Virtual power plant day-ahead optimization scheduling method considering multiple renewable energy sources

Abstract

The invention discloses a virtual power plant day-ahead optimization scheduling method considering various renewable energy sources. The method comprises the following steps: establishing a target function of renewable energy source virtual power plant day-ahead scheduling considering electric energy, heat energy and cold energy; establishing renewable energy virtual power plant operation constraints considering electric energy, heat energy and cold energy; and solving an optimal solution set of the day-ahead scheduling of the renewable energy virtual power plant considering the electric energy, the heat energy and the cold energy by adopting a wide kiss dolphin optimization algorithm BDO, and realizing the day-ahead optimal scheduling of the virtual power plant. According to the invention, the optimal day-ahead scheduling of the virtual power plant is carried out to meet the requirements of electricity, heat and cold energy; the solar micro combined power generation unit and the biomass fuel micro combined power generation unit are alternately merged into the virtual power plant; demand response is applied to electricity, heat and cold demands, so that a demand curve tends to be gentle, the system flexibility is improved, and the cost is reduced; and solving a virtual power plant scheduling problem by using a wide kiss dolphin optimizer algorithm, so that the net profit of a virtual power plant operator is maximized.

Description

Technical Field

[0001] The present application belongs to the field of electric power dispatching, and in particular, relates to a method for optimizing the day-ahead dispatching of a virtual power plant taking into account multiple renewable energy sources. Background Art

[0002] So far, the main part of energy demand comes from fossil fuels, and global greenhouse gas emissions have increased. Due to the major environmental issue of global warming, countries around the world are keen to discuss distributed power generation based on renewable energy. Wind power and photovoltaic power generation are the fastest growing technologies in renewable distributed energy; hydropower plants are also one of the main sources of renewable energy; combined heat and power is another more energy-efficient technology and micro combined heat and power is a small combined heat and power system, mainly used for power supply and heating in residential areas; plug-in electric vehicles have also gradually replaced traditional gasoline and diesel vehicles.

[0003] Since distributed power sources are usually connected near consumers, the system can benefit by reducing losses, lowering or delaying transmission costs, and improving power quality. However, the increasing penetration of distributed power sources in power systems that lack coordination and support will result in a surge in losses, poor voltage distribution, and imbalance between power generation and power demand. The introduction of virtual power plants is an effective way to overcome the problem of renewable distributed energy participating in system operation. A virtual power plant is a "power plant" consisting of a group of controllable and uncontrollable distributed power sources, energy storage, and controllable loads. These devices are controlled and coordinated to achieve the goal of minimizing costs and environmental pollution or maximizing benefits. Summary of the invention

[0004] Purpose of the invention: The present invention provides a virtual power plant day-ahead optimization scheduling method taking into account multiple renewable energy sources to improve the utilization rate of renewable energy.

[0005] Technical solution: The present invention discloses a method for optimizing the scheduling of a virtual power plant taking into account multiple renewable energy sources, which specifically includes the following steps:

[0006] (1) Establish an objective function for the day-ahead dispatch of a renewable energy virtual power plant that considers electricity, heat, and cooling energy;

[0007] (2) Establishing operating constraints for renewable energy virtual power plants that take into account electricity, heat, and cooling energy; including electricity, heat, and cooling energy balance constraints, hydropower output constraints, wind power output constraints, energy storage output constraints, electric vehicle output constraints, drainage constraints, water balance constraints, water storage constraints, micro-CHP unit output constraints, ramping constraints, and demand response constraints;

[0008] (3) The bottlenose dolphin optimization algorithm (BDO) is used to solve the optimal solution set for the day-ahead scheduling of renewable energy virtual power plants considering electricity, heat, and cooling energy, and to achieve the day-ahead optimal scheduling of virtual power plants.

[0009] Furthermore, the objective function in step (1) is:

[0010] NP=Revenue-Cost

[0011]

[0012] f it (Pw it )=Kw i ×P it +Ow it (Pw it )+Uw it (Pw it )

[0013]

[0014] Among them, NP, Revenue and Cost represent net profit, revenue and cost respectively; T represents the scheduling period; tariff elec,t , tariff ther,t and tariff cool,t are the prices of electricity, heat and cold energy at time t; P Dt , H Dt and C Dt are the predicted values ​​of electric load, heating load and cooling load at time t respectively; and are the electric power, thermal power and cooling power traded between the virtual power plant and the upstream network at time t; and are the electricity, heat and cold energy purchased by the virtual power plant from the upstream network at time t; N w , N cb and N cs They are the number of wind turbines, biomass fuel micro-combined generator sets and solar micro-combined generator sets; Indicates wind power; fcb it and fcs it Represent the cost functions of biomass fuel micro-combined power generation unit and solar micro-combined power generation unit respectively; Pcb it and Hcb it are the power and heat output of the biomass fuel micro-combined generator set at time t; Pcs it and Hcs it Respectively represent the output and heat output of the solar micro-combined generator set at time t; α bci , β bci , γ bci , δ bci , ε bci andbci are the cost coefficients of the i-th biomass fuel micro-combined power generation unit; α sci , β sci , γ sci , δ sci , ε bci and sci are the cost coefficients of the i-th solar micro-combined power generation unit; Uw it and Ow it are penalty cost and standby cost respectively; Kw i is the direct price coefficient of the i-th wind turbine; ow i and uw i are the reserve price and penalty price of the i-th wind turbine respectively; and represents the upper and lower limits of the output of the i-th wind turbine at time t; fw(y) indicates that wind power obeys the Weibull distribution.

[0015] Furthermore, the electrical, thermal and cold energy balance constraints are:

[0016] The power balance constraint is:

[0017]

[0018] Among them, N h and N EV Represent the number of small hydropower plants and electric vehicles respectively; Ph it is the output of the i-th small hydropower plant at time t; and is the charging and discharging power of the battery energy storage system at time t; and are the electrical energy used by the electric refrigeration unit and the ice storage regulator at time t respectively; is the charging power of the i-th electric vehicle at time t; when purchasing electricity from the upstream power grid, P tie,t When the power supply is positive, P tie,t is negative;

[0019] The thermal energy balance constraint is:

[0020]

[0021] in, and are the maximum heating power and maximum heat absorption power of the heat storage system respectively; Hac t represents the thermal power of the absorption chiller at time t; when purchasing thermal power from the upstream power grid, H tie,t When H is positive and provides thermal power to the upstream power grid tie,t is negative;

[0022] The cold energy balance constraint is:

[0023]

[0024] in, and They are the cooling power of the absorption chiller, electric chiller and ice storage regulator at time t; it is assumed here that when purchasing cooling energy from the upstream power grid, C tie,t When C is positive and provides cooling energy to the upstream power grid tie,t Negative; Cop ec 、Cop isc and Cop ac They are the coefficients of ice storage regulator, electric refrigeration unit and absorption chiller respectively.

[0025] Furthermore, the hydropower output constraint is:

[0026]

[0027] Among them, C 1i , C 2i , C 3i , C 4i , C 5i , C 6i is the coefficient of the i-th hydropower plant, which expresses the hydropower generation in terms of discharge and reservoir storage; Vh it and Qh it are the storage capacity and discharge capacity of the ith reservoir at time t respectively.

[0028] Furthermore, the wind power output constraint is:

[0029] P it =0,for Vf t <V in and Vf t >V out

[0030]

[0031] P it =Pwr i ,forVr t ≤Vf t <V out

[0032]

[0033] Among them, Vf t , V in , V out and VR tThey represent the predicted wind speed, cut-in wind speed, cut-out wind speed and rated wind speed at time t respectively; Pwr i is the rated power of the i-th wind turbine.

[0034] Furthermore, the energy storage output constraint is:

[0035]

[0036] (SOC) min ≤(SOC) t ≤(SOC) max

[0037]

[0038] in, and Charge and discharge power for energy storage system; and Respectively represent the charging and discharging efficiency of the battery energy storage system; (SOC) max and (SOC) min Pb is the upper and lower limits of the energy storage charge state; c,max and Pb d,max is the maximum charge and discharge power; and It is a binary variable, which indicates the state of the battery energy storage system at time t.

[0039] Furthermore, the electric vehicle output constraint is:

[0040]

[0041] in, represents the SOC of the i-th electric vehicle at time t; and are charging efficiency, discharging efficiency and total efficiency respectively; the power consumption and driving distance of the i-th electric vehicle at time t are respectively and given; in addition, and Respectively represent the maximum and minimum state of charge of the electric vehicle; and is the maximum charging and discharging power of the i-th electric vehicle; and It is a binary variable that characterizes the charging and discharging status of electric vehicles.

[0042] Furthermore, the displacement constraint is:

[0043]

[0044] in, and represents the minimum and maximum discharge of the ith reservoir;

[0045] The water balance constraint is:

[0046] V i(t+1) =Vh it +Ih it -Qh it -Sh it ,i∈N h t∈T

[0047] Among them, Ih it and Sh it are the runoff and overflow of the i-th reservoir at time t, respectively;

[0048] The water storage capacity constraint is:

[0049]

[0050] in, Represents the upper and lower limits of water storage capacity of the i-th reservoir.

[0051] Furthermore, the output constraint of the micro cogeneration unit is:

[0052]

[0053] The ramp rate constraint of the micro CHP unit is:

[0054] Pcb it -Pcb i(t-1) ≤UR bci ,i∈N cb t∈T

[0055] Pcb i(t-1) -Pcb it ≤DR bci ,i∈N cb t∈T

[0056] Pcs it -Pcs i(t-1) ≤UR sci ,i∈N cs t∈T

[0057] Pcs i(t-1) -Pcs it ≤DR sci ,i∈N cs t∈T

[0058] Among them, UR bci ,DR bcirepresents the upward / downward ramp rate limit of the i-th biomass fuel micro-combined power generation unit; UR sci ,DR sci is the up / down ramp rate limit of the i-th solar micro-combined power generation unit.

[0059] Furthermore, the implementation process of step (3) is as follows:

[0060] Use the fitness function to calculate the fitness of each dolphin and select the best dolphin as the driving dolphin:

[0061]

[0062]

[0063] Among them, They are dolphins D1, D2, ..., D P The fitness of , f represents the fitness function; represents the position of the driving dolphin at the nth moment, D q Indicates the position of the qth dolphin with the best fitness in the dolphin population;

[0064] During mud ring hunting, dolphins use the strategy random factor S according to the distance to the prey location. r Switches between the search phase and the hunting phase; S r According to the current iteration i and the total iteration I max The ratio is evaluated:

[0065]

[0066] θ is a constant; during the search, θ decreases linearly from 1 to θ min , limiting the search space to the global optimal position, the positions of the driving dolphin and the following dolphin are updated as:

[0067]

[0068] in, Indicates the updated position of the driver Dolphin. Indicates the position of the driving dolphin, rand is a random number between [-1,1], which helps to disperse the search ability; Indicates the updated position of the following dolphin, Indicates the current position of the dolphin. f is the acceleration factor that moves the following dolphin toward the driving dolphin, which can be adjusted according to the problem;

[0069] The motion of following the dolphin around the position of other prey is:

[0070]

[0071] In the hunting phase, in order to improve the efficiency of obtaining the global optimal solution, θ is changed from θ min Reduced to 0, the mathematical model of the driving dolphin and the following dolphin in this process is:

[0072]

[0073] Among them, r p for And P p equal r i is the rank of the i-th dolphin, and T is the total number of dolphins excluding the driving dolphin.

[0074] Beneficial effects: Compared with the prior art, the beneficial effects of the present invention are as follows: the present invention performs optimal day-ahead scheduling of virtual power plants to meet the demands for electricity, heat and cooling; alternately incorporates solar micro-combined power generation units and biomass fuel micro-combined power generation units into virtual power plants; applies demand response to electricity, heat and cooling demands to flatten the demand curve, improve system flexibility and reduce costs; utilizes bottlenose dolphin optimizer (BDO) to solve the virtual power plant scheduling problem and maximize the net profit of the virtual power plant operator. BRIEF DESCRIPTION OF THE DRAWINGS

[0075] Figure 1 It is a schematic diagram of the structure of a virtual power plant;

[0076] Figure 2 It is a schematic diagram of the feasible region of the micro CHP unit;

[0077] Figure 3 It is the flowchart of the bottlenose dolphin optimization algorithm;

[0078] Figure 4 It is a schematic diagram of the convergence characteristics of three different algorithms;

[0079] Figure 5 This is a schematic diagram of the net equivalent power generation of a virtual power plant obtained using three different algorithms. DETAILED DESCRIPTION

[0080] The present invention is further described in detail below based on the accompanying drawings.

[0081] The present invention provides a method for optimizing the scheduling of a virtual power plant by taking into account multiple renewable energy sources, which specifically includes the following steps:

[0082] Step 1: Build Figure 1 The virtual power plant shown in the figure establishes the objective function of the day-ahead dispatch of the renewable energy virtual power plant considering electricity, heat and cooling energy. The objective function is:

[0083] NP=Revenue-Cost

[0084]

[0085]

[0086] f it (Pw it )=Kw i ×P it +Ow it (Pw it )+Uw it (Pw it )

[0087]

[0088] Among them, NP, Revenue and Cost represent net profit, revenue and cost respectively; T represents the scheduling period; tariff elec,t , tariff ther,t and tariff cool,t are the prices of electricity, heat and cold energy at time t; P Dt , H Dt and C Dt are the predicted values ​​of electric load, heating load and cooling load at time t respectively; and are the electric power, thermal power and cooling power traded between the virtual power plant and the upstream network at time t; and are the electricity, heat and cold energy purchased by the virtual power plant from the upstream network at time t; N w , N cb and N cs are the number of wind turbines, biomass fuel micro-combined generator sets and solar micro-combined generator sets; Pw it Indicates wind power; fcb it and fcs it Represent the cost functions of biomass fuel micro-combined power generation unit and solar micro-combined power generation unit respectively; Pcb it and Hcb it are the power and heat output of the biomass fuel micro-combined generator set at time t; Pcs it and Hcs it Respectively represent the output and heat output of the solar micro-combined generator set at time t; α bci , β bci , γ bci , δ bci , ε bci and bciare the cost coefficients of the i-th biomass fuel micro-combined power generation unit; α sci , β sci , γ sci , δ sci , ε bci and sci are the cost coefficients of the i-th solar micro-combined power generation unit; Uw it and Ow it are penalty cost and standby cost respectively; Kw i is the direct price coefficient of the i-th wind turbine; ow i and uw i are the reserve price and penalty price of the i-th wind turbine respectively; and represents the upper and lower limits of the output of the i-th wind turbine at time t; fw(y) indicates that wind power follows the Weibull distribution. The direct cost coefficient of each wind turbine (K w ), reserve cost (ow i ) and penalty cost (uw i ) are 0.07, 0.02 and 0.01 respectively; the prices of electricity, heat and cold are shown in Table 1:

[0089] Table 1 Prices of electricity, heat and cold energy

[0090]

[0091] Step 2: Establish the day-ahead dispatch constraints of the renewable energy virtual power plant considering electricity, heat and cooling energy, including:

[0092] 1) Establish electrical energy, thermal energy and cooling energy balance constraints.

[0093] The power balance constraint is:

[0094]

[0095] Among them, N h and N EV Represent the number of small hydropower plants and electric vehicles respectively; Ph it is the output of the i-th small hydropower plant at time t; and is the charging and discharging power of the battery energy storage system at time t; and are the electrical energy used by the electric refrigeration unit and the ice storage regulator at time t respectively; is the charging power of the i-th electric vehicle at time t. Here, it is assumed that when purchasing electricity from the upstream power grid, P tie,t When the power supply is positive, P tie,t is negative.

[0096] The thermal energy balance constraint is:

[0097]

[0098] in, and are the maximum heating power and maximum heat absorption power of the heat storage system respectively; Hac t represents the thermal power of the absorption chiller at time t; when purchasing thermal power from the upstream power grid, H tie,t When H is positive and provides thermal power to the upstream power grid tie,t is negative.

[0099] The cold energy balance constraint is:

[0100]

[0101] in, and They are the cooling power of the absorption chiller, electric chiller and ice storage regulator at time t; it is assumed here that when purchasing cooling energy from the upstream power grid, C tie,t When C is positive and provides cooling energy to the upstream power grid tie,t Negative; Cop ec 、Cop isc and Cop ac They are the coefficients of ice storage regulator, electric refrigeration unit and absorption chiller respectively.

[0102] 2) Establish hydropower output constraints, the mathematical expression is as follows:

[0103]

[0104] Among them, C 1i , C 2i , C 3i , C 4i , C 5i , C 6i is the coefficient of the i-th hydropower plant, which expresses the hydropower generation in terms of discharge and reservoir storage; Vh it and Qh it are the storage and discharge of the i-th reservoir at time t. The hydropower output coefficient is shown in Table 2:

[0105] Table 2 Hydropower output coefficient

[0106]

[0107] 3) Establish wind power output constraints, the mathematical expression is as follows:

[0108] P it =0,for Vf t <V inand Vf t >V out

[0109]

[0110] P it =Pwr i ,for VR t ≤Vf t <V out

[0111]

[0112] Among them, Vf t , V in , V out and VR t They represent the predicted wind speed, cut-in wind speed, cut-out wind speed and rated wind speed at time t respectively; Pwr i is the rated power of the i-th wind turbine. Cut-in wind speed v in =5m / s, cut-out wind speed v o =25m / s, rated wind speed v r =15m / s; the rated power of wind turbines is P w1 =600KW and P w2 =400KW.

[0113] 4) Establish energy storage output constraints, the mathematical expression is as follows:

[0114]

[0115] (SOC) min ≤(SOC) t ≤(SOC) max

[0116]

[0117] in, and Charge and discharge power for energy storage system; and Respectively represent the charging and discharging efficiency of the battery energy storage system; (SOC) max and (SOC) min Pb is the upper and lower limits of the energy storage charge state; c,max and Pb d,max is the maximum charge and discharge power; and It is a binary variable, indicating the state of the battery energy storage system at time t. The installed capacity of the battery energy storage system is 300KW.

[0118] 5) Establish electric vehicle output constraints:

[0119]

[0120] in, represents the SOC of the i-th electric vehicle at time t; and are charging efficiency, discharging efficiency and total efficiency respectively; the power consumption and driving distance of the i-th electric vehicle at time t are respectively and given; in addition, and Respectively represent the maximum and minimum state of charge of the electric vehicle; and is the maximum charging and discharging power of the i-th electric vehicle; and It is a binary variable that characterizes the charging and discharging status of electric vehicles.

[0121] 6) Establish water discharge constraints, water balance constraints and water storage constraints:

[0122]

[0123] in, and represents the minimum and maximum discharge of the ith reservoir.

[0124] The water balance constraint is:

[0125] V i(t+1) =Vh it +Ih it -Qh it -Sh it ,i∈N h t∈T

[0126] Among them, Ih it and Sh it are the runoff and overflow of the ith reservoir at time t respectively.

[0127] The water storage capacity constraint is:

[0128]

[0129] in, Represents the upper and lower limits of water storage capacity of the i-th reservoir.

[0130] 7) Establish output constraints of micro CHP units and ramp rate constraints of micro CHP units:

[0131]

[0132]

[0133] The ramp rate constraint of the micro CHP unit is:

[0134] Pcb it -Pcb i(t-1) ≤UR bci ,i∈N cb t∈T

[0135] Pcb i(t-1) -Pcb it ≤DR bci ,i∈N cb t∈T

[0136] Pcs it ∈Pcs i(t-1) ≤UR sci ,i∈N cs t∈T

[0137] Pcs i(t-1) -Pcs it ≤DR sci ,i∈N cs t∈T

[0138] Among them, UR bci ,DR bci represents the upward / downward ramp rate limit of the i-th biomass fuel micro-combined power generation unit; UR sci ,DR sci is the up / down ramp rate limit of the i-th solar micro-combined power generation unit.

[0139] Step 3: Use the bottlenose dolphin optimization algorithm (BDO) to obtain the optimal solution set.

[0140] The bottlenose dolphin optimization algorithm imitates the mud ring feeding behavior of bottlenose dolphins. Dolphins search for the location of prey. Once the location of the prey is determined, the dolphin in the best position among all dolphins is called the driving dolphin and begins to surround the location of the prey. When the driving dolphin surrounds the prey, other dolphins follow it. The dolphin following the driving dolphin is called the following dolphin. In order to select a driving dolphin from the initialized population, the fitness function is used to calculate the fitness of each dolphin and select the best dolphin as the driving dolphin, as shown in formula (1):

[0141]

[0142]

[0143] Among them, They are dolphins D1, D2, ..., D P The fitness of , f represents the fitness function; represents the position of the driving dolphin at the nth moment, D q Indicates the position of the qth dolphin with the best fitness in the dolphin population.

[0144] During mud ring hunting, dolphins switch between the search phase and the hunting phase depending on the distance to the prey location. The selection of these phases is done using the strategy random factor S r To complete. r According to the current iteration i and the total iteration (I max ) is evaluated and expressed mathematically as follows:

[0145]

[0146] θ is a constant. During the search, θ decreases linearly from 1 to θ min , limiting the search space to the prey position (global optimal position), as shown in formula (4); the movement of the driving dolphin and the following dolphin (position update) in this stage is mathematically expressed as follows:

[0147]

[0148]

[0149]

[0150] in, Indicates the updated position of the driver Dolphin. Indicates the position of the driving dolphin, rand is a random number between [-1,1], which helps to disperse the search ability; Indicates the updated position of the following dolphin, Indicates the current position of the dolphin. f is the acceleration factor that moves the following dolphin toward the driving dolphin, and can be adjusted according to the problem.

[0151] Furthermore, the following dolphins around other prey locations follow a similar encirclement strategy as the driving dolphin. In this case, the current position of the following dolphin becomes the position of the prey, and a better optimal solution is sought at this position. The movement of the following dolphin around other prey locations is mathematically represented as follows:

[0152]

[0153] In the hunting phase, in order to improve the efficiency of obtaining the global optimal solution, θ is changed from θ min Reduced to 0, as shown in formula (8), the mathematical model of the driving dolphin and the following dolphin in this process is as follows:

[0154]

[0155]

[0156]

[0157] Among them, r p for And P p equal r i is the rank of the i-th dolphin, and T is the total number of dolphins excluding the driving dolphin.

[0158] like Figure 3 As shown, the corresponding algorithm flow is as follows:

[0159] S1: Initialize the number of dolphins and parameters S rf , a f ,θ min , I max , set i=1;

[0160] S2: Calculate the fitness of each dolphin, select the dolphin with the best fitness as the driving dolphin, and update S r (i);

[0161] S3: If S r (i)<S rf , then go to S4, otherwise go to S7;

[0162] S4: Update θ(i) and p according to equation (4), and use equation (5) to update the position of the driving dolphin; if p < 0.5, go to S5, otherwise go to S6;

[0163] S5: Update the position of the following dolphin according to formula (6);

[0164] S6: Update the position of the following dolphin according to formula (7);

[0165] S7: Update θ(i) according to equations (8) to (10), respectively, and the positions of the driving dolphin and the following dolphin;

[0166] S8: Check whether the solution is within the search range and modify it;

[0167] S9: If i<I max , then i=i+1, go to S2; otherwise, return to the position of driving the dolphin, which is the optimal solution.

[0168] This embodiment constructs a virtual power plant of renewable energy that considers electricity, heat, and cold energy, including 2 small hydropower plants, 2 wind turbines, 3 solar micro-combined generator sets, 3 biomass fuel micro-combined generator sets, 1 battery energy storage system, 15 electric vehicles, 1 electric refrigeration unit, 1 absorption refrigeration machine, 1 ice storage regulator, and 1 heat storage system for analysis. According to the present invention, a virtual power plant of renewable energy that considers the synergy of multiple energy sources is optimized and dispatched. Through BDO (parameter selection N p =100,a f =3,S r =0.8,θ min =0.2, the maximum number of iterations is selected as 100), the maximum, average and minimum values ​​and the average running time of the 100 solutions obtained by HPSO-TVAC and GWO are shown in Table 3.

[0169] Table 3 Performance comparison of three algorithms

[0170]

[0171] The convergence characteristics of the three algorithms are as follows: Figure 4 The net equivalent power generation obtained by the three algorithms is shown in Figure 5 As shown. It can be seen that the maximum net equivalent power generation obtained from BDO is the highest among all three algorithms. The maximum net equivalent power generation obtained from BDO is about 5.25% higher than the maximum net equivalent power generation obtained from HPSO-TVAC and about 9.82% higher than the maximum net equivalent power generation obtained from GWO.

[0172] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A method for optimizing the scheduling of virtual power plants considering multiple renewable energy sources, characterized in that: The following steps are involved: (1) Establish an objective function for the day-ahead dispatch of a renewable energy virtual power plant that considers electricity, heat, and cooling energy; (2) Establishing operating constraints for renewable energy virtual power plants that take into account electricity, heat, and cooling energy; including electricity, heat, and cooling energy balance constraints, hydropower output constraints, wind power output constraints, energy storage output constraints, electric vehicle output constraints, drainage constraints, water balance constraints, water storage constraints, micro-CHP unit output constraints, ramping constraints, and demand response constraints; (3) The bottlenose dolphin optimization algorithm (BDO) is used to solve the optimal solution set for the day-ahead scheduling of renewable energy virtual power plants considering electricity, heat, and cooling energy, and to achieve the day-ahead optimal scheduling of virtual power plants.

2. A method for optimizing the day-ahead scheduling of a virtual power plant considering multiple renewable energy sources according to claim 1, characterized in that: The objective function in step (1) is: NP=Revenue-Cost fw it (Pw it )=Kw i ×Pw it ×Ow it (Pw it )+Your it (Pw it ) Among them, NP, Revenue and Cost represent net profit, revenue and cost respectively; T represents the scheduling period; tariff elec,t , tariff ther,t and tariff cool,t are the prices of electricity, heat and cold energy at time t; P Dt , H Dt and C Dt are the predicted values ​​of electric load, heating load and cooling load at time t respectively; and are the electric power, thermal power and cooling power traded between the virtual power plant and the upstream network at time t; and are the electricity, heat and cold energy purchased by the virtual power plant from the upstream network at time t; N w , N cb and N cs are the number of wind turbines, biomass fuel micro-combined generator sets and solar micro-combined generator sets; Pw it Indicates wind power; fcb it and fcs it Represent the cost functions of biomass fuel micro-combined power generation unit and solar micro-combined power generation unit respectively; Pcb it and Hcb it are the power and heat output of the biomass fuel micro-combined generator set at time t; Pcs it and Hcs it Respectively represent the output and heat output of the solar micro-combined generator set at time t; α bci , β bci , γ bci , δ bci , ε bci and bci are the cost coefficients of the i-th biomass fuel micro-combined power generation unit; α sci , β sci , γ sci , δ sci , ε bci and sci are the cost coefficients of the i-th solar micro-combined power generation unit; Uw it and Ow it are penalty cost and standby cost respectively; Kw i is the direct price coefficient of the i-th wind turbine; ow i and uw i are the reserve price and penalty price of the i-th wind turbine respectively; and represents the upper and lower limits of the output of the i-th wind turbine at time t; fw(y) indicates that wind power obeys the Weibull distribution.

3. The method for optimizing the scheduling of a virtual power plant taking into account multiple renewable energy sources according to claim 1, characterized in that: The electrical, thermal and cooling energy balance constraints are: The power balance constraint is: Among them, N h and N EV Represent the number of small hydropower plants and electric vehicles respectively; Ph it is the output of the i-th small hydropower plant at time t; and is the charging and discharging power of the battery energy storage system at time t; P t ec and P t isc are the electrical energy used by the electric refrigeration unit and the ice storage regulator at time t respectively; is the charging power of the i-th electric vehicle at time t; when purchasing electricity from the upstream power grid, P tie,t When the power supply is positive, P tie,t is negative; The thermal energy balance constraint is: in, and are the maximum heating power and maximum heat absorption power of the heat storage system respectively; Hac t represents the thermal power of the absorption chiller at time t; when purchasing thermal power from the upstream power grid, H tie,t When H is positive and provides thermal power to the upstream power grid tie,t is negative; The cold energy balance constraint is: in, and They are the cooling power of the absorption chiller, electric chiller and ice storage regulator at time t; it is assumed here that when purchasing cooling energy from the upstream power grid, C tie,t When C is positive and provides cooling energy to the upstream power grid tie,t Negative; Cop ec 、Cop isc and Cop ac They are the coefficients of ice storage regulator, electric refrigeration unit and absorption chiller respectively.

4. The method for optimizing the scheduling of a virtual power plant taking into account multiple renewable energy sources according to claim 1, characterized in that: The hydropower output constraints are: Among them, C 1i , C 2i , C 3i , C 4i , C 5i , C 6i is the coefficient of the i-th hydropower plant, which expresses the hydropower generation in terms of discharge and reservoir storage; Vh it and Qh it are the storage capacity and discharge capacity of the ith reservoir at time t respectively.

5. The method for optimizing the scheduling of a virtual power plant taking into account multiple renewable energy sources according to claim 1, characterized in that: The wind power output constraint is: Pw it =0,forVf t <V in andVf t >V out Pw it =Pwr i ,forVr t ≤Vf t <V out Among them, Vf t , V in , V out and VR t They represent the predicted wind speed, cut-in wind speed, cut-out wind speed and rated wind speed at time t respectively; Pwr i is the rated power of the i-th wind turbine.

6. The method for optimizing the day-ahead scheduling of a virtual power plant considering multiple renewable energy sources according to claim 1, characterized in that: The energy storage output constraint is: (SOC) min ≤(SOC) t ≤(SOC) max in, and Charge and discharge power for energy storage system; and Respectively represent the charging and discharging efficiency of the battery energy storage system; (SOC) max and (SOC) min Pb is the upper and lower limits of the energy storage charge state; c,max and Pb d,max is the maximum charge and discharge power; and It is a binary variable, which indicates the state of the battery energy storage system at time t.

7. The method for optimizing the day-ahead scheduling of a virtual power plant considering multiple renewable energy sources according to claim 1, characterized in that: The electric vehicle output constraint is: in, represents the SOC of the i-th electric vehicle at time t; and are charging efficiency, discharging efficiency and total efficiency respectively; the power consumption and driving distance of the i-th electric vehicle at time t are respectively and given; in addition, and Respectively represent the maximum and minimum state of charge of the electric vehicle; and is the maximum charging and discharging power of the i-th electric vehicle; and It is a binary variable that characterizes the charging and discharging status of electric vehicles.

8. The method for optimizing the day-ahead scheduling of a virtual power plant considering multiple renewable energy sources according to claim 1, characterized in that: The displacement constraint is: in, and represents the minimum and maximum discharge of the ith reservoir; The water balance constraint is: Vh i(t+1) =Vh it +Ih it -Qh it -Sh it ,i∈N h t∈T Among them, Ih it and Sh it are the runoff and overflow of the i-th reservoir at time t, respectively; The water storage capacity constraint is: in, Represents the upper and lower limits of water storage capacity of the ith reservoir.

9. The method for optimizing the day-ahead scheduling of a virtual power plant considering multiple renewable energy sources according to claim 1, characterized in that: The output constraint of the micro CHP unit is: The ramp rate constraint of the micro CHP unit is: PCB it -PCB i(t-1) ≤UR bci ,i∈N cb t∈T PCB i(t-1) -PCB it ≤DR bci ,i∈N cb t∈T Pcs it -Pcs i(t-1) ≤UR sci ,i∈N cs t∈T Pcs i(t-1) -Pcs it ≤DR sci ,i∈N cs t∈T Among them, UR bci ,DR bci represents the upward / downward ramp rate limit of the i-th biomass fuel micro-combined power generation unit; UR sci ,DR sci is the up / down ramp rate limit of the i-th solar micro-combined power generation unit.

10. The method for optimizing the day-ahead scheduling of a virtual power plant considering multiple renewable energy sources according to claim 1, characterized in that: The implementation process of step (3) is as follows: Use the fitness function to calculate the fitness of each dolphin and select the best dolphin as the driving dolphin: Among them, They are dolphins D1, D2, ..., D P The fitness of, f represents the fitness function; represents the position of the driving dolphin at the nth moment, D q Indicates the position of the qth dolphin with the best fitness in the dolphin population; During mud ring hunting, dolphins use the strategy random factor S according to the distance to the prey location. r Switches between the search phase and the hunting phase; S r According to the current iteration i and the total iteration I max The ratio is evaluated: θ is a constant; during the search, θ decreases linearly from 1 to θ min , limiting the search space to the global optimal position, the positions of the driving dolphin and the following dolphin are updated as: in, Indicates the updated position of the driver Dolphin. Indicates the position of the driving dolphin, rand is a random number between [-1,1], which helps to disperse the search ability; Indicates the updated position of the following dolphin, Indicates the current position of the dolphin. f is the acceleration factor that moves the following dolphin toward the driving dolphin, which can be adjusted according to the problem; The motion of following the dolphin around the position of other prey is: In the hunting phase, in order to improve the efficiency of obtaining the global optimal solution, θ is changed from θ min Reduced to 0, the mathematical model of the driving dolphin and the following dolphin in this process is: Among them, r p for And P p equal r i is the rank of the i-th dolphin, and T is the total number of dolphins excluding the driving dolphin.

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