Power grid multi-resource joint collaborative optimization peak regulation scheduling method and system

Abstract

The invention discloses a power grid multi-resource joint collaborative optimization peak-load regulation scheduling method and a power grid multi-resource joint collaborative optimization peak-load regulation scheduling system. The method comprises the following steps: S1, collecting power grid data, and analyzing a support mechanism and a mutual coupling influence mechanism of source network load storage multi-resources on power grid peak regulation and supply guarantee according to the power grid data to obtain a resource analysis result; s2, establishing a peak regulation effect evaluation index system according to a resource analysis result in S1; s3, according to the resource analysis result in the S1 and the evaluation index system in the S2, establishing a power grid multi-resource joint collaborative optimization peak regulation scheduling model; and S4, substituting the resource analysis result in the S1 into the power grid multi-resource joint collaborative optimization peak regulation scheduling model in the S3, and solving to obtain an optimal scheduling plan of the power grid under the condition of difficult peak regulation and supply guarantee. According to the method, the scheduling result can be evaluated from multiple aspects of safety, adequacy, economy and high efficiency, and a scheduling scheme optimization reference is provided for scheduling personnel.

Description

Technical Field

[0001] The present invention relates to the technical field of power system dispatching, and more specifically to a method and system for power grid multi-resource joint collaborative optimization peak-shaving dispatching that takes into account multiple energy storage, efficient utilization of demand-side response, and the ramping capabilities of multiple power sources and grid constraints. Background Art

[0002] With the accelerated transformation of its energy mix, the power system is gradually shifting toward a clean, low-carbon, and efficient power system primarily based on clean energy. The power system is undergoing a multi-faceted reform across its power sources, grids, loads, and storage. On the power source side, clean energy, especially renewable energy sources like wind and solar, exhibits volatility, uncertainty, randomness, and anti-peaking characteristics. This introduces further uncertainty into the power grid and increases the difficulty of ensuring peak load regulation. Furthermore, the large-scale integration of clean energy into the grid will reduce the proportion of traditional generating units, weakening the power system's regulatory capacity and impacting the grid's security and availability. On the grid side, with the development and construction of new power systems, ultra-high voltage AC and DC transmission lines are being continuously built, increasing the number of power transmission channels and the amount of power transmitted. Transmission cross-section topology is becoming increasingly complex, leading to frequent power transmission disruptions caused by the grid structure, resulting in the frequent dilemma of power sources with excess power but unable to utilize it. On the load side, to address the new power system transformation situation, load-side management measures such as demand-side response are being vigorously researched and promoted. In domestic and international power markets, the demand side can participate in the primary energy market and ancillary service markets, coordinate and optimize user electricity consumption, and adapt to the large-scale integration of clean energy into the system through demand-side response, providing an effective way to help the grid optimize peak load regulation and scheduling. On the energy storage side, energy storage technology, as a key component of the new power system, not only enables the temporal and spatial shifting of electrical energy and system stability control, improving grid stability, but also reshapes the power system operating paradigm through the coordinated optimization of "source, grid, load, and storage."

[0003] Therefore, it is an urgent problem for those skilled in the art to propose a method and system for the joint coordinated optimization of peak-shaving scheduling of power grid resources, which takes into account multiple energy storage, efficient utilization of demand-side response, and the ramping capability of multiple power sources and grid constraints. Summary of the Invention

[0004] In view of this, the present invention provides a method and system for peak-shaving scheduling by jointly optimizing multiple resources in a power grid. By rationally calling upon energy storage and demand-side resources, it is possible to significantly reduce the load shedding penalty costs while introducing lower operating costs of energy storage and demand-side resources, thereby reducing the overall cost of the system and increasing the economy of the power system.

[0005] To achieve the above objectives, the present invention adopts the following technical solution: a method for peak-shaving scheduling of power grid multi-resource joint coordinated optimization, comprising:

[0006] S1: Collect grid data and analyze the support mechanism of multiple resources including source, grid, load and storage for peak load regulation and supply guarantee of the grid and their mutual coupling influence mechanism based on the grid data to obtain resource analysis results;

[0007] S2: Based on the resource analysis results in S1, establish a peak load regulation effect evaluation indicator system;

[0008] S3: Based on the resource analysis results in S1 and the evaluation index system in S2, a power grid multi-resource joint collaborative optimization peak-shaving dispatch model is established;

[0009] S4: Substitute the resource analysis results in S1 into the power grid multi-resource joint collaborative optimization peak-shaving scheduling model in S3 to obtain the optimal scheduling plan for the power grid under the condition of difficult peak-shaving and supply guarantee.

[0010] Specifically, the S1 includes:

[0011] S1.1: Collect grid data, including historical load data, power output, energy storage, and demand-side data, and analyze the characteristics of power generation resources, energy storage resources, and demand-side resources;

[0012] S1.2: Based on the actual operating data of conventional power units and taking into account their physical output characteristics, quantify the regulation capabilities of conventional units to reflect the peak load regulation and supply guarantee capabilities of the source side;

[0013] S1.3: Analyze the constraints of the grid structure on the transmission of multiple power backup sources, and reflect the impact of the grid side on the availability of regulation capacity;

[0014] S1.4: Analyze the load characteristics of user electricity demand and establish a price-based demand-side response (PDR) model and an incentive-based demand-side response (IDR) configuration strategy model based on demand-side flexibility resources.

[0015] S1.5: Based on the operating characteristics of electrochemical energy storage in the power grid, establish a multi-energy storage efficient utilization mechanism and obtain resource analysis results.

[0016] Specifically, S1.2 includes thermal power output characteristics, thermal power ramp characteristics, special thermal power unit state restrictions, thermal power unit start and stop phase output restrictions, thermal power unit minimum continuous start and stop time restrictions and hydropower output characteristics.

[0017] Specifically, the price-based demand-side response PDR model described in S1.4 is expressed as follows:

[0018]

[0019] Where, They represent the load of node n before and after PDR at time i, Refers to the load power change after PDR implementation, which has the following relationship with the time-of-use electricity price:

[0020]

[0021] Where E is the electricity price elasticity matrix, and its self-elasticity coefficient e ii It is used to describe the user's response to the current electricity price. It is a negative value. The mutual elasticity coefficient e ij It is used to describe the user's response to the electricity price at other times and is a non-negative value;

[0022] The incentive-based demand-side response IDR configuration strategy model is expressed as follows:

[0023]

[0024] Where, is the load power change after IDR implementation,

[0025] The resulting cost expression is as follows:

[0026]

[0027] Where C is the cost corresponding to the change in unit load power after adopting IDR, The load change power after IDR is adopted for node n.

[0028] Specifically, the peak load regulation effect evaluation index system of S2 includes power reserve index, reserve coverage rate, and power system operation economic cost;

[0029] The power reserve index considers the reserve capacity of the power system; the reserve capacity is divided into positive reserve capacity and negative reserve capacity, and the index characterization expression is as follows:

[0030]

[0031] Where R pos is the system standby at time t, R neg The system negative backup at time t; is the state variable of the i-th thermal power and hydropower unit; are the maximum technical outputs of the i-th thermal power unit and hydropower unit, are the minimum technical outputs of the i-th thermal power unit and hydropower unit respectively; E i,soct 、E i,socmax 、E i,socmin are the charge state of the i-th energy storage at time t, the upper limit of the i-th energy storage charge, and the lower limit of the i-th energy storage charge; P i,dis 、P i,ch are the discharging power and charging power of the i-th energy storage respectively;

[0032] The standby coverage expression is as follows:

[0033]

[0034] Where RCP represents the operating reserve coverage, T is the scheduling period, k t is a 0-1 variable. When the system reserve in period t is greater than or equal to the actual system reserve, k t Take 1, otherwise k t Take 0;

[0035] The system operation economy is a quantitative analysis from an economic perspective, which analyzes the impact of the model scheduling results after introducing multiple energy storage and demand-side response on the operation of the power system. The operation economic cost is analyzed in terms of source-load-storage. The expression is as follows:

[0036] C plant =C fire +C hydro

[0037] C new =C wind +C solar

[0038] C extra =C ess +C abd +C idr

[0039] Where C plant represents the operating cost of thermal power and hydropower, C new is the operating cost of wind and solar energy, C extra is the additional cost, which includes energy storage operation cost, penalty cost and incentive demand response cost.

[0040] Specifically, the S3 establishing the power grid multi-resource joint collaborative optimization peak-shaving dispatch model includes: determining the model objective function, constructing constraint conditions, combining the resource analysis results in S1 and the evaluation index system in S2 with the model objective function and jointly establishing the model constraint conditions;

[0041] The formula of the model objective function is as follows:

[0042] p glt,t =p load,t -p wind,t -p solar,t -p pdr,t

[0043]

[0044] f3=C fire +C hydro +Cnew +C ess +C renew_abd +C idr +C load_abd ;

[0045] Where p glt,t is the net load of the system at time t, f1 represents the net load variance throughout the day, which refers to the degree of net load fluctuation throughout the day and represents the difficulty of balancing the net load curve of the system. The smaller f1 is, the smaller the net load fluctuation is, the easier it is to balance the power, and the difficulty of peak load regulation and supply guarantee of the power grid is reduced. C represents the regulation capacity of the i-th resource at time t. For thermal power and hydropower units, it is necessary to consider their start and stop status, ramp speed, current output, and output upper and lower limits; fire is the thermal power operation cost, C hydro is the hydropower operation cost, C new is the operating cost of new energy, C ess is the energy storage operating cost, C renew_abd The penalty cost for curtailing clean energy, C idr is the incentive-based demand-side response cost, C load_abd Penalty cost for load shedding.

[0046] Specifically, the constraints include: conventional power output constraints, line flow constraints, conventional power ramp constraints, conventional power start and stop constraints, new energy output constraints, energy storage output constraints, energy storage energy constraints, energy storage equivalent call times constraints, and demand-side response-related constraints.

[0047] It also includes a power grid multi-resource joint collaborative optimization peak-shaving dispatching system:

[0048] Data acquisition and preprocessing module: used to collect and preprocess power grid data, and analyze the support mechanism of multiple resources of source, grid, load and storage for power grid peak load guarantee and their mutual coupling influence mechanism based on the power grid data, and obtain resource analysis results;

[0049] Evaluation index module: Based on the peak load regulation demand of the power grid and the results of resource analysis, a peak load regulation effect evaluation index system is established;

[0050] Multi-resource joint collaborative optimization peak-shaving scheduling module: used to establish a multi-resource joint collaborative optimization peak-shaving scheduling model for the power grid based on resource analysis results and evaluation indicator system;

[0051] Model solving and optimization module: used to substitute resource analysis results into the power grid multi-resource joint collaborative optimization peak-shaving scheduling model to solve the optimal scheduling plan for the power grid under the condition of peak-shaving and supply guarantee difficulties;

[0052] Dispatch plan generation module: Based on the model solution results, it generates a specific power grid dispatch plan and outputs optimized dispatch decisions to guide the actual operation and dispatch management of the power grid.

[0053] A computer device comprises a memory, a processor and a computer program stored in the memory and executable on the processor. When the processor executes the program, a method for multi-energy collaborative optimization and dispatching of a power grid is implemented.

[0054] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements a multi-energy collaborative optimization scheduling method for a power grid.

[0055] It can be seen from the above technical solutions that, compared with the prior art, the present invention provides a method and system for optimizing peak-shaving scheduling by joint collaborative optimization of multiple resources in a power grid, which has the following advantages:

[0056] 1. Refined modeling of multiple resources: Based on the various factors that need to be considered in the actual dispatch of the power grid, the support mechanism of various resources for the peak load regulation and supply guarantee of the power grid and their mutual coupling influence mechanism are analyzed to improve practicality.

[0057] 2. Multi-resource collaborative optimization: It integrates the coordinated scheduling of multiple resources, fully utilizes the advantages of various resources, and improves the efficiency and reliability of the overall system operation.

[0058] 3. Flexible adaptation to multiple scenarios: It can adapt to different operating scenarios of the power grid. By solving the optimization model, it can find the optimal dispatching strategy under various possible peak-shaving difficult situations, thereby improving the security and adequacy of the power grid.

[0059] 4. Comprehensive constraint management: The dispatch model takes into account multiple constraints such as the power output, power ramp, line flow, energy storage system, and demand side of the power grid to ensure the feasibility and safety of the optimized dispatch plan in actual operation.

[0060] 5. Data-driven decision support: Relying on data collection and preprocessing, modeling and analysis based on real-time and historical data, it provides powerful decision support capabilities and can adjust scheduling plans in a timely manner to cope with dynamically changing operating environments.

[0061] 6. The system is practical and efficient: By comprehensively considering the characteristics and interrelationships of different energy resources and modeling based on the actual operation of the power grid, this method can significantly improve the overall reliability and operational stability of the power grid in complex environments. The proposed method also has a fast computational speed while ensuring reliable operation. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are merely embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying any creative work.

[0063] Figure 1 This is a flow chart of the method for coordinated and optimized peak-shaving scheduling of multiple resources in a power grid provided by the present invention;

[0064] Figure 2 This is a schematic diagram of the specific calculation process of the power grid multi-resource collaborative joint optimization peak-shaving scheduling model provided by the present invention;

[0065] Figure 3 is a schematic diagram of a net load curve in each embodiment of the present invention;

[0066] Figure 4 is a schematic diagram of a peak regulation gap when regulation resources are not used in various embodiments of the present invention;

[0067] Figure 5 Schematic diagram of the adjustable capacity and standby coverage of multiple resources in Example 1 of the present invention;

[0068] Figure 6 This is a schematic diagram of the adjustable capacity and standby coverage of multiple resources in Example 2 of the present invention;

[0069] Figure 7 Schematic diagram of the adjustable capacity and standby coverage of multiple resources in Example 3 of the present invention;

[0070] Figure 8 This is a schematic diagram of the adjustable capability and standby coverage of multiple resources in Example 4 of the present invention. DETAILED DESCRIPTION

[0071] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0072] See also Figure 1 The embodiment of the present invention discloses a method for optimizing peak-shaving scheduling by joint collaborative optimization of multiple resources in a power grid, including:

[0073] S1: Collect grid data and analyze the support mechanism of multiple resources including source, grid, load and storage for peak load regulation and supply guarantee of the grid and their mutual coupling influence mechanism based on the grid data to obtain resource analysis results;

[0074] S2: Based on the resource analysis results in S1, establish a peak load regulation effect evaluation indicator system;

[0075] S3: Based on the resource analysis results in S1 and the evaluation index system in S2, a power grid multi-resource joint collaborative optimization peak-shaving dispatch model is established;

[0076] S4: Substitute the resource analysis results in S1 into the power grid multi-resource joint collaborative optimization peak-shaving scheduling model in S3 to obtain the optimal scheduling plan for the power grid under the condition of difficult peak-shaving and supply guarantee.

[0077] Preferably, the S1 includes:

[0078] S1.1: Collect grid data, including historical load data, power output, energy storage, and demand-side data, and analyze the characteristics of power generation resources, energy storage resources, and demand-side resources;

[0079] S1.2: Based on the actual operating data of conventional power units and taking into account their physical output characteristics, quantify the regulation capabilities of conventional units to reflect the peak load regulation and supply guarantee capabilities of the source side;

[0080] S1.3: Analyze the constraints of the grid structure on the transmission of multiple power backup sources, and reflect the impact of the grid side on the availability of regulation capacity;

[0081] S1.4: Analyze the load characteristics of user electricity demand and establish a price-based demand-side response (PDR) model and an incentive-based demand-side response (IDR) configuration strategy model based on demand-side flexibility resources.

[0082] S1.5: Based on the operating characteristics of electrochemical energy storage in the power grid, establish a multi-energy storage efficient utilization mechanism and obtain resource analysis results.

[0083] Preferably, the S1.2 specifically includes thermal power output characteristics, thermal power ramp characteristics, special thermal power unit state restrictions, thermal power unit start and stop phase output restrictions, thermal power unit minimum continuous start and stop time restrictions and hydropower output characteristics.

[0084] Furthermore, the thermal power output characteristic expression in S1.2 is as follows:

[0085]

[0086] The thermal power ramping characteristic expression is as follows:

[0087]

[0088] Where, ΔPi U , ΔP i D They are the maximum up-slope speed and the maximum down-slope speed respectively. Slack variables are added to the constraints to consider that the unit needs to go through a period of startup or shutdown time to climb up and down when it starts or stops.

[0089] The state restriction expression of special thermal power units is as follows:

[0090] In actual production operation, the units that need to be started and shut down according to manual regulations should maintain the corresponding status, including:

[0091]

[0092] Where, I is the operating status of the i-th unit at time t, 0 means shutdown, and 1 means normal operation. s1 , I s2 They represent the set of units that must be started and the set of units that must be stopped respectively.

[0093] The output limit expression of thermal power unit during start-up and shutdown is as follows:

[0094] When starting a thermal power plant, it is necessary to confirm whether it is a hot start or a cold start based on the downtime before startup. The two startup methods correspond to two different startup ramp curves.

[0095] The expression for the minimum continuous start and stop time limit of thermal power units is as follows:

[0096]

[0097] Where: T i on Indicates the minimum continuous operation time of thermal power units; T i off Indicates the minimum continuous downtime of thermal power units; [T / T i on ] means not more than T / T i on The largest positive integer.

[0098] The hydropower output characteristic expression is as follows:

[0099]

[0100] Furthermore, in S1.3, the constraint mechanism of the grid structure on the transmission of multiple power backup is analyzed. Considering the impact of the grid structure on the system backup transfer capability, the expression is as follows:

[0101]

[0102] Where: ΔP iIndicates the spare capacity transferred from unit i; ΔP indicates the capacity of ΔP i The vector formed; r i neg represents the negative reserve of unit i; r i pos Indicates the positive and standby status of unit i; H l-G Indicates the power transfer distribution factor of the unit to line l; H l-Load The power transfer distribution factor of the load to the line l; K Load Allocate the proportion of available standby power to each load node; ΔP l Indicates the remaining active power limit in the reverse direction of line 1; Indicates the remaining active power limit in the positive direction of line 1.

[0103] Preferably, the price-based demand-side response PDR model in S1.4 is expressed as follows:

[0104]

[0105] Where, They represent the load of node n before and after PDR at time i, Refers to the load power change after PDR implementation, which has the following relationship with the time-of-use electricity price:

[0106]

[0107] Where E is the electricity price elasticity matrix, and its self-elasticity coefficient e ii It is used to describe the user's response to the current electricity price. It is a negative value. The mutual elasticity coefficient e ij It is used to describe the user's response to the electricity price at other times and is a non-negative value;

[0108] The incentive-based demand-side response IDR configuration strategy model is expressed as follows:

[0109]

[0110] Where, is the load power change after IDR implementation,

[0111] The resulting cost expression is as follows:

[0112]

[0113] Where C is the cost corresponding to the change in unit load power after adopting IDR, The load change power after IDR is adopted for node n.

[0114] Furthermore, IDR helps the grid to regulate peak load by allowing users to reduce electricity usage during peak load periods, which requires certain compensation, so costs will be introduced; the model takes into account the economic efficiency of system operation and needs to take into account the calculation of the operating costs of various resources, so it is necessary to consider the increased compensation costs due to the use of IDR resources.

[0115] Preferably, the peak load regulation effect evaluation index system of S2 includes power reserve index, reserve coverage rate, and power system operation economic cost;

[0116] The power reserve index considers the reserve capacity of the power system; the reserve capacity is divided into positive reserve capacity and negative reserve capacity, and the index characterization expression is as follows:

[0117]

[0118] Where R pos is the system standby at time t, R neg The system negative backup at time t; is the state variable of the i-th thermal power and hydropower unit; are the maximum technical outputs of the i-th thermal power unit and hydropower unit, are the minimum technical outputs of the i-th thermal power unit and hydropower unit respectively; E i,soct 、E i,socmax 、E i,socmin are the charge state of the i-th energy storage at time t, the upper limit of the i-th energy storage charge, and the lower limit of the i-th energy storage charge; P i,dis 、P i,ch are the discharging power and charging power of the i-th energy storage respectively;

[0119] The standby coverage expression is as follows:

[0120]

[0121] Where RCP represents the operating reserve coverage, T is the scheduling period, k t is a 0-1 variable. When the system reserve in period t is greater than or equal to the actual system reserve, k t Take 1, otherwise k t Take 0;

[0122] The system operation economy is a quantitative analysis from an economic perspective, which analyzes the impact of the model scheduling results after introducing multiple energy storage and demand-side response on the operation of the power system. The operation economic cost is analyzed in terms of source-load-storage. The expression is as follows:

[0123] C plant =C fire +C hydro

[0124] C new=C wind +C solar

[0125] C extra =C ess +C abd +C idr (14)

[0126] Where C plant represents the operating cost of thermal power and hydropower, C new is the operating cost of wind and solar energy, C extra is the additional cost, which includes energy storage operation cost, penalty cost and incentive demand response cost.

[0127] Preferably, the step S3 of establishing a power grid multi-resource joint collaborative optimization peak-shaving dispatch model includes: determining a model objective function, constructing constraint conditions, combining the resource analysis results in S1 and the evaluation index system in S2 with the model objective function and establishing the model constraint conditions;

[0128] The formula of the model objective function is as follows:

[0129] p glt,t =p load,t -p wind,t -p solar,t -p pdr,t

[0130]

[0131] f3=C fire +C hydro +C new +C ess +C renew_abd +C idr +C load_abd (17);

[0132] Where p glt,t is the net load of the system at time t, f1 represents the net load variance throughout the day, which refers to the degree of net load fluctuation throughout the day and represents the difficulty of balancing the net load curve of the system. The smaller f1 is, the smaller the net load fluctuation is, the easier it is to balance the power, and the difficulty of peak load regulation and supply guarantee of the power grid is reduced. C represents the regulation capacity of the i-th resource at time t. For thermal power and hydropower units, it is necessary to consider their start and stop status, ramp speed, current output, and output upper and lower limits; fire is the thermal power operation cost, C hydro is the hydropower operation cost, C new is the operating cost of new energy, C ess is the energy storage operating cost, C renew_abd The penalty cost for curtailing clean energy, Cidr is the incentive-based demand-side response cost, C load_abd Penalty cost for load shedding.

[0133] Preferably, the constraints include: conventional power supply output constraints, line flow constraints, conventional power supply ramp constraints, conventional power supply start and shutdown constraints, new energy output constraints, energy storage output constraints, energy storage energy constraints, energy storage equivalent call times constraints and demand-side response-related constraints.

[0134] Furthermore, the conventional power output constraint expression is as follows:

[0135]

[0136] Where, They represent the on / off status of the i-th thermal power unit and hydropower unit at time t respectively. Respectively represent the minimum / maximum output of the i-th thermal power unit and hydropower unit. are the output values ​​of the i-th thermal power unit and hydropower unit at time t respectively.

[0137] The line power flow constraint expression is as follows:

[0138]

[0139] Where, They represent the upper and lower limits of active power allowed to pass through line ij, is the active power flowing through line ij at time t.

[0140] The conventional power supply ramp constraint expression is as follows:

[0141]

[0142] Where, ΔP i U , ΔP i D They are the maximum up-ramp speed and the maximum down-ramp speed respectively. Taking into account the start-up and shutdown time of the power supply during the actual operation of the power system, a slack variable is also added to the constraint, indicating that the unit needs a period of time to start or shut down.

[0143] The conventional power supply start and stop constraint expressions are as follows:

[0144]

[0145] Where: T i on Indicates the minimum continuous operation time of thermal power units; T i off Indicates the minimum continuous downtime of thermal power units; [T / Ti on ] means not more than T / T i on The largest positive integer.

[0146] The new energy output constraint expression is as follows:

[0147]

[0148] The energy storage output constraint expression is as follows:

[0149]

[0150] is the energy storage charging and discharging state at time t, For energy storage, it is also necessary to consider that its discharge s is constrained by the corresponding new energy output.

[0151] The energy storage constraint expression is as follows:

[0152]

[0153] SOC min ≤SOC(t)≤SOC max (25)

[0154] Where SOC(t) is the state of charge of the energy storage, E ess is the energy storage capacity, η ch ,η dis is the energy storage charging and discharging efficiency, They are respectively the energy storage charging and discharging power during the energy storage period t. max , SOC min They are the upper and lower limits of the energy storage state of charge respectively.

[0155] The equivalent call count constraint expression is as follows:

[0156] SOC(t)=SOC max When ε c,t =1

[0157]

[0158] SOC(t)=SOC min When ε d,t =1

[0159]

[0160] Where, ε c,t Indicates the number of times the energy storage is fully charged, ε d,t Indicates the number of times the energy storage is fully discharged. n max 、nmin Respectively represent the upper and lower limits of the number of equivalent calls.

[0161] The demand-side response constraint expressions are as follows:

[0162]

[0163] P min ≤P t ≤P max (28)

[0164] Where ΔL is the load change before and after the optimized response, is the load at time t before the optimal response, ΔP is the change in electricity price at time t, and P t is the electricity price at time t, and E is the electricity quantity and price elasticity matrix. max is the upper limit of electricity price, P min The lower limit of electricity price.

[0165] The power balance constraint expression is as follows:

[0166] P fire +P hydro +P new +P line +P ess =P load -P IDR (29)

[0167] Where, P fire 、P hydro 、P new 、P line 、P ess 、P load 、P IDR They respectively represent thermal power output, hydropower output, renewable energy output, interconnection line power, energy storage output, load size, and incentive-type demand-side response.

[0168] The tie line power constraint expression is as follows:

[0169]

[0170] Where, They represent the upper and lower limits of the tie line power respectively.

[0171] Furthermore, in the power grid system of the embodiment of the present invention, there are a total of 13 large thermal power plants, 8 large hydropower plants, and wind and solar energy stations as resources that require scheduling plans. The remaining small hydropower stations, nuclear power plants, gas power plants, interconnection line power, etc. are considered as fixed boundaries in the model, and a set of parameters are designed based on relevant data for calculation.

[0172] In this embodiment of the present invention, a total of eight main electrochemical energy storage stations are considered, of which two are supporting energy storage stations whose output is constrained by the new energy station, and six are independent energy storage stations directly managed by the power grid; the specific parameters of the energy storage system are shown in Table 1.

[0173] Table 1 Energy storage system parameters

[0174]

[0175] Table 2 shows the values ​​of the relevant parameters used in the model.

[0176] Table 2 Simulation parameters

[0177]

[0178]

[0179] Based on the relevant data of a typical difficult day for peak load regulation and supply guarantee of the power system, a model was established in PYTHON and the data was substituted. Four calculation embodiments were designed to solve the problem, and the scheduling results of various embodiments were evaluated according to the proposed peak load regulation effect evaluation index system. Among them: Example 1 (Example 1) does not introduce multiple energy storage and demand-side resources and does not consider the influence of the grid on the system regulation capability; Example 2 (Example 2) does not introduce multiple energy storage and demand-side resources and considers the influence of the grid on the system regulation capability; Example 3 (Example 3) introduces multiple energy storage and demand-side resources and does not consider the influence of the grid on the system regulation capability; Example 4 (Example 4) introduces multiple energy storage and demand-side resources and considers the influence of the grid on the system regulation capability. By analyzing the scheduling results corresponding to the four embodiments, it can be concluded that the source, grid, load and storage multiple resources have an impact on the peak load regulation and supply guarantee effect of the power grid, thereby guiding the dispatchers to formulate scheduling plans for difficult days for peak load regulation and supply guarantee. The specific calculation process of the model is as follows. Figure 2 shown.

[0180] like Figure 3 As shown in FIG, it is the net load curve output after the upper layer peak load optimization scheduling model in each embodiment. Figure 3 It can be seen that after adopting the multi-resource coordinated joint optimization peak-shaving scheduling model for the power grid that introduces multiple energy storage and demand-side response resources, the net load curve is greatly improved and the pressure on the power grid to ensure peak supply is reduced.

[0181] like Figure 4 The figure shows the peak load gap that exists when flexible resources are not used in each embodiment. Table 3 shows the net load fluctuation and load shedding amount in each embodiment on a typical day.

[0182] according to Figure 4 It can be seen from the peak-shaving gap and load shedding data in Table 3 that, when other conditions remain unchanged, considering grid constraints will increase the load shedding amount and increase the difficulty of peak-shaving and supply guarantee.

[0183] Table 3 Net load fluctuation and load shedding amount in each embodiment on a typical day

[0184]

[0185] like Figures 5 to 8 The following table shows the regulation capacity curves and reserve coverage of each resource obtained by solving the power grid multi-resource coordinated joint optimization peak-shaving scheduling model in each of the aforementioned embodiments. The reserve coverage at time t is 1 if the system reserve can cover the load shedding and a certain proportion of reserve capacity; otherwise, it is 0. Table 4 shows the regulation capacity and reserve coverage of each embodiment on a typical day.

[0186] Depend on Figures 5 to 8 As shown in Table 4, the introduction of multiple energy storage and demand-side response using the model and their reasonable and efficient deployment can improve the grid's ability to regulate sudden extreme imbalances between source and load, especially in short-term peak scenarios. Compared with not using this method, the grid's short-term average regulation capacity is increased by nearly 200%, and the reserve coverage rate is greatly improved, proving the high efficiency and advancement of the method in scientifically and reasonably deploying multiple grid resources such as multiple energy storage and demand-side response. At the same time, it can be seen that grid constraints place restrictions on the grid's adjustable regulation capacity, meaning that the system's configured regulation capacity may not be fully deployed, resulting in transmission congestion. The method takes into account the restrictions imposed by grid constraints on the transportation of flexible regulation resources during actual grid dispatch, optimizes the retention of flexible configurations of multiple resources, increases the proportion of adjustable regulation capacity in the regulation capacity configuration, and improves the efficient utilization of multiple resources.

[0187] Table 4 Adjustment capability and standby coverage of each embodiment on a typical day

[0188]

[0189]

[0190] The embodiments provided by the present invention also evaluate the scheduling plan obtained by the method from the perspective of system operation economics. The specific results are shown in Table 5. As can be seen from the table, the described grid multi-resource collaborative joint optimization peak-shaving scheduling model can reduce load shedding penalty costs, thereby increasing the grid's ability to regulate peak supply and improve system safety and adequacy, while also increasing the absorption of new energy and promoting the environmental protection of the power system. By rationally utilizing energy storage and demand-side resources, the load shedding penalty costs can be greatly reduced while introducing lower energy storage and demand-side resource operating costs, thereby reducing the overall system cost and improving the economic efficiency of the power system.

[0191] Table 5 Comparison of operating costs in various embodiments on a typical day

[0192]

[0193] Preferably, it also includes a power grid multi-resource joint collaborative optimization peak-shaving dispatching system:

[0194] Data acquisition and preprocessing module: used to collect and preprocess power grid data, and analyze the support mechanism of multiple resources of source, grid, load and storage for power grid peak load guarantee and their mutual coupling influence mechanism based on the power grid data, and obtain resource analysis results;

[0195] Evaluation index module: Based on the peak load regulation demand of the power grid and the results of resource analysis, a peak load regulation effect evaluation index system is established;

[0196] Multi-resource joint collaborative optimization peak-shaving scheduling module: used to establish a multi-resource joint collaborative optimization peak-shaving scheduling model for the power grid based on resource analysis results and evaluation indicator system;

[0197] Model solving and optimization module: used to substitute resource analysis results into the power grid multi-resource joint collaborative optimization peak-shaving scheduling model to solve the optimal scheduling plan for the power grid under the condition of peak-shaving and supply guarantee difficulties;

[0198] Dispatch plan generation module: Based on the model solution results, it generates a specific power grid dispatch plan and outputs optimized dispatch decisions to guide the actual operation and dispatch management of the power grid.

[0199] Preferably, a computer device is also included, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor can implement a multi-energy collaborative optimization scheduling method for a power grid when executing the program.

[0200] Furthermore, in one embodiment of the present invention, a terminal device is provided, which includes a processor and a memory, wherein the memory is used to store a computer program, the computer program includes program instructions, and the processor is used to execute the program instructions stored in the computer storage medium. The processor may be a central processing unit (CPU), or may be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. It is the computing core and control core of the terminal, which is suitable for implementing one or more instructions, specifically suitable for loading and executing one or more instructions to implement the corresponding method flow or corresponding function; the processor described in the embodiment of the present invention can be used for the operation of the multi-energy collaborative optimization scheduling method of the power grid.

[0201] Preferably, it also includes a computer-readable storage medium storing a computer program, which, when executed by a processor, implements a multi-energy collaborative optimization scheduling method for a power grid.

[0202] Furthermore, in one embodiment of the present invention, there is provided a storage medium, specifically a computer-readable storage medium (Memory), which is a memory device in a terminal device for storing programs and data. It is understandable that the computer-readable storage medium here can include both the built-in storage medium in the terminal device and, of course, the extended storage medium supported by the terminal device. The computer-readable storage medium provides a storage space, which stores the operating system of the terminal. In addition, one or more instructions suitable for being loaded and executed by the processor are also stored in the storage space. These instructions can be one or more computer programs (including program codes). It should be noted that the computer-readable storage medium here can be a high-speed RAM memory or a non-volatile memory, such as at least one disk memory.

[0203] One or more instructions stored in a computer-readable storage medium can be loaded and executed by a processor to implement the corresponding steps of the multi-energy collaborative optimization scheduling method for a power grid in the above-mentioned embodiment; one or more instructions in a computer-readable storage medium are loaded and executed by a processor.

[0204] It will be understood by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0205] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1A device that provides the functions specified in a block or multiple blocks.

[0206] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0207] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0208] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. Reference can be made to the common and similar parts between the various embodiments. For the devices disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the description is relatively simple, and the relevant parts can be referred to the method description.

[0209] The above description of the disclosed embodiments is intended to enable one skilled in the art to implement or use the present invention. Various modifications to these embodiments will be readily apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention is not limited to the embodiments shown herein but is intended to conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for peak-shaving scheduling of multiple resources in a power grid through joint collaborative optimization, characterized in that: include: S1: Collect grid data and analyze the support mechanism of multiple resources including source, grid, load and storage for peak load regulation and supply guarantee of the grid and their mutual coupling influence mechanism based on the grid data to obtain resource analysis results; S2: Based on the resource analysis results in S1, establish a peak load regulation effect evaluation indicator system; S3: Based on the resource analysis results in S1 and the evaluation index system in S2, a power grid multi-resource joint collaborative optimization peak-shaving dispatch model is established; S4: Substitute the resource analysis results in S1 into the power grid multi-resource joint collaborative optimization peak-shaving scheduling model in S3 to obtain the optimal scheduling plan for the power grid under the condition of difficult peak-shaving and supply guarantee.

2. A method for peak-shaving scheduling based on multi-resource joint coordinated optimization of power grid according to claim 1, characterized in that: Said S1 comprises: S1.1: Collect grid data, including historical load data, power output, energy storage, and demand-side data, and analyze the characteristics of power generation resources, energy storage resources, and demand-side resources; S1.2: Based on the actual operating data of conventional power units and considering their physical output characteristics, quantify the regulation capabilities of conventional units to reflect the peak regulation and supply guarantee capabilities of the source side; S1.3: Analyze the constraints of the grid structure on the transmission of multiple power backup sources, and reflect the impact of the grid side on the availability of regulation capacity; S1.4: Analyze the load characteristics of user electricity demand and establish a price-based demand-side response (PDR) model and an incentive-based demand-side response (IDR) configuration strategy model based on demand-side flexibility resources. S1.5: Based on the operating characteristics of electrochemical energy storage in the power grid, establish a multi-energy storage efficient utilization mechanism and obtain resource analysis results.

3. A method for peak-shaving scheduling based on multi-resource joint coordinated optimization of power grid according to claim 2, characterized in that: The S1.2 specifically includes thermal power output characteristics, thermal power ramp characteristics, special thermal power unit state restrictions, thermal power unit start and stop phase output restrictions, thermal power unit minimum continuous start and stop time restrictions and hydropower output characteristics.

4. A method for peak-shaving scheduling based on multi-resource joint coordinated optimization of power grid according to claim 2, characterized in that: The price-based demand-side response PDR model described in S1.4 is expressed as follows: Where, They represent the load of node n before and after PDR at time i, Refers to the load power change after PDR implementation, which has the following relationship with the time-of-use electricity price: Where E is the electricity price elasticity matrix, and its self-elasticity coefficient e ii It is used to describe the user's response to the current electricity price. It is a negative value. The mutual elasticity coefficient e ij It is used to describe the user's response to the electricity price at other times and is a non-negative value; The incentive-based demand-side response IDR configuration strategy model is expressed as follows: Where, is the load power change after IDR implementation, The resulting cost expression is as follows: Where C is the cost corresponding to the change in unit load power after adopting IDR, The load change power after IDR is adopted for node n.

5. A method for peak-shaving scheduling of power grid multi-resource joint coordinated optimization according to claim 1, characterized in that: The peak load regulation effect evaluation index system of S2 includes power reserve index, reserve coverage rate, and power system operation economic cost; The power reserve index considers the reserve capacity of the power system; the reserve capacity is divided into positive reserve capacity and negative reserve capacity, and the index characterization expression is as follows: Where R pos is the system standby at time t, R neg The system negative backup at time t; is the state variable of the i-th thermal power and hydropower unit; are the maximum technical outputs of the i-th thermal power unit and hydropower unit, are the minimum technical outputs of the i-th thermal power unit and hydropower unit respectively; E i,soct 、E i,socmax 、E i,socmin are the charge state of the i-th energy storage at time t, the upper limit of the i-th energy storage charge, and the lower limit of the i-th energy storage charge; P i,dis 、P i,ch are the discharging power and charging power of the i-th energy storage respectively; The standby coverage expression is as follows: Where RCP represents the operating reserve coverage, T is the scheduling period, k t is a 0-1 variable. When the system reserve in period t is greater than or equal to the actual system reserve, k t Take 1, otherwise k t Take 0; The system operation economy is a quantitative analysis from an economic perspective, which analyzes the impact of the model scheduling results after introducing multiple energy storage and demand-side response on the operation of the power system. The operation economic cost is analyzed in terms of source-load-storage. The expression is as follows: C plant =C fire +C hydro C new =C wind +C solar C extra =C ess +C abd +C idr Where C plant represents the operating cost of thermal power and hydropower, C new is the operating cost of wind and solar energy, C extra is the additional cost, which includes energy storage operation cost, penalty cost and incentive demand response cost.

6. A method for peak-shaving scheduling of power grid multi-resource joint coordinated optimization according to claim 1, characterized in that: The S3 establishes a multi-resource joint coordinated optimization peak-shaving dispatch model for the power grid, including: determining the model objective function, establishing constraints, combining the resource analysis results in S1 and the evaluation index system in S2 with the model objective function and establishing the model constraints; The formula of the model objective function is as follows: p glt,t =p load,t -p wind,t -p solar,t -p pdr,t f3=C fire +C hydro +C new +C ess +C renew_abd +C idr +C load_abd ; Where p glt,t is the net load of the system at time t, f1 represents the net load variance throughout the day, which refers to the degree of net load fluctuation throughout the day and represents the difficulty of balancing the net load curve of the system. The smaller f1 is, the smaller the net load fluctuation is, the easier it is to balance the power, and the difficulty of peak load regulation and supply guarantee of the power grid is reduced. C represents the regulation capacity of the i-th resource at time t. For thermal power and hydropower units, it is necessary to consider their start and stop status, ramp speed, current output, and output upper and lower limits; fire is the thermal power operation cost, C hydro is the hydropower operation cost, C new is the operating cost of new energy, C ess is the energy storage operating cost, C renew_abd The penalty cost for curtailing clean energy, C idr is the incentive-based demand-side response cost, C load_abd Penalty cost for load shedding.

7. A method for peak-shaving scheduling of power grid multi-resource joint coordinated optimization according to claim 6, characterized in that: The constraints include: conventional power supply output constraints, line flow constraints, conventional power supply ramp constraints, conventional power supply start and shutdown constraints, new energy output constraints, energy storage output constraints, energy storage energy constraints, energy storage equivalent call times constraints and demand-side response-related constraints.

8. A system using the power grid multi-resource joint collaborative optimization peak-shaving scheduling method according to any one of claims 1 to 7, characterized in that: include: Data acquisition and preprocessing module: used to collect and preprocess power grid data, and analyze the support mechanism of multiple resources of source, grid, load and storage for power grid peak load guarantee and their mutual coupling influence mechanism based on the power grid data, and obtain resource analysis results; Evaluation index module: Based on the peak load regulation demand of the power grid and the results of resource analysis, a peak load regulation effect evaluation index system is established; Multi-resource joint collaborative optimization peak-shaving scheduling module: used to establish a multi-resource joint collaborative optimization peak-shaving scheduling model for the power grid based on resource analysis results and evaluation indicator system; Model solving and optimization module: used to substitute resource analysis results into the power grid multi-resource joint collaborative optimization peak-shaving scheduling model to solve the optimal scheduling plan for the power grid under the condition of peak-shaving and supply guarantee difficulties; Dispatch plan generation module: Based on the model solution results, it generates a specific power grid dispatch plan and outputs optimized dispatch decisions to guide the actual operation and dispatch management of the power grid.

9. A computer device, characterized in that: The method comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the method for multi-energy collaborative optimization and dispatching of a power grid as described in any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium, characterized in that: A computer program is stored, and when the program is executed by a processor, the power grid multi-energy collaborative optimization scheduling method described in any one of claims 1 to 7 is implemented.

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