A generator unit portfolio optimization method considering independent energy storage participating in the power market

Abstract

The present invention discloses a generator set combination optimization method considering independent energy storage participating in the electricity market, which is applied to the field of power system resource planning. By obtaining the cost parameters of each generator set in the power system and the operating parameters of the independent energy storage system, and then based on the cost parameters of each generator set, a piecewise linearization method is used to segment the power generation cost of the generator set, and the power generation cost of each segment is calculated. An objective function is constructed according to the power generation cost of each segment and the operating parameters of the independent energy storage system, and a penalty function is obtained according to a penalty coefficient and constructed constraints. The penalty function is obtained by combining the objective function and the penalty function. The penalty function and the constraints are used to solve the problem with the goal of maximizing social benefits to obtain an optimal combination result. The above method significantly improves the accuracy of the generator set combination optimization result when considering independent energy storage participating in the electricity market.

Description

Technical Field

[0001] The present invention relates to the technical field of generator set combinations in power systems, and in particular to a generator set combination optimization method considering independent energy storage participating in a power market. Background Art

[0002] In the new power system environment, when independent energy storage participates in the electricity market as a major player, it can leverage flexible charging and discharging operations to engage in price arbitrage, provide ancillary services, and participate in multiple markets to generate revenue. However, other renewable energy sites, constrained by natural conditions, cannot flexibly adjust power generation and participate in market arbitrage like energy storage. The basic logic behind independent energy storage's participation in the electricity market is to charge when market prices are low and discharge and sell when prices are high, profiting from the price difference. Renewable energy power stations (such as wind power and photovoltaic power) can typically only provide electricity according to market prices and cannot flexibly adjust power generation like energy storage. At the same time, changes in electricity demand and fluctuations in electricity market prices further increase system complexity, placing higher demands on the real-time and accuracy of generator group combinations.

[0003] Therefore, to promote independent energy storage’s participation in the market, market organizers need to clarify the adaptability and shortcomings of the existing market mechanism, study participation methods suitable for independent energy storage, identify the mechanism elements that need to be adjusted, and select appropriate technical routes so that the market can better allocate independent energy storage resources. Summary of the Invention

[0004] In order to solve the above technical problems, an embodiment of the present invention provides a generator set combination optimization method considering independent energy storage participating in the electricity market, so as to solve the technical problem of low accuracy of optimization results when calculating the combination problem of generator sets when considering independent energy storage participating in the electricity market in the existing technology.

[0005] A first aspect of an embodiment of the present invention provides a method for optimizing the combination of generator sets considering independent energy storage participating in the electricity market, the method comprising:

[0006] Obtain the cost parameters of each generator set in the power system and the operating parameters of the independent energy storage system;

[0007] Based on the cost parameters of each generator set, the power generation cost of the generator set is segmented using the piecewise linearization method to obtain multiple segments. The power generation cost of each segment is obtained based on the power generation power of the generator set in each segment.

[0008] An objective function is constructed based on the power generation cost of each segment and the operating parameters of the independent energy storage system. The penalty term is used to combine the objective function and the constructed constraints to obtain a penalty function. Based on the penalty function and the constraints, the solution is solved with the goal of minimizing the total cost to obtain the optimal combination result.

[0009] In a possible implementation of the first aspect, based on the cost parameters of each generator set, a piecewise linearization method is used to segment the power generation cost of the generator set to obtain multiple segments, including:

[0010] Based on the cost parameters of each generator set, an initial power generation cost function is obtained;

[0011] The initial power generation cost function is discretized to obtain multiple segments.

[0012] In a possible implementation of the first aspect, obtaining the power generation cost of each segment according to the power generation power of the generator set of each segment includes:

[0013] According to the maximum power generation and minimum power generation of the generator set in each segment, the boundary of each segment is obtained. The boundary includes the maximum value and the minimum value of the boundary. The calculation formula of the maximum value of the boundary is:

[0014]

[0015] Where, For the The minimum generating capacity of each generator set, For the The maximum power generation capacity of each generator set, is the maximum value boundary, is the number of segments of piecewise linearization; The maximum number of segments for piecewise linearization.

[0016] The calculation formula for the boundary minimum is:

[0017]

[0018] Where, For the The minimum generating capacity of each generator set, For the The maximum power generation capacity of each generator set, is the maximum value boundary, is the number of segments of piecewise linearization; is the maximum number of segments for piecewise linearization;

[0019] According to the boundaries of each segment, the median of each segment is obtained, and according to the median of each segment, the power generation cost of each segment is obtained.

[0020] In a possible implementation of the first aspect, a penalty term is used to combine the objective function and the constructed constraint condition to obtain a penalty function, including:

[0021] The penalty function is obtained by combining the objective function and the penalty function, where the penalty function is:

[0022]

[0023] Where, In order to consider the maximum social benefits of electricity market participation and independent energy storage integration, For the current moment, is the number of piecewise linearization segments, is the number of generator sets, for The electricity price at the time, for Generator group at time s of efforts, is the power generation cost of the sth segment, For the Fixed costs of each generator set; for Moment The start and stop status of each generator set, =0, the generator set is in shutdown state. =1, the generator set is in the starting state; For the The startup cost of a generator set, is the first The starting state of the generator set, when =1, the generator set starts. For the Downtime cost of a generator set, for Moment The generator set is in shutdown state. =1, the generator set is shut down; is the penalty coefficient, which is used to control the degree of constraint violation; is the relaxation factor of the piecewise linearization; Relaxation factors for upper and lower limits of generator set processing; is the relaxation factor of the generator set's spare capacity.

[0024] In a possible implementation of the first aspect, based on the penalty function and the constraints, a solution is performed with the goal of minimizing the total cost to obtain an optimal combination result, including:

[0025] Determine power system constraints, where the constraints include unit combination constraints and independent energy storage system constraints;

[0026] Based on the penalty function and constraints, the linear programming toolkit solver is used to solve the problem with the goal of minimizing the total cost, and the optimal combination result is obtained.

[0027] In a possible implementation of the first aspect, the unit combination constraint conditions include upper and lower limit constraints on generator output, a power system load balancing requirement constraint, a generator unit ramp constraint, a generator unit minimum operating time constraint, a generator unit minimum downtime constraint, and a generator unit spare capacity constraint;

[0028] The constraints of the independent energy storage system include the capacity constraint of the independent energy storage system, the discharge power constraint of the independent energy storage system, the fast frequency regulation reserve capacity constraint of the independent energy storage, and the primary frequency regulation reserve capacity constraint of the thermal power unit.

[0029] To solve the same technical problem, a second aspect of an embodiment of the present invention provides a generator group combination optimization system considering independent energy storage participating in the power market, including:

[0030] An acquisition module, used to obtain cost parameters of each generator set in the power system and operating parameters of the independent energy storage system;

[0031] The power generation cost calculation module is used to segment the power generation cost of each generator set using a piecewise linearization method based on the cost parameters of each generator set to obtain multiple segments, and then obtain the power generation cost of each segment based on the power generation power of the generator set in each segment;

[0032] The solution module is used to construct an objective function based on the power generation cost of each segment and the operating parameters of the independent energy storage system. The objective function and the constructed constraints are combined using the penalty term to obtain the penalty function. Based on the penalty function and the constraints, the solution is performed with the goal of minimizing the total cost to obtain the optimal combination result.

[0033] In a possible implementation of the second aspect, the power generation cost calculation module includes an initial power generation cost function construction unit and a discretization unit, wherein:

[0034] The initial power generation cost function construction unit is used to obtain the initial power generation cost function based on the cost parameters of each power generation unit;

[0035] The discretization unit is used to discretize the initial power generation cost function to obtain multiple segments.

[0036] A third aspect of an embodiment of the present invention provides a computer device, including:

[0037] Memory for storing computer programs;

[0038] The processor is used to implement the steps of the generator set combination optimization method considering independent energy storage participating in the electricity market as described in the first aspect when executing the computer program.

[0039] A fourth aspect of an embodiment of the present invention provides a storage medium having a computer program stored thereon. When the computer program is executed by a processor, the steps of the generator set combination optimization method considering independent energy storage participating in the electricity market as in the first aspect are implemented.

[0040] The technical solution of the present invention has the following advantages:

[0041] The embodiment of the present invention provides a generator set combination optimization method considering the participation of independent energy storage in the power market. The method obtains the cost parameters of each generator set in the power system and the operating parameters of the independent energy storage system, and then uses a piecewise linearization method to segment the power generation cost of the generator set based on the cost parameters of each generator set to obtain multiple segments. The power generation cost of each segment is calculated according to the power generation power of the generator set in each segment. The objective function is constructed according to the power generation cost of each segment and the operating parameters of the independent energy storage system. The penalty function is obtained according to the penalty coefficient and the constructed constraints. The penalty function is obtained by combining the objective function and the penalty function. The penalty function and the constraints are used to solve the problem with the goal of minimizing the total cost to obtain the optimal combination result. The above method significantly improves the accuracy of the combination optimization result of the generator set when considering the participation of independent energy storage in the power market. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the specific embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0043] Figure 1 This is an optimization flow chart of a generator set combination optimization method considering independent energy storage participating in the power market in an embodiment of the present invention;

[0044] Figure 2 An implementation roadmap for a generator set combination optimization method considering independent energy storage participating in the power market in an embodiment of the present invention;

[0045] Figure 3 A flowchart of the power generation cost calculation for each section of the generator set combination optimization method considering independent energy storage participating in the power market in an embodiment of the present invention;

[0046] Figure 4This is a system block diagram of a generator set combination optimization system considering independent energy storage participating in the power market in an embodiment of the present invention;

[0047] Reference numerals: 400, generator group combination optimization system considering independent energy storage participating in the electricity market; 401, acquisition module; 402, power generation cost calculation module; 403, solution module. DETAILED DESCRIPTION

[0048] 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. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0049] The embodiment of the present invention provides a method for optimizing the combination of generator sets considering independent energy storage participating in the power market, such as Figure 1 As shown, Figure 1 The flowchart of the generator group combination optimization method considering independent energy storage participating in the power market includes steps S101 to S103, and each step is specifically as follows:

[0050] S101. Obtain cost parameters of each generator set in the power system and operating parameters of the independent energy storage system.

[0051] In this embodiment, cost parameters of each generator set in the power system and operating parameters of the independent energy storage system are obtained, where the cost parameters include but are not limited to power generation capacity, start-up and shutdown costs, maximum ramp limit, and start-up / shutdown time; the operating parameters of the independent energy storage system include but are not limited to independent energy storage system capacity, maximum charging power, minimum charging power, initial independent storage capacity, independent energy storage charging and discharging efficiency, maximum charging rate, minimum charging rate, and other independent energy storage system-related parameters.

[0052] At the same time, it can also collect real-time electricity price change data of the power market, collect the power demand of the power system in each time period, the carbon emission coefficient of the generator set, the total carbon emission limit and other environmental related parameters, and combine Figure 2 The proposed implementation route is calculated to obtain the optimal combination result. Figure 2 Implementation roadmap for generator unit portfolio optimization methods.

[0053] S102. Based on the cost parameters of each generator set, the power generation cost of the generator set is segmented using a piecewise linearization method to obtain multiple segments, and the power generation cost of each segment is obtained according to the power generation power of the generator set in each segment.

[0054] In this embodiment, a piecewise linearization method is used to perform segmented processing on the power generation cost. Specifically, the piecewise linearization method is used to discretize the continuous cost function of each generator and each power segment into multiple intervals and calculate the cost of each interval. The nonlinear function is approximated by linear combination, and the mixed integer quadratic programming problem is converted into a mixed integer linear programming problem, which can be solved by a solver, which can greatly reduce the solution difficulty and computational cost.

[0055] In one embodiment, based on the cost parameters of each generator set, a piecewise linearization method is used to segment the power generation cost of the generator set to obtain multiple segments, including:

[0056] Based on the cost parameters of each generator set, an initial power generation cost function is obtained;

[0057] The initial power generation cost function is discretized to obtain multiple segments.

[0058] In this embodiment, Figure 3 The calculation flow chart of each section’s power generation cost is as follows: Figure 3 As shown, first, based on the cost parameters of each generator set and according to the cost calculation formulas for each generator and each power segment, an initial power generation cost function is derived. A piecewise linearization approach is then used to discretize the continuous initial power generation cost function into multiple intervals, resulting in multiple segments. The cost of each segment is then calculated to facilitate linear solution. This is because the power generation cost of a generator set is typically a nonlinear function, such as a quadratic function. By converting the nonlinear problem into a linear programming (LP) or mixed-integer linear programming (MILP), solvers such as the GNU Linear Programming Kit (GLPK) can efficiently handle it.

[0059] In one embodiment, the power generation cost of each segment is obtained based on the power generation power of the generator set of each segment, including:

[0060] According to the maximum power generation and minimum power generation of the generator set in each segment, the boundary of each segment is obtained. The boundary includes the maximum value and the minimum value of the boundary. The calculation formula of the maximum value of the boundary is:

[0061]

[0062] Where, For the The minimum generating capacity of each generator set, For the The maximum power generation capacity of each generator set, is the maximum value boundary, is the number of segments of piecewise linearization; is the maximum number of segments for piecewise linearization;

[0063] The calculation formula for the boundary minimum is:

[0064]

[0065] Where, For the The minimum generating capacity of each generator set, For the The maximum power generation capacity of each generator set, is the maximum value boundary;

[0066] According to the boundaries of each segment, the median of each segment is obtained, and according to the median of each segment, the power generation cost of each segment is obtained.

[0067] In this embodiment, the process of calculating the segment boundary is as follows: using the maximum power generation and minimum power generation of the generator set in each segment, the boundary of each segment is obtained. The boundary includes the maximum value and the minimum value of the boundary. The calculation formula of the maximum value of the boundary is:

[0068]

[0069] Where, For the The minimum generating capacity of each generator set, For the The maximum power generation capacity of each generator set, is the maximum value boundary, is the number of segments of piecewise linearization; is the maximum number of segments for piecewise linearization;

[0070] The calculation formula for the boundary minimum is:

[0071]

[0072] Where, For the The minimum generating capacity of each generator set, For the The maximum power generation capacity of each generator set, is the maximum value boundary, is the number of segments of piecewise linearization; is the maximum number of segments for piecewise linearization;

[0073] The calculation process of the segment median is: use the minimum boundary value and the maximum boundary value to calculate the median of each segment to obtain the median of each segment. The calculation formula of the median is:

[0074]

[0075] Where, is the median value, is the minimum value of the boundary, is the maximum value of the boundary.

[0076] The calculation process of the segment cost is as follows: according to the median value of each segment, the power generation cost of each segment is obtained. The calculation formula of the power generation cost is:

[0077]

[0078] Where, is the power generation cost of the sth segment, For the The secondary cost coefficient of each generator set, For the The linear cost coefficient of each generator set.

[0079] S103. Construct an objective function based on the power generation cost of each segment and the operating parameters of the independent energy storage system. Use a penalty term to combine the objective function and the constructed constraints to obtain a penalty function. Based on the penalty function and the constraints, solve the problem with the goal of minimizing the total cost to obtain the optimal combination result.

[0080] In this embodiment, after determining the power generation costs for each segment, an objective function for power market participation and independent energy storage integration is constructed, comprehensively considering the unit's power generation costs, the charging and discharging costs of independent energy storage, and fluctuations in power market prices. Specifically, a penalty method is employed in constructing the objective function. This method combines the objective function and all constraints (inequality and equality constraints) through a penalty term to create a penalty function. Generally speaking, the penalty parameter should be large enough to ensure that the constraints are adequately penalized, but not too large to prevent excessive instability. Based on the penalty function and constraints, a solution is then performed with the goal of minimizing total cost to obtain the optimal combination.

[0081] It should be noted that the optimal combination result can be understood as the generator start-up and shutdown and output distribution plan that minimizes the total operating cost.

[0082] In one embodiment, a penalty term is used to combine the objective function with the constructed constraint conditions to obtain a penalty function, including:

[0083] Using the penalty term, the objective function and the constructed constraints are combined to obtain the penalty function, where the penalty function is:

[0084]

[0085] Where, In order to consider the maximum social benefits of electricity market participation and independent energy storage integration, For the current moment, is the number of piecewise linearization segments, is the number of generator sets, for The electricity price at the time, for Generator group at time s of efforts, is the power generation cost of the sth segment, For the The fixed cost of a generator set, for Moment The start and stop status of each generator set, =0, the generator set is in shutdown state. =1, the generator set is in the starting state; For the The startup cost of a generator set, is the first The starting state of the generator set, when =1, the generator set starts. For the Downtime cost of a generator set, for Moment The generator set is in shutdown state. =1, the generator set is shut down. is the penalty coefficient, which is used to control the degree of constraint violation. is the relaxation factor for piecewise linearization, Relaxation factors for the upper and lower limits of the generator set processing, is the relaxation factor of the generator set's spare capacity.

[0086] In this embodiment, an objective function is constructed that comprehensively considers the unit's power generation costs, the independent energy storage charging and discharging costs, and price changes implemented in the electricity market, with the goal of maximizing social benefits. The objective function and constraints (inequalities and equality constraints) are then combined through a penalty term to form a penalty function. All constraints are the combined constraints of the units under the constructed electricity market and the constraints of the independent energy storage system. The penalty function is:

[0087]

[0088] Where, In order to consider the maximum social benefits of electricity market participation and independent energy storage integration, For the current moment, is the number of piecewise linearization segments, is the number of generator sets, for The electricity price at the moment, unit: ￥, for Generator group at time s Output, unit: MW, is the power generation cost of the sth segment, unit: ￥ / time, For the Fixed cost of a generator set, unit: ￥ / time; for Moment The start and stop status of each generator set, =0, the generator set is in shutdown state. =1, the generator set is in the starting state; For the The startup cost of a generator set, unit: ￥ / time, is the first The starting state of the generator set, when =1, the generator set starts. For the The downtime cost of each generator set, unit: ￥ / time, for Moment The generator set is in shutdown state. =1, the generator set is shut down; is the penalty coefficient, which is used to control the degree of constraint violation; is the relaxation factor of the piecewise linearization; Relaxation factors for upper and lower limits of generator set processing; is the relaxation factor of the generator set's spare capacity.

[0089] In one embodiment, based on the penalty function and the constraints, the solution is performed with the goal of minimizing the total cost to obtain the optimal combination result, including:

[0090] Determine power system constraints, where the constraints include unit combination constraints and independent energy storage system constraints;

[0091] Based on the penalty function and constraints, the linear programming toolkit solver is used to solve the problem with the goal of minimizing the total cost, and the optimal combination result is obtained.

[0092] In this embodiment, the unit combination constraints and independent energy storage system constraints under the participation of the power market are constructed. Then, based on the penalty function, the unit combination constraints and the independent energy storage system constraints, the linear programming toolkit solver is used to solve the problem with the goal of minimizing the total cost to obtain the optimal combination result. Specifically, the unit combination method is obtained by the GLPK solver, and the values ​​of the relaxation factors and the penalty coefficients of each part are adjusted according to the obtained results to obtain the optimal combination scheme. First, the total power generation capacity of the generator set is determined, and the expression is:

[0093]

[0094] Where, Indicates the total power generation capacity of the generator set; Indicates the output upper limit of each generator set.

[0095] Set different unit combinations, solve the initial combination scheme based on the objective function and constraints, and then check the slack variables of the piecewise linearization , slack variables for upper and lower limits of generator set processing , slack variables of generator set spare capacity The value of is obtained, and the above slack variables are adjusted according to the obtained initial combination scheme. After obtaining the adjusted slack variables, the linear programming toolkit solver is used to solve the problem until the optimal combination result is obtained.

[0096] It should be noted that when the unit combination method is obtained using the GLPK solver, the relaxation factor and penalty coefficient will be adjusted based on the obtained results to optimize the solution. This is an iterative optimization process until the optimal combination result is obtained.

[0097] In one embodiment, the unit combination constraint conditions include upper and lower limit constraints on generator output, power system load balancing requirement constraints, generator unit ramping constraints, generator unit minimum operating time constraints, generator unit minimum downtime constraints, and generator unit spare capacity constraints;

[0098] The constraints of the independent energy storage system include the capacity constraint of the independent energy storage system, the discharge power constraint of the independent energy storage system, the fast frequency regulation reserve capacity constraint of the independent energy storage, and the primary frequency regulation reserve capacity constraint of the thermal power unit.

[0099] In this embodiment, the unit combination constraints include upper and lower limits of generator output, power system load balancing requirement constraints, generator unit ramping constraints, generator unit minimum operating time constraints, generator unit minimum downtime constraints, and generator unit spare capacity constraints. Specifically, the upper and lower limits of generator output are:

[0100]

[0101] Where, For the The lower limit of the output of each generator set, unit: MW; For the The upper limit of the output of each generator set, unit: MW; Slack variables for handling upper and lower limits for the generator set, for Moment The output of the generator set, for Moment Section unit of effort.

[0102] The load balancing demand constraint of the power system is:

[0103]

[0104] Where, is the total demand of the power system, for Moment Section unit of efforts, It is the independent energy storage discharge power; Charging power for independent energy storage; is the slack variable of the piecewise linearization.

[0105] The generator set climbing constraint is:

[0106]

[0107]

[0108] Where, for Moment Section unit contribution; for -1 moment Section unit contribution; Power ramp limit.

[0109] The above two constraints ensure that in the time series, the output change of the generator (i.e., the power increase or decrease) is subject to The limit will not increase or decrease too quickly.

[0110] The minimum operating time constraint of the generator set is:

[0111]

[0112] Where, for Moment The output of the generator set, is an indicator function, when The value is 1 when , otherwise it is 0; is the minimum running time, for Moment The output of the generator set, for Moment The output of the generator set.

[0113] The minimum downtime constraint of the generator set is:

[0114]

[0115] Where, For minimum downtime, for Moment The output of the generator set, for Moment The output of the generator set.

[0116] The spare capacity constraint is:

[0117]

[0118] Where, is the slack variable of the generator set’s spare capacity, is the slack variable of the piecewise linearization, is the total demand of the power system, is the reserve capacity requirement of the power system, for Moment Section unit of effort.

[0119] To ensure carbon emission limits, environmental constraints need to be added:

[0120]

[0121] Where, is the carbon emission coefficient, is the total carbon emission cap, for Moment Section unit of effort.

[0122] Then, the constraints of the independent energy storage system are constructed, where the capacity constraint of the independent energy storage system is:

[0123]

[0124] Where, for The energy status of the independent energy storage system at all times; is the capacity of an independent energy storage system.

[0125] The charging power constraint of the energy storage system is:

[0126]

[0127]

[0128] Where, For independent energy storage systems Charging power at the moment; is the maximum charging power; is the current charging state of the energy storage system. =1 means the energy storage system is currently in charging state; is the maximum charging rate of the energy storage system; is the capacity of an independent energy storage system.

[0129] The discharge power constraint of the independent energy storage system is:

[0130]

[0131]

[0132] Where, for Discharge power of independent energy storage at all times; is the maximum discharge power; is the current discharge state of the independent energy storage system. =1 means the current independent energy storage system is in charging state; is the maximum discharge rate of the independent energy storage system; is the capacity of an independent energy storage system.

[0133] Since independent energy storage systems cannot charge and discharge at the same time, + ≤1, is the current charging status of the independent energy storage system, It is the current discharge status of the independent energy storage system.

[0134] Energy balance constraints of independent energy storage systems considering charging and discharging efficiency:

[0135] for =1:

[0136]

[0137] for >1 hour:

[0138]

[0139] Where, for The energy of the independent energy storage system at all times; is the initial energy of the independent energy storage system; Charging efficiency of independent energy storage system; is the discharge efficiency of the independent energy storage system; For independent energy storage systems Charging power at the moment; For independent energy storage systems Discharge power at the moment.

[0140] The calculation process of independent energy storage reserve capacity and primary frequency regulation reserve capacity after participation is as follows:

[0141] Independent energy storage fast frequency regulation reserve power constraints:

[0142]

[0143] Where, It is an independent energy storage and fast frequency regulation backup power. For thermal power units The response time of one frequency modulation, is the duration of independent energy storage participating in frequency regulation. For the Rated power of the segment energy storage system in fast frequency regulation mode.

[0144] To make the lowest point of frequency drop above the low-frequency load reduction point, the independent energy storage must at least maintain The output time of , the backup capacity of independent energy storage for rapid frequency regulation is:

[0145]

[0146]

[0147] Where, For independent energy storage with fast frequency modulation response speed, The time required for independent energy storage to reach maximum output power, is the sum of the fast frequency modulation response speeds of the independent energy storage systems, is the first Rated power of the segment energy storage system in fast frequency regulation mode.

[0148] Calculation of primary frequency regulation reserve capacity of thermal power units:

[0149]

[0150]

[0151] Where, is the moment when the frequency curve reaches its lowest point, Fast frequency regulation response speed for thermal power units, It is the sum of the fast frequency regulation response speeds of all thermal power units.

[0152] Through the above constraints, the dynamic balance of energy of independent energy storage devices is ensured, reflecting the impact of charging and discharging operations on the independent energy storage level in each time period.

[0153] The embodiment of the present invention provides a generator group combination optimization system considering independent energy storage participating in the power market, such as Figure 4 As shown, Figure 4 A system block diagram of a generator unit portfolio optimization system 400 for considering independent energy storage participating in the electricity market includes:

[0154] An acquisition module 401 is used to acquire cost parameters of each generator set in the power system and operating parameters of the independent energy storage system;

[0155] The power generation cost calculation module 402 is used to segment the power generation cost of each generator set using a piecewise linearization method based on the cost parameters of each generator set to obtain multiple segments, and to obtain the power generation cost of each segment based on the power generation power of the generator set in each segment;

[0156] The solution module 403 is used to construct a function using penalty terms based on the power generation cost of each segment and the operating parameters of the independent energy storage system to obtain a penalty function. Based on the penalty function and constraints, the solution is performed with the goal of minimizing the total cost to obtain the optimal combination result.

[0157] The power generation cost calculation module 402 includes an initial power generation cost function construction unit and a discretization unit, wherein:

[0158] The initial power generation cost function construction unit is used to obtain the initial power generation cost function based on the cost parameters of each power generation unit;

[0159] The discretization unit is used to discretize the initial power generation cost function to obtain multiple segments.

[0160] The specific implementation of the generator set combination optimization system for considering independent energy storage participating in the electricity market is basically the same as the specific implementation of the generator set combination optimization method considering independent energy storage participating in the electricity market mentioned above, and will not be repeated here.

[0161] In one embodiment of the present application, a computer device is provided, which includes a memory and a processor, wherein a computer program is stored in the memory, and the above steps are implemented when the processor executes the computer program; the computer device provided in this embodiment has an implementation principle and technical effects similar to those of the above method embodiment, and will not be repeated here.

[0162] In one embodiment of the present application, a computer-readable storage medium is provided, on which a computer program is stored, and the above steps are implemented when the computer program is executed by a processor; the computer-readable storage medium provided in this embodiment has an implementation principle and technical effects similar to those of the above method embodiment, and will not be repeated here.

[0163] The technical features of the above embodiments can be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0164] The specific embodiments described above further illustrate the objectives, technical solutions, and beneficial effects of the present invention. It should be understood that the above descriptions are merely specific embodiments of the present invention and are not intended to limit the scope of protection of the present invention. In particular, it should be noted that any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included within the scope of protection of the present invention for those skilled in the art.

Claims

1. A method for optimizing the combination of generator sets considering independent energy storage participating in the power market, characterized in that: include: Obtain the cost parameters of each generator set in the power system and the operating parameters of the independent energy storage system; Based on the cost parameters of each generator set, a piecewise linearization method is used to segment the power generation cost of the generator set to obtain multiple segments; Obtaining boundaries of each segment according to the maximum power generation and the minimum power generation of the generator sets in each segment, wherein the boundaries include a maximum boundary value and a minimum boundary value; Obtaining a median value of each segment according to the boundaries of each segment, and obtaining a power generation cost of each segment according to the median value of each segment; An objective function is constructed based on the power generation cost of each segment and the operating parameters of the independent energy storage system. The objective function and the constructed constraint conditions are combined using a penalty term to obtain a penalty function. Based on the penalty function and the constraint conditions, a solution is performed with the goal of minimizing the total cost to obtain an optimal combination result, wherein the penalty function is: Where, In order to consider the maximum social benefits of electricity market participation and independent energy storage integration, For the current moment, is the number of piecewise linearization segments, is the number of generator sets, for The electricity price at the time, for Moment Section generator set of efforts, For the The power generation cost of the segment, For the The fixed cost of a generator set, for Moment The start and stop status of each generator set, =0, the generator set is in shutdown state. =1, the generator set is in the starting state; For the The startup cost of a generator set, is the first The starting state of the generator set, when =1, the generator set starts. For the Downtime cost of a generator set, for Moment The generator set is in shutdown state. =1, the generator set is shut down; is the penalty coefficient, which is used to control the degree of constraint violation; is the relaxation factor for piecewise linearization, Relaxation factors for the upper and lower limits of the generator set processing, is the relaxation factor of the generator set's spare capacity; Obtaining the optimal combination result includes: Determine the total generating capacity of the generator set, the expression is: Where, Indicates the total power generation capacity of the generator set; Indicates the output upper limit of each generator set; Set different unit combinations, solve and obtain the initial combination scheme according to the penalty function and the constraints, check the values ​​of the piecewise linearization relaxation factor, the relaxation factor of the upper and lower limits of the generator set processing, and the relaxation factor of the generator set spare capacity, and adjust the above relaxation factors according to the initial combination scheme to obtain the adjusted relaxation factors. Use the adjusted relaxation factors to continue to use the linear programming toolkit solver to solve until the optimal combination result is obtained.

2. The generator group combination optimization method considering independent energy storage participating in the power market according to claim 1 is characterized in that: Based on the cost parameters of each generator set, the power generation cost of the generator set is segmented using a piecewise linearization method to obtain multiple segments, including: Based on the cost parameters of each of the generator sets, an initial power generation cost function is obtained; The initial power generation cost function is discretized to obtain a plurality of segments.

3. The generator group combination optimization method considering independent energy storage participating in the power market as claimed in claim 1 is characterized in that: The calculation formula of the boundary maximum value is: Where, For the The minimum generating capacity of each generator set, For the The maximum power generation capacity of each generator set, is the maximum value boundary, is the number of segments of piecewise linearization; is the maximum number of segments for piecewise linearization; The calculation formula of the minimum boundary value is: Where, For the The minimum generating capacity of each generator set, For the The maximum power generation capacity of each generator set, is the maximum value boundary, is the number of segments of piecewise linearization; The maximum number of segments for piecewise linearization.

4. The method for optimizing the combination of generator sets considering independent energy storage participating in the electricity market according to claim 1, characterized in that: The method of solving the problem based on the penalty function and the constraint conditions with the goal of minimizing the total cost to obtain the optimal combination result includes: Determining constraints of the power system, wherein the constraints include unit combination constraints and independent energy storage system constraints; Based on the penalty function and the constraint conditions, a linear programming toolkit solver is used to solve the problem with the goal of minimizing the total cost to obtain the optimal combination result.

5. The generator group combination optimization method considering independent energy storage participating in the power market as claimed in claim 4 is characterized in that: The unit combination constraints include upper and lower limit constraints on generator output, power system load balancing demand constraints, generator unit ramping constraints, generator unit minimum operating time constraints, generator unit minimum downtime constraints and generator unit spare capacity constraints; The independent energy storage system constraints include independent energy storage system capacity constraints, independent energy storage system discharge power constraints, independent energy storage rapid frequency regulation reserve capacity constraints, and thermal power unit primary frequency regulation reserve capacity constraints.

6. A generator unit combination optimization system considering independent energy storage participating in the power market, characterized in that: include: An acquisition module, used to obtain cost parameters of each generator set in the power system and operating parameters of the independent energy storage system; a power generation cost calculation module, configured to segment the power generation cost of each generator set using a piecewise linearization method based on the cost parameters of each generator set to obtain a plurality of segments, obtain a boundary of each segment based on the maximum power generation and the minimum power generation of the generator set in each segment, wherein the boundary includes a maximum boundary value and a minimum boundary value, obtain a median value of each segment based on the boundary of each segment, and obtain a power generation cost of each segment based on the median value of each segment; A solution module is configured to construct an objective function based on the power generation cost of each segment and the operating parameters of the independent energy storage system, combine the objective function with the constructed constraints using a penalty term to obtain a penalty function, and solve the problem based on the penalty function and the constraints with the goal of minimizing the total cost to obtain an optimal combination result, wherein the penalty function is: Where, In order to consider the maximum social benefits of electricity market participation and independent energy storage integration, For the current moment, is the number of piecewise linearization segments, is the number of generator sets, for The electricity price at the time, for Moment Section generator set of efforts, For the The power generation cost of the segment, For the The fixed cost of a generator set, for Moment The start and stop status of each generator set, =0, the generator set is in shutdown state. =1, the generator set is in the starting state; For the The startup cost of a generator set, is the first The starting state of the generator set, when =1, the generator set starts. For the Downtime cost of a generator set, for Moment The generator set is in shutdown state. =1, the generator set is shut down; is the penalty coefficient, which is used to control the degree of constraint violation; is the relaxation factor for piecewise linearization, Relaxation factors for the upper and lower limits of the generator set processing, is the relaxation factor of the generator set's spare capacity; Obtaining the optimal combination result includes: Determine the total generating capacity of the generator set, the expression is: Where, Indicates the total power generation capacity of the generator set; Indicates the output upper limit of each generator set; Set different unit combinations, solve and obtain the initial combination scheme according to the penalty function and the constraints, check the values ​​of the piecewise linearization relaxation factor, the relaxation factor of the upper and lower limits of the generator set processing, and the relaxation factor of the generator set spare capacity, and adjust the above relaxation factors according to the initial combination scheme to obtain the adjusted relaxation factors. Use the adjusted relaxation factors to continue to use the linear programming toolkit solver to solve until the optimal combination result is obtained.

7. The generator group combination optimization system considering independent energy storage participating in the power market as claimed in claim 6 is characterized in that: The power generation cost calculation module includes an initial power generation cost function construction unit and a discretization unit, wherein: The initial power generation cost function construction unit is used to obtain an initial power generation cost function based on the cost parameters of each of the generator sets; The discretization unit is used to discretize the initial power generation cost function to obtain multiple segments.

8. A computer device, characterized in that: include: Memory for storing computer programs; A processor, configured to implement the generator set combination optimization method considering independent energy storage participating in the electricity market as described in any one of claims 1 to 5 when executing the computer program.

9. A storage medium, characterized in that: The storage medium stores a computer program, which, when executed by a processor, implements the steps of the generator set combination optimization method considering independent energy storage participating in the electricity market as described in any one of claims 1 to 5.

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