Optimized scheduling method and system for multi-energy complementary virtual power plant

Abstract

The invention discloses an optimal scheduling method for a multi-energy complementary virtual power plant, and the method comprises the following steps: building an objective function of an optimal scheduling model of the multi-energy complementary virtual power plant, the objective function comprising two optimization objectives of economic optimization and carbon emission optimization; establishing constraint conditions of the multi-energy complementary virtual power plant optimization scheduling model, wherein the constraint conditions comprise power supply system balance, gas supply system constraint, heat supply system constraint, heat supply network constraint and cold supply system constraint; based on the objective function and the constraint condition, a two-stage robust optimization model of the multi-energy complementary virtual power plant is constructed, and two types of adjustment resources of distributed energy storage and demand response load are introduced into the two-stage robust optimization model; and for the two-stage robust optimization model of the multi-energy complementary virtual power plant, an original problem is decomposed and iterative solution is carried out by using a column and constraint generation algorithm, and an optimal scheduling strategy for the multi-energy complementary virtual power plant is obtained. According to the invention, the power grid can be flexibly regulated and controlled by optimizing the scheduling strategy.

Description

Technical Field

[0001] The present application relates to the technical field of power system optimization and scheduling, and in particular to an optimization and scheduling method and system for a multi-energy complementary virtual power plant. Background Art

[0002] Although the 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 generation leads to the volatility of load demand, which increases the complexity and instability of system operation and poses a hidden danger to the safe and stable operation of power plants; the existing scheduling strategy lacks flexibility in dealing with internal uncertainties in the system, making it difficult to achieve economic and environmentally friendly scheduling goals while ensuring the user's electricity experience. An innovative optimization scheduling strategy is urgently needed to overcome the above technical deficiencies. Summary of the invention

[0003] In order to solve the deficiencies of the prior art, this application adopts the following technical solutions:

[0004] In a first aspect, the present application provides an optimization scheduling method for a multi-energy complementary virtual power plant, characterized in that the method comprises the following steps:

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

[0006] Establish the constraint conditions of the multi-energy complementary virtual power plant optimization scheduling model, which include power supply system balance, gas supply system constraints, heating system constraints, heating network constraints and cooling system constraints. Among them, the power supply system balance at least includes 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 of 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 decision for purchasing / selling electricity, charging / discharging, etc. under the known worst scenario, and the inner layer searches for the worst scenario and the corresponding scheduling strategy.

[0008] Aiming at 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 it to obtain the optimal scheduling strategy for multi-energy complementary virtual power plant.

[0009] According to the above description, the present application provides an optimization scheduling method for a multi-energy complementary virtual power plant. Based on the constructed objective function and constraints, distributed energy storage and demand response loads are used as regulation resources to establish a two-stage robust optimization model, obtain the optimization scheduling strategy for the multi-energy complementary virtual power plant, realize flexible scheduling of power grid electricity, and improve the utilization efficiency of power grid electricity.

[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] Where P DR,t is the actual dispatch power of the virtual power plant to the demand response load during period t, D DR is the total electricity demand of the demand response load during the dispatch period, is the minimum power demand of the demand response load in period t, is the maximum electricity demand of the demand response load in period t.

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

[0017]

[0018] Where U S,t Indicates the charging and discharging status of the energy storage device. When the value is 0, it indicates charging, and when the value is 1, it indicates discharging; N T is the scheduling period, E S (0) is the capacity of the energy storage device at the start time, is the maximum remaining capacity of energy storage during dispatch, is the minimum remaining capacity of energy storage during dispatch, is the maximum charge and discharge power allowed by the energy storage device, P ch,t represents the charging power input to the AC side of the energy storage inverter during period t, P dis,t It represents the discharge power outputted by the AC side of the energy storage inverter during 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 of x and y are as follows:

[0022]

[0023] In the formula, the optimization variable x contains two state variables, among which U M,t Indicates the state of power purchase and sale from the virtual power plant to the large power grid. When the value is 1, it means the power plant purchases power from the power grid, and when the value is 0, it means the power plant sells power to the power grid. S,t represents the charging and discharging state of the energy storage device. When the value is 0 / 1, it represents charging / discharging respectively. The optimization variable y contains the output level of various regulating resources in the virtual power plant during period t. GT,t is the output power of the micro-turbine at time t, P buy,t is the amount of electricity purchased by the virtual power plant from the large power grid at time t, P EC,t is the electric power consumed by EC at time t, P sell,t is the amount of electricity sold by the virtual power plant to the large power grid at time t, P PV,t is the photovoltaic output power at time t, P L,t is the power demand at time t; P ch,t represents the charging power input to the AC side of the distributed energy storage inverter during period t, P dis,t It represents the discharge power P outputted by the AC side of the distributed energy storage inverter during period t DR,t is the actual dispatch power of the virtual power plant to the demand response load during period t, P DR1,t , P DR2,t are the auxiliary variables introduced, G grid,t is the natural gas network supply at time t, G P2G,t is the gas production power of the P2G device during period t, G GT,t , G GB,t are the gas consumption power of the gas turbine and gas boiler during period t; H HB,t , H GB,t They represent the output of HB and GB at time t, respectively. L,t is the heat load at time t, H AC,t is the heat absorbed by AC at time t; T g,t and T h,t are the supply water temperature and return water temperature of the heating pipe, λ PMV is the predicted mean vote (PMV), which can describe the user's comfort level with the indoor thermal environment. n,t is the indoor temperature; Q AC,t , Q EC,t are the cooling power of AC and EC at time t, Q L,tis the total cooling power at time t, It is the indoor and outdoor temperature of the cooled building.

[0024] Further, decomposing the original problem and iteratively solving it using the column and constraint generation algorithm include the following steps:

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

[0026] Step S12: According to the worst scenario Solve the main problem and get the optimal solution

[0027] Step S13: Become the new Nether, Substitute into the linearized subproblem to get the optimal solution and Update the nether at the same time

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

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

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

[0031]

[0032] Where k is the current iteration number, y l is the solution to the subproblem after 1 iteration, is the value of the uncertain variable u in the worst scenario after one iteration, d and h are constant vectors, D, F, and G are the coefficient matrices of the variables under the corresponding constraints, It means "for all l that meet the conditions, they are less than or equal to k".

[0033] Furthermore, the inner subproblem of the two-stage robust optimization model is expressed as follows:

[0034]

[0035] In the formula, c is the coefficient column vector 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 the optimized dispatching strategy, distributed energy storage is preferentially dispatched to participate in peak-shaving. If the distributed energy storage cannot complete the peak-shaving instruction alone, the demand response load participates in peak-shaving.

[0037] In a second aspect, the present application also provides an optimization scheduling system for a virtual power plant, and the optimization scheduling system adopts any of the above-mentioned optimization scheduling methods. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 A schematic flow chart of an optimization scheduling method for a multi-energy complementary virtual power plant provided in one embodiment of the present application;

[0039] Figure 2 A schematic diagram of constraints in an optimization scheduling method for a multi-energy complementary virtual power plant provided in an embodiment of the present application;

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

[0041] Figure 4 A schematic diagram of the effect of equivalent thermal resistance on cooling buildings solved in an optimization scheduling method for a multi-energy complementary virtual power plant provided in an embodiment of the present application;

[0042] Figure 5 A schematic diagram of a demand response load in an optimization scheduling method for a multi-energy complementary virtual power plant provided in an embodiment of the present application;

[0043] Figure 6 A schematic diagram of a two-stage robust optimization model in an optimization scheduling method for a multi-energy complementary virtual power plant provided in an embodiment of the present application;

[0044] Figure 7 A schematic diagram of a demand response situation under an economically optimal target solved in an optimization scheduling method for a multi-energy complementary virtual power plant provided in an embodiment of the present application;

[0045] Figure 8 A diagram showing the influence of the PMV evaluation index on the heating system solved in the optimization scheduling method of a multi-energy complementary virtual power plant provided in one embodiment of the present application;

[0046] Fig. 9 A flowchart of decomposing the original problem and iterative solving steps using a column and constraint generation algorithm in an optimization scheduling method for a multi-energy complementary virtual power plant provided by an embodiment of the present application;

[0047] Fig.10 A schematic diagram of cooling power dispatching under an economically optimal target solved in an optimization dispatching method for a multi-energy complementary virtual power plant provided in an embodiment of the present application;

[0048] Fig.11A schematic diagram of a thermal energy dispatching situation under an economically optimal target solved in an optimization dispatching method of a multi-energy complementary virtual power plant provided in an embodiment of the present application;

[0049] Fig.12 A schematic diagram of a power dispatching situation under an economically optimal target solved in an optimization dispatching method for a multi-energy complementary virtual power plant provided in an embodiment of the present application;

[0050] Fig.13 A schematic diagram of the natural gas scheduling situation under the economic optimal goal solved in the optimization scheduling method of a multi-energy complementary virtual power plant provided in an embodiment of the present application. DETAILED DESCRIPTION

[0051] The present application will be described in detail below in conjunction with the specific implementation modes shown in the accompanying drawings, but these implementation modes do not limit the present application. Structural, methodological, or functional changes made by ordinary technicians in the field based on these implementation modes are included in the protection scope of the present application.

[0052] In order to address the deficiencies of the prior art, the purpose of this application is to provide an optimal scheduling method for a multi-energy complementary virtual power plant. Based on the constructed objective function and constraints, a two-stage robust optimization model is established with distributed energy storage and demand response loads as regulation resources to obtain an optimal scheduling strategy for the multi-energy complementary virtual power plant, thereby achieving flexible scheduling of grid electricity and improving the utilization efficiency of grid electricity.

[0053] First, as Figure 1 As shown, an embodiment of the present application provides an optimization scheduling method for a multi-energy complementary virtual power plant, the method comprising the following steps:

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

[0055] Specifically, in the optimization scheduling method of the multi-energy complementary virtual power plant in the embodiment of the present application, the economic optimal goal means that it is necessary to find a strategy that can maximize economic benefits under the premise of ensuring the normal operation of the multi-energy complementary virtual power plant. For example, when the uncertainty of photovoltaic output and load power is not considered, the objective function of the optimization scheduling model of the multi-energy complementary virtual power plant under the economic optimal goal can be expressed as follows:

[0056]

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

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

[0059]

[0060] In the formula, G grid,t is the natural gas network supply at time t, m gas is the carbon emissions generated by the unit of natural gas purchased by the virtual power plant from the natural gas grid, P buy,t is the amount of electricity purchased by the virtual power plant from the large power grid at time t, m ele is the carbon emissions generated by the unit electricity purchased by the virtual power plant from the large power grid. Furthermore, as an optional implementation method, the constraints of the multi-energy complementary virtual power plant optimization scheduling model under the carbon emission optimal goal can be the same as the economic optimal goal.

[0061] Step S102: Establish the constraint conditions of the multi-energy complementary virtual power plant optimization 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 which the power supply system balance includes at least demand response load constraints and energy storage charging and discharging power constraints.

[0062] Specifically, in the optimization scheduling method of the multi-energy complementary virtual power plant in the embodiment of the present application, the setting of constraint conditions ensures the feasibility and safety of the optimization scheduling strategy in actual operation. Among them, on the first aspect, the power supply system balance constraint ensures the real-time matching of power supply and demand, avoiding waste or insufficient supply caused by excess or shortage of power. In the power supply system balance, at least demand response load constraints and energy storage charging and discharging power constraints are included.

[0063] Based on the above description, the following will further explain it in combination with actual application scenarios:

[0064] like Figure 3 As shown, taking the cold, heat and electricity multi-energy complementary virtual power plant as an example, the power sources in its basic architecture can include new energy power generation (PV), micro gas turbines (GT) and large power grids (MG); the gas source consists of a natural gas network and a power-to-gas device (P2G); the heating resources include a gas boiler (GB) and a waste heat boiler (HB) connected to the cogeneration unit; the refrigeration device includes an absorption chiller (AC) and an electric chiller (EC). The application provides an optimization scheduling method for a multi-energy complementary virtual power plant, which introduces flexible adjustment resources including demand response loads and distributed energy storage into the multi-energy complementary virtual power plant operation platform, and realizes the coordinated optimization of internal resources according to the adjustable capabilities of different resources when responding to the power market or power grid demand.

[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 is the actual dispatch power of the virtual power plant to the demand response load during period t, D DR is the total electricity demand of the demand response load during the dispatch period, is the minimum power demand of the demand response load in period t, is the maximum electricity demand of the demand response load in period t.

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

[0071]

[0072]

[0073] Where U S,t Indicates the charging and discharging status of the energy storage device. When the value is 0, it indicates charging, and when the value is 1, it indicates discharging; N T is the scheduling period, E S (0) is the capacity of the energy storage device at the start time, is the maximum remaining capacity of energy storage during dispatch, is the minimum remaining capacity of energy storage during dispatch, is the maximum charge and discharge power allowed by the energy storage device, P ch,t represents the charging power input to the AC side of the energy storage inverter during period t, P dis,t It represents the discharge power outputted by the AC side of the energy storage inverter during period t, and η is the charging and discharging efficiency of the energy storage unit.

[0074] Secondly, the constraints of the gas, heating and cooling systems also follow a similar logic to the balance of the power supply system, ensuring the reasonable distribution and balance of energy among the systems and avoiding excessive load or energy waste in a certain system. The constraints of the heating network focus more on the stability and efficiency of the heat transmission network, ensuring that heat energy can be efficiently and evenly distributed to each demand point.

[0075] Specifically, the air 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 is the natural gas network supply at time t, G P2G,t is the gas production power of the P2G device during period t, G L,t is the natural gas demand in the virtual power plant during period t, G GT,t , G GB,t are the gas consumption power of the gas turbine and gas boiler during period t respectively.

[0078] Specifically, the thermal inertia in the heating system constraints can be described by the autoregressive moving average model. The indoor temperature of the building T n,t and the return water temperature T of the heating pipe h,t The relationship can be 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 is the water supply temperature of the heating pipe, T w,t is the outdoor temperature, J is the thermal inertia of the heating system, a j , b j , c j , θ1, φ1, w1 are the relevant parameters of the thermal inertia of the heating system and can be obtained through actual measurement.

[0082] Specifically, the temperature dynamic characteristics in the cooling system constraints can be described by an equivalent thermal parameter model, which can be shown as follows:

[0083]

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

[0085] like Figure 4 As shown in the figure, different equivalent thermal resistances have an impact on the building cooling load. It should be noted that a larger equivalent thermal resistance is associated with a lower cooling load, indicating that the inertial behavior of the cooling system has increased. The enhancement of the inertial behavior of the cooling system improves the system's ability to store "cold" and improves the flexibility of the cooling load. In addition, as the equivalent thermal resistance increases, the cooling system is more inclined to further reduce the temperature and use inertial cooling for energy storage.

[0086] The constraints for cooling buildings in the cooling system constraints 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 are the cooling powers of AC and EC at time t, is the lower limit of indoor temperature of cooling buildings. is the indoor temperature of the cooling building at time t, It is the upper limit of indoor temperature in cooling buildings.

[0090] Furthermore, the constraints of AC and EC can be 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] Among them, η AC , η EC They are the energy conversion efficiency of AC and EC respectively; They are the upper and lower limits of AC output power respectively; are the upper and lower limits of EC output power, H AC,t represents the heat supplied to the absorption refrigerator at time t, P EC,t Represents the amount of electricity consumed by the absorption chiller at time t.

[0096] Specifically, the supply and demand balance in the heating network constraint can be shown 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 They represent the output of HB and GB at time t, respectively. L,t is the heat load at time t, H AC,t is the heat absorbed by AC at time t.

[0099] The relationship between heat load and supply water temperature and return water temperature in the heating network constraint can be shown 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 constraints can be shown as follows:

[0102]

[0103] Among them, γ is the coefficient of relationship between heat load and supply and return water temperature, is the maximum supply water temperature, T h,t is the return water temperature in the heating network, T g,t is the supply water temperature in the heating network, H L,tis the amount of heat provided to the user at time t.

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

[0105]

[0106] H GB,t =G GB,t η GB (26);

[0107]

[0108] Among them, η GT , η HB , η GB They are GT heat generation efficiency, HB waste heat efficiency and gas boiler heat generation efficiency; They are the upper and lower limits of HB output heat power respectively; They are the upper and lower limits of GB output thermal power respectively.

[0109] Step S103: Based on the objective function and constraints, a two-stage robust optimization model of 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 searches for the best decision of purchasing / selling electricity, charging / discharging, etc. under the known worst scenario, and the inner layer searches for the worst scenario and the corresponding scheduling strategy.

[0110] Specifically, in the optimization scheduling method of the multi-energy complementary virtual power plant in the embodiment of the present application, based on the objective function and constraint conditions established in the above steps, step S103 can construct a two-stage robust optimization model of the multi-energy complementary virtual power plant. Figure 5 As shown, the model can include two stages: the outer decision-making stage and the inner search stage. In the outer layer, the model makes the best decisions such as purchasing / selling electricity, energy storage charging and discharging based on the known worst scenarios (such as extreme weather and equipment failure) to minimize economic losses and minimize carbon emissions in the worst case scenario; the inner layer is responsible for searching for the worst 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 electricity price mechanism. Under the constraint that the energy storage capacity is equal at the beginning and end times, the low-priced electricity at the valley end can be transferred to the peak period by using energy storage, effectively reducing the operating cost of the system.

[0112] Further, such as Figure 6As shown in the figure, demand response loads can include shiftable loads and curtailable loads. Curtailable loads can temporarily reduce some non-critical loads (such as non-continuous processes in industrial production) when the grid load is at peak, thereby alleviating grid pressure; shiftable loads can adjust the time and space distribution of loads and transfer loads during periods of relatively low demand, thereby reducing load pressure during peak periods and reducing the instability of system operation. Figure 7 As shown in the figure, through the shiftable load, the demand response load is strategically shifted to time periods with lower electricity prices and less overall consumption, specifically from 6:00 to 12:00, 20:00 to 22:00, and 24:00 to 5:00-6:00 and 16:00. This adjustment reduces the electricity cost during peak hours and relieves the pressure on system operation while maintaining various constraints.

[0113] The two-stage robust optimization model incorporates distributed energy storage and demand response loads as regulation resources, which enhances the system's ability to cope with the uncertainty of application scenarios and further improves 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 of x and y can be as follows:

[0117]

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

[0119] In the specific expressions of x and y, the optimization variable x contains two state variables, among which U M,t Indicates the state of power purchase and sale from the virtual power plant to the large power grid. When the value is 1, it means the power plant purchases power from the power grid, and when the value is 0, it means the power plant sells power to the power grid. S,t It indicates the charging and discharging state of the energy storage device. When the value is 0 / 1, it represents charging / discharging respectively. The optimization variable y contains the output level of various regulating resources in the virtual power plant during period t. FT,t is the output power of the micro-turbine at time t, P buy,tis the amount of electricity purchased by the virtual power plant from the large power grid at time t, P EC,t is the electric power consumed by EC at time t, P sell,t is the amount of electricity sold by the virtual power plant to the large power grid at time t, P PV,t is the photovoltaic output power at time t, P L,t is the power demand at time t; P ch,t represents the charging power input to the AC side of the distributed energy storage inverter during period t, P dis,t It represents the discharge power P outputted by the AC side of the distributed energy storage inverter during period t DR,t is the actual dispatch power of the virtual power plant to the demand response load during period t, P DR1,t , P DR2,t are the auxiliary variables introduced, G grid,t is the natural gas network supply at time t, G P2G,t is the gas production power of the P2G device during period t, G GT,t , G GB,t are the gas consumption power of the gas turbine and gas boiler during period t; H HB,t , H GB,t They represent the output of HB and GB at time t, respectively. L,t is the heat load at time t, H AC,t is the heat absorbed by AC at time t; T g,t and T h,t are the supply water temperature and return water temperature of the heating pipe, λ PMV is the predicted mean vote (PMV), which can describe the user's comfort level with the indoor thermal environment. n,t is the indoor temperature; Q AC,t , Q EC,t are the cooling power of AC and EC at time t, Q L,t is the total cooling power at time t, are the indoor and outdoor temperatures of the cooling building. PMV The mathematical equation can be expressed as follows:

[0120] λ PMV =(0.303e -0.036M +0.028){MW-3.05×10 -3 ×[5733-6.99(MW)-P a ]-0.42[(MW)-58.15]-1.7×10 -5 M(5867-P a )-0.0014M(34-T n )-3.96×10 -8 f c1 [(tc1 +273) 4 -(t r +273) 4 ]-f c1 h c (t c1 -T n )}(30);

[0124] In the expression, M is the energy metabolism rate of the human body, W is the corresponding mechanical power, and f c1 is the ratio of the area covered by clothing to the area not covered by clothing, h c is the surface heat transfer coefficient, T n is the air temperature around the human body, t c1 and t r are respectively the outer surface temperature of the clothing and the average radiation temperature. Since the most intuitive feeling of thermal comfort for the human body can be temperature, it is assumed that in addition to T n All other parameters are set to fixed values. In order to ensure comfort, PMV,t Can vary within a suitable range:

[0125] -σ≤λ PMV,t ≤σ(31);

[0126] like Figure 8 As shown in the figure, the PMV index has an impact on the heating system of the heating building. A smaller PMV constraint range means that the user has a higher temperature demand and needs to increase the heat input, which indicates that the heat storage capacity is reduced. Increasing the constraint range can enhance the flexibility of the heat load and improve the storage capacity, and it has a similar effect on the temperature control of the cooling system.

[0127] Step S104: for the two-stage robust optimization model of the multi-energy complementary virtual power plant, the original problem is decomposed and solved iteratively using the column and constraint generation algorithm to obtain the optimal scheduling strategy for the multi-energy complementary virtual power plant.

[0128] Specifically, in the optimization scheduling method of the multi-energy complementary virtual power plant in step S104, the original problem is decomposed by using the column and constraint generation algorithm and the iterative solution steps can be as follows: Fig. 9 As shown, the following steps are included:

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

[0130] Step S12: Based on the worst scenario Solve the main problem and get the optimal solution

[0131] Step S13: At this time Become the new Nether, Substitute into the linearized subproblem to get the optimal solution and Update the nether at the same time

[0132] Step S14: If UB-LB≤ε, the optimal solution is derived and the iteration ends; otherwise, the variable y is increased. k+1 and corresponding constraints, set k=k+1, and return to step S12;

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

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

[0135]

[0136] Where k is the current iteration number, y l is the solution to the subproblem after 1 iteration, is the value of the uncertain variable u in the worst scenario after one iteration, d and h are constant vectors, D, F, and G are the coefficient matrices of the variables under the corresponding constraints, It means "for all l that meet the conditions, they are less than or equal to k".

[0137] Specifically, the inner sub-problem of the two-stage robust optimization model can be expressed by the following formula:

[0138]

[0139] In the formula, c is the coefficient column vector 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 method, in the optimization scheduling strategy, distributed energy storage is preferentially scheduled to participate in peak regulation. If the distributed energy storage cannot complete the peak regulation instruction alone, the demand response load will participate in peak regulation.

[0141] Specifically, in the optimization scheduling method of the multi-energy complementary virtual power plant in the embodiment of the present application, the distributed energy storage has the characteristics of fast response and flexible adjustment, such as battery energy storage and supercapacitors. These energy storage devices can release the stored electric energy at the peak of the power grid load to supplement the power supply and reduce the pressure on the power generation side of the power grid; when the power grid load is low, it can absorb excess electric energy for charging to ensure the stability of the power grid. Therefore, in the optimization scheduling strategy, the distributed energy storage can be preferentially scheduled to participate in the peak regulation. When the charging and discharging capacity of the distributed energy storage cannot independently meet the peak regulation demand, the demand response load can participate in the peak regulation task as a supplementary means. The demand response load can include a shiftable load and a reducible load. The shiftable load adjusts the time and space distribution of the load to transfer the load during the period of relatively low demand, thereby reducing the load pressure during the peak period of the power grid; the reducible load can temporarily reduce some non-critical loads (such as non-continuous processes in industrial production) during the peak of the power grid load to relieve the pressure of the power grid. The above-mentioned collaborative scheduling mode improves the flexibility of the optimization scheduling strategy and improves the utilization efficiency of the power grid.

[0142] like Fig.10 , Fig.11 , Fig.12 and Fig.13 As shown in the figure, the optimal scheduling strategy of the optimization scheduling method of the multi-energy complementary virtual power plant for various energy sources in the application scenario is demonstrated. During the peak electricity consumption period (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 MG. From 12:00 to 15:00, the solar energy output reaches its peak, allowing MEVPP (Multi energy complementary virtual power plant) to sell electricity to MG and reduce costs. In order to take advantage of the lower electricity price, the demand response load is also shifted during this time period. At 17:00, the air conditioner starts to operate because the total thermal power of HB and GB is not enough to meet the thermal load demand.

[0143] Based on the above description, the following will further explain it in combination with actual application scenarios:

[0144] In this embodiment, a multi-energy complementary virtual power plant in the park is selected. The calculation data mainly refers to the demonstration project of active distribution network in Jianshan New District, Haining. The micro-gas turbine is 18MW, the photovoltaic power generation is 10MW, and the fluctuation deviation of load power and photovoltaic output is considered to be 10% and 15% of the predicted value respectively; the capacity of the electric energy storage device configured on the grid side is 2000kW·h, the unit charging and discharging cost after conversion is 0.38￥ / (kW·h), the charging and discharging efficiency is 0.95, and the maximum and minimum remaining capacities are 1800kW·h and 400kW·h respectively; the total power demand D of the demand response load in the dispatching period DR is 2940kW·h, and the unit dispatch cost is K DR Take 0.32￥ / (kW·h); use the 2023 Hangzhou residential electricity tiered electricity price as the day-ahead transaction electricity price for power exchange between the distribution network and the virtual power plant.

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

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

[0147] Step 3, calculate the economic optimum and carbon emission minimum objective functions respectively, and judge whether the termination condition is met. The criterion adopted here is that the difference between the upper and lower bounds of the current objective function is not greater than the preset convergence threshold. If it is met, stop the iteration process; otherwise, return and update the output of each operating unit in the column and constraint generation algorithm; determine the optimal solution for different objectives, and output the corresponding optimization scheduling plan.

[0148] Step 4: By solving the optimal dispatching model of the cold, heat and electricity multi-energy complementary virtual power plant, multiple groups of optimal dispatching schemes with different uncertainty adjustment parameters are obtained. The optimal solution can be selected from multiple groups of schemes according to demand. Taking the economic optimal solution as an example, the dispatching target differences shown in Table 1 below can be obtained:

[0149] Uncertain adjustment parameter Π Operating cost / yuan Carbon emissions / t <![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 The results of electricity purchase and gas purchase under different uncertainty parameters at the economic optimum are shown in Table 2:

[0151] Uncertain adjustment parameter Π Power Purchase / MWH Electricity sales / MWH Gas purchase volume / MWH <![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] It can be seen from Tables 1 and 2 that according to the optimization scheduling method of the virtual power plant provided in the embodiment of the present application, taking economic optimality as an example, the scheduling target differences of the power plant and the results of purchasing and selling electricity and gas change with the changes of uncertain adjustment parameters.

[0154] An embodiment of the present application also provides an optimization scheduling system for a virtual power plant, and the optimization scheduling system can be applied to any of the optimization scheduling methods described in the present application.

[0155] In summary, the present application provides an optimization scheduling method for a multi-energy complementary virtual power plant. Based on the constructed objective function and constraints, distributed energy storage and demand response loads are used as regulation resources, a two-stage robust optimization model is established, and an optimization scheduling strategy for the multi-energy complementary virtual power plant is obtained. Flexible scheduling of grid electricity is achieved, thereby improving the utilization efficiency of grid electricity.

[0156] It will be appreciated that the word "exemplary" as used herein means "serving as an example, instance, or illustration". Any embodiment described as "exemplary" is not necessarily preferred or superior to other embodiments and / or does not exclude the combination of features of other embodiments. It will be appreciated that certain features of the present application described in the context of separate embodiments for the sake of clarity may also be provided in a single embodiment by combination. Conversely, various features of the present application described in the context of a single embodiment for the sake of clarity may also be provided individually or in any suitable combination or as any other described embodiment of the present application.

[0157] The above disclosure is only the preferred embodiment of the present application, but it is not intended to limit the scope of rights of the present application. A person of ordinary skill in the art can understand that without departing from the spirit and scope of the present application and the appended claims, changes, modifications, substitutions, combinations, and simplifications should all be equivalent replacement methods and still fall within the scope of the invention.

[0158] The application fields of the technology of the present 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 solution of each embodiment of the present invention, it falls within the protection scope of the present invention.

Claims

1. An optimization scheduling method for a multi-energy complementary virtual power plant, characterized in that: The method comprises the following steps: Establishing an objective function of the multi-energy complementary virtual power plant optimization scheduling model, wherein the objective function includes two optimization objectives: economic optimization and carbon emission optimization; Establishing constraints for the optimal dispatch model of a multi-energy complementary virtual power plant, wherein the constraints include power supply system balance, gas supply system constraints, heating system constraints, heating network constraints, and cooling system constraints, wherein 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 of a multi-energy complementary virtual power plant is constructed. The two-stage robust optimization model introduces two types of regulation resources, namely distributed energy storage and demand response load. The outer layer of the two-stage robust optimization model searches for the best decision of purchasing / selling electricity, charging / discharging, etc. under the known worst scenario, and the inner layer searches for the worst scenario and the corresponding scheduling strategy. For the two-stage robust optimization model of the 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.

2. The optimization scheduling method according to claim 1, characterized in that: The demand response load constraint is expressed by the following formula: P DR,t -P DR*,t +P DR1,t -P DR2,t =0; P DR1,t ≥0,P DR2,t ≥0; Where P DR,t is the actual dispatch power of the virtual power plant to the demand response load during period t, D DR is the total electricity demand of the demand response load during the dispatch period, is the minimum power demand of the demand response load in period t, is the maximum electricity demand of the demand response load in period t.

3. The optimization scheduling method according to claim 2, characterized in that: The energy storage charging and discharging power constraint is expressed by the following formula: Where U S,t Indicates the charging and discharging status of the energy storage device. When the value is 0, it indicates charging, and when the value is 1, it indicates discharging; N T is the scheduling period, E S (0) is the capacity of the energy storage device at the start time, is the maximum remaining capacity of energy storage during dispatch, is the minimum remaining capacity of energy storage during dispatch, is the maximum charge and discharge power allowed by the energy storage device, P ch,t represents the charging power input to the AC side of the energy storage inverter during period t, P dis,t It represents the discharge power outputted by the AC side of the energy storage inverter during period t, and η is the charging and discharging efficiency of the energy storage unit.

4. The optimization scheduling method according to claim 3, characterized in that: The two-stage robust optimization model is expressed by the following formula: The specific expressions of x and y are as follows: In the formula, the optimization variable x contains two state variables, among which U M,t Indicates the state of power purchase and sale from the virtual power plant to the large power grid. When the value is 1, it means the power plant purchases power from the power grid, and when the value is 0, it means the power plant sells power to the power grid. S,t represents the charging and discharging state of the energy storage device. When the value is 0 / 1, it represents charging / discharging respectively. The optimization variable y contains the output level of various regulating resources in the virtual power plant during period t. GT,t is the output power of the micro-turbine at time t, P buy,t is the amount of electricity purchased by the virtual power plant from the large power grid at time t, P EC,t is the electric power consumed by EC at time t, P sell,t is the amount of electricity sold by the virtual power plant to the large power grid at time t, P PV,t is the photovoltaic output power at time t, P L,t is the power demand at time t; P ch,t represents the charging power input to the AC side of the distributed energy storage inverter during period t, P dis,t It represents the discharge power P outputted by the AC side of the distributed energy storage inverter during period t DR,t is the actual dispatch power of the virtual power plant to the demand response load during period t, P DR1,t , P DR2,t are the auxiliary variables introduced, G grid,t is the natural gas network supply at time t, G P2G,t is the gas production power of the P2G device during period t, G GT,t , G GB,t are the gas consumption power of the gas turbine and gas boiler during period t; H HB,t , H GB,t They represent the output of HB and GB at time t, respectively. L,t is the heat load at time t, H AC,t is the heat absorbed by AC at time t; T g,t and T h,t are the supply water temperature and return water temperature of the heating pipe, λ PMV is the predicted mean vote (PMV), which can describe the user's comfort level with the indoor thermal environment. n,t is the indoor temperature; Q AC,t , Q EC,t are the cooling power of AC and EC at time t, Q L,t is the total cooling power at time t, It is the indoor and outdoor temperature of the cooled building.

5. The optimization scheduling method according to claim 1, characterized in that: Decomposing the original problem and iteratively solving it by using the column and constraint generation algorithm includes the following steps: Step S11: Given the initial worst scenario and convergence threshold ε, set the number of iterations k = 1, and initialize the upper and lower bounds U0 = +∞, L0 = -∞; Step S12: According to the worst scenario Solve the main problem and get the optimal solution Step S13: Become the new Nether, Substitute into the linearized subproblem to get the optimal solution and Update the nether at the same time Step S14: If UB-LB≤ε, the optimal solution is derived and the iteration ends; otherwise, the variable y is increased. k+1 and corresponding constraints, set k=k+1, and return to step S12; Among them, LB is the lower bound of the operating cost corresponding to the final scheduling solution, and ε is the convergence threshold.

6. The optimization scheduling method according to claim 5, characterized in that: The main problem in the outer layer of the two-stage robust optimization model is expressed by the following formula: Where k is the current iteration number, y l is the solution to the subproblem after 1 iteration, is the value of the uncertain variable u in the worst scenario after one iteration, d and h are constant vectors, D, F, and G are the coefficient matrices of the variables under the corresponding constraints, It means "for all l that meet the conditions, they are less than or equal to k".

7. The optimization scheduling method according to claim 6, characterized in that: The inner sub-problem of the two-stage robust optimization model is expressed by the following formula: In the formula, c is the coefficient column vector corresponding to the objective function, y is the optimization variable, and c T y is the optimal solution of the objective function.

8. The optimization scheduling method according to claim 1, characterized in that: The demand response load includes shiftable load and curtailable load.

9. The optimization scheduling method according to claim 8, characterized in that: In the optimization scheduling strategy, distributed energy storage is preferentially scheduled to participate in peak-shaving. If the distributed energy storage cannot complete the peak-shaving instruction alone, the demand response load participates in peak-shaving.

10. An optimization scheduling system for a virtual power plant, the optimization scheduling system adopts the optimization scheduling method according to any one of claims 1 to 9.

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