Day-ahead optimization method for source-grid-load-storage flexibility resources

Abstract

The present invention discloses a method for optimizing the day-ahead source-grid-load-storage flexibility resources, including: S1. Obtaining the operating parameters of conventional units on the source side, the topological structure and line parameters on the grid side, the power consumption information and the demand response load scale on the load side, and the operating parameters of the battery on the energy storage side, and constructing a day-ahead optimization model for the source-grid-load-storage flexibility resources based on the short-term forecast information of renewable energy power generation and load; S2. Converting the day-ahead optimization model into a mixed integer linear optimization model for solving, and calculating the day-ahead optimization scheme for the source-grid-load-storage flexibility resources. The present invention can optimize the absorption range of renewable energy and the load demand response fluctuation range, ensuring the feasibility, safety and economy of the day-ahead optimization strategy.

Description

Technical Field

[0001] The present invention relates to the field of electric power systems, and in particular to a method for day-ahead optimization of source-grid-load-storage flexibility resources. Background Art

[0002] Under the "dual carbon" development goal, the development and utilization of renewable energy power generation represented by wind power and photovoltaics will continue to maintain a rapid development trend. However, as the penetration rate of wind power and photovoltaic power generation in the power grid continues to increase, the uncertainty of their power generation has put the safe and economical operation of the power system under severe test. Studies have shown that the flexibility resources of the power system are an important guarantee for improving the absorption capacity of wind power and photovoltaics. The flexibility transformation of thermal power units, automatic power generation control technology and the investment in energy storage devices are common ways to improve the flexibility of the power system.

[0003] In recent years, electrochemical energy storage power stations represented by lithium-ion batteries have attracted much attention in the research and application of peak and frequency regulation on the grid side due to their advantages of flexible site selection, short construction period, high energy and power density, and high charging and discharging efficiency. At the same time, due to the shortage of transmission corridors and the increase in large-scale wind power and photovoltaic grid-connected capacity, the dynamic capacity expansion technology of transmission lines, as an important flexibility resource on the grid side, has high economic benefits and provides support for the efficient operation of transmission lines. In addition, existing studies have shown that adjusting the user-side load demand through load demand response can improve the wind power and photovoltaic absorption capacity. There are two main types of load-side demand response: incentive demand response (IDR) and power price demand response (PDR). Since IDR is contractually mandatory, the uncertainty is small; while PDR is based on the principle of voluntary participation of users and has strong randomness. In the context of energy Internet, the coordinated operation of source-grid-load-storage flexibility resources needs to consider not only the uncertainty brought by wind power and photovoltaics, but also the uncertainty of PDR on the load side.

[0004] The research on the day-ahead optimization problem considering the uncertainty of both source and load ends currently focuses on the coordinated optimization operation of source-load or source-load-storage type flexible resources. In-depth research is urgently needed on the coordinated operation strategy of multiple types of flexible resources including source, grid, load and storage. The peak-shaving and frequency-regulating capabilities of flexible resources including source, grid, load and storage are related to their operating characteristics. Under the premise of considering the uncertainty of both source and load ends, it is necessary to weigh the economy and safety of renewable energy consumption according to the operating characteristics of various types of flexible resources including source, grid, load and storage, and reasonably optimize the consumption range of renewable energy and the fluctuation range of load demand response to ensure the feasibility of the day-ahead optimization strategy. Summary of the invention

[0005] In view of this, the purpose of the present invention is to overcome the defects in the prior art and provide a day-ahead optimization method for source-grid-load-storage flexibility resources, which can optimize the consumption range of renewable energy and the load demand response fluctuation range, thereby ensuring the feasibility, safety and economy of the day-ahead optimization strategy.

[0006] The method for optimizing the source-grid-load-storage flexibility resources of the present invention comprises the following steps:

[0007] S1. Construct the day-ahead optimization model F of the source-grid-load-storage flexibility resources:

[0008]

[0009] Where T is the day-ahead scheduling period; N T is the number of thermal power units; is the starting state coefficient of thermal power unit i; is the stop state coefficient of thermal power unit i; is the upward adjustment consumption coefficient of thermal power unit i; The downward adjustment consumption coefficient of thermal power unit i; u i,t is the startup state variable of thermal power unit i in period t; v i,t is the stop state variable of thermal power unit i in period t; It is the difference between the predicted upward fluctuation of PDR and the allowed fluctuation; It is the difference between the downward predicted volatility of PDR and the allowed volatility; i,t is the power generation of thermal power unit i in period t; x i,t is the operating status of thermal power unit i in period t; a i 、b i 、c i are the coefficients of fuel consumption of thermal power units; N W is the total number of renewable energy stations; is the wind / solar abandonment penalty coefficient of renewable energy station w; are the load shedding penalty coefficients of renewable energy station w respectively; is the amount of wind / light abandoned by renewable energy station w in period t; is the load shedding amount of renewable energy station w in period t; N N is the number of transmission lines; Dynamically increase the equipment and operation and maintenance consumption per unit capacity for the transmission line m; is the capacity increase power of transmission line m in time period t; N P It is a set of load nodes for electricity price type demand response; is the upward fluctuation deviation penalty coefficient of demand response load n; is the penalty coefficient for downward fluctuation deviation of demand response load n; is the upward fluctuation deviation of the demand response load n in time period t; N is the downward fluctuation deviation of the demand response load n in time period t; B is the number of energy storage power stations; is the operation and maintenance consumption per unit capacity of energy storage power station b; is the operation and maintenance consumption per unit power of energy storage power station b; is the rated capacity of energy storage power station b; is the rated power of energy storage station b; α b is the recovery factor, r is the discount rate, is the actual cycle life of energy storage power station b; The revenue from upward regulation of unit power for energy storage power stations; The revenue from downward adjustment of unit power for energy storage power stations; To adjust the power upward for energy storage in the charging state; To regulate the power downward for energy storage in the charging state; To regulate the power upwards for energy storage in the discharge state; The downward regulation power of the energy storage in the discharge state;

[0010] S2. Adjust the parameter values ​​in the day-ahead optimization model F of the source, grid, load and storage flexibility resources so that the optimization model F achieves the minimum value, and calculate the day-ahead optimization plan of the source, grid, load and storage flexibility resources using the parameters set when the optimization model F achieves the minimum value; the day-ahead optimization plan includes the benchmark output plan of conventional units, the power generation capacity of renewable energy, the allowable fluctuation range of load demand response, the time-of-use electricity price strategy, the dynamic capacity expansion strategy of the transmission line, and the charging and discharging strategy of energy storage.

[0011] Furthermore, the constraints of the source-grid-load-storage flexibility resource day-ahead optimization model F include conventional unit constraints, renewable energy station power constraints, transmission line dynamic capacity expansion constraints, PDR response constraints, energy storage power station operation constraints, system power balance constraints, system spinning reserve constraints, and network transmission capacity security constraints.

[0012] Furthermore, the system power balance constraint is:

[0013]

[0014] Among them, N L is the number of load nodes; is the predicted power of renewable energy station w in time period t; The regulating power of the energy storage in the discharge state; The regulating power of energy storage in charging state; is the predicted value of the load at node l in period t; The predicted total response of the PDR at node n in time period t.

[0015] Furthermore, the system spinning reserve constraints are:

[0016]

[0017] in, The upward spinning reserve provided for adjustable unit a during period t; The downward spinning reserve provided for adjustable unit a in period t; N S Represents the total number of conventional units and energy storage power stations; is the upper limit of the forecast error of the absorptive power of renewable energy station w in time period t; is the lower limit of the forecast error of the renewable energy station w in the period t; binary variable and Both are used to constrain the conservativeness of the uncertainty set of renewable energy station w in time period t; are the upper limits of the permissible response of the PDR at node n in time period t; is the lower limit of the permissible response of the PDR at node n in time period t; binary variable and are the uncertainty set conservative control variables of PDR at node n in time period t; p a,t is the planned output of adjustable unit a in period t; x a,t is the operating status of adjustable unit a in the tth period, and Characterizes the charging state of energy storage unit b in time period t, Characterize the discharge state of energy storage unit b in time period t; is the upper limit of the technical output of adjustable unit a in period t; is the lower limit of the technical output of adjustable unit a in period t; is the climbing rate of adjustable unit a; is the ramp rate of adjustable unit a; Δt a is the release time of spinning reserve.

[0018] Furthermore, the network transmission capacity security constraint is:

[0019]

[0020] Among them, λ a,s is the output power transfer distribution factor of the adjustable unit; n,s is the PDR output power transfer distribution factor; w,s is the renewable energy output power transfer distribution factor; and They represent the upper limit of the bidirectional transmission power of branch se respectively; Δp a,t is the adjustment amount of adjustable unit a in time period t; is the fluctuation of PDR at node n in time period t; is the fluctuation of renewable energy station w in period t;

[0021] Furthermore, the system spinning reserve constraint is linearized to obtain a new system spinning reserve constraint:

[0022]

[0023] in, and They are all introduced as auxiliary variables.

[0024] Further, the network transmission capacity security constraint is transformed to obtain a new network transmission capacity security constraint:

[0025] in, H ε,t , K ε,t and G ε,t They are all introduced as auxiliary variables.

[0026] The beneficial effects of the present invention are as follows: a method for optimizing the source-grid-load-storage flexibility resources on the day-ahead disclosed by the present invention takes into account the uncertainties of both the source and the load, optimizes the consumption range of renewable energy and the load demand response fluctuation range, and ensures the feasibility of the day-ahead optimization strategy; at the same time, based on the equivalent full cycle number model of energy storage charging and discharging, the life constraints of energy storage operation are taken into account, ensuring the economy and feasibility of the energy storage charging and discharging strategy; in addition, the dynamic capacity increase constraints of transmission lines and the transmission capacity safety constraints are also taken into account to ensure the safety of network operation. BRIEF DESCRIPTION OF THE DRAWINGS DETAILED DESCRIPTION

[0027] The present invention is further described below:

[0028] The method for optimizing the source-grid-load-storage flexibility resources of the present invention comprises the following steps:

[0029] S1. Obtain the operating parameters of conventional units on the source side, the topological structure and line parameters on the grid side, the power consumption information and demand response load scale on the load side, and the operating parameters of the battery on the energy storage side, and build a day-ahead optimization model F for source-grid-load-storage flexibility resources based on the short-term forecast information of renewable energy generation and load:

[0030]

[0031] Where T is the day-ahead scheduling period; N T is the number of thermal power units; is the starting state coefficient of thermal power unit i; is the stop state coefficient of thermal power unit i; is the upward adjustment consumption coefficient of thermal power unit i; The downward adjustment consumption coefficient of thermal power unit i; u i,t is the startup state variable of thermal power unit i in period t; v i,t is the stop state variable of thermal power unit i in period t; It is the difference between the predicted upward fluctuation of PDR and the allowed fluctuation; It is the difference between the downward predicted volatility of PDR and the allowed volatility; i,t is the power generation of thermal power unit i in period t; x i,t is the operating status of thermal power unit i in period t; a i 、b i 、c i are the coefficients of fuel consumption of thermal power units; N W is the total number of renewable energy stations; is the wind / solar abandonment penalty coefficient of renewable energy station w; are the load shedding penalty coefficients of renewable energy station w respectively; is the amount of wind / light abandoned by renewable energy station w in period t; is the load shedding amount of renewable energy station w in period t; N N is the number of transmission lines; Dynamically increase the equipment and operation and maintenance consumption per unit capacity for the transmission line m; is the capacity increase power of transmission line m in time period t; N P It is a set of load nodes for electricity price type demand response; is the upward fluctuation deviation penalty coefficient of demand response load n; is the penalty coefficient for downward fluctuation deviation of demand response load n; is the upward fluctuation deviation of the demand response load n in time period t; N is the downward fluctuation deviation of the demand response load n in time period t; B is the number of energy storage power stations; is the operation and maintenance consumption per unit capacity of energy storage power station b; is the operation and maintenance consumption per unit power of energy storage power station b; is the rated capacity of energy storage power station b; is the rated power of energy storage station b; α b is the recovery factor, r is the discount rate, is the actual cycle life of energy storage power station b; The revenue from upward regulation of unit power for energy storage power stations; The revenue from downward adjustment of unit power for energy storage power stations; To adjust the power upward for energy storage in the charging state; To regulate the power downward for energy storage in the charging state; To regulate the power upwards for energy storage in the discharge state; The power is downwardly regulated for energy storage in a discharge state; the renewable energy includes wind power and photovoltaic power; the renewable energy station includes a wind farm and a photovoltaic power station;

[0032] S2. Adjust the parameter values ​​in the day-ahead optimization model F of the source-grid-load-storage flexibility resources so that the optimization model F obtains the minimum value, and use the parameters set when the optimization model F obtains the minimum value to calculate the day-ahead optimization plan of the source-grid-load-storage flexibility resources; the day-ahead optimization plan includes the benchmark output plan of conventional units, the power generation power of renewable energy, the allowable fluctuation range of load demand response, the time-of-use electricity price strategy, the dynamic capacity expansion strategy of the transmission line, and the charging and discharging strategy of energy storage. Among them, by converting the day-ahead optimization model F of the source-grid-load-storage flexibility resources into a mixed integer linear optimization model, and then calling a commercial solver to solve the linear programming problem of the mixed integer linear optimization model, the day-ahead optimization plan of the source-grid-load-storage flexibility resources can be calculated.

[0033] In this embodiment, in order to enable the source-grid-load-storage flexibility resource day-ahead optimization model F to better calculate the day-ahead optimization plan, the constraints of the source-grid-load-storage flexibility resource day-ahead optimization model F include conventional unit constraints, renewable energy station power constraints, transmission line dynamic capacity expansion constraints, PDR response constraints, energy storage power station operation constraints, system power balance constraints, system spinning reserve constraints and network transmission capacity safety constraints; wherein, the conventional unit constraints include minimum start-stop time constraints, technical output constraints, unit ramp constraints and spinning reserve release constraints. It should be noted that when the resource day-ahead optimization model F is converted into a mixed integer linear optimization model, the above constraints will also be converted accordingly, thereby ensuring the solution of the linear programming problem.

[0034] In this embodiment, the system power balance constraint is:

[0035]

[0036] Among them, N L is the number of load nodes; is the predicted power of renewable energy station w in time period t; The regulating power of the energy storage in the discharge state; The regulating power of energy storage in charging state; is the predicted value of the load at node l in period t; The predicted total response of the PDR at node n in time period t.

[0037] In this embodiment, the system spinning reserve constraints are:

[0038]

[0039] in, The upward spinning reserve provided for adjustable unit a during period t; The downward spinning reserve provided for adjustable unit a in period t; N S Represents the total number of conventional units and energy storage power stations; is the upper limit of the forecast error of the absorptive power of renewable energy station w in time period t; is the lower limit of the forecast error of the renewable energy station w in the period t; binary variable and Both are used to constrain the conservativeness of the uncertainty set of renewable energy station w in time period t; are the upper limits of the permissible response of the PDR at node n in time period t; is the lower limit of the permissible response of the PDR at node n in time period t; binary variable and are the uncertainty set conservative control variables of PDR at node n in time period t; p a,t is the planned output of adjustable unit a in period t; x a,t is the operating status of adjustable unit a in the tth period, and Characterizes the charging state of energy storage unit b in time period t, Characterize the discharge state of energy storage unit b in time period t; is the upper limit of the technical output of adjustable unit a in period t; is the lower limit of the technical output of adjustable unit a in period t; is the climbing rate of adjustable unit a; is the ramp rate of adjustable unit a; Δt a is the release time of spinning reserve, Δt a It is usually taken for 5-10 minutes.

[0040] In this embodiment, the network transmission capacity security constraint is:

[0041]

[0042] Among them, λ a,s is the output power transfer distribution factor of the adjustable unit; n,s is the PDR output power transfer distribution factor; w,s is the renewable energy output power transfer distribution factor; and They represent the upper limit of the bidirectional transmission power of branch se respectively; Δp a,t is the adjustment amount of adjustable unit a in time period t; is the fluctuation of PDR at node n in time period t; is the fluctuation of renewable energy station w in period t.

[0043] In this embodiment, the system spinning reserve constraint is linearized to obtain a new system spinning reserve constraint:

[0044]

[0045] in, and They are all introduced as auxiliary variables.

[0046] In this embodiment, the network transmission capacity security constraint is transformed to obtain a new network transmission capacity security constraint:

[0047]

[0048] in, H ε,t , K ε,t and G ε,t They are all introduced as auxiliary variables.

[0049] The feasibility and effectiveness of the present invention are analyzed and verified by taking the modified IEEE 30-node test system as an example:

[0050] Taking wind power access as an example, thermal power units are connected to nodes 1, 2, 22, 23, and 27, with installed capacities of 160MW, 160MW, 100MW, 60MW, and 110MW, respectively. The wind farm is connected to node 5, with an installed capacity of 400MW. The minimum load demand is 280.69MW, the maximum is 731.62MW, and the average is 513.34MW. The PDR load is located at node 8, and the response prediction error does not exceed 5%. The maximum charging and discharging power of the energy storage battery is 180MW, and the capacity is 450MWh. The energy storage installation costs are 20,000 yuan / MW and 30,000 yuan / MWh, respectively. The penalty cost coefficient for abandoned wind power and the penalty cost coefficient for downward fluctuation deviation of load demand response are 400 yuan / MW, and the penalty cost coefficient for load shedding power, the penalty cost coefficient for upward fluctuation deviation of load demand response, and the dynamic capacity increase cost coefficient of transmission lines are 600 yuan / MW.

[0051] The following three operating scenarios are defined:

[0052] Scenario 1: Considering the uncertainty of source and load, the source, grid, load and storage are coordinated and optimized

[0053] Scenario 2: Uncertainty of source and load is not taken into account, and the source, grid, load and storage are coordinated and optimized

[0054] Scenario 3: Considering the uncertainty of source and load, the source, load and storage are coordinated and optimized

[0055] As shown in Table 1, under the operation mode without taking into account the uncertainty of source and load, the operation cost of thermal power units is the lowest and the load demand response capability is the weakest. The energy storage power station needs to cope with the fluctuation of wind power forecast error and load demand response through frequent charging and discharging cycles. The wind power absorption capacity is the weakest among the three operation scenarios. Comparing scenario 1 and scenario 3, it can be seen that under the coordinated operation scenario of source, grid, load and storage, the operation cost of thermal power units is slightly increased compared with that under the coordinated operation mode of source, load and storage, and the operation life of energy storage power station is slightly reduced; however, the wind power absorption capacity and the ability to respond to load demand fluctuations in scenario 1 are significantly improved compared with scenario 3, and the total cost of the optimization target is further reduced, which verifies the feasibility and effectiveness of the method of the present invention.

[0056] Table 1 Comparison of calculation results under different scenarios

[0057]

[0058] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solution of the present invention can be modified or replaced by equivalents without departing from the purpose and scope of the technical solution of the present invention, which should be included in the scope of the claims of the present invention.

Claims

1. A method for optimizing the source-grid-load-storage flexibility resources on the day-ahead, characterized in that: The steps include: S1. Construct the day-ahead optimization model F of the source-grid-load-storage flexibility resources: Where T is the day-ahead scheduling period; N T is the number of thermal power units; is the starting state coefficient of thermal power unit i; is the stop state coefficient of thermal power unit i; is the upward adjustment consumption coefficient of thermal power unit i; The downward adjustment consumption coefficient of thermal power unit i; u i,t is the startup state variable of thermal power unit i in period t; v i,t is the stop state variable of thermal power unit i in period t; It is the difference between the predicted upward fluctuation of PDR and the allowed fluctuation; The difference between the downward predicted volatility of PDR and the allowed volatility; i,t is the power generation of thermal power unit i in period t; x i,t is the operating status of thermal power unit i in period t; a i 、b i 、c i are the coefficients of fuel consumption of thermal power units; N W is the total number of renewable energy stations; is the wind / solar abandonment penalty coefficient of renewable energy station w; is the load shedding penalty coefficient of renewable energy station w; is the amount of wind / light abandoned by renewable energy station w in period t; is the load shedding amount of renewable energy station w in period t; N N is the number of transmission lines; Dynamically increase the equipment and operation and maintenance consumption per unit capacity for the transmission line m; is the capacity increase power of transmission line m in time period t; N P It is a set of load nodes for electricity price type demand response; is the upward fluctuation deviation penalty coefficient of demand response load n; is the penalty coefficient for downward fluctuation deviation of demand response load n; is the upward fluctuation deviation of the demand response load n in time period t; N is the downward fluctuation deviation of the demand response load n in time period t; B is the number of energy storage power stations; is the operation and maintenance consumption per unit capacity of energy storage power station b; is the operation and maintenance consumption per unit power of energy storage power station b; is the rated capacity of energy storage power station b; is the rated power of energy storage station b; α b is the recovery factor, r is the discount rate, is the actual cycle life of energy storage power station b; The revenue from upward regulation of unit power for energy storage power stations; The revenue from downward adjustment of unit power for energy storage power stations; To regulate the power upward for energy storage in the charging state; To regulate the power downward for energy storage in the charging state; To regulate the power upwards for energy storage in the discharge state; is the downward adjustment power of energy storage in the discharge state; the constraints of the source-grid-load-storage flexibility resource day-ahead optimization model F include conventional unit constraints, renewable energy station power constraints, transmission line dynamic capacity expansion constraints, electricity price-based demand response constraints, energy storage power station operation constraints, system power balance constraints, system spinning reserve constraints, and network transmission capacity security constraints; S2. Adjust the parameter values ​​in the day-ahead optimization model F of the source, grid, load and storage flexibility resources so that the optimization model F achieves the minimum value, and calculate the day-ahead optimization plan of the source, grid, load and storage flexibility resources using the parameters set when the optimization model F achieves the minimum value; the day-ahead optimization plan includes the benchmark output plan of conventional units, the power generation capacity of renewable energy, the allowable fluctuation range of load demand response, the time-of-use electricity price strategy, the dynamic capacity expansion strategy of the transmission line, and the charging and discharging strategy of energy storage.

2. The method for day-ahead optimization of source-grid-load-storage flexibility resources according to claim 1, characterized in that: The system power balance constraint: Among them, N L is the number of load nodes; is the predicted power of renewable energy station w in time period t; The regulating power of the energy storage in the discharge state; The regulating power of energy storage in charging state; is the predicted value of the load at node l in period t; The predicted total response of the PDR at node n in time period t.

3. The method for day-ahead optimization of source-grid-load-storage flexibility resources according to claim 1, characterized in that: The system spinning reserve constraints are: in, The upward spinning reserve provided for adjustable unit a during period t; The downward spinning reserve provided for adjustable unit a in period t; N S Represents the total number of conventional units and energy storage power stations; is the upper limit of the forecast error of the absorptive power of renewable energy station w in time period t; is the lower limit of the forecast error of the renewable energy station w in the period t; binary variable and Both are used to constrain the conservativeness of the uncertainty set of renewable energy station w in time period t; is the upper limit of the permissible response of the PDR at node n in time period t; is the lower limit of the permissible response of the PDR at node n in time period t; binary variable and are the uncertainty set conservative control variables of PDR at node n in time period t; p a,t is the planned output of adjustable unit a in period t; x a,t is the operating status of adjustable unit a in the tth period, and Characterizes the charging state of energy storage unit b in time period t, Characterize the discharge state of energy storage unit b in time period t; is the upper limit of the technical output of adjustable unit a in period t; is the lower limit of the technical output of adjustable unit a in period t; is the climbing rate of adjustable unit a; is the ramp rate of adjustable unit a; Δt a is the release time of spinning reserve.

4. The method for day-ahead optimization of source-grid-load-storage flexibility resources according to claim 1, characterized in that: The network transmission capacity security constraint is: Among them, λ a,s is the output power transfer distribution factor of the adjustable unit; n,s is the PDR output power transfer distribution factor; w,s is the renewable energy output power transfer distribution factor; and They represent the upper limit of the bidirectional transmission power of branch se respectively; Δp a,t is the adjustment amount of adjustable unit a in time period t; is the fluctuation of PDR at node n in time period t; is the fluctuation of renewable energy station w in period t.

5. The method for day-ahead optimization of source-grid-load-storage flexibility resources according to claim 3, characterized in that: The system spinning reserve constraint is linearized to obtain a new system spinning reserve constraint: in, and They are all introduced as auxiliary variables.

6. The method for day-ahead optimization of source-grid-load-storage flexibility resources according to claim 4, characterized in that: The network transmission capacity security constraint is transformed to obtain a new network transmission capacity security constraint: in, H ε,t , K ε,t and G ε,t They are all introduced as auxiliary variables.

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