A Source-Load Collaborative Optimal Scheduling Method and Terminal for Flexible Supply-Demand Balance

Abstract

The present invention discloses a method and a terminal for collaborative optimal scheduling of source and load with flexible supply and demand balance, which obtain the first operation data of thermal power units, the second operation data of energy storage devices, and the active power load of the power grid; establish a power-side flexibility model, an energy storage flexibility model, a demand-side flexibility model and corresponding flexibility evaluation indexes based on the first operation data of the thermal power units, the second operation data of the energy storage devices, and the active power load of the power grid; establish a multi-objective collaborative optimal scheduling model of source and load based on the flexibility evaluation indexes, solve the multi-objective collaborative optimal scheduling model of source and load to obtain a scheduling scheme, make flexibility become a part considered in the scheduling model, the flexibility evaluation index can be used to evaluate the flexibility degree of the above three models, consider the flexibility of both the source and load sides, and establish a multi-objective collaborative optimal scheduling model through source-load interaction, so as to realize the comprehensive optimization of the flexibility and economy of power grid operation under extreme typical scenarios.

Description

Technical Field

[0001] The present invention relates to the technical field of new energy, and particularly to a source-load collaborative optimization scheduling method and terminal for flexible supply-demand balance. Background Art

[0002] In a new power system, due to the double fluctuations on the power supply side and the load side, the mode of simply increasing standby power supplies to handle various emergencies in the power grid is difficult to sustain technically and economically. The flexibility of the power system is a measure of the ability of the power grid to effectively utilize existing resources to handle various emergencies, and is one of the core issues concerned in power grid planning and operation.

[0003] Currently, most of the research on power grid operation scheduling focuses on improving economic efficiency and is difficult to adapt to the situation of high-proportion new energy grid connection in the future. And for the research on the flexibility of the power system, most of it focuses on the one-way analysis of flexible supply resources, that is, the source unidirectionally matches the load, without considering the two-way interaction between the source and the load, and cannot reflect the physical mechanism of the flexible supply-demand balance of the system. Summary of the Invention

[0004] The technical problem to be solved by the present invention is: to provide a source-load collaborative optimization scheduling method and terminal for flexible supply-demand balance, which can achieve the comprehensive optimization of the flexibility and economic efficiency of the power system.

[0005] To solve the above technical problem, a technical solution adopted by the present invention is:

[0006] A source-load collaborative optimization scheduling method for flexible supply-demand balance, comprising the steps of:

[0007] Obtain the first operation data of thermal power units, the second operation data of energy storage devices, and the active power load of the power grid;

[0008] Based on the first operation data of the thermal power units, the second operation data of the energy storage devices, and the active power load of the power grid, establish a power supply side flexibility model, an energy storage flexibility model, a demand side flexibility model, and corresponding flexibility evaluation indexes;

[0009] Based on the flexibility evaluation indexes, establish a source-load multi-objective collaborative optimization scheduling model, and solve the source-load multi-objective collaborative optimization scheduling model to obtain a scheduling plan.

[0010] To solve the above technical problem, another technical solution adopted by the present invention is:

[0011] A source-load collaborative optimization scheduling terminal for flexible supply-demand balance, comprising a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the computer program, the following steps are implemented:

[0012] Obtain the first operating data of the thermal power unit, the second operating data of the energy storage device, and the active power load of the power grid;

[0013] Based on the first operating data of the thermal power unit, the second operating data of the energy storage device, and the active power load of the power grid, establish a power-side flexibility model, an energy storage flexibility model, a demand-side flexibility model, and corresponding flexibility evaluation indexes;

[0014] Based on the flexibility evaluation indexes, establish a source-load multi-objective collaborative optimization scheduling model, and solve the source-load multi-objective collaborative optimization scheduling model to obtain a scheduling plan.

[0015] The beneficial effects of the present invention are as follows: Based on the first operating data of the thermal power unit, the second operating data of the energy storage device, and the active power load of the power grid, establish a power-side flexibility model, an energy storage flexibility model, a demand-side flexibility model, and corresponding flexibility evaluation indexes; based on the flexibility evaluation indexes, establish a source-load multi-objective collaborative optimization scheduling model, and solve the source-load multi-objective collaborative optimization scheduling model to obtain a scheduling plan. Through the power-side flexibility model, the energy storage flexibility model, and the demand-side flexibility model, flexibility can be made a part considered in the scheduling model. The flexibility evaluation indexes can be used to evaluate the flexibility degree of the above three models. Considering the flexibility on both the source and load sides, establish a multi-objective collaborative optimization scheduling model through source-load interaction, so as to realize the comprehensive optimization of the flexibility and economy of power grid operation under extreme typical scenarios. Description of the Drawings

[0016] Figure 1 It is a step flow chart of a source-load collaborative optimization scheduling method for flexibility supply-demand balance according to an embodiment of the present invention;

[0017] Figure 2 It is a structural schematic diagram of a source-load collaborative optimization scheduling terminal for flexibility supply-demand balance according to an embodiment of the present invention;

[0018] Figure 3 It is an optimization scheduling flow chart in the source-load collaborative optimization scheduling method for flexibility supply-demand balance according to an embodiment of the present invention. Detailed Embodiments

[0019] To describe in detail the technical content, the achieved objectives, and the effects of the present invention, the following is described in conjunction with the embodiments and with reference to the drawings.

[0020] Please refer to Figure 1 , an embodiment of the present invention provides a source-load collaborative optimization scheduling method for flexibility supply-demand balance, including the steps of:

[0021] Obtain the first operating data of the thermal power unit, the second operating data of the energy storage device, and the active power load of the power grid;

[0022] Based on the first operating data of the thermal power unit, the second operating data of the energy storage device, and the active power load of the power grid, a power-side flexibility model, an energy storage flexibility model, a demand-side flexibility model, and corresponding flexibility evaluation indexes are established;

[0023] Based on the flexibility evaluation indexes, a source-load multi-objective collaborative optimization scheduling model is established, and the source-load multi-objective collaborative optimization scheduling model is solved to obtain a scheduling plan.

[0024] As can be seen from the above description, the beneficial effects of the present invention are as follows: Based on the first operating data of the thermal power unit, the second operating data of the energy storage device, and the active power load of the power grid, a power-side flexibility model, an energy storage flexibility model, a demand-side flexibility model, and corresponding flexibility evaluation indexes are established. Based on the flexibility evaluation indexes, a source-load multi-objective collaborative optimization scheduling model is established, and the source-load multi-objective collaborative optimization scheduling model is solved to obtain a scheduling plan. Through the power-side flexibility model, the energy storage flexibility model, and the demand-side flexibility model, flexibility can be made a part of the consideration in the scheduling model. The flexibility evaluation indexes can be used to evaluate the flexibility degree of the above three models. The flexibility of both the source and the load is considered. A multi-objective collaborative optimization scheduling model is established through source-load interaction, so as to realize the comprehensive optimization of the flexibility and economy of power grid operation under extreme typical scenarios.

[0025] Further, the first operating data of the thermal power unit includes the maximum output power, the minimum output power, the upward regulation power rate, the downward regulation power rate, and the actual output power at any moment;

[0026] The second operating data of the energy storage device includes the charging power, the discharging power, the rated energy storage capacity, and the state of charge;

[0027] The establishment of the power-side flexibility model, the energy storage flexibility model, the demand-side flexibility model, and the corresponding flexibility evaluation indexes based on the first operating data of the thermal power unit, the second operating data of the energy storage device, and the active power load of the power grid includes:

[0028] Based on the maximum output power, the minimum output power, the upward regulation power rate, the downward regulation power rate, and the actual output power, a power-side flexibility model is established;

[0029] Based on the charging power, the discharging power, the rated energy storage capacity, and the state of charge, an energy storage flexibility model is established;

[0030] Based on the active power load of the power grid, a demand-side flexibility model is established;

[0031] According to the power-side flexibility model, the energy storage flexibility model, and the demand-side flexibility model, corresponding flexibility evaluation indexes are established.

[0032] As can be seen from the above description, instead of only considering the unidirectional action of the source and load as in the prior art, the bidirectional interaction between the source and load is considered by establishing a flexibility model for the power supply side, a flexibility model for energy storage, and a flexibility model for the demand side, and it can ensure that the power grid has sufficient flexibility to achieve the balance of flexibility supply and demand.

[0033] Furthermore, the flexibility evaluation index is as follows:

[0034]

[0035] In the formula, represents the upward overall flexibility deficit rate at time t, represents the downward overall flexibility deficit rate at time t, ΔP t up represents the upward change in load power at time t, ΔP t down represents the downward change in load power at time t, represents the upward flexible supply of the thermal power unit at time t, represents the downward flexible supply of the thermal power unit at time t, represents the upward flexible supply of the energy storage device at time t, represents the downward flexible supply of the energy storage device at time t, represents the upward flexibility of the demand side at time t, represents the downward flexibility of the demand side at time t.

[0036] As can be seen from the above description, the flexibility evaluation index is used to evaluate the flexibility of the flexibility model for the power supply side, the flexibility model for energy storage, and the flexibility model for the demand side, so as to ensure that the final dispatching scheme can achieve flexibility balance.

[0037] Furthermore, the flexibility model for the power supply side is as follows:

[0038]

[0039] In the formula, represents the maximum output power of the thermal power unit, represents the minimum output power of the thermal power unit, P G,t represents the actual output power of the thermal power unit at time t, represents the upward regulation power rate of the thermal power unit at time t, Δt represents the time scale, represents the downward regulation power rate of the thermal power unit at time t.

[0040] As described above, a flexibility model of the power supply side is established based on the maximum output power, minimum output power, upward regulation power rate, downward regulation power rate, and actual output power of a thermal power unit, thereby taking into account the flexibility of the power supply side.

[0041] Furthermore, the energy storage flexibility model is as follows:

[0042]

[0043] In the formula, P dis,t represents the discharge power of the energy storage device at time t, P ch,t represents the charging power of the energy storage device at time t, E ess represents the rated energy of the energy storage, SOC t represents the state of charge of the energy storage device at time t, SOC min represents the minimum state of charge of the energy storage device, SOC max represents the maximum state of charge of the energy storage device, and Δt represents the time scale.

[0044] As described above, an energy storage flexibility model is established based on the charging power, discharge power, rated energy of the energy storage, and state of charge, thereby ensuring the flexibility of the energy storage device.

[0045] Furthermore, before obtaining the first operation data of the thermal power unit, the second operation data of the energy storage device, and the active power load of the power grid, it includes:

[0046] Obtain the benchmark electricity price of the preset regional power grid;

[0047] Use the stepped elastic load curve modeling method to formulate a dynamic electricity price according to the benchmark electricity price;

[0048] Based on the correspondence between the electricity price grades and the expected load response rate, smooth the initial load curve of the power grid according to the dynamic electricity price until the user's electricity consumption behavior adapts to the system dispatching requirements, and obtain the active power load of the power grid:

[0049]

[0050] In the formula, P t load represents the active power load after the user's electricity consumption behavior adapts to the system dispatching requirements at time t, α kt represents a 0-1 discrete variable, the flag of each electricity price grade, η kt represents the load response rate at the kth electricity price grade at time t, P t f represents the predicted active power value of the load at time t, ρ trepresents the dynamic electricity price after the user's electricity consumption behavior at time t adapts to the system scheduling requirements, ρ 0 represents the reference electricity price, β kt represents the price rate at the kth electricity price level at time t;

[0051] The demand-side flexibility model is as follows:

[0052]

[0053] In the formula, represents the actual active power load at time t + 1, represents the actual active power load at time t.

[0054] As can be seen from the above description, since the dynamic electricity price corresponds to the dynamic load, based on the correspondence between the electricity price level and the expected load response rate, the initial load curve of the power grid is smoothed according to the dynamic electricity price until the user's electricity consumption behavior adapts to the system scheduling requirements. By changing the electricity price, the load curve can be changed, making the load curve smoother and easier to formulate a scheduling strategy. A demand-side flexibility model is established based on the active power load of the power grid, taking into account the flexibility of both the power supply side and the demand side at the same time.

[0055] Furthermore, the source-load multi-objective collaborative optimization scheduling model established based on the flexibility evaluation index includes:

[0056] Construct a first objective function according to the power generation cost of the thermal power unit and the operation cost of the energy storage device;

[0057] Construct a second objective function according to the wind curtailment cost, light curtailment cost and load shedding cost of the power system;

[0058] Construct a comprehensive flexibility deficiency rate according to the flexibility evaluation index;

[0059] Based on the first objective function, the second objective function and the comprehensive flexibility deficiency rate, establish a total objective function and the corresponding membership function, and establish the constraint conditions of the total objective function;

[0060] Based on the total objective function, the membership function and the constraint conditions, obtain the source-load multi-objective collaborative optimization scheduling model.

[0061] As can be seen from the above description, based on the total objective function, the membership function and the constraint conditions, a source-load multi-objective collaborative optimization scheduling model is obtained. This model comprehensively considers multiple objectives such as economic cost and comprehensive flexibility deficiency rate, and can make the decision maker most satisfied with the scheduling plan, so as to realize the effective collaborative optimization scheduling of both the source and load sides.

[0062] Furthermore, the first objective function F 1 is:

[0063]

[0064] Wherein, C G represents the power generation cost of the thermal power unit, C ESS represents the operating cost of the energy storage device, a represents the first coefficient of the power generation cost of the thermal power unit, b represents the second coefficient of the power generation cost of the thermal power unit, c represents the third coefficient of the power generation cost of the thermal power unit, P G,t represents the actual output power of the thermal power unit at time t, ρ t represents the dynamic electricity price after the user's electricity consumption behavior adapts to the system scheduling requirements at time t, P dis,t represents the discharge power of the energy storage device at time t, P ch,t represents the charging power of the energy storage device at time t;

[0065] The second objective function F 2 is:

[0066]

[0067] Wherein, C wind represents the wind curtailment cost of the power system, C light represents the PV curtailment cost, C load represents the load shedding cost, C w represents the wind curtailment penalty coefficient, C pv represents the PV curtailment penalty coefficient, C l represents the load shedding penalty coefficient, represents the actual wind curtailment power, represents the actual PV curtailment power, represents the actual load shedding power;

[0068] The comprehensive flexibility deficiency rate F 3 is:

[0069]

[0070] Wherein, ξ represents the weight coefficient;

[0071] The total objective function F(x) is:

[0072]

[0073] The membership function μ is:

[0074]

[0075] Wherein, F min represents the minimum value of the total objective function, Fmax Represents the maximum value of the total objective function.

[0076] As can be seen from the above description, by using the descending half-linear function as the membership function, the membership function value represents the satisfaction degree of the decision maker, thus ensuring that the satisfaction degree corresponding to the scheduling plan finally output by the model is the highest and most capable of meeting the requirements of power system scheduling.

[0077] Furthermore, the constraints for establishing the total objective function include:

[0078] Establish the load power constraint, energy storage device constraint, supply-demand balance constraint, thermal power output constraint, load shedding constraint, wind abandonment constraint, and photovoltaic abandonment constraint for the total objective function.

[0079] As can be seen from the above description, the finally output scheduling plan can determine the power output of each power source while satisfying the load power constraint, energy storage device constraint, supply-demand balance constraint, thermal power output constraint, load shedding constraint, wind abandonment constraint, and photovoltaic abandonment constraint.

[0080] Please refer to Figure 2 , Another embodiment of the present invention provides a source-load collaborative optimization scheduling terminal for flexible supply-demand balance, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the computer program, each step in the above-mentioned source-load collaborative optimization scheduling method for flexible supply-demand balance is implemented.

[0081] The above-mentioned source-load collaborative optimization scheduling method and terminal of the present invention can be applied to a power system, which will be described below through specific embodiments:

[0082] Embodiment 1

[0083] Please refer to Figure 1 and Figure 3 , A source-load collaborative optimization scheduling method for flexible supply-demand balance in this embodiment includes the steps:

[0084] S0. Obtain the benchmark electricity price of the preset regional power grid;

[0085] In an alternative embodiment, obtain the benchmark electricity price of the preset regional power grid and initial data, where the initial data includes an initial load curve;

[0086] In another alternative embodiment, the initial data further includes wind power data and photovoltaic data;

[0087] S1. Use the stepped elastic load curve modeling method to formulate a dynamic electricity price according to the benchmark electricity price, as Figure 3 shown;

[0088] S2. Based on the correspondence between electricity price tiers and the expected load response rate, smooth the initial load curve of the power grid according to the dynamic electricity price until the user's electricity consumption behavior adapts to the system dispatching requirements, and obtain the active load of the power grid:

[0089]

[0090] In the formula, P t load represents the active load after the user's electricity consumption behavior adapts to the system dispatching requirements at time t, α kt represents a 0-1 discrete variable, the flag of each electricity price tier, η kt represents the load response rate at time t under the kth electricity price tier, P t f represents the predicted active value of the load at time t, ρ t represents the dynamic electricity price after the user's electricity consumption behavior adapts to the system dispatching requirements at time t, ρ 0 represents the benchmark electricity price, β kt represents the price rate at time t under the kth electricity price tier;

[0091] Among them, the correspondence between the electricity price tiers and the expected load response rate is shown in Table 1;

[0092] Table 1 Correspondence between electricity price tiers and expected load response rate

[0093]

[0094]

[0095] As shown in Table 1, different price rates correspond to different load response rates, that is, the dynamic electricity price corresponds to dynamic load. Therefore, the load curve can be changed by changing the electricity price, making the load curve smoother, enabling the user's electricity consumption behavior to adapt to the system dispatching requirements, and making it easier to formulate dispatching strategies;

[0096] S3. Obtain the first operation data of the thermal power unit, the second operation data of the energy storage device, and the active load of the power grid;

[0097] Among them, the first operation data of the thermal power unit includes the maximum output power, the minimum output power, the upward regulation power rate, the downward regulation power rate, and the actual output power at any moment; the second operation data of the energy storage device includes the charging power, the discharging power, the rated energy storage capacity, and the state of charge;

[0098] S4. Based on the first operation data of the thermal power unit, the second operation data of the energy storage device, and the active load of the power grid, establish a power source side flexibility model, an energy storage flexibility model, a demand side flexibility model, and corresponding flexibility evaluation indicators, specifically including:

[0099] S41. Establish a flexibility model for the power supply side based on the maximum output power, minimum output power, upward regulation power rate, downward regulation power rate, and actual output power;

[0100] Among them, the flexibility model for the power supply side is:

[0101]

[0102] In the formula, represents the upward flexible supply of the thermal power unit at time t, represents the downward flexible supply of the thermal power unit at time t, represents the maximum output power of the thermal power unit, represents the minimum output power of the thermal power unit, P G,t represents the actual output power of the thermal power unit at time t, represents the upward regulation power rate of the thermal power unit at time t, and Δt represents the time scale, represents the downward regulation power rate of the thermal power unit at time t;

[0103] S42. Establish a flexibility model for energy storage based on the charging power, discharging power, rated energy storage capacity, and state of charge;

[0104] Among them, the flexibility model for energy storage is:

[0105]

[0106] In the formula, represents the upward flexible supply of the energy storage device at time t, represents the downward flexible supply of the energy storage device at time t, P dis,t represents the discharging power of the energy storage device at time t, P ch,t represents the charging power of the energy storage device at time t, E ess represents the rated energy storage capacity, SOC t represents the state of charge of the energy storage device at time t, SOC min represents the minimum state of charge of the energy storage device, SOC max represents the maximum state of charge of the energy storage device, and Δt represents the time scale;

[0107] S43. Establish a flexibility model for the demand side based on the active power load of the power grid;

[0108] Among them, the flexibility model for the demand side is:

[0109]

[0110] In the formula, represents the upward flexibility of the demand side at time t, represents the downward flexibility of the demand side at time t, represents the actual active power load at time t + 1, represents the actual active power load at time t;

[0111] S44. Establish corresponding flexibility evaluation indexes according to the power source side flexibility model, the energy storage flexibility model and the demand side flexibility model;

[0112] Among them, the flexibility evaluation index is:

[0113]

[0114] In the formula, represents the upward overall flexibility deficit rate at time t, represents the downward overall flexibility deficit rate at time t, ΔP t up represents the upward change amount of the load power at time t, ΔP t down represents the downward change amount of the load power at time t, represents the upward flexible supply of the thermal power unit at time t, represents the downward flexible supply of the thermal power unit at time t, represents the upward flexible supply of the energy storage device at time t, represents the downward flexible supply of the energy storage device at time t, represents the upward flexibility of the demand side at time t, represents the downward flexibility of the demand side at time t;

[0115] The flexibility evaluation index uses the overall flexibility deficit rate as a quantitative index, which can accurately evaluate the flexibility of the above three models;

[0116] S5. Establish a source-load multi-objective collaborative optimization scheduling model based on the flexibility evaluation index, and solve the source-load multi-objective collaborative optimization scheduling model to obtain a scheduling plan, specifically including:

[0117] S51. Establish a source-load multi-objective collaborative optimization scheduling model based on the flexibility evaluation index, specifically including:

[0118] S511. Construct a first objective function according to the power generation cost of the thermal power unit and the operation cost of the energy storage device;

[0119] Among them, to simplify the calculation, the cost of the thermal power unit only considers the fuel power generation cost, and the operating cost of the energy storage device considers the charge and discharge costs. The first objective function F 1 is as follows:

[0120]

[0121] In the formula, C G represents the power generation cost of the thermal power unit, C ESS represents the operating cost of the energy storage device, a represents the first coefficient of the power generation cost of the thermal power unit, b represents the second coefficient of the power generation cost of the thermal power unit, c represents the third coefficient of the power generation cost of the thermal power unit, P G,t represents the actual output power of the thermal power unit at time t, ρ t represents the dynamic electricity price after the user's electricity consumption behavior adapts to the system scheduling requirements at time t, P dis,t represents the discharge power of the energy storage device at time t, P ch,t represents the charging power of the energy storage device at time t;

[0122] S512. Construct a second objective function according to the curtailment cost of wind power, curtailment cost of photovoltaic power and load shedding cost in the power system;

[0123] Among them, the second objective function F 2 is as follows:

[0124]

[0125] In the formula, C wind represents the curtailment cost of wind power in the power system, C light represents the curtailment cost of photovoltaic power, C load represents the load shedding cost, C w represents the curtailment penalty coefficient of wind power, C pv represents the curtailment penalty coefficient of photovoltaic power, C l represents the load shedding penalty coefficient, represents the actual curtailment power of wind power, represents the actual curtailment power of photovoltaic power, represents the actual load shedding power;

[0126] S513. Construct a comprehensive flexibility deficiency rate according to the flexibility evaluation index;

[0127] Among them, the comprehensive flexibility deficiency rate F 3 is as follows:

[0128]

[0129] In the formula, ξ represents the weight coefficient;

[0130] S514. Establish the total objective function and the corresponding membership function based on the first objective function, the second objective function, and the comprehensive flexibility deficiency rate, and establish the constraint conditions of the total objective function, specifically including:

[0131] S5141. Establish the total objective function and the corresponding membership function based on the first objective function, the second objective function, and the comprehensive flexibility deficiency rate;

[0132] Among them, the total objective function F(x) is:

[0133]

[0134] The total objective function consists of multiple objectives, aiming to maximize the decision-maker's satisfaction with the scheduling plan and comprehensively consider multiple objectives such as economic cost and comprehensive flexibility deficit rate. Therefore, the solution result not only ensures that the power grid has sufficient flexibility, that is, realizes flexibility balance, but also takes into account the economy of the system. By constructing a fuzzy membership function, the membership function value represents the decision-maker's satisfaction, that is, the membership of the function should be the largest. Here, a downward semi-linear function is used as the membership function;

[0135] The membership function μ is:

[0136]

[0137] In the formula, F min represents the minimum value of the total objective function, and F max represents the maximum value of the total objective function;

[0138] S5142. Establish the constraint conditions of the total objective function;

[0139] Specifically, establish the load power constraint, energy storage device constraint, supply-demand balance constraint, thermal power output constraint, load shedding constraint, wind curtailment constraint, and photovoltaic curtailment constraint of the total objective function;

[0140] Among them, the load power constraint is:

[0141]

[0142] The energy storage device constraint is:

[0143]

[0144]

[0145]

[0146] SOC min ≤SOCt ≤SOC max ;

[0147] In the formula, represents the maximum charge-discharge power of the energy storage device, and E 0 represents the initial charge of the energy storage device;

[0148] The supply-demand balance constraint is:

[0149] P t wind +P t pv +P G,t =P t load +P dis,t +P ch,t ;

[0150] In the formula, P t wind represents the actual power output of wind power at time t, and P t pv represents the actual power output of photovoltaic power at time t;

[0151] The thermal power output constraint is:

[0152]

[0153]

[0154] In the formula, P G,t+1 represents the actual output power of the thermal power unit at time t + 1;

[0155] The load shedding constraint is:

[0156]

[0157] The wind power curtailment constraint is:

[0158]

[0159] In the formula, represents the actual power in the typical extreme scenario of wind power at time t;

[0160] The photovoltaic power curtailment constraint is:

[0161]

[0162] In the formula, represents the actual power in the typical extreme scenario of photovoltaic power at time t;

[0163] S51. Obtain a source-load multi-objective collaborative optimization scheduling model based on the total objective function, the membership function, and the constraint conditions;

[0164] S52. Solve the source-load multi-objective collaborative optimization scheduling model to obtain a scheduling plan;

[0165] Specifically, solve the source-load multi-objective collaborative optimization scheduling model, and determine whether the source-load multi-objective collaborative optimization scheduling model converges. If so, output the scheduling plan; if not, return to execute S1;

[0166] Table 2 Comparison of optimization scheduling results of different methods

[0167] Index Plan 1 Plan 2 Plan 3 Cost of curtailed wind and solar power / 10,000 yuan 1767.7 3837.5 2217 Cost of thermal power generation / 10,000 yuan 5625.2 7116.9 5257.6 Upward adjustment of flexibility deficit rate / % -648.05 -733.09 -1087.82 Downward adjustment of flexibility deficit rate / % -245.42 -448.00 -582.19

[0168] As shown in Table 2, among them, both Scheme 1 and Scheme 2 are traditional optimization scheduling methods, and Scheme 3 is the source-load collaborative optimization scheduling method for flexible supply-demand balance described in the present invention. The smaller the values of the upward and downward flexibility deficit rates, the higher the flexibility level of the system. Comparing Scheme 1 with Scheme 3, it can be seen that the economic costs of Scheme 1 and Scheme 3 are similar, but the flexibility level of Scheme 3 is much higher than that of Scheme 1. Comparing Scheme 2 with Scheme 3, it can be seen that in terms of economic cost and flexibility level, Scheme 3 is superior to Scheme 2. Therefore, the scheduling plan finally output by the present invention has the highest satisfaction and can best meet the scheduling requirements of decision-makers.

[0169] In summary, a method and a terminal for collaborative optimization scheduling of source and load with flexible supply and demand balance provided by the present invention are based on the obtained first operation data of thermal power units, the second operation data of the energy storage device, and the active power load of the power grid to establish a power source side flexibility model, an energy storage flexibility model, a demand side flexibility model, and corresponding flexibility evaluation indicators; construct a first objective function according to the power generation cost of the thermal power units and the operation cost of the energy storage device; construct a second objective function according to the wind power abandonment cost, photovoltaic power abandonment cost, and load shedding cost of the power system; construct a comprehensive flexibility deficiency rate according to the flexibility evaluation indicators; establish a total objective function and a corresponding membership function based on the first objective function, the second objective function, and the comprehensive flexibility deficiency rate, and establish the constraint conditions of the total objective function; obtain a source-load multi-objective collaborative optimization scheduling model based on the total objective function, the membership function, and the constraint conditions. This model comprehensively considers multiple objectives such as economic cost and comprehensive flexibility deficiency rate, and can make the decision maker most satisfied with the scheduling plan; solve the source-load multi-objective collaborative optimization scheduling model to obtain a scheduling plan; through the power source side flexibility model, the energy storage flexibility model, and the demand side flexibility model, flexibility can be made a part of the scheduling model consideration, and the flexibility evaluation indicators can be used to evaluate the flexibility degree of the above three models. Considering the flexibility of both the source and load sides, a multi-objective collaborative optimization scheduling model is established through source-load interaction, so as to realize the comprehensive optimization of the flexibility and economy of power grid operation under extreme typical scenarios.

[0170] The above are only the embodiments of the present invention, and do not limit the patent scope of the present invention accordingly. Any equivalent transformation made by using the content of the specification and drawings of the present invention, or directly or indirectly applied in the related technical fields, shall be included in the patent protection scope of the present invention by the same token.

Claims

1. A source-load collaborative optimal scheduling method for flexible supply-demand balance, characterized in that, it includes the steps of: Obtain the first operation data of thermal power units, the second operation data of energy storage devices, and the active power load of the power grid; Based on the first operation data, the second operation data, and the active power load of the power grid, establish a power-side flexibility model, an energy storage flexibility model, a demand-side flexibility model, and corresponding flexibility evaluation indicators; Based on the flexibility evaluation indicators, establish a source-load multi-objective collaborative optimal scheduling model, and solve the scheduling model to obtain a scheduling plan; The first operation data includes the maximum output power, the minimum output power, the upward regulation power rate, the downward regulation power rate, and the actual output power at any moment; The second operation data includes the charging power, the discharging power, the rated energy storage capacity, and the state of charge; The establishment of the power-side flexibility model, the energy storage flexibility model, the demand-side flexibility model, and the corresponding flexibility evaluation indicators based on the first operation data, the second operation data, and the active power load of the power grid includes: Establish a power-side flexibility model based on the maximum output power, the minimum output power, the upward regulation power rate, the downward regulation power rate, and the actual output power; Establish an energy storage flexibility model based on the charging power, the discharging power, the rated energy storage capacity, and the state of charge; Establish a demand-side flexibility model based on the active power load of the power grid; Establish corresponding flexibility evaluation indicators according to the power-side flexibility model, the energy storage flexibility model, and the demand-side flexibility model; The flexibility evaluation indicator is: ； For increasing the overall flexibility deficit rate at time t, For decreasing the overall flexibility deficit rate at time t, For the upward change in load power at time t, For the downward change in load power at time t, For the upward flexible supply of the thermal power unit at time t, For the downward flexible supply of the thermal power unit at time t, For the upward flexible supply of the energy storage device at time t, For the downward flexible supply of the energy storage device at time t, For the upward flexibility of the demand side at time t, For the downward flexibility of the demand side at time t; Before obtaining the first operation data of thermal power units, the second operation data of energy storage devices, and the active power load of the power grid, it includes: Obtain the benchmark electricity price of the preset regional power grid; Use the stepped elastic load curve modeling method to formulate a dynamic electricity price according to the benchmark electricity price; Based on the correspondence between the electricity price grades and the expected load response rate, smooth the initial load curve of the power grid according to the dynamic electricity price until the user's electricity consumption behavior adapts to the system scheduling requirements, and obtain the active power load of the power grid: ； The active power load after the user's electricity consumption behavior at time t adapts to the system scheduling requirements Indicates a 0~1 discrete variable, the flag of each electricity price level The load response rate at time t under the kth electricity price level The predicted active power value of the load at time t The dynamic electricity price after the user's electricity consumption behavior at time t adapts to the system scheduling requirements The reference electricity price The price rate at time t under the kth electricity price level The demand-side flexibility model is: ； is the actual active power load at time t + 1, is the actual active power load at time t; The establishment of the source-load multi-objective collaborative optimal scheduling model based on the flexibility evaluation indicators includes: Construct a first objective function according to the generation cost of thermal power units and the operation cost of energy storage devices; Construct a second objective function according to the wind power abandonment cost, the photovoltaic power abandonment cost, and the load shedding cost of the power system; Construct a comprehensive flexibility deficiency rate according to the flexibility evaluation indicators; Based on the first objective function, the second objective function, and the comprehensive flexibility deficiency rate, establish a total objective function and a corresponding membership function, and establish the constraint conditions of the total objective function; Obtain the source-load multi-objective collaborative optimal scheduling model based on the total objective function, the membership function, and the constraint conditions.

2. A source-load collaborative optimal scheduling method for flexible supply-demand balance according to claim 1, characterized in that, the power-side flexibility model is: ； In the formula, represents the maximum output power of the thermal power unit, represents the minimum output power of the thermal power unit, represents the actual output power of the thermal power unit at time t, represents the upward power regulation rate of the thermal power unit at time t, represents the time scale, represents the downward power regulation rate of the thermal power unit at time t.

3. A source-load collaborative optimal scheduling method for flexible supply-demand balance according to claim 1, characterized in that, the energy storage flexibility model is: ； Wherein, represents the discharge power of the energy storage device at time t, represents the charging power of the energy storage device at time t, represents the rated energy of the energy storage, represents the state of charge of the energy storage device at time t, represents the minimum state of charge of the energy storage device, represents the maximum state of charge of the energy storage device, represents the time scale.

4. A source-load collaborative optimal scheduling method for flexible supply-demand balance according to claim 1, It is characterized in that The first objective function is as follows: ； Wherein, represents the power generation cost of the thermal power unit, represents the operating cost of the energy storage device, represents the first coefficient of the power generation cost of the thermal power unit, represents the second coefficient of the power generation cost of the thermal power unit, represents the third coefficient of the power generation cost of the thermal power unit, represents the actual output power of the thermal power unit at time t, represents the dynamic electricity price after the user's electricity consumption behavior adapts to the system scheduling requirements at time t, represents the discharge power of the energy storage device at time t, represents the charging power of the energy storage device at time t; The second objective function is as follows: ； Wherein, represents the curtailment cost of wind power in the power system, represents the curtailment cost of photovoltaic power, represents the curtailment cost of load, represents the curtailment penalty coefficient of wind power, represents the curtailment penalty coefficient of photovoltaic power, represents the curtailment penalty coefficient of load, represents the actual curtailment power of wind power, represents the actual curtailment power of photovoltaic power, represents the actual curtailment power of load; The comprehensive flexibility deficiency rate is as follows: ； In the formula, represents the weight coefficient; The total objective function is as follows: ； The membership function is as follows: ； In the formula, represents the minimum value of the total objective function, represents the maximum value of the total objective function.

5. A source-load collaborative optimal scheduling method for flexible supply-demand balance according to claim 1 It is characterized in that The constraint conditions for establishing the total objective function include Load power constraint, energy storage device constraint, supply-demand balance constraint, thermal power output constraint, load shedding constraint, wind curtailment constraint, and photovoltaic curtailment constraint for establishing the total objective function 6. A source-load collaborative optimal scheduling terminal for flexible supply-demand balance, including a memory, a processor, and a computer program stored on the memory and executable on the processor It is characterized in that When the processor executes the computer program, it realizes each step in a source-load collaborative optimal scheduling method for flexible supply-demand balance according to any one of claims 1 to 5

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