Source network load storage flexibility resource collaborative planning optimization method

Abstract

The invention relates to a source-grid-load-storage flexible resource collaborative planning optimization method, belongs to the field of smart power grids, and solves the problems of low flexible resource utilization efficiency and difficulty in new energy consumption of an existing power system scheduling scheme. Comprising the steps of obtaining historical data of a regional power grid in a predetermined scheduling period; constructing a flexible capacity supply quantification model and a demand quantification model of the source network load storage resources of the regional power grid; based on the flexibility capacity supply quantification model and the demand quantification model, constructing a flexibility resource collaborative planning model with a minimum flexibility shortage amount as a target; wherein the flexible resource collaborative planning model is a deterministic planning model; the flexible resource collaborative planning model is converted into a basic robust optimization model, and a weak robust optimization planning model considering uncertainty is constructed based on a traditional robust optimization model; and solving the weak robust optimization planning model by using a CPLEX solver based on historical data in a predetermined scheduling period to obtain a planning scheme of the regional power grid power system.

Description

Technical Field

[0001] The present invention relates to the field of smart grid technology, and in particular to a method for collaborative planning and optimization of source-grid-load-storage flexibility resources. Background Art

[0002] As the construction of the new power system steadily advances, a pattern will be formed in which large power grids dominate and various power grid forms coexist. The one-way process of the traditional power system, "generation-transmission-transformation-distribution-consumption", will be transformed into an integrated cycle process of "source-grid-load-storage", thereby increasing the proportion of new energy power generation and consumption. Due to the volatility and intermittent nature of new energy output, a large amount of flexibility resources still need to be allocated to provide auxiliary services to ensure the safe and stable operation of the new power system. The operating conditions and levels of different flexibility resources vary. Therefore, the rational allocation of flexibility resources and the scientific and economical scheduling of flexibility resources play an important role in improving the safety and stability of the power system.

[0003] Currently, research on flexible resource coordination models, addressing the uncertainty of renewable energy output caused by natural factors such as wind speed, sunlight, and runoff, mostly uses traditional robust optimization models for optimal scheduling. However, traditional robust optimization models often make decisions based on worst-case scenarios, which rarely occur in actual operation, resulting in overly conservative decision-making. Overly conservative scheduling plans can lead to reduced dispatch flexibility in the power system. Summary of the Invention

[0004] In view of the above analysis, an embodiment of the present invention aims to provide a collaborative planning and optimization method for source, grid, load and storage flexibility resources, so as to solve the technical problems that the existing collaborative planning and optimization method for flexibility resources adopts traditional robust optimization models in new power systems, resulting in overly conservative scheduling schemes for existing power systems, low flexibility resource utilization efficiency and difficulty in accommodating new energy.

[0005] The purpose of the present invention is mainly achieved through the following technical solutions:

[0006] The present invention provides a method for collaborative planning and optimization of source-grid-load-storage flexibility resources, comprising the following steps:

[0007] Obtain historical data within the scheduled dispatch period of the regional power grid;

[0008] Construct a quantitative model for the supply and demand of the flexibility capacity of regional power grid source, grid, load and storage resources;

[0009] Based on the flexibility capacity supply quantification model and the demand quantification model, a flexibility resource collaborative planning model is constructed with the goal of minimizing the flexibility shortage; wherein the flexibility resource collaborative planning model is a deterministic planning model;

[0010] The flexibility resource collaborative planning model is converted into a basic robust optimization model, and a weak robust optimization planning model considering uncertainty is constructed based on the traditional robust optimization model; based on the historical data within the predetermined scheduling period, the weak robust optimization planning model is solved using the CPLEX solver to obtain a planning scheme for the regional power grid system.

[0011] Furthermore, based on the flexibility capacity supply of conventional coal-fired power units, flexible coal-fired power units, adjustable hydropower units, transmission lines, flexible loads and energy storage units, a quantitative model for the flexibility capacity supply of the regional power grid source, grid, load and storage resources is constructed.

[0012] Furthermore, the quantitative model for the flexibility capacity supply of the regional power grid source, grid, load and storage resources is as follows:

[0013]

[0014] in, They are the upward and downward flexible supply of source, grid, load and storage resources at time t; I is the set of conventional coal-fired power units; J is the set of flexible coal-fired power units; K is the set of adjustable hydropower units; B is the set of transmission lines; H is the set of energy storage units; C is the set of flexible loads; are the upward and downward flexibility capacity supply of coal-fired power unit i at time t, respectively; are the upward and downward flexibility capacity supply of coal-fired power unit j after flexibility transformation at time t; are the upward and downward flexibility capacity supply of hydropower unit k at time t, respectively; are the upward and downward flexibility capacity supply of transmission line b at time t, respectively; are the upward and downward flexibility capacity supply of flexible load c at time t respectively; They are the upward and downward flexibility capacity supply of energy storage h at time t respectively.

[0015] Furthermore, the flexibility capacity demand quantification model of the regional power grid source-grid-load-storage resources includes the net load demand and flexibility demand of the regional power grid;

[0016] Net load demand P at time t NL,t , calculated as follows:

[0017] P NL,t =P L,t -P W,t -P PV,t

[0018] Among them, P L,t 、P W,t and PPV,t are the load demand, wind power and photovoltaic power at time t respectively;

[0019] Flexibility demand F at time t DE,t , calculated as follows:

[0020]

[0021] Among them, P NL,t-1 is the net load demand at time t-1; They are the upward and downward flexibility demands at time t respectively.

[0022] Furthermore, the flexibility resource collaborative planning model includes an objective function with the goal of minimizing the flexibility shortage and a set of constraint conditions;

[0023] The objective function f, which aims to minimize the flexibility deficit, is as follows:

[0024]

[0025] Among them, λ up ,λ dn are the weight coefficients of upward and downward flexibility shortage respectively; They are the upward and downward flexibility shortages of the regional power grid system at time t.

[0026] Furthermore, the constraint condition set includes conventional coal-fired power unit operation constraints, flexibility-modified coal-fired power unit operation constraints, adjustable hydropower unit operation constraints, transmission line operation constraints, flexible load operation constraints, energy storage unit operation constraints, flexibility shortage constraints, power balance constraints and power system standby constraints.

[0027] Further, the flexibility resource collaborative planning model is converted into a basic robust optimization model;

[0028] Based on the basic robust optimization model, slack variables are introduced into the objective function and constraint conditions, and it is transformed into a weak robust optimization planning model that takes into account the uncertainty of wind power and photovoltaic power.

[0029] Furthermore, the planning scheme of the regional power grid system includes the planning values ​​of multiple decision variables of the regional power grid, namely, the power of coal-fired power units Power of transformed coal-fired power units Adjustable hydropower unit power P HP,k,t , transmission line transmission power P Net,b,t , flexible load power P TL,t And the energy storage unit power generation power P PS,h,t .

[0030] Furthermore, the weak robust optimization planning model is as follows:

[0031]

[0032] Where x is the decision variable matrix with A rows and B columns, A is the number of decision variables, and B is the number of sampling points in the scheduling period; c T is the coefficient matrix of the decision variable matrix; γ is the slack variable; d T is the coefficient matrix corresponding to the slack variable; s is the constraint condition The sth constraint in ; For constraints The coefficient matrix of the decision variable matrix x in b; s For constraints The uncertain parameter matrix in γ s is the slack variable matrix in the sth constraint; For constraints The coefficient matrix of the uncertain parameters in ; is the upper limit of the slack variable in the sth constraint; N S,new is the number of constraints in the weak robust optimization model; b s,j represents the jth uncertain parameter in the sth constraint; is the mean value of the jth uncertain parameter in the sth constraint; is the maximum fluctuation range of the jth uncertain parameter in the sth constraint condition; ξ s,j is the uncertain parameter b s,j The degree of fluctuation; Ω s for ξ s,j Box uncertain set; Γ s is the robustness coefficient.

[0033] Furthermore, the power balance constraint is as follows:

[0034]

[0035] in, is the total power generation of all conventional coal-fired power units at time t; is the total power generation capacity of all transformed coal-fired power units at time t; is the total power generation of all hydropower units at time t; is the total transmission power of all transmission lines at time t; is the total power generation of all energy storage units at time t; is the total power of all flexible loads at time t.

[0036] Compared with the prior art, the present invention can achieve at least one of the following beneficial effects:

[0037] 1. This invention avoids the overly conservative decision-making problem of traditional robust optimization models by introducing an improved weak robust optimization model. This improvement makes the power system scheduling scheme more flexible and can better adapt to the uncertainty and volatility of renewable energy output;

[0038] 2. The present invention improves the utilization efficiency of flexible resources through scientific and reasonable flexibility resource scheduling. It can more effectively utilize resources such as conventional coal-fired power units, flexible coal-fired power units, hydropower units, energy storage units, and flexible loads to flexibly respond to changes in supply and demand in the power system.

[0039] 3. This invention enhances the capacity to absorb new energy by building a flexible resource collaborative planning optimization model, which helps to increase the absorption ratio of new energy power generation. It not only helps to reduce the phenomenon of wind and solar power curtailment, but also promotes the wider use of renewable energy.

[0040] 4. The present invention enhances the adaptability of the power system to uncertainty and improves the safety and stability of the power system by optimizing the configuration and scheduling of flexible resources, which is crucial for ensuring the reliable operation and power supply quality of the regional power grid system.

[0041] In the present invention, the above-mentioned technical solutions can be combined with each other to achieve more preferred combinations. Other features and advantages of the present invention will be described in the following description, and some advantages will become apparent from the description or be learned through practice of the present invention. The objectives and other advantages of the present invention can be realized and obtained through the contents particularly pointed out in the description and drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] The accompanying drawings are only for the purpose of illustrating particular embodiments and are not to be considered limiting of the present invention. Like reference symbols denote like parts throughout the drawings.

[0043] Figure 1 This is a flow chart of a method for collaborative planning and optimization of source, grid, load and storage flexibility resources in an embodiment of the present invention. DETAILED DESCRIPTION

[0044] The preferred embodiments of the present invention will be described in detail below in conjunction with the accompanying drawings, wherein the accompanying drawings constitute a part of this application and are used together with the embodiments of the present invention to illustrate the principles of the present invention, and are not used to limit the scope of the present invention.

[0045] The present invention takes the flexibility resources on each side of the source, grid, load and storage as the research object, including conventional coal-fired power units, flexible coal-fired power units, adjustable hydropower units, transmission lines, flexible loads and energy storage units, takes the minimum flexibility shortage as the objective function, and constructs a traditional robust optimization model. Then, on the basis of the traditional robust optimization model, slack variables are introduced to construct a weak robust optimization planning model that considers the uncertainty of wind power and photovoltaic power, and obtains a coordinated operation optimization model for the source, grid, load and storage flexibility resources. The weak robust optimization planning model with the introduction of slack variables can avoid the power system scheduling plan being too conservative, and is helpful to scientifically and efficiently schedule the flexibility resources on each side of the power system, improve the utilization efficiency of flexibility resources, and promote the consumption of new energy electricity. Among them, the flexibility shortage is the sum of the upward flexibility shortage and the downward flexibility shortage of the regional power grid power system; the upward flexibility shortage is the upward flexibility demand minus the upward flexibility supply, and the downward flexibility shortage is the downward flexibility demand minus the downward flexibility supply.

[0046] A specific embodiment of the present invention discloses a method for collaborative planning and optimization of source, grid, load and storage flexibility resources, such as Figure 1 As shown, the following steps are included:

[0047] Step S1: Acquire historical data within a scheduled dispatch period of a regional power grid;

[0048] Step S2: constructing a quantitative supply model and a quantitative demand model for the flexibility capacity of the regional power grid source, grid, load and storage resources;

[0049] Step S3: Based on the flexibility capacity supply quantitative model and the demand quantitative model, construct a flexibility resource collaborative planning model with the goal of minimizing the flexibility shortage; wherein the flexibility resource collaborative planning model is a deterministic planning model;

[0050] Step S4: convert the flexibility resource collaborative planning model into a traditional robust optimization model, and construct a weak robust optimization planning model that takes uncertainty into account based on the traditional robust optimization model; based on the historical data within the predetermined scheduling period, use the CPLEX solver to solve the weak robust optimization planning model to obtain a planning scheme for the regional power grid system.

[0051] Step S1 includes steps S11-S12.

[0052] Step S11: Acquire historical data within a certain scheduling period.

[0053] Exemplarily, the preset scheduling period T is within one year.

[0054] At the sampling point at time t in the dispatch cycle, the historical data of the regional power grid is obtained as follows:

[0055] (1) For conventional coal-fired power units: obtain the set of conventional coal-fired power units, as well as the upward flexibility capacity supply, downward flexibility capacity supply, and maximum ramp rate of each coal-fired power unit i in the set; the power, upper and lower limits of the power generation capacity of the coal-fired power unit i; the operating status, shortest startup time, and minimum shutdown time;

[0056] (2) For the coal-fired power units with flexibility transformation: obtain the set of coal-fired power units with flexibility transformation, as well as the upward flexibility capacity supply, downward flexibility capacity supply, and maximum ramp rate of the coal-fired power unit j with flexibility transformation; the power, upper limit of power generation, lower limit of power generation, and newly added peak regulation depth of the coal-fired power unit j with flexibility transformation; the operating status, shortest startup time, and shortest shutdown time;

[0057] Flexibility-modified thermal power units and conventional thermal power units coexist in the regional power grid.

[0058] Coal-fired power units that have undergone flexibility transformation have a wider adjustment range and faster climbing rate than conventional thermal power units. For example, the load rate of conventional thermal power units is 40% to 100% and the climbing rate is 60MW / h. The load rate of coal-fired power units that have undergone flexibility transformation can be changed to 20% to 100% and the climbing rate is 100MW / h.

[0059] (3) For adjustable hydropower units: obtain the set of adjustable hydropower units, as well as the upward flexibility capacity supply and downward flexibility capacity supply of adjustable hydropower unit k in the set; the power, upper and lower limits of the power generation of adjustable hydropower unit k; the conversion efficiency of water energy to electric energy; the maximum and minimum power generation flow rates; the water head height; and the hydropower generation allocated to a typical day by the dispatcher according to the annual power generation plan;

[0060] (4) For transmission lines: obtain the set of transmission lines, as well as the upward flexibility capacity supply, downward flexibility capacity supply, transmission power, power upper limit, and power lower limit of each transmission line b in the set; the minimum output percentage of the transmission line; and the rated transmission capacity of the transmission line;

[0061] (5) For flexible loads: obtain the flexible load set, as well as the upward flexibility capacity supply, downward flexibility capacity supply, transmission power, transmission power upper limit, and transmission power lower limit of each flexible load c in the set.

[0062] (6) For energy storage units: obtain the energy storage unit set, as well as the upward flexibility capacity supply, downward flexibility capacity supply, operating power, operating power upper limit, operating power lower limit, charging efficiency, discharge efficiency, storage capacity, storage upper limit, storage lower limit; power generation status, charging status; power generation upper limit, power generation lower limit; charging power upper limit, charging power lower limit; storage capacity at the initial and end times of the sampling point within the scheduling period;

[0063] (7) For net load demand: load demand, wind power and photovoltaic power at time t.

[0064] (8) Flexibility requirements: upward and downward flexibility requirements at time t.

[0065] (9) Meteorological data: Meteorological data that affects the output of renewable energy, such as wind speed, temperature, humidity, sunshine time, etc. at time t.

[0066] For example, these historical data can be obtained through a regional power grid power system database.

[0067] Step S12: pre-process the acquired historical data.

[0068] After acquiring the historical data within these scheduled scheduling cycles, data preprocessing is performed, including data cleaning, missing value processing, and outlier detection, to ensure the quality and reliability of the historical data. This historical data will serve as the basis for building models and optimizing scheduling.

[0069] The purpose of step S1 is to obtain and pre-process the historical data of the regional power grid within the predetermined dispatching period, and provide basic data support for the transformation and solution of the regional power grid power system flexibility resource collaborative planning optimization model in the subsequent steps.

[0070] Step S2 includes steps S21-S22.

[0071] Step S21: Construct a quantitative model for the flexibility capacity supply of regional power grid source, grid, load and storage resources.

[0072] Based on the flexibility capacity supply of conventional coal-fired power units, flexible coal-fired power units, adjustable hydropower units, transmission lines, flexible loads and energy storage units, a quantitative model for the flexibility capacity supply of source, grid, load and storage resources of the regional power grid is constructed.

[0073] (1) Calculate the flexibility capacity supply of conventional coal-fired power units.

[0074] The flexible capacity supply of conventional coal-fired power units is mainly limited by technical output and ramp rate, as shown below:

[0075]

[0076] in, Provide upward flexibility capacity for coal-fired power unit i; Provide downward flexibility capacity for coal-fired power unit i; is the maximum ramp rate of coal-fired power unit i; Δt is the operating time window; is the power of coal-fired power unit i; is the upper limit of power generation capacity of coal-fired power unit i; is the lower limit of the power generation capacity of coal-fired power unit i.

[0077] (2) Calculate the flexibility capacity supply of coal-fired power units that have undergone flexibility transformation.

[0078] The flexibility transformation of coal-fired power units has increased the peak load depth of the units and optimized the unit regulation parameters, as shown below:

[0079]

[0080] in, The upward flexibility capacity supply for the coal-fired power unit j that has undergone flexibility transformation; The downward flexibility capacity supply for the coal-fired power unit j that has undergone flexibility transformation; The maximum ramp rate of coal-fired power unit j for flexibility modification; The power of coal-fired power unit j converted for flexibility; The upper limit of the power generation capacity of the coal-fired power unit j after flexibility modification; P is the lower limit of the power generation capacity of the coal-fired power unit j after flexibility transformation; FR It is the newly added peak regulation depth of coal-fired power unit j after flexibility transformation.

[0081] (3) Calculate the flexibility capacity supply of adjustable hydropower units.

[0082] The power generation capacity of the adjustable hydropower unit is related to the amount of water in the storage, as shown below:

[0083]

[0084] in, Provide upward flexibility capacity for adjustable hydropower unit k; P is the downward flexibility capacity supply of the adjustable hydropower unit k; HP,k,t is the power of the adjustable hydropower unit k; P HP,k,max is the upper limit of the power generation capacity of the adjustable hydropower unit k; P HP,k,min is the lower limit of the power generation of the adjustable hydropower unit k; ρ is the density of water, which is 1×10 3 kg / m 3 , η is the conversion efficiency of water energy to electrical energy; qmax is the maximum power generation flow, q min is the minimum power generation flow; h is the water head height.

[0085] (4) Calculate the flexibility capacity supply of transmission lines.

[0086] The flexibility capacity supply of transmission lines is mainly limited by the capacity of transmission tie lines, as shown below:

[0087]

[0088] in, Upward flexibility capacity supply for transmission line b; The downward flexibility capacity supply for transmission line b; is the transmission power of transmission line b; is the upper power limit of transmission line b; is the lower power limit of transmission line b.

[0089] (5) Calculate the flexibility capacity supply of flexible loads.

[0090] Flexible loads participating in demand response can change the time of electricity consumption to provide flexible capacity, such as electric vehicle charging, as shown below:

[0091]

[0092] in, It is the upward flexibility capacity supply for the flexible load c; The downward flexibility capacity supply for the flexible load c; is the transmission power of the flexible load c; is the upper limit of the transmission power of the flexible load c; is the lower limit of the transmission power of the flexible load c.

[0093] (6) Calculate the flexibility capacity supply of the energy storage unit.

[0094] The flexible supply capacity of energy storage units is limited by technical output and stored electricity, as shown below:

[0095]

[0096] in, It is the upward flexibility capacity supply of energy storage h; is the downward flexibility capacity supply for energy storage h; is the operating power of the energy storage h; is the upper limit of the operating power of the energy storage h; is the lower limit of the operating power of the energy storage h; η C is the charging efficiency of energy storage h; ηD is the discharge efficiency of energy storage; is the storage capacity of energy storage h; E h,max is the upper limit of energy storage h; E h,min is the lower limit of energy storage h.

[0097] Based on the above (1)-(6), a quantitative model for the flexibility capacity supply of regional power grid source, grid, load and storage resources is constructed.

[0098] The quantitative model for the flexibility capacity supply of the regional power grid source, grid, load and storage resources is as follows:

[0099]

[0100] in, They are the upward and downward flexible supply of source, grid, load and storage resources at time t; I is the set of conventional coal-fired power units; J is the set of flexible coal-fired power units; K is the set of adjustable hydropower units; B is the set of transmission lines; H is the set of energy storage units; C is the set of flexible loads; are the upward and downward flexibility capacity supply of coal-fired power unit i at time t, respectively; are the upward and downward flexibility capacity supply of coal-fired power unit j after flexibility transformation at time t; are the upward and downward flexibility capacity supply of hydropower unit k at time t, respectively; are the upward and downward flexibility capacity supply of transmission line b at time t, respectively; are the upward and downward flexibility capacity supply of the flexible load c at time t, respectively; They are the upward and downward flexibility capacity supply of energy storage h at time t respectively.

[0101] Step S22: Construct a quantitative model for the flexibility capacity demand of regional power grid source, grid, load and storage resources.

[0102] Affected by the dual uncertainties of new energy (wind power, photovoltaics) and load, the net load shows variability and uncertainty. The regional power grid system needs to have sufficient adjustment capabilities to balance the fluctuations of the net load, thus creating flexibility needs.

[0103] The flexibility capacity demand quantitative model of the regional power grid source-grid-load-storage resources includes the net load demand and flexibility demand of the regional power grid;

[0104] Net load demand P at time t NL,t , calculated as follows:

[0105] P NL,t =P L,t -P W,t -P PV,tFormula (8)

[0106] Among them, P L,t 、P W,t and P PV,t are the load demand, wind power and photovoltaic power at time t respectively;

[0107] Flexibility demand F at time t DE,t , calculated as follows:

[0108]

[0109] Among them, P NL,t-1 is the net load demand at time t-1; They are the upward and downward flexibility demands at time t respectively.

[0110] The purpose of step S2 is to construct a quantitative model of the flexibility capacity supply and demand of regional power grid source, grid, load and storage resources, so as to quantify the flexibility supply capacity and demand of various resources in the power system, and provide a basis for subsequent coordinated planning and optimized scheduling.

[0111] Step S3, specifically.

[0112] The present invention takes minimizing the flexibility deficit as the objective function, combines the operation constraints of various resources (such as output range, climbing rate), power balance constraints and backup constraints, and constructs a flexible resource collaborative planning optimization model.

[0113] The flexibility resource collaborative planning model includes an objective function with the goal of minimizing the flexibility shortage and a set of constraint conditions;

[0114] The objective function f, which aims to minimize the flexibility deficit, is as follows:

[0115]

[0116] Among them, λ up ,λ dn are the weight coefficients of upward and downward flexibility shortage respectively; They are the upward and downward flexibility shortages of the regional power grid system at time t.

[0117] When flexibility resources are insufficient to adjust upward, wind and solar power curtailment can occur. Conversely, when flexibility resources are insufficient to adjust downward, load shedding can occur. To reduce wind and solar power curtailment and load shedding, the model objective function is set to minimize the flexibility shortfall.

[0118] The constraint condition set includes conventional coal-fired power unit operation constraints, flexibility-modified coal-fired power unit operation constraints, adjustable hydropower unit operation constraints, transmission line operation constraints, flexible load operation constraints, energy storage unit operation constraints, flexibility shortage constraints, power balance constraints and power system reserve constraints.

[0119] The constraint set is as follows:

[0120] (1) Conventional coal-fired power generation unit operation constraints: Coal-fired power generation units have large single-unit capacity and stable operation, but the minimum output of coal-fired power without flexibility transformation remains high, the ramp rate is slow, and the start-up and shutdown time is long. As shown below:

[0121]

[0122] Among them, u i,t u is a Boolean variable representing the operating status of coal-fired power unit i at time t. When it is 1, it means that coal-fired power unit i is turned on, and when it is 0, it means that coal-fired power unit i is turned off; i,t-1 is a Boolean variable representing the operating status of the coal-fired power unit at time t-1; T i on is the shortest start-up time of coal-fired power units; T i off The minimum shutdown time of coal-fired power units; is the power of coal-fired power unit i at time t-1; u i,k is a Boolean variable representing the operating status of coal-fired power unit i at time k.

[0123] Power Constraints: represents the output power of coal-fired power unit i at time t must be at its minimum output power and maximum output power To ensure that the output power of coal-fired power units is within a safe range.

[0124] Hill climbing constraints: Indicates that the power change of coal-fired power unit i between two adjacent moments t and t-1 cannot exceed its maximum ramp rate This constraint takes into account the dynamic responsiveness of coal-fired power units and prevents damage to the units or grid equipment due to rapid power changes.

[0125] Start-stop constraint: u i,t -u i,t-1 -u i,k ≤0 means that if coal-fired power unit i is started at time t (i.e. u i,t =1), then the machine must also be turned on at time t-1 (i.e. u i,t-1 =1), that is, the unit cannot be started instantly;

[0126] ui,t-1 -u i,t +u i,k ≤1 means that if coal-fired power unit i stops at time t (i.e. u i,t =0), then the machine must also be shut down at time t-1 (i.e. u i,t-1 =0), that is, the coal-fired power unit cannot be shut down instantly.

[0127] Minimum boot time constraint: u i,t-1 -u i,t +u i,k ≤1 means that if coal-fired power unit i is shut down at time t, then from time t to T i off +t, the coal-fired power unit must remain in the shutdown state; if coal-fired power unit i is started at time t, then from time t to T i on +t, coal-fired power units must remain in operation.

[0128] These constraints ensure the safe, stable, and efficient operation of coal-fired power units, preventing equipment damage and grid instability caused by frequent unit starts and stops and rapid power fluctuations. They also provide an important reference for grid scheduling and optimization.

[0129] (2) Operational constraints of coal-fired power units after flexibility transformation: Coal-fired power units with flexibility transformation have great economic advantages and will be the main supplier of flexibility. After flexibility transformation of coal-fired power units, the minimum output is reduced, the ramp rate is increased, and the start-up time is shortened. This is shown below:

[0130]

[0131] Among them, u j,t u is a Boolean variable representing the operating status of coal-fired power unit j at time t after flexibility transformation. When it is 1, it indicates startup, and when it is 0, it indicates shutdown. i,t-1 is a Boolean variable representing the operating status of coal-fired power unit j at time t-1 after flexibility transformation; The shortest startup time of coal-fired power units after flexibility transformation; The shortest shutdown time of coal-fired power units after flexibility transformation; is the power of the flexible coal-fired power unit j at time t-1; u j,k is a Boolean variable indicating the operating status of coal-fired power unit j at time k after flexibility transformation.

[0132] (3) Operational constraints of adjustable hydropower units: Adjustable hydropower has strong regulation capabilities and can ramp up and start and stop very quickly. The generation capacity of adjustable hydropower is related to the amount of water flowing in, which varies seasonally. The water allocation of adjustable hydropower needs to be arranged on a long-term scale throughout the year. This is shown below:

[0133]

[0134] Among them, E HP,k It is the hydropower generation allocated to a typical day by the dispatcher according to the annual power generation plan; T is the dispatch period; for example, T is one year.

[0135] P HP,k,min ≤P HP,k,t ≤P HP,k,max It represents the power generation power P of the kth adjustable hydropower unit at any time t. HP,k,t The hydropower unit must be operated within a safe range between its minimum and maximum power generation;

[0136] Indicates the total power generation of the kth adjustable hydropower unit in the scheduling period T It must be equal to the hydropower generation E allocated to a typical day by the dispatcher according to the annual power generation plan HP,k , ensuring that the power generation plan of the hydropower unit matches the annual power generation target.

[0137] (4) Transmission line operation constraints: Transmission lines can improve the security capabilities of receiving areas and increase the space for renewable energy consumption in sending areas. This is shown below:

[0138]

[0139] Among them, α net is the minimum output percentage of the transmission line; is the rated transmission capacity of transmission line b.

[0140] (5) Flexible load operation constraints: Flexible loads participating in demand response must meet the following constraints, as shown below:

[0141]

[0142] Flexible loads are those that can adjust their timing or amount of electricity consumption to respond to grid demand, such as electric vehicle charging and electricity consumption for non-critical industrial processes. Adjusting these loads can help the grid balance supply and demand, improving its operational efficiency and reliability.

[0143] Indicates the power of the flexible load TL at any time t Between the lower and upper limits of transmission power;

[0144] It means that within the dispatch period T, the total power of the flexible load is zero; within the dispatch period, the increase and decrease of the flexible load offset each other in total amount; ensuring that the adjustment of the flexible load will not affect the total load balance of the power grid.

[0145] This constraint ensures that flexible loads can provide necessary ancillary services in response to grid demand without impacting the grid's overall load balance. By properly adjusting flexible loads, the grid's flexibility and reliability can be improved, better adapting to the volatility and uncertainty of renewable energy.

[0146] (6) Energy storage unit operation constraints: The energy storage capacity is determined by the energy storage capacity at the previous moment and the charge and discharge capacity at the current moment. The energy storage needs to meet the charge and discharge power constraints, charge and discharge state constraints, and energy storage balance constraints. As shown below:

[0147]

[0148] in, It is a Boolean variable indicating the power generation state of the energy storage unit at time t. When it is 1, it indicates power generation, and when it is 0, it indicates other states. It is a Boolean variable indicating the charging state of the energy storage unit at time t. When it is 1, it indicates charging, and when it is 0, it indicates other states. The upper limit of the energy storage power generation capacity; is the lower limit of the energy storage power generation; The upper limit of the charging power of the energy storage; is the lower limit of the charging power of the energy storage; is the amount of energy stored at time t-1; and It is the storage capacity at the initial and end time of the sampling point in the scheduling period.

[0149] (7) Flexibility quota constraint: The system flexibility quota is not the lower the better. Instead, it should maintain a certain margin while meeting the demand. Excessive capacity redundancy will lead to resource waste, as shown below:

[0150]

[0151] in, The upward flexibility lacks a quota limit coefficient; The upward flexibility limit coefficient.

[0152] (8) Power balance constraint: Power balance is a fundamental safety and stability issue for power systems. In traditional power systems, the main consideration is the balance between power consumption and power generation at the time of maximum load, which can meet the balance requirements at any other time. In new power systems with a high proportion of renewable energy, the balance standard is more complex. Power consumption and power generation need to be balanced in real time, relying on the regulation function of multiple flexible resources.

[0153] The power balance constraints are as follows:

[0154]

[0155] in, is the total power generation of all conventional coal-fired power units at time t; is the total power generation capacity of all transformed coal-fired power units at time t; is the total power generation of all hydropower units at time t; is the total transmission power of all transmission lines at time t; is the total power generation of all energy storage units at time t; is the total power of all flexible loads at time t.

[0156] This power balance constraint ensures that at any time t, the total generated power in the regional power system equals the total power demand, thus maintaining the power balance of the power system. In new power systems with a high proportion of renewable energy, due to the volatility and uncertainty of renewable energy sources such as wind and photovoltaic power, achieving power balance relies more heavily on the regulation of multiple flexible resources, such as coal-fired power units, hydropower units, energy storage units, and flexible loads. The coordinated regulation of these resources can better adapt to the fluctuations of renewable energy and ensure the stable operation of the regional power system.

[0157] (9) Power system reserve constraints: To ensure real-time power balance, coal-fired power plants must reserve a certain amount of reserve capacity to prevent imbalances caused by renewable energy forecast deviations, load forecast deviations, and various operational accidents. Power system reserve constraints include positive reserve constraints and negative reserve constraints.

[0158] (1) Positive reserve constraint (i.e., upward adjustment of reserve), as follows:

[0159]

[0160] Among them, r i U Positive reserve rate.

[0161] This constraint states that at any time t, the sum of the difference between the maximum power generation capacity and the actual power generation capacity (i.e., the reserve capacity) of all conventional coal-fired power units i and flexible coal-fired power units j in the power system must be greater than or equal to the positive reserve rate r i U Multiply by the total electricity demand P L,t ; Ensure that the power system has sufficient positive reserve capacity to cope with possible power generation shortages.

[0162] (2) Negative reserve constraint (i.e., downward adjustment of reserve)

[0163]

[0164] Among them, r t D Negative reserve ratio.

[0165] This constraint states that at any time t, the sum of the difference between the actual power generation capacity and the minimum power generation capacity (i.e., the power generation that can be reduced) of all conventional coal-fired power units j and flexible modified coal-fired power units j in the power system must be less than or equal to the negative reserve rate r t D Multiply by the total electricity demand P L,t ; Ensure that the power system has sufficient negative reserve capacity to cope with possible overgeneration.

[0166] When constructing the flexibility resource collaborative planning model, it is assumed that all relevant input parameters are known and determined; the historical data of the regional power grid system are used as fixed values ​​of these parameters; therefore, the flexibility resource collaborative planning model is a deterministic planning model.

[0167] The model's objective function is calculated based on fixed mathematical expressions and parameters, without any uncertainty. The set of constraints is also based on fixed rules and parameters. These constraints do not change over time or with external conditions. By ignoring uncertainty, deterministic models simplify the complexity of the problem.

[0168] Although deterministic models are useful in simplifying problems and providing benchmark solutions, they cannot fully reflect the uncertainties in actual power system operation. Therefore, in step S4, the present invention transforms the deterministic model into a weakly robust optimization model that considers uncertainty to improve the practicality and robustness of the model.

[0169] The purpose of step S3 is to build a deterministic flexibility resource collaborative planning model. By quantifying flexibility supply and demand (the objective function of minimizing the shortage) and nine types of physical constraints, it provides a basic mathematical model for the collaborative planning of source, grid, load and storage resources to support subsequent robust optimization improvements.

[0170] Step S4 includes steps S41-S43.

[0171] Converting the flexible resource collaborative planning model into a basic robust optimization model;

[0172] Based on the basic robust optimization model, slack variables are introduced into the objective function and constraint conditions, and it is transformed into a weak robust optimization planning model that takes into account the uncertainty of wind power and photovoltaic power.

[0173] Step S41: converting the flexible resource collaborative planning model into a basic robust optimization model.

[0174] The flexible resource collaborative planning model constructed in step S3 is a deterministic model. This model doesn't account for the uncertainty of renewable energy output caused by natural factors like wind speed and sunlight. This makes planning results prone to deviating from actual conditions when applied, reducing the credibility of the regional power system planning results. Therefore, step S4 builds on the flexible resource system planning model from step S3 by using robust optimization methods to account for wind speed and sunlight uncertainties, thereby forming a flexible resource collaborative planning optimization model.

[0175] In the flexible resource system planning model of step S3 of the present invention, six decision variables are: coal-fired power unit power Power of transformed coal-fired power units Adjustable hydropower unit power P HP,k,t , transmission line transmission power P Net,b,t , flexible load power P TL,t And the energy storage unit power generation power P PS,h,t ; In specific applications, additions and deletions can be made according to specific needs.

[0176] First, the flexible resource collaborative planning model is converted into a basic robust optimization model in the traditional robust optimization form, as shown below:

[0177]

[0178] Wherein, x is a decision variable matrix with A rows and B columns, for example, the flexible resource coordination model in step S3 has A decision variables, and the values ​​of each decision variable at B sampling points within the preset scheduling period are included; c T is the coefficient matrix of the decision variable matrix, corresponding to the coefficient of each decision variable in x, such as λ in formula (10) up ,λ dn Weight coefficient; s is the sth constraint in the constraint set; N S For constraints The number of constraints in is the coefficient matrix of the decision variable matrix x in the constraint conditions, and its form is the same as c T Similarly, for example, in formula (14) Transmission line operation constraints, is the decision variable in the transmission line operation constraint condition. If its constant term is 1, then the corresponding element in its coefficient matrix is ​​1; b s Represents the uncertain parameter matrix in the constraint condition set. The uncertain parameters in the present invention include wind power output P W,t With photovoltaic output P PV,t ; is the coefficient matrix of the uncertain parameters in the sth constraint condition, and c T 、 similar.

[0179] The objective function of formula (10) is the weighted sum of the upward and downward flexibility shortages; the upward flexibility shortage is the upward flexibility demand minus the upward flexibility supply; the downward flexibility shortage is the downward flexibility demand minus the downward flexibility supply;

[0180] Since the upward flexibility demand and the downward flexibility demand are deterministic, they are deterministic net load demands. Therefore, the objective function (10) mainly reflects the upward flexibility supply and the downward flexibility supply. The power of each unit plays a decisive role in the supply, and the response for each unit is the flexibility supply of each unit at each time t. Therefore, for formula (21), the objective function is reflected in the decision variable matrix x and the coefficient matrix c of the decision variable matrix T The product of is the abstract representation of formula (10).

[0181] Step S42: Based on the basic robust optimization model, slack variables are introduced into the objective function and constraint conditions to transform it into a weak robust optimization planning model that takes into account the uncertainty of wind power and photovoltaic power.

[0182] Based on the basic robust optimization model, the present invention introduces slack variables to change the robustness of the planning scheme, improves the basic robust optimization model into a weak robust optimization model, and uses box set constraints and 1-norm constraints to describe the uncertain parameter polyhedron set, thereby achieving an improvement on the traditional robust optimization model (the basic robust optimization model is the traditional robust optimization model) and improving the conservatism of the model.

[0183] The basic robust optimization model requires that all constraints in the constraint set be satisfied under all circumstances, and makes decisions based on a worst-case scenario. However, in the actual operation of regional power systems, the worst-case scenario rarely occurs, and this model results in overly conservative decisions. Therefore, the basic robust optimization model is improved to a weak robust optimization model, using box set constraints and 1-norm constraints to describe the uncertain parameter polyhedron set. The improved weak robust optimization model is shown below.

[0184] The weak robust optimization planning model is as follows:

[0185]

[0186] Where x is the decision variable matrix with A rows and B+1 columns, A is the number of decision variables, and B is the number of sampling points in the scheduling period; c T is the coefficient matrix of the decision variable matrix; γ is the slack variable; d T is the coefficient matrix corresponding to the slack variable; s is the sth constraint in the constraint set; is the coefficient matrix of the decision variable matrix x in the constraint condition set; b s For constraints The uncertain parameter matrix in γ s is the slack variable matrix in the sth constraint; For constraints The coefficient matrix of the uncertain parameters in ; For constraints The upper limit of the slack variable in the sth constraint; N S,new For constraints The number of constraints in b s,j represents the jth uncertain parameter in the sth constraint; is the mean value of the jth uncertain parameter in the sth constraint; is the maximum fluctuation range of the jth uncertain parameter in the sth constraint condition; ξ s,j is the uncertain parameter b s,j The degree of fluctuation; Ω s for ξ s,j Box uncertain set; Γ s is the robustness coefficient.

[0187] Formula (22) introduces a slack variable γ in the objective function and constraints based on Formula (21), facilitating the solution within a larger feasible domain. The slack variable γ has no actual physical meaning and changes continuously during the model solution, thereby obtaining a feasible planning solution.

[0188] The 0th column of x is the planned value of the decision variable to be solved, and the 1st to Bth columns are the historical data values ​​of the decision variables at each sampling point within the predetermined scheduling period T.

[0189] d T is the coefficient matrix corresponding to the slack variable, which has the same meaning as And similar. is the upper limit of the slack variable in the sth constraint.

[0190] Since there are multiple uncertain parameters in the model of the present invention, b s,jrepresents the jth uncertain parameter in the sth constraint; Then it represents the mean value of the jth uncertain parameter in the sth constraint, which is calculated from the historical data of the uncertain parameter; represents the maximum fluctuation range of the jth uncertain parameter in the sth constraint, which is set by the historical data of the uncertain parameter;

[0191] ξ s,j is the uncertain parameter b s,j The degree of fluctuation of , the value is between 0 and 1;

[0192] Ω s Yes s,j Box uncertain set; Γ s is the robustness coefficient. By presetting its value, the expected range of the fluctuation of the uncertain parameters is controlled, and the total amount of fluctuation is controlled.

[0193] For example, the robustness coefficient Γ s The default value is 1.5; in actual operation, it can be adjusted according to specific needs. This means that the model will consider 1.5 times the range of wind speed variation, and the regional power system will also be able to maintain stable operation.

[0194] In the above-mentioned resource planning process for source-grid-load-storage flexibility, both wind power and photovoltaic output have certain uncertainties. The corresponding uncertainty constraints are as follows:

[0195]

[0196] Among them, ξ WT is the uncertain parameter of wind power; ξ PV is the photovoltaic uncertainty parameter; Γ is the robust coefficient of wind power and photovoltaic; Γ1 and Γ2 are the robust coefficients of wind power and photovoltaic respectively.

[0197] Step S43: Based on the historical data within the predetermined scheduling period, the weak robust optimization planning model is solved by using a CPLEX solver to obtain a planning scheme for the regional power grid system.

[0198] The proposed method for collaborative planning and optimization of source-grid-load-storage flexibility resources is applicable to the planning stage of a regional power grid. For example, a weakly robust optimization planning model is solved using the CPLEX solver to obtain the final planning solution for the regional power grid.

[0199] The planning scheme of the regional power grid system includes the planning values ​​of multiple decision variables of the regional power grid, namely, the power of coal-fired power units Power of transformed coal-fired power units Adjustable hydropower unit power P HP,k,t, transmission line transmission power P Net,b,t , flexible load power P TL,t And the energy storage unit power generation power P PS,h,t .

[0200] In summary, the method for collaborative planning and optimization of source-grid-load-storage flexibility resources according to the embodiment of the present invention has the following beneficial effects:

[0201] 1. This invention avoids the overly conservative decision-making problem of traditional robust optimization models by introducing an improved weak robust optimization model. This improvement makes the power system scheduling scheme more flexible and can better adapt to the uncertainty and volatility of renewable energy output;

[0202] 2. The present invention improves the utilization efficiency of flexible resources through scientific and reasonable flexibility resource scheduling. It can more effectively utilize resources such as conventional coal-fired power units, flexible coal-fired power units, hydropower units, energy storage units, and flexible loads to flexibly respond to changes in supply and demand in the power system.

[0203] 3. This invention enhances the capacity to absorb new energy by building a flexible resource collaborative planning optimization model, which helps to increase the absorption ratio of new energy power generation. It not only helps to reduce the phenomenon of wind and solar power curtailment, but also promotes the wider use of renewable energy.

[0204] 4. The present invention enhances the adaptability of the power system to uncertainty and improves the safety and stability of the power system by optimizing the configuration and scheduling of flexible resources, which is crucial for ensuring the reliable operation and power supply quality of the regional power grid system.

[0205] Those skilled in the art will appreciate that all or part of the process steps of the above-described embodiments can be implemented by instructing related hardware through a computer program, and the program can be stored in a computer-readable storage medium, such as a magnetic disk, an optical disk, a read-only memory, or a random access memory.

[0206] The above description is only a preferred specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any changes or substitutions that can be easily thought of by any technician familiar with this technical field within the technical scope disclosed by the present invention should be covered by the scope of protection of the present invention.

Claims

1. A method for collaborative planning and optimization of source, grid, load and storage flexibility resources, characterized in that: include: Obtain historical data within the scheduled dispatch period of the regional power grid; Construct a quantitative model for the supply and demand of the flexibility capacity of regional power grid source, grid, load and storage resources; Based on the flexibility capacity supply quantification model and the demand quantification model, a flexibility resource collaborative planning model is constructed with the goal of minimizing the flexibility shortage; wherein the flexibility resource collaborative planning model is a deterministic planning model; The flexibility resource collaborative planning model is converted into a basic robust optimization model, and a weak robust optimization planning model considering uncertainty is constructed based on the traditional robust optimization model; based on the historical data within the predetermined scheduling period, the weak robust optimization planning model is solved using the CPLEX solver to obtain a planning scheme for the regional power grid system.

2. The method for collaborative planning and optimization of source-grid-load-storage flexibility resources according to claim 1 is characterized in that: Based on the flexibility capacity supply of conventional coal-fired power units, flexible coal-fired power units, adjustable hydropower units, transmission lines, flexible loads and energy storage units, a quantitative model for the flexibility capacity supply of source, grid, load and storage resources of the regional power grid is constructed.

3. The method for collaborative planning and optimization of source-grid-load-storage flexibility resources according to claim 2 is characterized in that: The quantitative model for the flexibility capacity supply of the regional power grid source, grid, load and storage resources is as follows: in, They are the upward and downward flexible supply of source, grid, load and storage resources at time t; I is the set of conventional coal-fired power units; J is the set of flexible coal-fired power units; K is the set of adjustable hydropower units; B is the set of transmission lines; H is the set of energy storage units; C is the set of flexible loads; are the upward and downward flexibility capacity supply of coal-fired power unit i at time t, respectively; are the upward and downward flexibility capacity supply of coal-fired power unit j after flexibility transformation at time t; are the upward and downward flexibility capacity supply of hydropower unit k at time t, respectively; are the upward and downward flexibility capacity supply of transmission line b at time t, respectively; are the upward and downward flexibility capacity supply of flexible load c at time t respectively; They are the upward and downward flexibility capacity supply of energy storage h at time t respectively.

4. The method for collaborative planning and optimization of source-grid-load-storage flexibility resources according to claim 3 is characterized in that: The flexibility capacity demand quantitative model of the regional power grid source-grid-load-storage resources includes the net load demand and flexibility demand of the regional power grid; Net load demand P at time t NL,t , calculated as follows: P NL,t =P L,t -P W,t -P PV,t Among them, P L,t 、P W,t and P PV,t are the load demand, wind power and photovoltaic power at time t respectively; Flexibility demand F at time t DE,t , calculated as follows: Among them, P NL,t-1 is the net load demand at time t-1; They are the upward and downward flexibility demands at time t respectively.

5. The method for collaborative planning and optimization of source, grid, load and storage flexibility resources according to claim 4 is characterized in that: The flexibility resource collaborative planning model includes an objective function with the goal of minimizing the flexibility shortage and a set of constraint conditions; The objective function f, which aims to minimize the flexibility deficit, is as follows: Among them, λ up ,λ dn are the weight coefficients of upward and downward flexibility shortage respectively; They are the upward and downward flexibility shortages of the regional power grid system at time t.

6. The method for collaborative planning and optimization of source, grid, load and storage flexibility resources according to claim 5 is characterized in that: The constraint condition set includes conventional coal-fired power unit operation constraints, flexibility-modified coal-fired power unit operation constraints, adjustable hydropower unit operation constraints, transmission line operation constraints, flexible load operation constraints, energy storage unit operation constraints, flexibility shortage constraints, power balance constraints and power system reserve constraints.

7. The method for collaborative planning and optimization of source, grid, load and storage flexibility resources according to claim 5 is characterized in that: Converting the flexible resource collaborative planning model into a basic robust optimization model; Based on the basic robust optimization model, slack variables are introduced into the objective function and constraint conditions, and it is transformed into a weak robust optimization planning model that takes into account the uncertainty of wind power and photovoltaic power.

8. The method for collaborative planning and optimization of source-grid-load-storage flexibility resources according to any one of claims 1 to 7, characterized in that: The planning scheme of the regional power grid system includes the planning values ​​of multiple decision variables of the regional power grid, namely, the power of coal-fired power units Power of transformed coal-fired power units Adjustable hydropower unit power P HP,k,t , transmission line transmission power P Net,b,t , flexible load power P TL,t And the energy storage unit power generation power P PS,h,t .

9. The method for collaborative planning and optimization of source-grid-load-storage flexibility resources according to claim 8, characterized in that: The weak robust optimization planning model is as follows: Where x is the decision variable matrix with A rows and B columns, A is the number of decision variables, and B is the number of sampling points in the scheduling period; c T is the coefficient matrix of the decision variable matrix; γ is the slack variable; d T is the coefficient matrix corresponding to the slack variable; s is the constraint condition The sth constraint in ; For constraints The coefficient matrix of the decision variable matrix x in b; s For constraints The uncertain parameter matrix in γ s is the slack variable matrix in the sth constraint; For constraints The coefficient matrix of the uncertain parameters in ; is the upper limit of the slack variable in the sth constraint; N S,new is the number of constraints in the weak robust optimization model; b s,j represents the jth uncertain parameter in the sth constraint; is the mean value of the jth uncertain parameter in the sth constraint; is the maximum fluctuation range of the jth uncertain parameter in the sth constraint condition; ξ s,j is the uncertain parameter b s,j The degree of fluctuation; Ω s for ξ s,j Box uncertain set; Γ s is the robustness coefficient.

10. The method for collaborative planning and optimization of source, grid, load and storage flexibility resources according to claim 6, characterized in that: The power balance constraints are as follows: in, is the total power generation of all conventional coal-fired power units at time t; is the total power generation capacity of all transformed coal-fired power units at time t; is the total power generation of all hydropower units at time t; is the total transmission power of all transmission lines at time t; is the total power generation of all energy storage units at time t; is the total power of all flexible loads at time t.

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