual power plant, characterized in that, The method includes the following steps: An objective function is established for the optimal scheduling model of a multi-energy complementary virtual power plant, which includes two optimization objectives: economic optimization and carbon emission optimization. The constraints for establishing the optimal scheduling model of a multi-energy complementary virtual power plant include power supply system balance, gas supply system constraints, heating system constraints, heating network constraints, and cooling system constraints. The power supply system balance includes at least demand response load constraints and energy storage charging and discharging power constraints. Based on the objective function and the constraints, a two-stage robust optimization model for a multi-energy complementary virtual power plant is constructed. The two-stage robust optimization model introduces two types of regulating resources: distributed energy storage and demand response load. The outer layer of the two-stage robust optimization model seeks the best decisions for purchasing / selling electricity and charging / discharging under the known worst-case scenario, while the inner layer searches for the worst-case scenario and the corresponding scheduling strategy. For the two-stage robust optimization model of the multi-energy complementary virtual power plant, the column and constraint generation algorithm is used to decompose the original problem and iteratively solve it to obtain the optimized scheduling strategy for the multi-energy complementary virtual power plant. The two-stage robust optimization model is expressed by the following formula: ； The specific expressions for x and y are as follows: ； In the formula, the optimization variable x includes two state variables, where This indicates the power purchase and sale status of the virtual power plant to the main power grid. A value of 1 indicates that the power plant purchases electricity from the grid, and a value of 0 indicates that the power plant sells electricity to the grid. This represents the charging and discharging state of the energy storage device, with values ​​of 0 and 1 representing charging and discharging respectively. The optimization variable y includes the output levels of various regulating resources within the virtual power plant during time period t. Let t be the output power of the micro gas turbine. Let t represent the amount of electricity the virtual power plant purchases from the main power grid. Let t be the electrical power consumed by EC. Let t represent the amount of electricity sold by the virtual power plant to the main power grid. Let be the photovoltaic output power at time t. Let t be the electricity demand at time t; This represents the charging power input to the AC side of the distributed energy storage inverter during time period t. This represents the discharge power output from the AC side of the distributed energy storage inverter during time period t. The actual dispatch power of the virtual power plant in response to demand load during time period t. , These are the introduced auxiliary variables, The gas production capacity of the P2G unit during time period t. These represent the gas consumption power of the gas turbine and the gas boiler during time period t, respectively. , These represent the output of HB and GB at time t, respectively. It is the magnitude of the heat load at time t. It is the heat absorbed by AC at time t; and These are the supply water temperature and return water temperature of the heating pipes, respectively. It is the predicted mean vote (PMV), which can describe users' comfort level with the indoor thermal environment. It is the indoor temperature; , These are the cooling capacities of AC and EC at time t, respectively. It is the total cooling power at time t. These are the indoor and outdoor temperatures of the refrigerated building; The demand response load constraint is expressed by the following formula: ； ； ； ； In the formula, for t The actual dispatch power of the virtual power plant in response to demand load during the time period. The total electricity demand of the demand response load during the dispatch period. For demand response load in t Minimum electricity demand for a given time period For demand response load in t Maximum electricity demand during a given time period; The energy storage charging and discharging power constraint is expressed by the following formula: ； ； ； ； In the formula, This indicates the charging and discharging status of the energy storage device; a value of 0 indicates charging, and a value of 1 indicates discharging. For the scheduling period, The capacity of the energy storage device at the start of operation. This represents the maximum remaining capacity of energy storage during scheduling. This represents the minimum remaining capacity of energy storage during scheduling. The maximum allowable charge and discharge power of the energy storage device. express t The charging power input to the AC side of the time-of-use energy storage inverter. express t The discharge power output from the AC side of the time-of-use energy storage inverter. The charging and discharging efficiency of the energy storage unit.

2. The optimized scheduling method according to claim 1, characterized in that, The algorithm for decomposing the original problem and iteratively solving it using column and constraint generation includes the following steps: Step S11: Given the initial worst-case scenario and convergence threshold Set the number of iterations =1, initialize upper and lower bounds. ; Step S12: Based on the worst-case scenario Solve the main problem to obtain the optimal solution. ; Step S13, at this time Becoming the new Nether will Substituting the solutions into the linearized subproblem yields the optimal solution. and At the same time, update the lower bound. ; Step S14, if If the optimal solution is found, the iteration ends; otherwise, the variable is increased. And corresponding constraints, let Return to step S12; Where LB is the lower bound of the operating cost corresponding to the final scheduling scheme. This is the convergence threshold.

3. The optimized scheduling method according to claim 2, characterized in that, The outer principal problem of the two-stage robust optimization model is expressed by the following formula: ； In the formula, It is the current iteration number. It is iteration Solution to the next subproblem It is iteration The subsequent uncertainties in the worst-case scenario The value, Let be a constant vector, and D, F, and G be the coefficient matrices of the variables under the corresponding constraints. It means that "for all l that satisfy the condition, it is less than or equal to k".

4. The optimized scheduling method according to claim 3, characterized in that, The inner subproblems of the two-stage robust optimization model are represented by the following formula: ； In the formula, c is the column vector of coefficients corresponding to the objective function, and y is the optimization variable. This represents the optimal solution to the objective function.

5. The optimized scheduling method according to claim 1, characterized in that, The demand response load includes shiftable loads and reduceable loads.

6. The optimized scheduling method according to claim 5, characterized in that, In the optimized scheduling strategy, distributed energy storage is prioritized for peak shaving. If distributed energy storage cannot complete the peak shaving command on its own, the demand response load participates in the peak shaving.

7. An optimized scheduling system for a virtual power plant, wherein the optimized scheduling system employs the optimized scheduling method as described in any one of claims 1 to 6.

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