Energy storage configuration planning method and system based on fire storage combined peak regulation

Abstract

The invention discloses an energy storage configuration planning method and system based on fire-storage combined peak regulation, and the method comprises the steps: building a deep peak regulation cost revenue function and a scheduling planning model of a thermal power generating unit system, and the model comprises unit start-stop planning, output planning and peak regulation stage conversion planning; establishing a deep peak regulation cost revenue function and a scheduling planning model of the energy storage system, wherein the model comprises an energy storage dynamic life calculation method; a thermal power unit and energy storage combined deep peak regulation double-layer planning model is established on the basis of a thermal power unit and energy storage independent deep peak regulation model, a thermal power unit and energy storage combined deep peak regulation scheduling model is established on the upper layer with the maximum value of the thermal power unit and energy storage combined peak regulation income in a single day as a target function, and an optimal scheduling scheme of the thermal power unit and energy storage is solved. And the lower layer adopts a present value method to calculate the maximum net income of the energy storage whole life cycle, and the maximum net income is used as a target function to solve the optimal configuration of energy storage. The optimal configuration of energy storage is calculated according to the bilevel programming model, so that the benefit of fire-storage combined peak regulation can be effectively improved.

Description

Technical Field

[0001] The present invention relates to the technical field of auxiliary service storage configuration for thermal power units, and in particular to a method for planning energy storage configuration based on combined peak regulation of thermal power units and energy storage. Background Art

[0002] As the penetration rate of wind power gradually increases, the dispatch of power systems is increasingly affected by the inherent characteristics of wind power. The introduction of a certain proportion of flexible power sources can effectively improve the system's wind power absorption capacity. In recent years, large-capacity energy storage technology has become increasingly mature, and it plays an important role in peak-shaving, frequency regulation, and improving power quality of power systems. Introducing energy storage systems of appropriate capacity and incorporating thermal storage systems into power grid power generation will help improve the flexibility of system dispatch planning. Especially for peak-shaving auxiliary services, on the one hand, the unit faces the dilemma of slow ramping rate and inability to meet the peak-shaving needs of the power grid. On the other hand, when participating in deep peak-shaving, the unit cannot maintain extremely low output for a long time, and energy storage is needed to absorb part of the thermal power output to artificially increase the unit load rate.

[0003] With the continuous implementation of the dual carbon strategy, energy storage has become more and more commercialized, but most of the existing technologies do not involve improving the deep peak-shaving auxiliary service revenue of thermal power units by rationally allocating storage for thermal power units. The prior art publication number CN112653180B discloses an environmental economic dispatching method and system for a wind-fire-storage combined system; the publication number CN110137938B discloses an optimized dispatching method for a wind-fire-storage combined system based on an improved bat algorithm, both of which are aimed at improving the overall spare capacity and wind power absorption capacity of the wind-fire-storage system, and are not suitable for a combined thermal storage system. This situation not only reduces the revenue of thermal power plants, but also reduces the efficiency of power grid peak-shaving. Therefore, it is necessary to improve the energy storage system according to the specific situation of the combined thermal storage system and provide a corresponding energy storage planning method. Summary of the invention

[0004] 1. Technical problems to be solved:

[0005] In view of the above technical problems, the present invention provides an energy storage configuration planning method based on combined peak-shaving of thermal power plants and energy storage, adopts an energy storage system to improve the existing thermal power plant system, and provides a corresponding energy storage configuration method to improve the benefits of thermal power plants participating in peak-shaving auxiliary services.

[0006] 2. Technical solution:

[0007] A method for energy storage configuration planning based on combined peak load regulation of thermal energy storage, characterized in that it comprises the following steps:

[0008] Step 1: Considering the unit start-up and shutdown planning, unit output planning and unit peak load conversion planning, establish the deep peak load cost-benefit function and dispatch planning model of the thermal power unit system;

[0009] Step 2: Considering the dynamic life of the energy storage system, establish the deep peak load regulation cost-benefit function and dispatch planning model of the energy storage system;

[0010] Step 3: Based on the two models constructed in steps 1 and 2, a two-layer planning model for combined deep peak regulation of thermal energy storage is established; the upper layer takes the maximum peak regulation benefit of the combined thermal energy storage system within a single day as the objective function, establishes a combined deep peak regulation scheduling model for thermal energy storage, solves the optimal scheduling scheme of thermal power units and energy storage at each moment and the maximum benefit within a single day, and transmits the maximum benefit value to the lower layer; the lower layer uses a preset calculation method to calculate the maximum net benefit of combined peak regulation of thermal energy storage over the entire life cycle, and uses it as the objective function to solve the optimal configuration of energy storage, and transmits it back to the upper layer for further iteration;

[0011] Step 4: Based on the two-level planning model, the preset algorithm can be used to calculate the optimal configuration of energy storage input into the thermal power system.

[0012] Furthermore, step one specifically includes:

[0013] S11: Construct a deep peak-shaving cost-benefit function for the thermal power unit system, which includes the unit's operating electricity revenue, deep peak-shaving compensation revenue, coal consumption cost, and deep peak-shaving unit loss cost; this is achieved by calculating the daily cost-benefit function of a single thermal power unit on a single day; the daily cost-benefit function of a single thermal power unit is expressed as follows, where deep peak-shaving is subsidized by a progressive billing model, and different billing levels correspond to different electricity prices:

[0014]

[0015] R ele,t =π sell ·P G,i (t)·Δt

[0016]

[0017] In the above formula, R G，i is the revenue of the ith thermal power unit participating in peak load regulation in a single day; R ele,t is the electricity sales revenue of the thermal power unit in period t; R deep,t is the deep peak regulation benefit of the unit, C coal,t is the coal consumption cost of the unit in period t, is the deep peak load loss cost of the thermal power unit in time period t, Δt is the sampling period, and T is the number of sampling points in a day; P G,i (t) is the output of the thermal power unit in time period t; π sell (t) is the on-grid price of thermal power; is the unit price of deep peak regulation compensation when the load rate of thermal power units is at the i-th level of deep peak regulation; is the lower limit of the output of the ith subsidy, where the level is the corresponding progressive billing mode billing level; is the upper limit of thermal power unit output; p coal is the coal price; a, b, c are the quadratic coefficient, linear coefficient and constant term of the coal consumption function respectively; β is the actual operation loss coefficient of the thermal power plant; S J,i is the purchase cost of the i-th thermal power unit; N F,i,t is the number of rotor cracking cycles of the i-th thermal power unit at time t;

[0018] S12: Establish a scheduling and planning model for thermal power units; the scheduling and planning model for thermal power units includes corresponding constraints for unit start-stop planning, unit output planning, and unit peak-shaving phase conversion planning; specifically, the unit start-stop constraints included in the unit start-stop planning, the unit power balance constraints and ramp constraints included in the unit output planning, and the corresponding output constraints for each peak-shaving phase of the unit included in the unit peak-shaving phase conversion planning; wherein each peak-shaving phase of the unit includes conventional peak-shaving, deep peak-shaving without oil injection, and deep peak-shaving with oil injection; the specific expression of the scheduling and planning model for a single thermal power unit is as follows:

[0019] (B i,t-1 -B i,t )(T on,i,t-1 -T on,i )≥0

[0020] (B i,t -B i,t-1 )(T off,i,t-1 -T off,i )≥0

[0021]

[0022] 0≤B G,1 (t)+B G,2 (t)+B G,3 (t)≤1

[0023] In the above formula, B i,t represents the start and stop status of the i-th unit at time period t, B i,t =1 When the unit is in operation, B i,t =0 when the unit is in shutdown state; T on,i,t , T off,i,t are the continuous operation time and continuous downtime time of the i-th unit in time period t, T on,i is the minimum continuous operation time of the i-th unit, T off,i is the minimum continuous downtime of the i-th unit; L(t) is the load in period t, n represents the total number of thermal power units; P RG,i is the upper limit of the climbing speed of the i-th unit; B G,1 , B G,2 , B G,3 They are 0-1 variables representing the three stages of conventional peak regulation, deep peak regulation without oil injection, and deep peak regulation with oil injection.

[0024] Furthermore, step 2 specifically includes the following steps:

[0025] S21: Establish a cost-benefit function for deep peak regulation of the energy storage system; the cost-benefit of the energy storage system specifically includes peak shaving and valley filling benefits, energy storage subsidy benefits, environmental benefits, and depreciation recovery. The cost includes investment cost, operation and maintenance cost, and supporting facility cost; the single-day benefit R of the constructed energy storage system ESS,d , Life cycle cost LCC function R ESS The cost and benefits of each part are shown in the following formula:

[0026]

[0027] C r =a(C inv +C op,d )

[0028] C inv =c e E+c p P

[0029]

[0030] In the formula P is the peak-valley arbitrage income of energy storage within a day, that is, the energy storage device discharges when the electricity price is peak and charges when the electricity price is low, and income can be obtained through the price difference between peak and valley periods. d (t) is the energy storage discharge power, P c (t) is the energy storage charging power, π toll (t) is the time-of-use electricity price; R ESS is the life cycle cost function, Y is the energy storage service life, unit: year; d, m, y represent the summed ordinal number of day, month, and year respectively; is the peak-shaving subsidy for energy storage in time period t; p0 is the electricity price for energy storage output subsidy, which is affected by local policies; is the environmental benefit of energy storage in time period t; that is, using energy storage to peak-shift to replace part of the output of thermal power units. The output of the replaced thermal power units is often accompanied by pollutant emissions. Using energy storage to replace them can reduce the corresponding pollutant emissions; where K is the total amount of pollutant emissions from the production of electricity using thermal power units, ρ k is the emission density of the kth pollutant per unit of electricity produced, π con,kis the unit emission cost of the kth pollutant, k≤K; C r is the residual value recovery income of energy storage depreciation, that is, the value of the precious metals and other parts of the equipment in the energy storage system that can be recycled after it is scrapped; assuming that the residual value of the energy storage equipment is linearly related to the sum of its construction and operation and maintenance costs, a is the residual value coefficient of the energy storage equipment, and C op,d The single-day energy storage operation and maintenance cost;

[0031] C inv is the energy storage investment cost, where c e is the unit capacity investment cost, c p is the unit power investment cost, E is the rated capacity, P is the rated power; C op is the annual operation and maintenance cost of energy storage, which consists of fixed operation and maintenance cost and variable operation and maintenance cost. The fixed operation and maintenance cost is related to the rated power of the energy storage system, and the variable operation and maintenance cost is related to the charge and discharge capacity of the energy storage; c F op is the annual fixed operation and maintenance cost per unit power of the energy storage system; c V op is the variable operation and maintenance cost per unit capacity of the energy storage system; C anc The cost of supporting facilities for energy storage refers to the purchase funds for supporting auxiliary equipment of the energy storage system except for energy storage components and PCS, which is determined by the rated capacity or rated power of energy storage. p anc is the auxiliary cost per unit power; c e anc is the auxiliary cost per unit capacity;

[0032] S22: The scheduling planning model of the energy storage system is shown in the following formula, which specifically includes output constraints, state of charge SOC constraints, output continuity constraints, and energy storage charging and discharging state constraints:

[0033] 0≤P c (t)≤P

[0034] 0≤P d (t)≤P

[0035] SOC min ≤SOC(t)≤SOC max

[0036] SOC(t)=SOC(t-1)+(η c P c (t)-P d (t) / η d )Δt / E

[0037] P c (t)·P d (t) = 0

[0038] Where P c is the energy storage charging power, P d is the energy storage discharge power; SOC min is the minimum energy storage SOC, SOC max is the maximum energy storage SOC; η c is the energy storage charging efficiency; η d is the energy storage discharge efficiency;

[0039] S23: Calculate the dynamic life of the energy storage system; the effective discharge capacity of the energy storage system depends on the ratio of the actual discharge depth to the rated discharge depth; under the same operating conditions, the number of cycles is a decreasing function of the discharge depth as shown in the following formula:

[0040]

[0041] Where D r is the rated discharge depth, and the corresponding number of cycles is N r ;D a is the discharge depth of the actual discharge process, and the corresponding number of cycles is N a ;

[0042] If operated at the rated depth of discharge, the total amount of electricity discharged by the energy storage system during its service life is:

[0043] E r =N r D r E

[0044] Where E is the rated capacity of the battery energy storage system;

[0045] Remember m DOD is the conversion factor, then:

[0046]

[0047] In a planned horizontal year, the discharge process of different discharge depths is converted to the annual discharge capacity at the rated discharge depth as follows:

[0048]

[0049] In the above formula, N S Indicates that the planning level includes N S The number of research stages; N D For each research phase, N D day; N means the number of discharge processes included in the jth day; E bijn 、m DOD ijnis the discharge capacity of the energy storage system in the nth discharge process on the jth day of the i-th stage and the conversion factor corresponding to the actual discharge depth; the dynamic life of the energy storage system is shown as follows:

[0050]

[0051] Where Y b is the service life of the energy storage system; E r E is the total discharge capacity of the battery energy storage within its service life at the rated discharge depth; a The annual discharge capacity at the rated discharge depth is converted from the discharge process at different discharge depths within the planning level year.

[0052] Furthermore, in step 3, the upper layer takes the maximum peak-shaving benefit of the combined fire-storage system in a single day as the objective function, and the objective function is expressed as follows:

[0053]

[0054] The upper constraint conditions are the scheduling planning model of the thermal power unit and the scheduling planning model of the energy storage obtained in step S12 and step S22, and the power balance constraint in step S12 is changed to:

[0055]

[0056] Furthermore, in step three, the objective function of solving the optimal configuration of energy storage in the lower layer is the maximum benefit of the combined peak regulation of thermal storage in the whole life cycle; after the solution is completed, the optimal configuration of energy storage is transmitted to the upper layer; the two layers are connected by the particle swarm intelligent algorithm PSO; the total benefit in the whole life cycle is set as the fitness function of the particle swarm intelligent algorithm PSO to continuously iterate and optimize the optimal configuration of energy storage, and the upper layer adopts Cplex or Mosek or Gurobi solver in MATLAB to solve.

[0057] Furthermore, in step 4, a preset algorithm is used to solve the model in Cplex, Mosek or Gurobi.

[0058] Furthermore, the cost present value is used to calculate the maximum benefit of combined peak load regulation with thermal energy storage over the entire life cycle; then the corresponding objective function of the lower layer is:

[0059]

[0060] Where i0 is the benchmark discount rate; f2 is also the fitness function of the particle swarm intelligence algorithm PSO.

[0061] An energy storage configuration planning system based on thermal energy storage combined peak load regulation, comprising:

[0062] A deep peak-shaving cost-benefit function and a scheduling planning module for a thermal power unit system; a deep peak-shaving cost-benefit function and a scheduling planning model for a unit are established based on the conversion relationship of different peak-shaving stages of a single unit in the thermal power unit system; the scheduling planning model includes the unit's climbing constraints, power balance constraints, climbing constraints, and the output corresponding to the unit's peak-shaving stage conversion plan; the output corresponding to the unit's peak-shaving stage conversion plan includes the corresponding output constraints of each peak-shaving stage of the unit; a deep peak-shaving benefit and scheduling planning module for energy storage; a deep peak-shaving benefit function and a scheduling planning model for energy storage are established based on the output characteristics and benefit mode of energy storage; the scheduling planning model includes the output constraints, state of charge constraints, output continuity constraints, and charge and discharge state constraints of energy storage;

[0063] A two-layer planning module for deep peak regulation of thermal power units and energy storage. Based on the deep peak regulation cost-benefit function and scheduling planning model of the thermal power unit system and the deep peak regulation cost-benefit function and scheduling planning model of the energy storage system, and on the basis of following the constraints of each individual model, it is additionally required that the thermal storage jointly output to maintain power balance, thereby constructing a two-layer planning model for deep peak regulation of thermal storage. The upper layer of the two-layer planning model optimizes the scheduling strategy of thermal power units and energy storage with the maximum daily benefit of deep peak regulation of thermal storage as the objective function, and the lower layer optimizes the optimal configuration of energy storage with the maximum benefit of the combined peak regulation of thermal storage over the entire life cycle as the objective function.

[0064] The solution module solves the model in Cplex, Mosek or Gurobi based on the established two-level programming model to obtain the maximum benefit of combined peak regulation of thermal energy storage and the optimal configuration of energy storage.

[0065] 3. Beneficial effects:

[0066] (1) This method is a method and system for energy storage configuration planning based on combined peak-shaving of thermal energy storage, which constructs a two-layer planning model for combined deep peak-shaving of thermal energy storage. The upper layer model takes the maximum peak-shaving benefit of the combined thermal energy storage system as the objective function; the lower layer model takes the maximum benefit of the combined peak-shaving of thermal energy storage over the entire life cycle as the objective function, and transmits the configuration obtained by the lower layer model to the upper layer model to realize iterative optimization of energy storage planning.

[0067] (2) In the energy storage configuration planning method and system based on combined peak-shaving of thermal power plants, the maximum net benefit of combined deep peak-shaving of thermal power plants is taken as the objective function. Considering the present value method and the life loss model of energy storage, a scheduling planning model of combined peak-shaving of thermal power plants that fits the reality is established. The established model can be solved using a commercial solver to obtain the optimal configuration of energy storage and the optimal scheduling and operation plan of thermal power units, so as to improve the peak-shaving benefits and energy utilization efficiency of thermal power plants and promote the implementation of the "dual carbon strategy". BRIEF DESCRIPTION OF THE DRAWINGS

[0068] Figure 1 A flowchart of the method is shown in FIG.

[0069] Figure 2 This is a schematic diagram of the double-layer planning model for combined deep peak regulation of fire and storage constructed in this method;

[0070] Figure 3 It is the energy storage SOC diagram and energy storage output diagram in the specific embodiment;

[0071] Figure 4 It is the output diagram of 200MW, 300MW and 600MW units in the specific embodiment;

[0072] Figure 5 It is the original net load curve and the comparison diagram of the unit and energy storage output in the specific embodiment;

[0073] Figure 6 It is the fitness evolution curve obtained in the specific embodiment. DETAILED DESCRIPTION

[0074] The present invention will be described in detail below in conjunction with the accompanying drawings.

[0075] As attached Figure 1 A method for energy storage configuration planning based on combined peak load regulation of thermal energy storage, characterized in that it comprises the following steps:

[0076] Step 1: Considering the unit start-up and shutdown planning, unit output planning and unit peak load conversion planning, establish the deep peak load cost-benefit function and dispatch planning model of the thermal power unit system;

[0077] Step 2: Considering the dynamic life of the energy storage system, establish the deep peak load regulation cost-benefit function and dispatch planning model of the energy storage system;

[0078] Step 3: Based on the two models constructed in steps 1 and 2, a two-layer planning model for deep peak regulation of thermal energy storage is established; the upper layer takes the maximum peak regulation benefit of the thermal energy storage combined system within a single day as the objective function, establishes a thermal energy storage combined deep peak regulation scheduling model, solves the optimal scheduling scheme of thermal power units and energy storage at each moment and the maximum benefit within a single day, and transmits the maximum benefit value to the lower layer; the lower layer uses a preset calculation method to calculate the maximum net benefit of thermal energy storage combined peak regulation over the entire life cycle, and uses it as the objective function to solve the optimal configuration of energy storage, and transmits it back to the upper layer for further iteration; its specific structure is shown in the attached figure. Figure 2 shown.

[0079] Step 4: Based on the two-level planning model, the preset algorithm can be used to calculate the optimal configuration of energy storage input into the thermal power system.

[0080] Furthermore, step one specifically includes:

[0081] S11: Construct a deep peak-shaving cost-benefit function for the thermal power unit system, which includes the unit's operating electricity revenue, deep peak-shaving compensation revenue, coal consumption cost, and deep peak-shaving unit loss cost; this is achieved by calculating the daily cost-benefit function of a single thermal power unit on a single day; the daily cost-benefit function of a single thermal power unit is expressed as follows, where deep peak-shaving is subsidized by a progressive billing model, and different billing levels correspond to different electricity prices:

[0082]

[0083] R ele,t =π sell ·P G,i (t)·Δt

[0084]

[0085] In the above formula, R G，i is the revenue of the ith thermal power unit participating in peak load regulation in a single day; R ele,t is the electricity sales revenue of the thermal power unit in period t; R deep,t is the deep peak regulation benefit of the unit, C coal,t is the coal consumption cost of the unit in period t, is the deep peak load loss cost of the thermal power unit in time period t, Δt is the sampling period, and T is the number of sampling points in a day; P G,i (t) is the output of the thermal power unit in time period t; π sell (t) is the on-grid price of thermal power; is the unit price of deep peak regulation compensation when the load rate of thermal power units is at the i-th level of deep peak regulation; is the lower limit of the output of the ith subsidy, where the level is the corresponding progressive billing mode billing level; is the upper limit of thermal power unit output; p coal is the coal price; a, b, c are the quadratic coefficient, linear coefficient and constant term of the coal consumption function respectively; β is the actual operation loss coefficient of the thermal power plant; S J,i is the purchase cost of the i-th thermal power unit; N F,i,t is the number of rotor cracking cycles of the i-th thermal power unit at time t;

[0086] S12: Establish a scheduling and planning model for thermal power units; the scheduling and planning model for thermal power units includes corresponding constraints for unit start-stop planning, unit output planning, and unit peak-shaving phase conversion planning; specifically, the unit start-stop constraints included in the unit start-stop planning, the unit power balance constraints and ramp constraints included in the unit output planning, and the corresponding output constraints for each peak-shaving phase of the unit included in the unit peak-shaving phase conversion planning; wherein each peak-shaving phase of the unit includes conventional peak-shaving, deep peak-shaving without oil injection, and deep peak-shaving with oil injection; the specific expression of the scheduling and planning model for a single thermal power unit is as follows:

[0087] (B i,t-1 -B i,t )(T on,i,t-1 -T on,i )≥0

[0088] (B i,t -B i,t-1 )(T off,i,t-1 -T off,i )≥0

[0089]

[0090] 0≤B G,1 (t)+B G,2 (t)+B G,3 (t)≤1

[0091] In the above formula, B i,t represents the start and stop status of the i-th unit at time period t, B i,t =1 When the unit is in operation, B i,t =0 when the unit is in shutdown state; T on,i,t , T off,i,t are the continuous operation time and continuous downtime time of the i-th unit in time period t, T on,i is the minimum continuous operation time of the i-th unit, T off,i is the minimum continuous downtime of the i-th unit; L(t) is the load in period t, n represents the total number of thermal power units; P R G,i is the upper limit of the climbing speed of the i-th unit; B G,1 , B G,2 , B G,3 They are 0-1 variables representing the three stages of conventional peak regulation, deep peak regulation without oil injection, and deep peak regulation with oil injection.

[0092] Furthermore, step 2 specifically includes the following steps:

[0093] S21: Establish a cost-benefit function for deep peak regulation of the energy storage system; the cost-benefit of the energy storage system specifically includes peak shaving and valley filling benefits, energy storage subsidy benefits, environmental benefits, and depreciation recovery. The cost includes investment cost, operation and maintenance cost, and supporting facility cost; the single-day benefit R of the constructed energy storage system ESS,d , Life cycle cost LCC function R ESS The cost and benefits of each part are shown in the following formula:

[0094]

[0095] C r =a(C inv +C op,d )

[0096] C inv =c e E+c p P

[0097]

[0098] In the formula P is the peak-valley arbitrage income of energy storage within a day, that is, the energy storage device discharges when the electricity price is peak and charges when the electricity price is low, and income can be obtained through the price difference between peak and valley periods. d (t) is the energy storage discharge power, P c (t) is the energy storage charging power, π toll (t) is the time-of-use electricity price; R ESS is the life cycle cost function, Y is the energy storage service life, unit: year; d, m, y represent the summed ordinal number of day, month, and year respectively; is the peak-shaving subsidy for energy storage in time period t; p0 is the electricity price for energy storage output subsidy, which is affected by local policies; is the environmental benefit of energy storage in time period t; that is, using energy storage to peak-shift to replace part of the output of thermal power units. The output of the replaced thermal power units is often accompanied by pollutant emissions. Using energy storage to replace them can reduce the corresponding pollutant emissions; where K is the total amount of pollutant emissions from the production of electricity using thermal power units, ρ k is the emission density of the kth pollutant per unit of electricity produced, π con,k is the unit emission cost of the kth pollutant, k≤K; C r is the residual value recovery income of energy storage depreciation, that is, the value of the precious metals and other parts of the equipment in the energy storage system that can be recycled after it is scrapped; assuming that the residual value of the energy storage equipment is linearly related to the sum of its construction and operation and maintenance costs, a is the residual value coefficient of the energy storage equipment, and C op,d The single-day energy storage operation and maintenance cost;

[0099] C inv is the energy storage investment cost, where ce is the unit capacity investment cost, c p is the unit power investment cost, E is the rated capacity, P is the rated power; C op is the annual operation and maintenance cost of energy storage, which consists of fixed operation and maintenance cost and variable operation and maintenance cost. The fixed operation and maintenance cost is related to the rated power of the energy storage system, and the variable operation and maintenance cost is related to the charge and discharge capacity of the energy storage; c F op is the annual fixed operation and maintenance cost per unit power of the energy storage system; c V op is the variable operation and maintenance cost per unit capacity of the energy storage system; C anc The cost of supporting facilities for energy storage refers to the purchase funds for supporting auxiliary equipment of the energy storage system except for energy storage components and PCS, which is determined by the rated capacity or rated power of energy storage. p anc is the auxiliary cost per unit power; c e anc is the auxiliary cost per unit capacity;

[0100] S22: The scheduling planning model of the energy storage system is shown in the following formula, which specifically includes output constraints, state of charge SOC constraints, output continuity constraints, and energy storage charging and discharging state constraints:

[0101] 0≤P c (t)≤P

[0102] 0≤P d (t)≤P

[0103] SOC min ≤SOC(t)≤SOC max

[0104] SOC(t)=SOC(t-1)+(η c P c (t)-P d (t) / η d )Δt / E

[0105] P c (t)·P d (t) = 0

[0106] Where P c is the energy storage charging power, P d is the energy storage discharge power; SOC min is the minimum energy storage SOC, SOC max is the maximum energy storage SOC; η c is the energy storage charging efficiency; η d is the energy storage discharge efficiency;

[0107] S23: Calculate the dynamic life of the energy storage system; the effective discharge capacity of the energy storage system depends on the ratio of the actual discharge depth to the rated discharge depth; under the same operating conditions, the number of cycles is a decreasing function of the discharge depth as shown in the following formula:

[0108]

[0109] Where D r is the rated discharge depth, and the corresponding number of cycles is N r ;D a is the discharge depth of the actual discharge process, and the corresponding number of cycles is N a ;

[0110] If operated at the rated depth of discharge, the total amount of electricity discharged by the energy storage system during its service life is:

[0111] E r =N r D r E

[0112] Where E is the rated capacity of the battery energy storage system;

[0113] Remember m DOD is the conversion factor, then:

[0114]

[0115] In a planned horizontal year, the discharge process of different discharge depths is converted to the annual discharge capacity at the rated discharge depth as follows:

[0116]

[0117] In the above formula, N S Indicates that the planning level includes N S The number of research stages; N D For each research phase, N D day; N means the number of discharge processes included in the jth day; E bijn 、m DOD ijn is the discharge capacity of the energy storage system in the nth discharge process on the jth day of the i-th stage and the conversion factor corresponding to the actual discharge depth; the dynamic life of the energy storage system is shown as follows:

[0118]

[0119] Where Y b is the service life of the energy storage system; E r E is the total discharge capacity of the battery energy storage within its service life at the rated discharge depth; aThe annual discharge capacity at the rated discharge depth is converted from the discharge process at different discharge depths within the planning level year.

[0120] Furthermore, in step 3, the upper layer takes the maximum peak-shaving benefit of the combined fire-storage system in a single day as the objective function, and the objective function is expressed as follows:

[0121]

[0122] The upper constraint conditions are the scheduling planning model of the thermal power unit and the scheduling planning model of the energy storage obtained in step S12 and step S22, and the power balance constraint in step S12 is changed to:

[0123]

[0124] Furthermore, in step three, the objective function of solving the optimal configuration of energy storage in the lower layer is the maximum benefit of the combined peak regulation of thermal storage in the whole life cycle; after the solution is completed, the optimal configuration of energy storage is transmitted to the upper layer; the two layers are connected by the particle swarm intelligent algorithm PSO; the total benefit in the whole life cycle is set as the fitness function of the particle swarm intelligent algorithm PSO to continuously iterate and optimize the optimal configuration of energy storage, and the upper layer adopts Cplex or Mosek or Gurobi solver in MATLAB to solve.

[0125] Furthermore, in step 4, a preset algorithm is used to solve the model in Cplex, Mosek or Gurobi.

[0126] Furthermore, the cost present value is used to calculate the maximum benefit of combined peak load regulation with thermal energy storage over the entire life cycle; then the corresponding objective function of the lower layer is:

[0127]

[0128] Where i0 is the benchmark discount rate; f2 is also the fitness function of the particle swarm intelligence algorithm PSO.

[0129] An energy storage configuration planning system based on thermal energy storage combined peak load regulation, comprising:

[0130] A deep peak-shaving cost-benefit function and a scheduling planning module for a thermal power unit system; a deep peak-shaving cost-benefit function and a scheduling planning model for a unit are established based on the conversion relationship of different peak-shaving stages of a single unit in the thermal power unit system; the scheduling planning model includes the unit's climbing constraints, power balance constraints, climbing constraints, and the output corresponding to the unit's peak-shaving stage conversion plan; the output corresponding to the unit's peak-shaving stage conversion plan includes the corresponding output constraints of each peak-shaving stage of the unit; a deep peak-shaving benefit and scheduling planning module for energy storage; a deep peak-shaving benefit function and a scheduling planning model for energy storage are established based on the output characteristics and benefit mode of energy storage; the scheduling planning model includes the output constraints, state of charge constraints, output continuity constraints, and charge and discharge state constraints of energy storage;

[0131] A two-layer planning module for deep peak regulation of thermal power units and energy storage. Based on the deep peak regulation cost-benefit function and scheduling planning model of the thermal power unit system and the deep peak regulation cost-benefit function and scheduling planning model of the energy storage system, and on the basis of following the constraints of each individual model, it is additionally required that the thermal storage jointly output to maintain power balance, thereby constructing a two-layer planning model for deep peak regulation of thermal storage. The upper layer of the two-layer planning model optimizes the scheduling strategy of thermal power units and energy storage with the maximum daily benefit of deep peak regulation of thermal storage as the objective function, and the lower layer optimizes the optimal configuration of energy storage with the maximum benefit of the combined peak regulation of thermal storage over the entire life cycle as the objective function.

[0132] The solution module solves the model in Cplex, Mosek or Gurobi based on the established two-level programming model to obtain the maximum benefit of combined peak regulation of thermal energy storage and the optimal configuration of energy storage. Specific embodiment:

[0134] In this example, there are 5 200MW, 5 300MW and 2 600MW units, of which the 600MW units can be used for deep peak load regulation. Figure 4 The energy storage uses lithium iron phosphate batteries, and its SOC diagram and energy storage output diagram are shown in the attached Figure 3 The parameters of the unit are shown in Table 1 below, and the storage parameters are shown in Table 2 below.

[0135] Table 1 Unit parameters

[0136]

[0137] Table 2 Energy storage parameters

[0138]

[0139]

[0140] The boiler fuel of the unit is coking coal, the price is 685 yuan / t; the on-grid electricity price of thermal power is 0.375 yuan / (kWh). In the calculation using this method, it is assumed that the 200MW and 300MW units only perform conventional peak regulation, and the minimum load rate in the conventional peak regulation stage is 50%. Assuming that the 600MW unit participates in deep peak regulation, its relevant parameters during deep peak regulation are as follows: ① The minimum load rate of the unit in the conventional peak regulation stage is 50%, the minimum load rate in the deep peak regulation stage without oil injection is 40%, and the minimum load rate in the deep peak regulation stage with oil injection is 30%. ② The operating loss coefficient β in the deep peak regulation stage without oil injection is 1.2, and the operating loss coefficient β in the deep peak regulation stage with oil injection is 1.5. ③ The unit construction cost is 3464 yuan / kW. ④ The relationship between the rotor cracking cycle N and the unit power P in the deep peak regulation stage is N(P)=0.005778P 3 -2.682P 2 +484.8P-8411. ⑤ The oil consumption of the unit during the deep peak regulation phase is 4.8t / h, and the oil price is 6130 yuan / t. ⑥ The compensation price for thermal power units participating in deep peak regulation is 0.4 yuan / (kW×h).

[0141] Bringing the above parameters into this method, the calculated results are as follows:

[0142] (1) Optimal energy storage power: 77.1MW, optimal energy storage capacity: 334.2MWh

[0143] (2) Coal consumption cost: 10,220,970.7 yuan / day

[0144] (3) Deep peak load regulation income: 576,000.0 yuan / day

[0145] (4) Revenue from electricity sales of thermal power units: RMB 18,628,714.5 per day

[0146] (5) Peak-to-valley arbitrage: RMB 234,059.8 per day

[0147] (6) Environmental benefits: RMB 227,484.3 per day

[0148] (7) Combined income from fire and storage: RMB 8,472,583.1 per day.

[0149] Figure 5 , 6 It is the original net load curve of this embodiment and the comparison diagram of the unit and energy storage output and the fitness evolution curve.

[0150] Although the present invention has been disclosed as above in terms of preferred embodiments, they are not intended to limit the present invention. Anyone skilled in the art can make various changes or modifications without departing from the spirit and scope of the present invention. Therefore, the scope of protection of the present invention should be based on the scope of protection defined by the claims of this application.

Claims

1. A method for energy storage configuration planning based on combined peak load regulation of thermal energy storage, characterized in that: The following steps are involved: Step 1: Considering the unit start-up and shutdown planning, unit output planning and unit peak load conversion planning, establish the deep peak load cost-benefit function and dispatch planning model of the thermal power unit system; Step 2: Considering the dynamic life of the energy storage system, establish the deep peak load regulation cost-benefit function and dispatch planning model of the energy storage system; Step 3: Based on the two models constructed in steps 1 and 2, a two-layer planning model for combined deep peak regulation of thermal energy storage is established; the upper layer takes the maximum peak regulation benefit of the combined thermal energy storage system within a single day as the objective function, establishes a combined deep peak regulation scheduling model for thermal energy storage, solves the optimal scheduling scheme of thermal power units and energy storage at each moment and the maximum benefit within a single day, and transmits the maximum benefit value to the lower layer; the lower layer uses a preset calculation method to calculate the maximum net benefit of combined peak regulation of thermal energy storage over the entire life cycle, and uses it as the objective function to solve the optimal configuration of energy storage, and transmits it back to the upper layer for further iteration; Step 4: Based on the two-level planning model, the preset algorithm can be used to calculate the optimal configuration of energy storage input into the thermal power system.

2. The energy storage configuration planning method based on combined peak load regulation of thermal energy storage according to claim 1 is characterized by: Step 1 specifically includes: S11: Construct a deep peak-shaving cost-benefit function for the thermal power unit system, which includes the unit's operating electricity revenue, deep peak-shaving compensation revenue, coal consumption cost, and deep peak-shaving unit loss cost; this is achieved by calculating the daily cost-benefit function of a single thermal power unit on a single day; the daily cost-benefit function of a single thermal power unit is expressed as follows, where deep peak-shaving is subsidized by a progressive billing model, and different billing levels correspond to different electricity prices: R ele,t =π sell ·P G,i (t)·Δt In the above formula, R G，i is the revenue of the ith thermal power unit participating in peak load regulation in a single day; R ele,t is the electricity sales revenue of the thermal power unit in period t; R deep,t is the deep peak regulation benefit of the unit, C coal,t is the coal consumption cost of the unit in period t, is the deep peak load loss cost of the thermal power unit in time period t, Δt is the sampling period, and T is the number of sampling points in a day; P G,i (t) is the output of the thermal power unit in time period t; π sell (t) is the price of thermal power on the grid; p i deep is the unit price of deep peak regulation compensation when the load rate of thermal power units is in the i-th level of deep peak regulation; η i min is the output lower limit of the ith subsidy, where the level is the corresponding progressive billing mode billing level; P G,i max is the upper limit of thermal power unit output; p coal is the coal price; a, b, c are the quadratic coefficient, linear coefficient and constant term of the coal consumption function respectively; β is the actual operation loss coefficient of the thermal power plant; S J,i is the purchase cost of the i-th thermal power unit; N F,i,t is the number of rotor cracking cycles of the i-th thermal power unit at time t; S12: Establish a scheduling and planning model for thermal power units; the scheduling and planning model for thermal power units includes corresponding constraints for unit start-stop planning, unit output planning, and unit peak-shaving phase conversion planning; specifically, the unit start-stop constraints included in the unit start-stop planning, the unit power balance constraints and ramp constraints included in the unit output planning, and the corresponding output constraints for each peak-shaving phase of the unit included in the unit peak-shaving phase conversion planning; wherein each peak-shaving phase of the unit includes conventional peak-shaving, deep peak-shaving without oil injection, and deep peak-shaving with oil injection; the specific expression of the scheduling and planning model for a single thermal power unit is as follows: (B i,t-1 -B i,t )(T on,i,t-1 -T on,i )≥0 (B i,t -B i,t-1 )(T off,i,t-1 -T off,i )≥0 0≤B G,1 (t)+B G,2 (t)+B G,3 (t)≤1 In the above formula, B i,t represents the start and stop status of the i-th unit at time period t, B i,t =1 When the unit is in operation, B i,t =0 when the unit is in shutdown state; T on,i,t 、T off,i,t are the continuous operation time and continuous downtime time of the i-th unit in time period t, T on,i is the minimum continuous operation time of the i-th unit, T off,i is the minimum continuous downtime of the i-th unit; L(t) is the load in period t, n represents the total number of thermal power units; P R G,i is the upper limit of the climbing speed of the i-th unit; B G,1 , B G,2 , B G,3 They are 0-1 variables representing the three stages of conventional peak regulation, deep peak regulation without oil injection, and deep peak regulation with oil injection.

3. The energy storage configuration planning method based on combined peak load regulation of thermal energy storage according to claim 2 is characterized by: Step 2 specifically includes the following steps: S21: Establish a cost-benefit function for deep peak regulation of the energy storage system; the cost-benefit of the energy storage system specifically includes peak shaving and valley filling benefits, energy storage subsidy benefits, environmental benefits, and depreciation recovery. The cost includes investment cost, operation and maintenance cost, and supporting facility cost; the single-day benefit R of the constructed energy storage system ESS,d , Life cycle cost LCC function R ESS The cost and benefits of each part are shown in the following formula: C r =a(C inv +C op,d ) C inv =c e E+c p P In the formula P is the peak-valley arbitrage income of energy storage within a day, that is, the energy storage device discharges when the electricity price is peak and charges when the electricity price is low, and income can be obtained through the price difference between peak and valley periods. d (t) is the energy storage discharge power, P c (t) is the energy storage charging power, π toll (t) is the time-of-use electricity price; R ESS is the life cycle cost function, Y is the energy storage service life, unit: year; d, m, y represent the summed ordinal number of day, month, and year respectively; is the peak-shaving subsidy for energy storage in time period t; p0 is the electricity price for energy storage output subsidy, which is affected by local policies; is the environmental benefit of energy storage in time period t; that is, using energy storage to peak-shift to replace part of the output of thermal power units. The output of the replaced thermal power units is often accompanied by pollutant emissions. Using energy storage to replace them can reduce the corresponding pollutant emissions; where K is the total amount of pollutant emissions from the production of electricity using thermal power units, ρ k is the emission density of the kth pollutant per unit of electricity produced, π con,k is the unit emission cost of the kth pollutant, k≤K; C r is the residual value recovery income of energy storage depreciation, that is, the value of the precious metals and other parts of the equipment in the energy storage system that can be recycled after it is scrapped; assuming that the residual value of the energy storage equipment is linearly related to the sum of its construction and operation and maintenance costs, a is the residual value coefficient of the energy storage equipment, and C op,d The single-day energy storage operation and maintenance cost; C inv is the energy storage investment cost, where c e is the unit capacity investment cost, c p is the unit power investment cost, E is the rated capacity, P is the rated power; C op is the annual operation and maintenance cost of energy storage, which consists of fixed operation and maintenance cost and variable operation and maintenance cost. The fixed operation and maintenance cost is related to the rated power of the energy storage system, and the variable operation and maintenance cost is related to the charge and discharge capacity of the energy storage; c F op is the annual fixed operation and maintenance cost per unit power of the energy storage system; c V op is the variable operation and maintenance cost per unit capacity of the energy storage system; C anc The cost of supporting facilities for energy storage refers to the purchase funds for supporting auxiliary equipment of the energy storage system except for energy storage components and PCS, which is determined by the rated capacity or rated power of energy storage. p anc is the auxiliary cost per unit power; c e anc is the auxiliary cost per unit capacity; S22: The scheduling planning model of the energy storage system is shown in the following formula, which specifically includes output constraints, state of charge SOC constraints, output continuity constraints, and energy storage charging and discharging state constraints: 0≤P c (t)≤P 0≤P d (t)≤P SOC min ≤SOC(t)≤SOC max SOC(t)=SOC(t-1)+(η c P c (t)-P d (t) / η d )Δt / E P c (t)·P d (t)=0 Where P c is the energy storage charging power, P d is the energy storage discharge power; SOC min is the minimum energy storage SOC, SOC max is the maximum energy storage SOC; η c is the energy storage charging efficiency; η d is the energy storage discharge efficiency; S23: Calculate the dynamic life of the energy storage system; the effective discharge capacity of the energy storage system depends on the ratio of the actual discharge depth to the rated discharge depth; under the same operating conditions, the number of cycles is a decreasing function of the discharge depth as shown in the following formula: Where D r is the rated discharge depth, and the corresponding number of cycles is N r ;D a is the discharge depth of the actual discharge process, and the corresponding number of cycles is N a ; If operated at the rated depth of discharge, the total amount of electricity discharged by the energy storage system during its service life is: E r =N r D r E Where E is the rated capacity of the battery energy storage system; Remember m DOD is the conversion factor, then: In a planned horizontal year, the discharge process of different discharge depths is converted to the annual discharge capacity at the rated discharge depth as follows: In the above formula, N S Indicates that the planning level includes N S The number of research stages; N D For each research phase, N D day; N means the number of discharge processes included in the jth day; E bijn 、m DOD ijn is the discharge capacity of the energy storage system in the nth discharge process on the jth day of the i-th stage and the conversion factor corresponding to the actual discharge depth; the dynamic life of the energy storage system is shown as follows: Where Y b is the service life of the energy storage system; E r E is the total discharge capacity of the battery energy storage within its service life at the rated discharge depth; a The annual discharge capacity at the rated discharge depth is converted from the discharge process at different discharge depths within the planning level year.

4. The energy storage configuration planning method based on combined peak load regulation of thermal energy storage according to claim 3 is characterized by: In step 3, the upper layer takes the maximum peak-shaving benefit of the combined fire-storage system in a single day as the objective function, and the objective function is expressed as follows: The upper constraint conditions are the scheduling planning model of the thermal power unit and the scheduling planning model of the energy storage obtained in step S12 and step S22, and the power balance constraint in step S12 is changed to:

5. The energy storage configuration planning method based on combined peak load regulation of thermal energy storage according to claim 4 is characterized by: In step three, the objective function of solving the optimal configuration of energy storage in the lower layer is the maximum benefit of the combined peak regulation of thermal storage in the whole life cycle; after the solution is completed, the optimal configuration of energy storage is transmitted to the upper layer; the two layers are connected by the particle swarm intelligent algorithm PSO; the total benefit in the whole life cycle is set as the fitness function of the particle swarm intelligent algorithm PSO to continuously iterate and optimize the optimal configuration of energy storage, and the upper layer uses Cplex or Mosek or Gurobi solver in MATLAB for solution.

6. The energy storage configuration planning method based on combined peak load regulation of thermal energy storage according to claim 5 is characterized by: In step 4, a preset algorithm is used to solve the model in Cplex, Mosek or Gurobi.

7. The energy storage configuration planning method based on combined peak load regulation of thermal energy storage according to claim 6 is characterized by: The present value of cost is used to calculate the maximum benefit of combined peak regulation of thermal energy storage over the entire life cycle; the corresponding lower-level objective function is: Where i0 is the benchmark discount rate; f2 is also the fitness function of the particle swarm intelligence algorithm PSO.

8. An energy storage configuration planning system based on combined peak load regulation of thermal energy storage, which adopts the energy storage configuration planning method as claimed in any one of claims 1 to 7 to carry out energy storage configuration planning for the combined thermal energy storage system, characterized in that: include A deep peak-shaving cost-benefit function and a scheduling planning module for a thermal power unit system; a deep peak-shaving cost-benefit function and a scheduling planning model for a unit are established based on the conversion relationship of different peak-shaving stages of a single unit in the thermal power unit system; the scheduling planning model includes the unit's ramp constraints, power balance constraints, ramp constraints, and the output corresponding to the unit's peak-shaving stage conversion plan; the output corresponding to the unit's peak-shaving stage conversion plan includes the corresponding output constraints for each peak-shaving stage of the unit; Energy storage deep peak-shaving benefit and dispatch planning module: Establish the energy storage deep peak-shaving benefit function and dispatch planning model based on the output characteristics and benefit mode of energy storage; the dispatch planning model includes energy storage output constraints, state of charge constraints, output continuity constraints and charge and discharge state constraints; A two-layer planning module for deep peak regulation of thermal power units and energy storage. Based on the deep peak regulation cost-benefit function and scheduling planning model of the thermal power unit system and the deep peak regulation cost-benefit function and scheduling planning model of the energy storage system, and on the basis of following the constraints of each individual model, it is additionally required that the thermal storage jointly output to maintain power balance, thereby constructing a two-layer planning model for deep peak regulation of thermal storage. The upper layer of the two-layer planning model optimizes the scheduling strategy of thermal power units and energy storage with the maximum daily benefit of deep peak regulation of thermal storage as the objective function, and the lower layer optimizes the optimal configuration of energy storage with the maximum benefit of the combined peak regulation of thermal storage over the entire life cycle as the objective function. The solution module solves the model in Cplex, Mosek or Gurobi based on the established two-level programming model to obtain the maximum benefit of combined peak regulation of thermal energy storage and the optimal configuration of energy storage.

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