CN117196173A - Virtual power plant distributed scheduling method considering operation risk and network transmission - Google Patents
Virtual power plant distributed scheduling method considering operation risk and network transmission Download PDFInfo
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Abstract
The invention discloses a virtual power plant distributed scheduling method considering operation risk and network transmission, which comprises the following steps: 1) Establishing an objective function of a virtual power plant distributed scheduling model considering operation risk and network transmission; 2) Establishing constraint conditions of a virtual power plant distributed scheduling model considering operation risks and network transmission; 3) Solving a virtual power plant distributed scheduling model considering the running risk and network transmission to obtain a virtual power plant distributed scheduling decision; 4) Calculating the risk loss cost of the virtual power plant on the basis of the step (3); 5) And establishing a virtual power plant profit distribution model based on generalized Nash bargaining, and realizing profit distribution of virtual power plant distributed scheduling. According to the method, the transmission constraint of the power distribution network is considered, so that the virtual power plant is guided to change the scheduling strategy, and the transmission loss of the power distribution network is reduced; according to the method, the operation risk of the virtual power plant is considered, so that the obtained virtual power plant scheduling strategy can avoid possible risk loss as much as possible.
Description
Technical Field
The invention belongs to the field of distributed transaction of virtual power plants, and particularly relates to a distributed scheduling method of a virtual power plant, which considers operation risks and network transmission.
Background
The rapid development of high-proportion distributed resources advances the construction of novel electric energy systems. However, the distributed resources have the characteristics of dispersion and small capacity, and the difficulty in regulating and controlling the electric energy system is increased. Therefore, the novel electric energy main body of the virtual power plant is generated, distributed resources such as user side power generation, energy storage, flexible load and the like are aggregated, and the functions of data analysis, prediction, decision optimization and the like are realized through unified regulation and control of an internal electric energy management system. By aggregating the distributed resources to stabilize the random fluctuation of the renewable energy output, the flexibility and schedulability of the distributed resources at the user side are improved, and the response to price signals is more sensitive. Compared with the traditional consumers, the virtual power plant has more diversified sources of electric energy supply, the willingness to participate in electric energy transaction is enhanced, besides the electric energy transaction is participated in to meet the internal electricity demand, the virtual power plant can also participate in auxiliary service transaction, and scheduling flexibility is fully exerted. Therefore, a suitable virtual power plant distributed scheduling and transaction mechanism needs to be formulated, so that the contradiction between supply and demand at the power supply side and the user side is effectively relieved.
In the virtual power plant scheduling, if network constraint is not considered, in order to ensure system safety, a transaction strategy is not necessarily realized; meanwhile, the output of renewable energy sources in the virtual power plant is affected by environmental factors such as illumination, weather and the like, and the output has uncertainty. Therefore, it is critical to consider operational risk and network transmissions in virtual power plant scheduling. In addition, the adjacent virtual power plants in the domain share electric energy by forming the alliance, so that the transaction amount with operators is reduced, and the alliance transaction cost is reduced. However, how to fairly distribute the alliance revenue, attracting more virtual power plants to join the alliance, is still a core problem, and how to improve the fairness of revenue distribution still needs further research.
Disclosure of Invention
The technical scheme is as follows: in order to solve the technical problems mentioned in the background art, the invention provides a virtual power plant distributed scheduling method considering operation risk and network transmission, which comprises the following steps:
(1) Establishing an objective function of a virtual power plant distributed scheduling model considering operation risk and network transmission;
(2) Establishing constraint conditions of a virtual power plant distributed scheduling model considering operation risks and network transmission;
(3) Solving a virtual power plant distributed scheduling model considering the running risk and network transmission to obtain a virtual power plant distributed scheduling decision;
(4) Calculating the risk loss cost of the virtual power plant on the basis of the step (3);
(5) And establishing a virtual power plant profit distribution model based on generalized Nash bargaining, and realizing profit distribution of virtual power plant distributed scheduling.
Further, in step (1), an objective function of a virtual power plant distributed scheduling model is established, which considers the operation risk and the network transmission:
wherein,
wherein i is the number of the virtual power plant; t is the scheduling period number; n (N) i Is the total number of virtual power plants; t is the total time period number; k (k) i The degree of deviation predicted value of the renewable energy output in the virtual power plant i;predicted output of renewable energy sources in the virtual power plant i at the t period;The actual output of renewable energy sources in the virtual power plant i is t time periods; c (C) obj Objective function values for deterministic models; beta is the deviation ratio of a specified objective function; c (C) DSO The network power consumption cost is;The transaction cost of the electric energy, the carbon emission and the standby service of the virtual power plant i in the t period is respectively calculated;The loss cost of the battery caused by energy storage charge and discharge in the virtual power plant i in the t period;The cost of user comfort loss in the virtual power plant i is generated by adjusting a central air conditioner and a flexible load;And (5) the running cost of the fuel cell in the i-type fuel cell of the virtual power plant is t.
Further, in the step (2), constraint conditions of establishing a virtual power plant multi-variety resource distributed scheduling model considering operation risk and network transmission are as follows:
(201) Establishing a calculation formula of target cost, wherein the calculation formula comprises a network electric energy loss cost, a transaction cost, an energy storage loss cost, a user comfort loss cost and a fuel cell operation cost calculation formula;
a) Network power consumption cost:
wherein B is S The method comprises the steps of collecting system branches; mn is the branch resistance number between nodes m and n; r is (r) mn A branch resistance between nodes m and n; l (L) mn,t The square of the current amplitude on the branch between nodes m and n at time t;the price coefficient of the network electric energy loss is t time period;
b) Transaction cost:
in the method, in the process of the invention,and->The prices of purchase and sale of electric energy in the period t respectively;And->The electric energy purchased and sold by the virtual power plant i in the period t respectively;And->Purchase and sale prices for the carbon emissions at the t period, respectively;And->Respectively t timesThe carbon emission amount purchased and sold by the section virtual power plant i from the carbon trading platform;And->Purchase and sale prices for the standby service for period t, respectively;And->Spare capacities purchased and sold for the t-period virtual power plant i, respectively;
c) Energy storage loss cost:
in the method, in the process of the invention,and->Respectively, the charge and discharge quantity of energy stored in the virtual power plant i at t time intervals, < >>And->Respectively charging and discharging dissipation coefficients of energy stored in the virtual power plant i;
d) User comfort loss cost:
wherein ρ anda user discomfort factor;The indoor temperature of the user in the virtual power plant i is t time periods; t (T) i ref The user body feeling most comfortable temperature in the virtual power plant i;The flexible load value of the user in the virtual power plant i at the t period is set;The load reference value of the user in the virtual power plant i at the t period;
e) Fuel cell operating cost:
in the method, in the process of the invention,the unit power generation cost of the fuel cell in the virtual power plant i is set;Generating power of the fuel cell in the virtual power plant i at the t period;
(202) Establishing a safe operation constraint of the power distribution network after the virtual power plant is accessed:
wherein m, n and k are the system node numbers; n (N) S Is a system node set;and->Active power and reactive power of the root node at t time intervals respectively;Is a system power factor; p (P) i∈m,t Active power and reactive power of the node i in the t period; p (P) mn,t And Q mn,t Active power and reactive power on the branch mn of the t period are respectively; p (P) km,t And Q km,t Active power and reactive power on the branch km of the t period are respectively; f (F) m A set of end nodes which are branches taking node m as a head end node; t (T) m A head end node set which is a branch taking a node m as an end node; r is (r) mn The resistance is branch mn; x is x mn Reactance for branch mn; r is (r) km The resistance is a branch km; x is x km Reactance is a branch km; l (L) mn,t The square of the current amplitude on branch mn at time t; l (L) km,t The square of the current amplitude on the branch km at the t time interval; v (V) n,t The square of the voltage amplitude on node n at time t; v (V) m,t The square of the voltage amplitude on node m for the period t;And->Respectively the minimum value and the maximum value of the square of the voltage amplitude of the node m;For the current amplitude on branch mnA maximum value of the square;
(203) Establishing balance constraint of electric energy, carbon emission and standby capacity of a virtual power plant:
wherein: p (P) i,t 、E i,t 、R i,t The method comprises the steps that electric energy, carbon emission and standby capacity of a virtual power plant i and other virtual power plants in t time periods are respectively traded;the refrigeration power of a central air conditioner in the virtual power plant i in the t period;Carbon emission allowance for the fuel cell in the virtual power plant i; g i,t The method comprises the steps of reducing the discharge capacity of a photovoltaic nuclear certificate in a virtual power plant i under a scene of a t period s; f (F) i,t The carbon emission of the fuel cell in the virtual power plant i is t time periods;The reserve capacity provided by adjusting the flexible load in the virtual power plant i for the period t;The standby capacity provided for the central air conditioner in the virtual power plant i in the t period;Spare capacity provided for fuel cells in the t-period virtual power plant i;Standby requirement for t-period virtual power plant iCalculating the quantity;
(204) Establishing element electric energy, standby and carbon emission constraints in a virtual power plant:
a) Central air conditioning electric energy and standby constraint
Wherein alpha is i,t 、β i 、γ i Parameters describing building cold accumulation characteristics and weather conditions in the virtual power plant i are related to building characteristics and outdoor temperature; sigma (sigma) i The energy efficiency ratio of the central air conditioning water chiller in the virtual power plant i is set; t (T) in,min And T in,max The minimum indoor temperature and the maximum indoor temperature acceptable to the user are respectively;providing standby indoor temperature for a central air conditioner in the virtual power plant i in the t-1 period;Providing standby indoor temperature for a central air conditioner in the virtual power plant i at the t period;
b) Power and backup constraints for flexible loads:
wherein:and->Respectively adjusting the minimum load and the maximum load of the flexible load of the virtual power plant i in the t period;
c) Power, standby and carbon emission constraints of fuel cells:
wherein P is i max The maximum power of the fuel cell in the virtual power plant i;and->Minimum reserve capacity and maximum reserve capacity available for the fuel cells in the virtual power plant i, respectively;The standby capacity provided for the fuel cell in the virtual power plant i under the scene of t time period s;The power generation power of the fuel cell in the i-type power plant is virtual for the t-1 period; r is (r) i u And r i d The upward regulating quantity and the downward regulating quantity of the fuel cell in the adjacent time period are used for the virtual power plant i; upsilon (v) i The carbon emission intensity of the unit output of the fuel cell in the virtual power plant i;
d) Stored energy power and backup constraints:
wherein P is i c,max And P i d,max Respectively storing maximum charging power and maximum discharging power of energy in the virtual power plant i;and->The charge and discharge quantity of the spare capacity in the virtual power plant i at the t period are respectively; s is S i,t-1 The energy storage capacity of energy stored in the virtual power plant i at the t-1 period; s is S i,t The energy storage capacity of energy stored in the virtual power plant i at the t period;And->Respectively storing the minimum electricity storage quantity and the maximum electricity storage quantity of the energy in the virtual power plant i;And->Respectively the charging efficiency and the discharging efficiency of energy storage in the virtual power plant i;
(205) Establishing a virtual power plant distributed transaction constraint:
wherein: p (P) i,t ＜0、E i,t ＜0、R i,t < 0 respectively represents the selling of electric energy, carbon emission and spare capacity of the virtual power plant i to other virtual power plants in the period of t, P i,t ＞0、E i,t ＞0、R i,t Each of the virtual power plants i in the period of t is more than 0 to purchase electric energy, carbon emission and spare capacity from other virtual power plants, P i,t ＝0、E i,t ＝0、R i,t =0 indicates that the virtual power plant i does not conduct distributed transactions for the t period, respectively.
Further, in step (4), on the basis of step (3), the risk loss cost of the virtual power plant is calculated, as follows:
in the method, in the process of the invention,virtual power plant risk cost for t period;Compensating the price for the generated energy of the t period; omega is a risk coefficient; omega > 1, & lt + & gt>The electric energy purchase price of the virtual power plant is t time periods; n is n s The number of the photovoltaic scenes is the number; p (P) t risk The total loss load of the virtual power plant in the t period;The actual output of renewable energy sources under the scene s is the virtual power plant i in the period t;The amount of load loss in the scene s for the virtual power plant i for period t.
Further, the specific process of step (5) is as follows:
(501) Establishing a virtual power plant profit allocation model based on generalized Nash bargaining:
wherein:
in the method, in the process of the invention,trading costs for virtual power plant i;Adding distributed transaction costs for other virtual power plants serving as transaction objects for the virtual power plant i;The distributed transaction revenue obtained for the virtual power plant i; θ i The competitive coefficient of the virtual power plant i;increased network transmission costs for virtual power plant i distributed transactions;The network transmission cost of the virtual power plant i;Network transmission cost for distributed transaction with other virtual power plants as transaction objects is added for the virtual power plant i;
(502) The virtual power plant profit allocation model objective function based on generalized Nash bargaining is converted by adopting a logarithmic and negative number taking method to obtain the virtual power plant profit allocation model objective function based on generalized Nash bargaining:
in delta i Cost saving is achieved after the virtual power plant i participates in the distributed transaction;
(502) Solving (44) by using a Lagrange multiplier method to obtain a corresponding Lagrange equation:
wherein λ is a dual variable;
(503) Obtaining a first-order partial derivative of (45):
(504) Will beAnd->Substitution (46), yield:
(505) Obtaining the profit allocation cost of the distributed scheduling of the virtual power plants:
the beneficial effects are that: compared with the prior art, the technical scheme of the invention has the following beneficial technical effects:
according to the method, the transmission constraint of the power distribution network is considered, so that the virtual power plant is guided to change the scheduling strategy, and the transmission loss of the power distribution network is reduced; the method considers the running risk of the virtual power plant, so that the obtained virtual power plant scheduling strategy can avoid possible risk loss as much as possible.
Drawings
FIG. 1 is a flow chart of the method of the present invention;
FIG. 2 is an IEEE33 node system and virtual power plant distribution;
FIG. 3 is a graph that accounts for the impact of operating risk on a virtual power plant.
Detailed Description
The technical scheme of the present invention will be described in detail below with reference to the accompanying drawings.
The invention designs a virtual power plant distributed scheduling method considering operation risk and network transmission, which comprises the following specific steps as shown in fig. 1:
(1) Establishing an objective function of a virtual power plant distributed scheduling model considering operation risk and network transmission;
(2) Establishing constraint conditions of a virtual power plant distributed scheduling model considering operation risks and network transmission;
(3) Solving a virtual power plant distributed scheduling model considering the running risk and network transmission to obtain a virtual power plant distributed scheduling decision;
(4) Calculating the risk loss cost of the virtual power plant on the basis of the step (3);
(5) And establishing a virtual power plant profit distribution model based on generalized Nash bargaining, and realizing profit distribution of virtual power plant distributed scheduling.
Further, in step (1), an objective function of a virtual power plant distributed scheduling model is established, which considers the operation risk and the network transmission:
wherein,
wherein i is the number of the virtual power plant; t is the scheduling period number; n (N) i Is the total number of virtual power plants; t is the total time period number; k (k) i The degree of deviation predicted value of the renewable energy output in the virtual power plant i;predicted output of renewable energy sources in the virtual power plant i at the t period;The actual output of renewable energy sources in the virtual power plant i is t time periods; c (C) obj Objective function values for deterministic models; beta is the deviation ratio of a specified objective function; c (C) DSO The network power consumption cost is;The transaction cost of the electric energy, the carbon emission and the standby service of the virtual power plant i in the t period is respectively calculated;The loss cost of the battery caused by energy storage charge and discharge in the virtual power plant i in the t period;The cost of user comfort loss in the virtual power plant i is generated by adjusting a central air conditioner and a flexible load;And (5) the running cost of the fuel cell in the i-type fuel cell of the virtual power plant is t.
Further, in the step (2), constraint conditions of establishing a virtual power plant multi-variety resource distributed scheduling model considering operation risk and network transmission are as follows:
(201) Establishing a calculation formula of target cost, wherein the calculation formula comprises a network electric energy loss cost, a transaction cost, an energy storage loss cost, a user comfort loss cost and a fuel cell operation cost calculation formula;
a) Network power consumption cost:
wherein B is S The method comprises the steps of collecting system branches; mn is the branch resistance number between nodes m and n; r is (r) mn A branch resistance between nodes m and n; l (L) mn,t The square of the current amplitude on the branch between nodes m and n at time t;the price coefficient of the network electric energy loss is t time period;
b) Transaction cost:
in the method, in the process of the invention,and->The prices of purchase and sale of electric energy in the period t respectively;And->The electric energy purchased and sold by the virtual power plant i in the period t respectively;And->Purchase and sale prices for the carbon emissions at the t period, respectively;And->The carbon emission amount purchased and sold from the carbon trading platform by the virtual power plant i in the period t respectively;And->Purchase and sale prices for the standby service for period t, respectively;And->Spare capacities purchased and sold for the t-period virtual power plant i, respectively;
c) Energy storage loss cost:
in the method, in the process of the invention,and->Respectively, the charge and discharge quantity of energy stored in the virtual power plant i at t time intervals, < >>And->Respectively charging and discharging dissipation coefficients of energy stored in the virtual power plant i;
d) User comfort loss cost:
wherein ρ anda user discomfort factor;The indoor temperature of the user in the virtual power plant i is t time periods; t (T) i ref The user body feeling most comfortable temperature in the virtual power plant i;The flexible load value of the user in the virtual power plant i at the t period is set;The load reference value of the user in the virtual power plant i at the t period;
e) Fuel cell operating cost:
in the method, in the process of the invention,the unit power generation cost of the fuel cell in the virtual power plant i is set;Generating power of the fuel cell in the virtual power plant i at the t period;
(202) Establishing a safe operation constraint of the power distribution network after the virtual power plant is accessed:
wherein m, n and k are the system node numbers; n (N) S Is a system node set;and->Active power and reactive power of the root node at t time intervals respectively;Is a system power factor; p (P) i∈m,t Active power and reactive power of the node i in the t period; p (P) mn,t And Q mn,t Active power and reactive power on the branch mn of the t period are respectively; p (P) km,t And Q km,t Active power and reactive power on the branch km of the t period are respectively; f (F) m A set of end nodes which are branches taking node m as a head end node; t (T) m A head end node set which is a branch taking a node m as an end node; r is (r) mn The resistance is branch mn; x is x mn Reactance for branch mn; r is (r) km The resistance is a branch km; x is x km Reactance is a branch km; l (L) mn,t The square of the current amplitude on branch mn at time t; l (L) km,t The square of the current amplitude on the branch km at the t time interval; v (V) n,t The square of the voltage amplitude on node n at time t; v (V) m,t The square of the voltage amplitude on node m for the period t;And->Respectively the minimum value and the maximum value of the square of the voltage amplitude of the node m;The maximum value of the square of the current amplitude on the branch mn;
(203) Establishing balance constraint of electric energy, carbon emission and standby capacity of a virtual power plant:
wherein: p (P) i,t 、E i,t 、R i,t The method comprises the steps that electric energy, carbon emission and standby capacity of a virtual power plant i and other virtual power plants in t time periods are respectively traded;the refrigeration power of a central air conditioner in the virtual power plant i in the t period;Carbon emission allowance for the fuel cell in the virtual power plant i; g i,t The method comprises the steps of reducing the discharge capacity of a photovoltaic nuclear certificate in a virtual power plant i under a scene of a t period s; f (F) i,t The carbon emission of the fuel cell in the virtual power plant i is t time periods;The reserve capacity provided by adjusting the flexible load in the virtual power plant i for the period t;The standby capacity provided for the central air conditioner in the virtual power plant i in the t period;Spare capacity provided for fuel cells in the t-period virtual power plant i;The standby demand of the virtual power plant i is t time periods;
(204) Establishing element electric energy, standby and carbon emission constraints in a virtual power plant:
a) Central air conditioning electric energy and standby constraint
Wherein alpha is i,t 、β i 、γ i Parameters describing cold accumulation characteristics and weather conditions of building in virtual power plant iRelated to building characteristics and outdoor temperature; sigma (sigma) i The energy efficiency ratio of the central air conditioning water chiller in the virtual power plant i is set; t (T) in,min And T in,max The minimum indoor temperature and the maximum indoor temperature acceptable to the user are respectively;providing standby indoor temperature for a central air conditioner in the virtual power plant i in the t-1 period;Providing standby indoor temperature for a central air conditioner in the virtual power plant i at the t period;
b) Power and backup constraints for flexible loads:
wherein:and->Respectively adjusting the minimum load and the maximum load of the flexible load of the virtual power plant i in the t period;
c) Power, standby and carbon emission constraints of fuel cells:
wherein P is i max The maximum power of the fuel cell in the virtual power plant i;and->Minimum reserve capacity and maximum reserve capacity available for the fuel cells in the virtual power plant i, respectively;The standby capacity provided for the fuel cell in the virtual power plant i under the scene of t time period s;The power generation power of the fuel cell in the i-type power plant is virtual for the t-1 period; r is (r) i u And r i d The upward regulating quantity and the downward regulating quantity of the fuel cell in the adjacent time period are used for the virtual power plant i; upsilon (v) i The carbon emission intensity of the unit output of the fuel cell in the virtual power plant i;
d) Stored energy power and backup constraints:
wherein P is i c,max And P i d,max Respectively storing maximum charging power and maximum discharging power of energy in the virtual power plant i;and->The charge and discharge quantity of the spare capacity in the virtual power plant i at the t period are respectively; s is S i,t-1 The energy storage capacity of energy stored in the virtual power plant i at the t-1 period; s is S i,t The energy storage capacity of energy stored in the virtual power plant i at the t period;And->Respectively storing the minimum electricity storage quantity and the maximum electricity storage quantity of the energy in the virtual power plant i;And->Respectively the charging efficiency and the discharging efficiency of energy storage in the virtual power plant i;
(205) Establishing a virtual power plant distributed transaction constraint:
wherein: p (P) i,t ＜0、E i,t ＜0、R i,t < 0 respectively represents the selling of electric energy, carbon emission and spare capacity of the virtual power plant i to other virtual power plants in the period of t, P i,t ＞0、E i,t ＞0、R i,t Each of the virtual power plants i in the period of t is more than 0 to purchase electric energy, carbon emission and spare capacity from other virtual power plants, P i,t ＝0、E i,t ＝0、R i,t =0 indicates that the virtual power plant i does not conduct distributed transactions for the t period, respectively.
Further, in step (4), on the basis of step (3), the risk loss cost of the virtual power plant is calculated, as follows:
in the method, in the process of the invention,virtual power plant risk cost for t period;Compensating the price for the generated energy of the t period; omega is a risk coefficient; omega > 1, & lt + & gt>The electric energy purchase price of the virtual power plant is t time periods; n is n s The number of the photovoltaic scenes is the number; p (P) t risk For t period of virtual power plant total loss loadAn amount of;The actual output of renewable energy sources under the scene s is the virtual power plant i in the period t;The amount of load loss in the scene s for the virtual power plant i for period t.
Further, the specific process of step (5) is as follows:
(501) Establishing a virtual power plant profit allocation model based on generalized Nash bargaining:
wherein:
in the method, in the process of the invention,trading costs for virtual power plant i;Adding distributed transaction costs for other virtual power plants serving as transaction objects for the virtual power plant i;The distributed transaction revenue obtained for the virtual power plant i; θ i The competitive coefficient of the virtual power plant i;is deficiency typeThe network transmission cost of the i distributed transaction of the quasi-power plant is increased;The network transmission cost of the virtual power plant i;Network transmission cost for distributed transaction with other virtual power plants as transaction objects is added for the virtual power plant i; />
(502) The virtual power plant profit allocation model objective function based on generalized Nash bargaining is converted by adopting a logarithmic and negative number taking method to obtain the virtual power plant profit allocation model objective function based on generalized Nash bargaining:
in delta i Cost saving is achieved after the virtual power plant i participates in the distributed transaction;
(502) Solving (44) by using a Lagrange multiplier method to obtain a corresponding Lagrange equation:
wherein λ is a dual variable;
(503) Obtaining a first-order partial derivative of (45):
(504) Will beAnd->Substitution (46), yield:
(505) Obtaining the profit allocation cost of the distributed scheduling of the virtual power plants:
three virtual power plants accessing an IEEE33 node system are taken as an example, wherein distributed resources in the virtual power plant 1 comprise renewable energy sources, energy storage, a central air conditioner, flexible loads and fuel cells, and distributed resources in the virtual power plants 2 and 3 comprise renewable energy sources, energy storage, a central air conditioner and flexible loads. IEEE33 node System As shown in FIG. 2, three virtual power plants access nodes 4, 19, 32, respectively.
To verify the impact of distributed scheduling on network loss costs and virtual power plant costs, 6 virtual power plant trading schemes are set for comparison:
scheme 1: considering a network transmission virtual power plant to conduct electric energy transaction;
scheme 2: considering a network transmission virtual power plant to conduct electric energy and reserve capacity transaction;
scheme 3: taking network transmission virtual power plants into consideration to conduct electric energy, carbon emission and reserve capacity transaction;
scheme 4: considering network transmission virtual power plants to conduct electric energy transaction, transaction objects are added with other virtual power plants
Scheme 5: considering network transmission virtual power plants to conduct electric energy and reserve capacity transaction, and adding other virtual power plants to transaction objects;
scheme 6: and considering the network transmission virtual power plant to conduct electric energy, carbon emission and reserve capacity transactions, and adding other virtual power plants by the transaction objects.
The comparison results are shown in Table 1. In comparison to schemes 1-3 and 4-6, when the standby and carbon transactions are considered, the standby, carbon and operating costs are significantly reduced and thus the overall cost of the virtual power plant is reduced, although the electricity transaction cost of the virtual power plant 1 is increased. In schemes 1-3, the network power consumption cost of the virtual power plant increases because the virtual power plant increases the power trade volume, and the network power consumption cost increases with the increase in the power trade cost. And the schemes 4-6 consider the distributed transaction among the virtual power plants, the network electric energy loss cost is jointly determined by the electric energy transaction amount and the distributed transaction amount, and the network electric energy loss cost is similar under the three schemes. As can be seen from comparing the schemes 1 and 4, the schemes 2 and 5, and the schemes 3 and 6, considering the reduction of the transaction cost of the virtual power plant after the distributed transaction, the operation cost and the network power consumption cost are increased by calling the distributed resources in the virtual power plant, but the increase amplitude of the operation cost and the network power consumption cost is smaller than the reduction amplitude of the transaction cost, and the total cost of the virtual power plant is obviously reduced. In summary, considering multiple varieties of resources and distributed scheduling can reduce the overall cost of the virtual power plant.
TABLE 1 virtual plant cost
To illustrate the impact of considering operational risk on a virtual power plant, the virtual power plant target cost, risk cost, and cost change under consideration risk are shown in FIG. 3. The target cost of the virtual power plant is linearly reduced along with the increase of the target coefficient, the increase of the risk cost is in a trend of slowing down and then increasing quickly along with the increase of the target coefficient, and the cost under consideration of the risk is gradually reduced and then slowly increased along with the increase of the target coefficient. When the objective function exceeds a certain value, the reduction of the objective cost is insufficient to compensate for the loss caused by the risk of fluctuation of renewable energy sources, and the cost under consideration of the risk is gradually increased. This shows that the virtual power plant can set the deviation ratio according to the expected cost of the virtual power plant per se, so that the transaction cost under risk is reduced.
The embodiments are only for illustrating the technical idea of the present invention, and the protection scope of the present invention is not limited by the embodiments, and any modification made on the basis of the technical scheme according to the technical idea of the present invention falls within the protection scope of the present invention.
Claims (5)
1. A virtual power plant distributed scheduling method considering operational risk and network transmission, comprising the steps of:
(1) Establishing an objective function of a virtual power plant distributed scheduling model considering operation risk and network transmission;
(2) Establishing constraint conditions of a virtual power plant distributed scheduling model considering operation risks and network transmission;
(3) Solving a virtual power plant distributed scheduling model considering the running risk and network transmission to obtain a virtual power plant distributed scheduling decision;
(4) Calculating the risk loss cost of the virtual power plant on the basis of the step (3);
(5) And establishing a virtual power plant profit distribution model based on generalized Nash bargaining, and realizing profit distribution of virtual power plant distributed scheduling.
2. The method for distributed scheduling of virtual power plants in consideration of operational risk and network traffic according to claim 1, wherein in step (1), an objective function of a distributed scheduling model of virtual power plants in consideration of operational risk and network traffic is established:
wherein,
wherein i is the number of the virtual power plant; t is the scheduling period number; n (N) i Is the total number of virtual power plants; t is the total time period number; k (k) i The degree of deviation predicted value of the renewable energy output in the virtual power plant i;predicted output of renewable energy sources in the virtual power plant i at the t period;The actual output of renewable energy sources in the virtual power plant i is t time periods; c (C) obj Objective function values for deterministic models; beta is the deviation ratio of a specified objective function; c (C) DSO The network power consumption cost is;The transaction cost of the electric energy, the carbon emission and the standby service of the virtual power plant i in the t period is respectively calculated;The loss cost of the battery caused by energy storage charge and discharge in the virtual power plant i in the t period;The cost of user comfort loss in the virtual power plant i is generated by adjusting a central air conditioner and a flexible load;And (5) the running cost of the fuel cell in the i-type fuel cell of the virtual power plant is t.
3. The method for distributed scheduling of virtual power plants in consideration of operational risk and network transmission according to claim 2, wherein in step (2), constraint conditions for establishing a distributed scheduling model of virtual power plant multi-variety resources in consideration of operational risk and network transmission are as follows:
(201) Establishing a calculation formula of target cost, wherein the calculation formula comprises a network electric energy loss cost, a transaction cost, an energy storage loss cost, a user comfort loss cost and a fuel cell operation cost calculation formula;
a) Network power consumption cost:
wherein B is S The method comprises the steps of collecting system branches; mn is the branch resistance number between nodes m and n; r is (r) mn A branch resistance between nodes m and n; l (L) mn,t The square of the current amplitude on the branch between nodes m and n at time t;the price coefficient of the network electric energy loss is t time period;
b) Transaction cost:
in the method, in the process of the invention,and->The prices of purchase and sale of electric energy in the period t respectively;And->The electric energy purchased and sold by the virtual power plant i in the period t respectively;And->Purchase and sale prices for the carbon emissions at the t period, respectively;And->The carbon emission amount purchased and sold from the carbon trading platform by the virtual power plant i in the period t respectively;And->Purchase and sale prices for the standby service for period t, respectively;And->Spare capacities purchased and sold for the t-period virtual power plant i, respectively;
c) Energy storage loss cost:
in the method, in the process of the invention,and->Respectively, the charge and discharge quantity of energy stored in the virtual power plant i at t time intervals, < >>And->Respectively charging and discharging dissipation coefficients of energy stored in the virtual power plant i;
d) User comfort loss cost:
wherein ρ anda user discomfort factor;The indoor temperature of the user in the virtual power plant i is t time periods; t (T) i ref The user body feeling most comfortable temperature in the virtual power plant i;The flexible load value of the user in the virtual power plant i at the t period is set;The load reference value of the user in the virtual power plant i at the t period;
e) Fuel cell operating cost:
in the method, in the process of the invention,the unit power generation cost of the fuel cell in the virtual power plant i is set;Generating power of the fuel cell in the virtual power plant i at the t period;
(202) Establishing a safe operation constraint of the power distribution network after the virtual power plant is accessed:
wherein m, n and k are the system node numbers; n (N) S Is a system node set;and->Active power and reactive power of the root node at t time intervals respectively;Is a system power factor; p (P) i∈m,t Active power and reactive power of the node i in the t period; p (P) mn,t And Q mn,t Active power and reactive power on the branch mn of the t period are respectively; p (P) km,t And Q km,t Active power and reactive power on the branch km of the t period are respectively; f (F) m A set of end nodes which are branches taking node m as a head end node; t (T) m A head end node set which is a branch taking a node m as an end node; r is (r) mn The resistance is branch mn; x is x mn Reactance for branch mn; r is (r) km The resistance is a branch km; x is x km Reactance is a branch km; l (L) mn,t The square of the current amplitude on branch mn at time t; l (L) km,t The square of the current amplitude on the branch km at the t time interval; v (V) n,t The square of the voltage amplitude on node n at time t; v (V) m,t The square of the voltage amplitude on node m for the period t;And->Respectively the minimum value and the maximum value of the square of the voltage amplitude of the node m;The maximum value of the square of the current amplitude on the branch mn;
(203) Establishing balance constraint of electric energy, carbon emission and standby capacity of a virtual power plant:
wherein: p (P) i,t 、E i,t 、R i,t The method comprises the steps that electric energy, carbon emission and standby capacity of a virtual power plant i and other virtual power plants in t time periods are respectively traded;the refrigeration power of a central air conditioner in the virtual power plant i in the t period;Carbon emission allowance for the fuel cell in the virtual power plant i; g i,t The method comprises the steps of reducing the discharge capacity of a photovoltaic nuclear certificate in a virtual power plant i under a scene of a t period s; f (F) i,t The carbon emission of the fuel cell in the virtual power plant i is t time periods;The reserve capacity provided by adjusting the flexible load in the virtual power plant i for the period t;The standby capacity provided for the central air conditioner in the virtual power plant i in the t period;Spare capacity provided for fuel cells in the t-period virtual power plant i;The standby demand of the virtual power plant i is t time periods;
(204) Establishing element electric energy, standby and carbon emission constraints in a virtual power plant:
a) Central air conditioning electric energy and standby constraint
Wherein alpha is i,t 、β i 、γ i Parameters describing building cold accumulation characteristics and weather conditions in the virtual power plant i are related to building characteristics and outdoor temperature; sigma (sigma) i The energy efficiency ratio of the central air conditioning water chiller in the virtual power plant i is set; t (T) in,min And T in,max The minimum indoor temperature and the maximum indoor temperature acceptable to the user are respectively;providing standby indoor temperature for a central air conditioner in the virtual power plant i in the t-1 period;Providing standby indoor temperature for a central air conditioner in the virtual power plant i at the t period;
b) Power and backup constraints for flexible loads:
wherein:and->Respectively adjusting the minimum load and the maximum load of the flexible load of the virtual power plant i in the t period;
c) Power, standby and carbon emission constraints of fuel cells:
wherein P is i max The maximum power of the fuel cell in the virtual power plant i;and->Minimum reserve capacity and maximum reserve capacity available for the fuel cells in the virtual power plant i, respectively;The standby capacity provided for the fuel cell in the virtual power plant i under the scene of t time period s;The power generation power of the fuel cell in the i-type power plant is virtual for the t-1 period; r is (r) i u And r i d Is a virtual power plant iAn upward adjustment amount and a downward adjustment amount of the internal fuel cell in adjacent periods; upsilon (v) i The carbon emission intensity of the unit output of the fuel cell in the virtual power plant i;
d) Stored energy power and backup constraints:
wherein P is i c,max And P i d,max Respectively storing maximum charging power and maximum discharging power of energy in the virtual power plant i;andthe charge and discharge quantity of the spare capacity in the virtual power plant i at the t period are respectively; s is S i,t-1 The energy storage capacity of energy stored in the virtual power plant i at the t-1 period; s is S i,t The energy storage capacity of energy stored in the virtual power plant i at the t period;And->Respectively is the most energy storage in the virtual power plant iSmall and maximum power storage;And->Respectively the charging efficiency and the discharging efficiency of energy storage in the virtual power plant i;
(205) Establishing a virtual power plant distributed transaction constraint:
wherein: p (P) i,t ＜0、E i,t ＜0、R i,t < 0 respectively represents the selling of electric energy, carbon emission and spare capacity of the virtual power plant i to other virtual power plants in the period of t, P i,t ＞0、E i,t ＞0、R i,t Each of the virtual power plants i in the period of t is more than 0 to purchase electric energy, carbon emission and spare capacity from other virtual power plants, P i,t ＝0、E i,t ＝0、R i,t =0 indicates that the virtual power plant i does not conduct distributed transactions for the t period, respectively.
4. A method of distributed scheduling of virtual power plants taking into account operational risk and network transmissions according to claim 3, wherein in step (4) the risk loss costs of the virtual power plants are calculated on the basis of step (3), expressed as follows:
λ t p ＝ωλ t b (37)
in the method, in the process of the invention,virtual power plant risk cost for t period;Compensating the price for the generated energy of the t period; omega is a risk coefficient; omega > 1, & lt + & gt>The electric energy purchase price of the virtual power plant is t time periods; n is n s The number of the photovoltaic scenes is the number; p (P) t risk The total loss load of the virtual power plant in the t period;the actual output of renewable energy sources under the scene s is the virtual power plant i in the period t;The amount of load loss in the scene s for the virtual power plant i for period t.
5. The method for distributed scheduling of virtual power plants taking into account operational risk and network traffic as defined in claim 4, wherein the specific process of step (5) is as follows:
(501) Establishing a virtual power plant profit allocation model based on generalized Nash bargaining:
wherein:
in the method, in the process of the invention,trading costs for virtual power plant i;Adding distributed transaction costs for other virtual power plants serving as transaction objects for the virtual power plant i;The distributed transaction revenue obtained for the virtual power plant i; θ i The competitive coefficient of the virtual power plant i;Increased network transmission costs for virtual power plant i distributed transactions;The network transmission cost of the virtual power plant i;Network transmission cost for distributed transaction with other virtual power plants as transaction objects is added for the virtual power plant i;
(502) The virtual power plant profit allocation model objective function based on generalized Nash bargaining is converted by adopting a logarithmic and negative number taking method to obtain the virtual power plant profit allocation model objective function based on generalized Nash bargaining:
in delta i Cost saving is achieved after the virtual power plant i participates in the distributed transaction;
(502) Solving (44) by using a Lagrange multiplier method to obtain a corresponding Lagrange equation:
wherein λ is a dual variable;
(503) Obtaining a first-order partial derivative of (45):
(504) Will beAnd->Substitution (46), yield:
(505) Obtaining the profit allocation cost of the distributed scheduling of the virtual power plants:
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